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Missing data 7 EM algorithm 6 Econometrics 6 Probability Theory and Stochastic Processes 6 Statistical consulting 6 Statistics for Business, Management, Economics, Finance, Insurance 6 Statistics, general 6 Estimation 5 Explainable artificial intelligence 5 Interpretable machine learning 5 Moments 5 Panel data 5 Cointegration 4 Computer experiments 4 Factor analysis 4 Feature importance 4 Measurement error 4 Mixture models 4 Multiple imputation 4 Structural equation modeling 4 Analysis of variance 3 Asymptotic normality 3 Capture–recapture 3 Deep learning 3 Design of experiments 3 Forecasting 3 Functional data 3 Heterogeneity 3 Imputation 3 Monte Carlo simulation 3 Monte Carlo study 3 Regularization 3 Sample selection 3 Statistical process control 3 Stochastic differential equations 3 products of random variables 3 Bayesian analysis 2 Bayesian statistics 2 Beta regression 2 Bradley–Terry model 2
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Undetermined 261 Free 41
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Article 302
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Article 39 Book Review 2
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Undetermined 261 English 41
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Uebe, Götz 11 Nadarajah, Saralees 10 Kauermann, Göran 8 Kneib, Thomas 6 Härdle, Wolfgang 5 Schlittgen, Rainer 5 Schmid, Wolfgang 5 Groll, Andreas 4 Böhning, Dankmar 3 Golosnoy, Vasyl 3 Hassler, Uwe 3 Haupt, Harry 3 Hsiao, Cheng 3 Kotz, Samuel 3 Krumbholz, Wolf 3 Münnich, Ralf 3 Okhrin, Yarema 3 Pauly, Markus 3 Rendtel, Ulrich 3 Schmidt, Klaus 3 Singer, Hermann 3 Webel, Karsten 3 Weiß, Christian 3 Weißbach, Rafael 3 Ötting, Marius 3 Aßenmacher, Matthias 2 Behnke, Joachim 2 Berger, Ursula 2 Bischl, Bernd 2 Blesch, Kristin 2 Brefeld, Ulf 2 Bresson, Georges 2 Böckenhoff, Annette 2 Demetrescu, Matei 2 Doblhammer, Gabriele 2 Dörre, Achim 2 Engel, Joachim 2 Fahrmeir, Ludwig 2 Fickel, Norman 2 Fink, Anne 2
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AStA Advances in Statistical Analysis 302
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RePEc 261 EconStor 41
Showing 21 - 30 of 302
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Conditional feature importance for mixed data
Blesch, Kristin; Watson, David S.; Wright, Marvin N. - In: AStA Advances in Statistical Analysis 108 (2023) 2, pp. 259-278
Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical perspective, a major distinction is between analysing a variable’s importance before and after adjusting for...
Persistent link: https://www.econbiz.de/10015404263
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Action rate models for predicting actions in soccer
Dick, Uwe; Brefeld, Ulf - In: AStA Advances in Statistical Analysis 107 (2022) 1, pp. 29-49
We present a data-driven approach to predict the next action in soccer. We focus on passing actions of the ball possessing player and aim to forecast the pass itself and when, in time, the pass will be played. At the same time, our model estimates the probability that the player loses possession...
Persistent link: https://www.econbiz.de/10015197735
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On the role of data, statistics and decisions in a pandemic
Jahn, Beate; Friedrich, Sarah; Behnke, Joachim; Engel, … - In: AStA Advances in Statistical Analysis 106 (2022) 3, pp. 349-382
A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be...
Persistent link: https://www.econbiz.de/10015205216
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Authors’ response: on the role of data, statistics and decisions in a pandemic
Jahn, Beate; Friedrich, Sarah; Behnke, Joachim; Engel, … - In: AStA Advances in Statistical Analysis 106 (2022) 3, pp. 403-405
Persistent link: https://www.econbiz.de/10015205397
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Control charts for measurement error models
Golosnoy, Vasyl; Hildebrandt, Benno; Köhler, Steffen; … - In: AStA Advances in Statistical Analysis 107 (2022) 4, pp. 693-712
We consider a linear measurement error model (MEM) with AR(1) process in the state equation which is widely used in applied research. This MEM could be equivalently re-written as ARMA(1,1) process, where the MA(1) parameter is related to the variance of measurement errors. As the MA(1) parameter...
Persistent link: https://www.econbiz.de/10015165616
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Multiple imputation of ordinal missing not at random data
Hammon, Angelina - In: AStA Advances in Statistical Analysis 107 (2022) 4, pp. 671-692
We introduce a selection model-based imputation approach to be used within the Fully Conditional Specification (FCS) framework for the Multiple Imputation (MI) of incomplete ordinal variables that are supposed to be Missing Not at Random (MNAR). Thereby, we generalise previous work on this topic...
Persistent link: https://www.econbiz.de/10015173563
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Editorial special issue: Statistics in sports
Groll, Andreas; Liebl, Dominik - In: AStA Advances in Statistical Analysis 107 (2022) 1, pp. 1-7
Triggered by advances in data gathering technologies, the use of statistical analyzes, predictions and modeling techniques in sports has gained a rapidly growing interest over the last decades. Today, professional sports teams have access to precise player positioning data and sports scientists...
Persistent link: https://www.econbiz.de/10015323844
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[Rezension] Discussion on On the role of data, statistics and decisions in a pandemic
Berger, Ursula; Kauermann, Göran; Küchenhoff, Helmut - In: AStA Advances in Statistical Analysis 106 (2022) 3, pp. 387-390
The authors make an important contribution presenting a comprehensive and thoughtful overview about the many different aspects of data, statistics and data analyses in times of the recent COVID-19 pandemic discussing all relevant topics. The paper certainly provides a very valuable reflection of...
Persistent link: https://www.econbiz.de/10015327386
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Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections
De Nicola, Giacomo; Schneble, Marc; Kauermann, Göran; … - In: AStA Advances in Statistical Analysis 106 (2022) 3, pp. 407-426
Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context,...
Persistent link: https://www.econbiz.de/10015327400
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[Rezension] Comment on: On the role of data, statistics and decisions in a pandemic statistics for climate protection and health—dare (more) progress!
Radermacher, Walter J. - In: AStA Advances in Statistical Analysis 106 (2022) 3, pp. 391-397
In the Corona pandemic, it became clear with burning clarity how much good quality statistics are needed, and at the same time how unsuccessful we are at providing such statistics despite the existing technical and methodological possibilities and diverse data sources. It is therefore more than...
Persistent link: https://www.econbiz.de/10015327414
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