Computational statistics

The text includes a preface and contents overview, followed by various studies and analyses related to statistical properties and methodologies. It discusses the Most Probable Number (MPN) procedure for estimating microorganism density through computer simulations and explores bilinear models for time series analysis and forecasting. The annotation covers tests for uniform distribution and compares classical ruin problems with electronic roulette machines. It addresses price formation in the chemical industry of Germany through econometric modeling, as well as vector AR processes in macroeconomics. The robustness of distributed lag estimators and linear regression estimates via simulation are examined. Nonlinear regression is discussed, focusing on parameter estimation in linearizable models. A test for the independence of dichotomous stochastic variables on a two-dimensional lattice is presented, along with computational experiences in automatic transfer function modeling. The text also includes comparisons between empirical and theoretical characteristics of ARMA models for model validation, the role of APL in teaching statistics, and optimal stratification under a bivariate lognormal distribution assumption. Nonparametric tests for randomness at various discretization levels are mentioned, alongside a Monte Carlo study on linear combinations of estimators in errors-in-variables models and the robustness of sampling plans fo