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X-WR-CALDESC:Veranstaltungen für Fächergruppe Mathematik und Statistik
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200318T154500
DTEND;TZID=Europe/Berlin:20200318T171500
DTSTAMP:20200129T094942Z
CREATED:20200129T094804Z
LAST-MODIFIED:20200129T094942Z
UID:871-1584546300-1584551700@www.hsu-hh.de
SUMMARY:Yves Breitmoser (Uni Bielefeld)
DESCRIPTION:An axiomatic foundation of conditional logit\nThis paper considers a decision maker choosing from a set of options when options have multiple real-valued attributes. Assuming DM chooses all options with positive probability\, four invariance assumptions are necessary and sufficient for choice\nprobabilities to take McFadden’s conditional logit form: independence of irrelevant alternatives\, translation invariance\, presentation independence and context independence. Variations on these assumptions yield generalized logit and contextual logit\nmodels. This shows that even specific logit models have behavioral foundations in simple invariance assumptions involving observables only\, which therefore are\ndirectly testable.
URL:https://www.hsu-hh.de/statistik/event/an-axiomatic-foundation-of-conditional-logit
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200304T154500
DTEND;TZID=Europe/Berlin:20200304T171500
DTSTAMP:20200129T120142Z
CREATED:20200129T093447Z
LAST-MODIFIED:20200129T120142Z
UID:862-1583336700-1583342100@www.hsu-hh.de
SUMMARY:Annette Möller (TU Clausthal)
DESCRIPTION:Vine copula based post-processing of ensemble forecasts for temperature\nTo account for forecast uncertainty in numerical weather prediction (NWP) models it has become common practice to employ ensemble prediction systems generating probabilistic forecast ensembles by multiple runs of the NWP\nmodel\, each time with variations in the details of the numerical model and/or initial and boundary conditions. However\, forecast ensembles typically exhibit biases and dispersion errors as they are not able to fully represent uncertainty in NWP models. Therefore\, statistical postprocessing models are employed to correct ensembles for biases and dispersion errors in conjunction with recently observed forecast errors. We propose a novel postprocessing approach for temperature forecasts based on D-vine copula quantile regression. It is a multivariate regression approach predicting quantiles of the response (temperature observations) conditioned on a set of predictor variables (the ensemble forecasts)\, while not making specific assumptions about the shape of the conditional quantiles. It exploits the dependence between observation and predictors\, accounting for non-gaussian dependencies in a flexible and data driven way. In a comparative study with temperature forecasts of different forecast horizons from the European Center for Medium Range Weather Forecast (ECMWF) the D-vine postprocessing approach shows to be highly competitive to the state-of-the-art EMOS model\, improving over standard EMOS especially for larger forecast horizons. Furthermore\, an exploratory data analysis revealed that the dependency between temperature observations and its ensemble forecasts is indeed non-Gaussian\, pointing to the need to employ vine copula models for postprocessing\, as they allow for more flexibility in the dependence structure than state-of-the-art models.
URL:https://www.hsu-hh.de/statistik/event/vine-copula-based-post-processing
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200129T154500
DTEND;TZID=Europe/Berlin:20200129T171500
DTSTAMP:20200129T115746Z
CREATED:20191008T114046Z
LAST-MODIFIED:20200129T115746Z
UID:811-1580312700-1580318100@www.hsu-hh.de
SUMMARY:Houssem Brairi (USTHB Algerien)
DESCRIPTION:Testing discrete-valued time series for whiteness\nWe consider the problem of testing a univariate discrete-valued time series for whiteness in the sequency domain\, using Walsh–Fourier analysis. We show that the distribution of the lag window estimator of the Walsh spectral density is a scaled chi-square distribution\, where the scale and degrees of freedom\, both depend on the bandwidth of the smoothing window associated with the estimator. The definition of the bandwidth is extended from the frequency to the sequency domain. To address our problem\, we propose three tests: the first one is based on the cumulative Walsh periodogram\, and is shown to converge to a Brownian bridge. The second test is based on applying the Cramer–von Mises functional to an estimate of the Walsh spectral density\, and is shown to converge to a Normal distribution\, while the last test is based on a distance to whiteness\, and is shown to have an approximate scaled chi-square distribution. Simulations are reported on the performance of the tests. Finally\, we apply the proposed tests to the brain functional connectivity of schizophrenic patients.
URL:https://www.hsu-hh.de/statistik/event/testing-discrete-valued-time-series-for-whiteness
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20191002T161500
DTEND;TZID=Europe/Berlin:20191002T174500
DTSTAMP:20190927T062601Z
CREATED:20190911T163844Z
LAST-MODIFIED:20190927T062601Z
UID:800-1570032900-1570038300@www.hsu-hh.de
SUMMARY:Alexander Schnurr (Uni Siegen)
DESCRIPTION:Ordinal Patterns and Ordinal Pattern Dependence\nOrdinal patterns describe the order structure of data points over a small time horizon. Using a moving window approach we reduce the complexity of a time series by analyzing the sequence of ordinal patterns instead of the original data. We present limit theorems for ordinal pattern probabilities and tests for structural breaks in the short-range dependent as well as in the long-range dependent setting. In the long-range dependent case\, we investigate the ordinal information of a subordinated Gaussian process with a non-summable autocovariance function. We establish the asymptotic behavior of different estimators for ordinal pattern probabilities by using a multivariate Hermite decomposition. \nOrdinal pattern dependence is a new way of measuring the degree of dependence between time series. Since it only relies on the ordinal structure of the data\, it is robust against monotone transformations and measurement errors. This method has proved to be useful already in the context of hydrological\, financial as well as medical data. Using this concept it is possible to analyze whether the dependence structure between two time series changes over time.
URL:https://www.hsu-hh.de/statistik/event/ordinal-patterns-and-ordinal
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190820T154500
DTEND;TZID=Europe/Berlin:20190820T154500
DTSTAMP:20190716T115847Z
CREATED:20190703T195914Z
LAST-MODIFIED:20190716T115847Z
UID:790-1566315900-1566315900@www.hsu-hh.de
SUMMARY:Burcu Aytacoglu (Ege University)
DESCRIPTION:Effect of estimation under non-normality on the phase II performance of linear profile monitoring approaches\nRecently\, there have been several studies about control charts to monitor profiles\, where the quality of a process/product is expressed as function of response and explanatory variable(s). Mostly\, it is assumed that the in-control parameter values are known and the error terms are normally distributed. However\, these assumptions are rarely satisfied in practice. In this study\, we focused on three popular methods (EWMA-R\, EWMA-3\, and EWMA3(d2)) for monitoring simple linear profiles and the performance of them is examined via simulation where the in-control parameters are estimated and error terms have a Student’s t distribution or gamma distribution. In order to capture the sampling variation among different practitioners\, average and standard deviation of the average run length (ARL) are used as performance measures. In conclusion\, it is seen that the estimation effect becomes more severe as the error term distribution deviates from normality to a greater extent. In addition\, although the average ARL values get closer to the desired values as the amount of Phase I data increases\, their standard deviations remain far away from the acceptable level indicating a high practitioner-to-practitioner variability.
URL:https://www.hsu-hh.de/statistik/event/effect-of-estimation-under-non-normality
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190612T150000
DTEND;TZID=Europe/Berlin:20190612T163000
DTSTAMP:20190424T123239Z
CREATED:20190424T123213Z
LAST-MODIFIED:20190424T123239Z
UID:777-1560351600-1560357000@www.hsu-hh.de
SUMMARY:Johannes Bracher (Uni Zürich)
DESCRIPTION:Some extensions to the endemic-epidemic model class for infectious disease surveillance counts \nThe endemic-epidemic class (Held et al 2005\, DOI 10.1191=1471082X05st098oa) is a modelling framework for multivariate infectious disease surveillance counts closely related to INGARCH models. It allows to model counts stratified by e.g. disease type\, geographical area or age group and is readily implemented in the R package surveillance. I will present some recent developments for this model class. In a first part I will talk about an extension to higher-order lags and some properties of the resulting model class\, in particular their periodically stationary properties. These can be used for model assessment and to link retrospective modelling to prospective outbreak detection. In a second part I will talk about the problem of underreporting\, which is very common in infectious disease epidemiology. I will show how inference for models with an additional reporting step can be done using an approximate maximum likelihood scheme. Moreover I will address some biases which can occur in naive analyses where underreporting is ignored. The different extensions are illustrated using various data examples.
URL:https://www.hsu-hh.de/statistik/event/some-extensions-to-the-endemic-epidemic-model
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190529T154500
DTEND;TZID=Europe/Berlin:20190529T171500
DTSTAMP:20190416T124132Z
CREATED:20190416T124033Z
LAST-MODIFIED:20190416T124132Z
UID:767-1559144700-1559150100@www.hsu-hh.de
SUMMARY:Rainer A. Schüssler (Uni Rostock)
DESCRIPTION:Forecasting the Equity Premium: Mind the News! \nThis paper introduces a novel strategy for predicting the monthly equity premium based on extracted news from more than 700\,000 newspaper articles\, published in The New York Times and Washington Post between 1980 and 2018. We propose a flexible data-adaptive switching approach to map a large set of different news-topics into forecasts of aggregate stock returns. The information embedded in our extracted news are not captured by established equity premium predictors. Compared to the historical mean between 1999 and 2018\, we find large out-of-sample (OOS) gains with an R²oos of 6.52% and sizeable utility gains for a mean-variance investor. The empirical results imply that (geo-)political rather than economic news are more valuable to forecast the equity premium out of sample. Prediction gains arise in down markets.
URL:https://www.hsu-hh.de/statistik/event/forecasting-the-equity-premium-mind
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190522T154500
DTEND;TZID=Europe/Berlin:20190522T171500
DTSTAMP:20190416T122243Z
CREATED:20190416T122242Z
LAST-MODIFIED:20190416T122243Z
UID:755-1558539900-1558545300@www.hsu-hh.de
SUMMARY:Uwe Saint-Mont (HS Nordhausen)
DESCRIPTION:Auf der Suche nach relevanten Merkmalen \nDie Selektion relevanter Merkmale ist ein zentrales Problem der Statistik und der empirischen Wissenschaften im Allgemeinen. Zwar lassen sich heute problemlos große Datenmengen erheben\, also viele Merkmale zahlreicher statistischer Merkmalsträger festhalten\, doch welche davon sind wichtig? Wie ist das kausale Gefüge und welche Variablen steuern das Geschehen? \nSchon im 19. Jahrhundert hat John Stuart Mill diese Fragen mit einer überschaubaren Menge von Argumentationsmustern überzeugend beantwortet. Im 20. Jahrhundert hat die angewandte Statistik diese Ideen aufgegriffen und weiterentwickelt: Entweder man untersucht in einem Experiment wenige Merkmale sehr präzise oder man versucht aus einer Vielzahl von „Kandidaten“ die interessanten zu isolieren. So kommt man einerseits zu experimentellen Designs und andererseits zur statistischen Modellierung. \nBis heute sind daraus randomisierte kontrollierte Studien\, Metaanalysen\, Strukturgleichungsmodelle\, Regressionsanalysen\, epidemiologische Korrelationsstudien und kausalen Graphen hervorgegangen. So verschiedenartig die einzelnen Ansätze auch wirken: Immer ist man den Ursachen auf der Spur und wird gar nicht so selten fündig. \nAuch wenn konkrete Verfahren eine gewisse Rolle spielen werden\, so geht es im Vortrag vor allem um die wesentlichen Ideen\, also um die großen „strategischen“ Zusammenhänge.
URL:https://www.hsu-hh.de/statistik/event/auf-der-suche-nach-relevanten-merkmalen
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190424T154500
DTEND;TZID=Europe/Berlin:20190424T171500
DTSTAMP:20190416T122017Z
CREATED:20190315T113011Z
LAST-MODIFIED:20190416T122017Z
UID:748-1556120700-1556126100@www.hsu-hh.de
SUMMARY:Arne Johannssen (Uni Hamburg)
DESCRIPTION:Health Care Monitoring by Hypergeometric Control Charts for Fractions Non-Conforming \nProcess monitoring in health care organisations is one of the core tasks of an efficient medical risk management system for detecting\, assessing\, mitigating\, and preventing risks. Statistical control charts as adequate tools for process monitoring are well-suited to observe\, measure\, and improve health care outcomes as well as to minimize the occurrence of adverse events. In this presentation we focus on various improvements of control charts for „fraction non-conforming“ and their application to health care monitoring\, since (hospital) quality indicators are often binary at the patient level\, presented as proportions\, risk-adjusted or standardised rates. In particular\, we introduce a new class of statistical control charts based on the (negative) Hypergeometric distribution that comprises improvements of Binomial p-charts (with approximative or exact control limits)\, Geometric g-charts\, and negative Binomial cumulative count of conforming charts. The proposed class of Hypergeometric control charts meets numerous requirements of efficient health care monitoring and is especially useful when monitoring high-yield processes.
URL:https://www.hsu-hh.de/statistik/event/health-care-monitoring-by-hypergeometric-control
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190417T154500
DTEND;TZID=Europe/Berlin:20190417T171500
DTSTAMP:20190305T094258Z
CREATED:20190305T094258Z
LAST-MODIFIED:20190305T094258Z
UID:738-1555515900-1555521300@www.hsu-hh.de
SUMMARY:Dhouha Mejri (TU Dortmund)
DESCRIPTION:Adaptive Control charts for identifying concept drift in nonstationary environment \nTime adjusting dynamic systems whose underlying changing distribution should be continuously\nmonitored to track abnormal behaviors is one of the most recent challenges in many real life\napplications. In fields such as sensor networks\, intrusion detection\, credit card fraud detection and\nprocess monitoring\, the arriving data change over time and the target concept to be learned changes\naccordingly causing the problem of “concept drift. Adaptive control charts from SPC domain and\ndynamic ensemble methods from data mining field are the most widely used techniques to track\nconcept drift. In order to perform the change identification in data stream processes\, the first\nenhancement of ensemble methods in SPC proposed in Mejri et al\, 2018 entitled Dynamic Weighted\nMajority Control Chart will be presented. The new adaptive method has not only the ability to\ncombine more than two control charts but also uses the expertise of dynamic weighted majorityWinnow (DWM)-WIN in\nidentifying and learning changes during the monitoring. First\, it transforms\nthe task of determining the state of the process into a classification problem by treating control charts\nas attributes where the drift has to be predicted. Second\, DWM-WIN mechanism is applied to learn the\nshift and to combine the decision of different individuals. Third\, a prediction of class label is used to\nhelp in classifying the shift during the changing of the process toward the approximated right\ndirection. The three main steps of this combined control chart as well comparative results will be\npresented and discussed in this talk
URL:https://www.hsu-hh.de/statistik/event/adaptive-control-charts-for-identifying-concept
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190313T154500
DTEND;TZID=Europe/Berlin:20190313T171500
DTSTAMP:20190226T152341Z
CREATED:20190226T140207Z
LAST-MODIFIED:20190226T152341Z
UID:727-1552491900-1552497300@www.hsu-hh.de
SUMMARY:Aisouda Hoshiyar (HSU)
DESCRIPTION:Challenging the commonly used log-link in statistical models for count data with an application to infectious disease data \nA response function is an essential part of any generalized linear model\, but its choice\nis rarely questioned. In particular\, if the modeled expected value is restricted to be\ngreater than zero\, the choice often falls on the exponential function. Even for a response\nvariable\, for which the exponential function corresponds to the canonical link\, there is\nno indication that this is the true response function in general. Therefore\, we propose to\ntake the softplus function as response function into consideration. The softplus function\,\nwhich is technically used in the context of neural networks\, enables the modeling of the\nconditional mean in an additive way and therefore ensures a linear interpretation of the\nregression coefficients while respecting the positivity boundary of the conditional mean\nat the same time. The central research question to be discussed in this study is: Does\nthe softplus activating function represent an adequate substitute of the commonly used\nlog-link with an application to infectious diseases? In the first step\, a simulation study\ngives insight into the robustness of the estimated coefficients under various circumstances.\nFurthermore\, the framework for the analysis of multivariate infection disease data yield\nby Held et al. (2005) is self-implemented via the open source software R. By doing so\,\nthe softplus function is introduced to the model class applied. The estimation results\nfrom Held et al. (2005) are reproduced and compared to those concerning the softplus\nlink function with respect to the predictive quality. One-step-ahead-predictions build the\nbasis for mean-squared prediction errors and coverage frequencies of the upper prediction\nlimits. The results have been obtained using general optimisation routines via maximum\nlikelihood estimation.
URL:https://www.hsu-hh.de/statistik/event/challenging-the-commonly-used-log-link
LOCATION:Gebäude H1\, Raum 2151
CATEGORIES:Kolloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190213T154500
DTEND;TZID=Europe/Berlin:20190213T171500
DTSTAMP:20190109T141313Z
CREATED:20190109T141209Z
LAST-MODIFIED:20190109T141313Z
UID:691-1550072700-1550078100@www.hsu-hh.de
SUMMARY:Annika Homburg (HSU)
DESCRIPTION:Point Forecasting in Discrete Time Series Analysis\nIn this work we determine central and non-central coherent point forecasts of various discrete valued time series models.\nEach coherent integer forecast is compared to its approximation\, derived from the model representing the continuous counterpart to each respective discrete model. Several INAR(1) processes and the influence of their distribution parameters\nare analyzed.
URL:https://www.hsu-hh.de/statistik/event/point-forecasting-in-discrete-time-series-analysis
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190206T161500
DTEND;TZID=Europe/Berlin:20190206T174500
DTSTAMP:20190128T161126Z
CREATED:20190121T162340Z
LAST-MODIFIED:20190128T161126Z
UID:704-1549469700-1549475100@www.hsu-hh.de
SUMMARY:Andreas Groll (TU Dortmund)
DESCRIPTION:Effect Selection in Cox Frailty Models by Regularization Methods\nIn all sorts of regression problems it has become more and more important to deal with complex and high dimensional data with lots of potentially influential covariates. A possible solution is to apply estimation methods that aim at the detection of the relevant effect structure by using regularization methods. In this talk\, the effect structure in the Cox frailty model\, which is the most widely used model that accounts for heterogeneity in survival data\, is investigated. Since in survival models one has to account for possible variation of the effect strength over time the selection of the relevant features has to distinguish between several cases: covariates can have time-varying effects\, can have time-constant effects or be irrelevant. A regularization approach is proposed that is able to distinguish between these types of effects to obtain a sparse representation that includes the relevant effects in a proper form. The method is applied to a real world data set\, illustrating that the complexity of the influence structure can be strongly reduced by using the proposed regularization approach.
URL:https://www.hsu-hh.de/statistik/event/effect-selection-in-cox-frailty-models-by-regularization-methods
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190108T154500
DTEND;TZID=Europe/Berlin:20190108T171500
DTSTAMP:20181226T095130Z
CREATED:20181226T094910Z
LAST-MODIFIED:20181226T095130Z
UID:669-1546962300-1546967700@www.hsu-hh.de
SUMMARY:Maria Mohr (Uni Hamburg)
DESCRIPTION:Changepoint detection in a nonparametric time series regression model\nA weakly dependent time series is considered\, for which we develop a strategy to detect whether the nonparametric conditional mean function is stable in time. The strategy allows for autoregressive effects and heteroscedasticity. Our proposal is based on a modified CUSUM-type test procedure\, which uses a sequential marked empirical process of residuals. We show weak convergence of the considered process to a centered Gaussian process under the null ”mt(·) = m(·) for all t” and a stationarity assumption. This requires some sophisticated arguments for sequential empirical processes of weakly dependent variables. As a consequence we obtain the convergence of Kolmogorov-Smirnov\nand Cramér-von Mises type test statistics. The procedure acquires a very simple limiting distribution and nice consistency properties against changepoint alternatives\, features from which related tests are lacking. Further considerations include a bootstrap procedure as well as a test for change in the conditional variance function. Finally\, a simulation study is conducted to investigate the finite sample performance of our tests.
URL:https://www.hsu-hh.de/statistik/event/changepoint-detection-in-a-nonparametric-time
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20181211T154500
DTEND;TZID=Europe/Berlin:20181211T171500
DTSTAMP:20181226T094847Z
CREATED:20181108T111402Z
LAST-MODIFIED:20181226T094847Z
UID:655-1544543100-1544548500@www.hsu-hh.de
SUMMARY:Tobias A. Möller (HSU)
DESCRIPTION:Integer-valued max-autoregressive models\nThe talk addresses an introduction to integer-valued max-autoregressive models. The parameter estimation for such models\, e.g.\, the max-INAR(1) model\, seems to be straightforward. The max-INAR(1) model is a Markov chain and maximum likelihood estimation with numerical optimization routines seems to be easily applicable. But if the observed counts attain very large values\, numerical issues frustrate this plan. The structure of the max-INAR(1) process will be used to show up a way to circumvent this problem. An example of the parameter estimation procedure with real data (counts of cinema visitors) demonstrates the application.
URL:https://www.hsu-hh.de/statistik/event/integer-valued-max-autoregressive-models
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20181113T154500
DTEND;TZID=Europe/Berlin:20181113T171500
DTSTAMP:20180828T120904Z
CREATED:20180828T120839Z
LAST-MODIFIED:20180828T120904Z
UID:626-1542123900-1542129300@www.hsu-hh.de
SUMMARY:Christian Weiss (HSU)
DESCRIPTION:Distance-based Analysis of Ordinal Data and Ordinal Time Series\nThe dissimilarity of ordinal categories can be expressed with a distance measure. By considering expected distances of ordinal random variables\, well-interpretable measures of location\, dispersion or symmetry of ordinal random variables are defined\, and also measures of serial dependence for ordinal processes. For special types of distance\, these analytic tools lead to known approaches for ordinal or real-valued random variables. We also analyze the sample counterparts of the proposed measures and derive asymptotic results for practically important cases. Two real applications about the economic situation in Germany and the credit rating of European countries are presented.
URL:https://www.hsu-hh.de/statistik/event/distance-based-analysis-of-ordinal-data-and-ordinal-time-series
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20181106T154500
DTEND;TZID=Europe/Berlin:20181106T171500
DTSTAMP:20181212T115833Z
CREATED:20181009T121459Z
LAST-MODIFIED:20181212T115833Z
UID:643-1541519100-1541524500@www.hsu-hh.de
SUMMARY:Peter-Theodor Wilrich (FU)
DESCRIPTION:Minensuche – ein statistisches Problem?\nDie in diesem Vortrag vorgestellte Untersuchung begann im Jahre 2002\nmit dem Ziel\, durch systematische Tests die Zuverlässigkeit von Methoden zur\nRäumung von Landminen zu quantifizieren. Insbesondere ging es um Klärung\nder Fragen\, von welchen Einflussgrößen die Entdeckungswahrscheinlichkeit\n(probability of detection\, POD) und die Rate falscher Alarme (false alarm\nrate\, FAR) abhängen\, wie sich POD erhöhen und FAR verringern lassen\, ob\nTrainingsprogramme für Minensucher erforderlich sind und wie die Experimente\nzur Beantwortung dieser Fragen geplant und deren Ergebnisse ausgewertet\nwerden müssen.\nDie in die Minensuchexperimente einbezogenen Faktoren waren die Minentypen\,\ndie Minentiefen\, die Suchgerätetypen (device types)\, die Exemplare\nder Geräte (specimen)\, die Minensucher (operators) und die Versuchsfelder\n(lanes) mit verschiedener Bodenbeschaffenheit.\nDabei hatte der Statistiker folgende Aufgaben: \n1. Entwicklung eines Algorithmus zur Allokation der Minen in Versuchsfeldern.\nAnfangs gab es zwar mit Minen bestückte Versuchsfelder\, aber\nkeine Regeln\, nach denen vorzugehen ist\, um Fehlinformationen zu vermeiden.\n2. Bereitstellung von Versuchsplänen für die Minensuche. Anfangs gab es\nkeinerei statistische Versuchsplanung. Zunächst wurden griechisch-lateinische\nVersuchspläne benutzt\, später eigens entwickelte balancierte faktorielle\nVersuchspläne für Faktoren mit unterschiedlichen Anzahlen von Faktorstufen.\n3. Bereitstellung von Methoden zur statistischen Analyse der in den Experimenten\ngewonnenen Daten. Da die Erebnisse der Experimente binäre\nDaten (0 = nicht gefunden\, 1 = gefunden zw. falscher Alarm) bestehen\,\nerfolgte die statistische Analyse mit verallgemeinerten linearen Modellen.\n4. Erstellung von konsistenten web-basierten Programmen zur Durchführung\nder Planungs- und Analyseschritte\, basierend auf R\, benutzbar aber ohne\nKenntnisse von R. \nDie ersten drei dieser Aufgaben werden im Vortrag angesprochen. Einige\nUntersuchungsergebnisse werden vorgestellt.
URL:https://www.hsu-hh.de/statistik/event/minensuche-ein-statistisches-problem
LOCATION:Helmut-Schmidt-Universität / Universität der Bundeswehr\, Holstenhofweg 85\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20181023T154500
DTEND;TZID=Europe/Berlin:20181023T171500
DTSTAMP:20180830T133757Z
CREATED:20180830T133757Z
LAST-MODIFIED:20180830T133757Z
UID:632-1540309500-1540314900@www.hsu-hh.de
SUMMARY:Gabriel Frahm (HSU)
DESCRIPTION:Pricing and Valuation under the Real-World Measure\nIn general it is not clear which kind of information is supposed to be used for calculating the fair value of a contingent claim. Even if the information is specified\, it is not guaranteed that the fair value is uniquely determined by the given information. A further problem is that asset prices are typically expressed in terms of a risk-neutral measure. This makes it difficult to transfer the fundamental results of financial mathematics to econometrics. I show that the aforementioned problems evaporate if the financial market is complete and sensitive. In this case\, after an appropriate choice of the numéraire\, the discounted price processes turn out to be uniformly integrable martingales under the real-world measure. This leads to a Law of One Price and a simple real-world valuation formula in a model-independent framework where the number of assets as well as the lifetime of the market can be finite or infinite.
URL:https://www.hsu-hh.de/statistik/event/pricing-and-valuation-under-the-real-world-measure
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20181016T154500
DTEND;TZID=Europe/Berlin:20181016T171500
DTSTAMP:20180823T085353Z
CREATED:20180823T085353Z
LAST-MODIFIED:20180823T085353Z
UID:620-1539704700-1539710100@www.hsu-hh.de
SUMMARY:Lena Hubig (LMU München)
DESCRIPTION:Statistical Process Control in Quality Assurance of Inpatient Care\nStatistical Process Control (SPC) in hospital benchmarking using control charts is a common instrument for monitoring clinical performance and early detection of quality deficits. The external quality assurance program (EQA) of German hospitals does not yet employ SPC. Previous work has failed to come up with suggestions for efficient application of SPC. There is also a lack of focus on the importance of preventing false positive signals.\nIn this contribution we study control limits for defined false signal probability and their dependence on specific features such as hospital volume\, risk score and patient mix. We also determine the detection quality of specific control switches. We conduct simulation studies in order to investigate optimal designs for crude and risk-adjusted performance indicators of the log-likelihood CUSUM chart of Steiner et al. (Biostatistics 1.4 (2000)\, pp. 441-52). Examples are taken from the EQA in Bavaria\, Germany.\nFocusing on signal probability instead of average run length allows control of the false signal probability and performance evaluation of control charts. Thus it was possible to construct CUSUM charts for different hospital volumes and failure probabilities. We gained better understanding of the influence of control switches in constructing CUSUM charts. We also compare our results to run-length based control strategies.\nThe presented results are useful for regulatory decision making and help to implement CUSUM charts within EQA. We expect application of CUSUM control charts to significantly improve early detection of quality deficits with appropriate adjustment for different case mix and hospital volume.
URL:https://www.hsu-hh.de/statistik/event/statistical-process-control-in-quality-assurance-of-inpatient-care
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20180626T154500
DTEND;TZID=Europe/Berlin:20180626T171500
DTSTAMP:20180509T094258Z
CREATED:20180509T094202Z
LAST-MODIFIED:20180509T094258Z
UID:600-1530027900-1530033300@www.hsu-hh.de
SUMMARY:Sebastian Ottenstreuer (HSU)
DESCRIPTION:A Combined Shewhart-CUSUM Chart with Switching Limit\nThe common Shewhart-CUSUM chart deploys an additional Shewhart limit to expand a single CUSUM chart by triggering quick alarms for large changes in the parameter of interest. Here\, we utilize this supplementary limit to initiate the CUSUM accumulation. That is\, we switch between an accumulation and a silent phase. The new switching limit’s value resides between the reference value of the CUSUM chart and the usual Shewhart limit. Thus\, for the case that the CUSUM statistic is equal to zero\, a further observation has to be more substantial than this new limit to engage the summing process. We demonstrate the setup and analyze the new combination for independent Poisson distributed data and a more involved time series model with Poisson marginals\, the Poisson INAR(1). Moreover\, we also test the novel chart’s robustness against hypothetical misspecification such as undetected overdispersion or autocorrelation. It turns out that this kind of combination features patterns between a pure CUSUM and a stand-alone Shewhart chart and\, hence\, constitutes a solid alternative to both single charts as well as to the ordinary Shewhart-CUSUM. Finally\, in the context of possible extensions\, a real data set from semiconductor industry with apparently overdispersed counts is considered and the application to Gaussian variables data is briefly discussed.
URL:https://www.hsu-hh.de/statistik/event/a-combined-shewhart-cusum-chart-with-switching-limit
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20180607T143000
DTEND;TZID=Europe/Berlin:20180607T160000
DTSTAMP:20180605T133017Z
CREATED:20180605T133017Z
LAST-MODIFIED:20180605T133017Z
UID:612-1528381800-1528387200@www.hsu-hh.de
SUMMARY:Philipp Wittenberg (HSU)
DESCRIPTION:Performance of risk-adjusted CUSUM chart under an incorrectly specified binary logistic regression model\nQuality control charts used in a healthcare environment\, for example\, to monitor surgical performance are becoming more common. Risk-adjusted (RA) CUSUM charts\, that utilize only raw risk scores like the Parsonnet score to assess the preoperative risk\, may lead to a deterioration of the chart’s properties\, in particular the false alarm behavior. Our approach considers the application of power transformations in the logistic regression model to improve the fit to the binary outcome data. From a list of alternatives\, we derive an appropriate value for the power exponent δ. The average run length (ARL) to false alarm is calculated with the popular Markov chain approximation more efficiently by utilizing the Toeplitz structure of the transition matrix. A sensitivity analysis of the in-control ARL against the actually used value δ shows possible effects of incorrect choices of δ depending on the underlying patient mix. We show that these results can vary from robustness to severe effects (doubled number of false alarms).
URL:https://www.hsu-hh.de/statistik/event/performance-of-risk-adjusted-cusum-chart-under-an
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20180605T154500
DTEND;TZID=Europe/Berlin:20180605T171500
DTSTAMP:20180406T130723Z
CREATED:20180406T130644Z
LAST-MODIFIED:20180406T130723Z
UID:581-1528213500-1528218900@www.hsu-hh.de
SUMMARY:Monika Doll (Uni Erlangen-Nürnberg)
DESCRIPTION:Tests on Asymmetry for Ordered Categorical Variables\nSkewness is a well-established statistical concept for continuous and to a lesser extent for discrete quantitative statistical variables. However\, for ordered categorical variables almost no literature concerning skewness exists\, although this type of variables is common for behavioral\, educational\, and social sciences. Suitable measures of skewness for ordered categorical variables have to be invariant with respect to the group of strictly increasing\, continuous transformations. Therefore\, they have to depend on the corresponding maximal-invariants. Based on these maximal-invariants we propose a new class of skewness functionals\, show that members of this class preserve a suitable ordering of skewness and derive the asymptotic distribution of the corresponding skewness statistic. Finally\, we show the good power behavior of the corresponding skewness tests and illustrated these tests by applying real data examples.
URL:https://www.hsu-hh.de/statistik/event/tests-on-asymmetry-for-ordered-categorical-variables
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20180508T154500
DTEND;TZID=Europe/Berlin:20180508T171500
DTSTAMP:20180430T171245Z
CREATED:20180430T171119Z
LAST-MODIFIED:20180430T171245Z
UID:591-1525794300-1525799700@www.hsu-hh.de
SUMMARY:Ralf Wunderlich (BTU Cottbus-Senftenberg)
DESCRIPTION:On Some Stochastic Optimal Control Problems for an Energy Storage Facility \nWe address the valuation of an energy storage facility in the presence of stochastic energy prices as it arises in the case of a hydro-electric pump station. The valuation problem is related to the problem of determining the optimal charging/discharging strategy that maximizes the expected value of the resulting discounted cash flows over the lifetime of the storage. We use a regime-switching model for the energy price which allows for a changing economic environment described by a finite state Markov chain. For the latter we consider the fully as well as the partially observed case. \nThe valuation problem is formulated as a stochastic control problem with regime switching in continuous time. For this control problem we derive the associated Hamilton-Jacobi-Bellman (HJB) equation which is not strictly elliptic. Therefore we study the HJB equation using regularization arguments. \nWe use numerical methods for computing approximations of the value function and the optimal strategy. Finally\, we present some numerical results.
URL:https://www.hsu-hh.de/statistik/event/on-some-stochastic-optimal-control-problems-for-an-energy-storage-facility
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20180206T140000
DTEND;TZID=Europe/Berlin:20180206T153000
DTSTAMP:20180129T134159Z
CREATED:20180129T134158Z
LAST-MODIFIED:20180129T134159Z
UID:575-1517925600-1517931000@www.hsu-hh.de
SUMMARY:Boris Aleksandrov (HSU)
DESCRIPTION:Parameter estimation and diagnostic tests for INMA(1) processes\nThe INMA(1) model for count time series\, an integer-valued counterpart to the usual moving-average model of order~1\, was introduced by Al-Osh & Alzaid (1988) and McKenzie (1988). During the last years\, it gained increasing interest for applications. For instance\, it was used by Cossette et al. (2011) to model the number of claims in the area of insurance\, and Zhang et al. (2015) applied the model in the area of reinsurance. Furthermore\, Hu et al. (2017) point out application areas where the claim numbers may exhibit overdispersion. While stochastic properties of this model\, in particular for the special case of the Poisson INMA(1) model\, have been comprehensively studied in the literature\, only little is known about statistical inference concerning this model. \nWe start with a central limit theorem for Poisson INMA(1) processes\, which allows to explicitly derive the asymptotic distribution of moment and frequency related statistics. In particular\, we consider the asymptotic distribution (including bias correction) for diverse moment estimators\, for the index of dispersion\, and for the autocorrelation function. We apply these results for constructing confidence intervals for model parameters\, and for deriving hypothesis tests to check the marginal distribution (e.g.\, with respect to the Poisson’s equidispersion property) as well as the autocorrelation function (to diagnose the moving average structure). We also show simulation results for INMA(1) time series with different parameters to demonstrate the finite-sample performance of the asymptotic approximations for the above mentioned statistics.
URL:https://www.hsu-hh.de/statistik/event/parameter-estimation-and-diagnostic-tests-for-inma1-processes
LOCATION:Gebäude H1\, Raum 1505\, Holstenhofweg 85\, Hamburg\, Hamburg\, 22043\, Deutschland
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
END:VCALENDAR