- PART 1: Plenary Lectures: W. Stute, Stairway to hell.- I. Gijbels, R. Karim, A. Verhasselt, Quantile estimation in a generalized asymmetric distributional setting.- M. Ljungdahl, M. Podolskij, A note on parametric estimation of Levy moving average processes.-
- PART 2: Theory and Related Topics: K. Knight, A continuous-time iteratively reweighted least squares algorithm for $L_\infty$ estimation.- M. Bibinger, M. Trabs, On central limit theorems for power variations of the solution to the stochastic heat equation.- P. Gapeev, Perpetual dual American barrier options for short sellers.- P. Lachout, A criterion for weak convergence in vector Skorokhod spaces.- E. Liebscher, On combining star-shaped distributions and copulas.- E. Skubalska-Rafajlowicz, Stability of the Random-Projection Based Classifiers. The Bayes error perspective.- A. Ishii, K. Yata, M. Aoshima, A quadratic classifier for high-dimension, low-sample-size data under the strongly spiked eigenvalue model.- Z. Hlavka, M. Huskova, Doubly paired change-point analysis.- B. Darkhovsky, A. Piryatinska, Detection of changes in binary sequences.- Q. Liu, R. Zhang, Y. Xie, Distributed Change Detection via Average Consensus over Networks.- A. Steland, E. Rafajlowicz, The Hotelling-like T^2 control chart modified for detecting changes in images having the matrix normal distribution.- S. Vogel, Universal Confidence Sets for Solutions of Stochastic Optimization Problems - A Contribution to Quantification of Uncertainty.- Y. Liu, J. Wishart.- Local polynomial $M$-estimation in random design regression with dependent errors.- L. Smaga, Projection-based repeated measures analysis for functional data.- C. Weiss, On the Sample Coefficient of Nominal Variation.- B. Aleksandrov, A Negative-Binomial Index Considering Dispersion and Zero Probability.-
- PART 3: Stochastic Models, Methods and Simulations: J. P. Burgard, J. Krause, H. Merkle, R. Munnich, S. Schmaus, Conducting a Dynamic Microsimulation for Care Research: Data Generation, Transition Probabilities and Sensitivity Analysis.- M. E. Silva, I. Silva, C. Torres, Modelling overdispersion with integer-valued moving average processes.- E. Goncalves, N. Mendes-Lopes, Zero-distorted Compound Poisson INGARCH models.- T. A. Moeller, An Application of the Max-INAR(1) Model to Counts of Cinema Visitors.- J. Bracher, A new INARMA(1, 1) model with Poisson marginal.- S. Buscher, M. Batram, D. Bauer, Using motifs for population synthesis in multi-agent mobility simulation models.- U. Falkenhagen, W. Koessler, Hans-J. Lenz, A likelihood ratio test for inlier detection.-
- PART 4: Applications and Algorithms: A. Tordeux, M. Chraibi, A. Seyfried, A. Schadschneider, Artificial neural networks predicting pedestrian dynamics in complex buildings.- A. Gramacki, M. Kowal, M. Mazurkiewicz, J. Gramacki, A. Plawiak-Mowna, Automatic breast cancer diagnostics based on statistical analysis of shape and texture features of individual cell nuclei.- P. Sliwinski, P. Wachel, A. Galeziowski, Stochastic framework for contrast-detection autofocusing.- S. Kroemer, W. Stummer, A new toolkit for mortality data analytics.- T. Gorecki, P. Piasecki, A comprehensive comparison of distance measures for time series classification.- A. Homburg, Criteria to Validate Count Data Model Selection.- M. Romaniuk, On Some Applications of Simulations in Estimation of Maintenance Costs and in Statistical Tests for Fuzzy Settings.
- (source: Nielsen Book Data)
This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance. .
(source: Nielsen Book Data)