Trends and Perspectives in Linear Statistical Inference

Trends and Perspectives in Linear Statistical Inference

LinStat, Istanbul, August 2016

Tez, Muejgan; von Rosen, Dietrich

Springer International Publishing AG

06/2019

257

Mole

Inglês

9783319892429

15 a 20 dias

454

Descrição não disponível.
Foreword.- Comparison of estimation methods for inverse Weibull distribution (F. G. Akguel, B. Senoglu).- Liu-type negative binomial regression (Y. Asar).- Appraisal of performance of three tree-based classification methods (H. D. Asfha, B. K. Kilinc).- High-dimensional CLTs for individual Mahalanobis distances (D. Dai, T. Holgersson).- Bootstrap type-1 fuzzy functions approach for time series forecasting (A. Z. Dalar, E. Egrioglu).- A weighted ensemble learning by SVM for longitudinal data: Turkish bank bankruptcy (B. E. Erdogan, S. OE. Akyuez).- The complementary exponential phase type distribution (S. Eryilmaz).- Best linear unbiased prediction: Some properties of linear prediction sufficiency in the linear model (J. Isotalo, A. Markiewicz, S. Puntanen).- A note on circular m-consecutive-k-out-of-n: F Systems (C. Kan).- A categorical principal component regression on computer assisted instruction in probability domain (T. Kapucu, O. Ilk, I. Batmaz).- Contemporary robust optimal design strategies (T. E. O'Brien).- Alternative approaches for the use of uncertain prior information to overcome the rank-deficiency of a linear model (B. Schaffrin, K. Snow, X. Fang).- Exact likelihood-based point and interval estimation for lifetime characteristics of Laplace distribution based on hybrid Type-I and Type-II censored data (F. Su, N. Balakrishnan, X. Zhu).- Statistical inference for two-compartment model parameters with bootstrap method and genetic algorithm (OE. Tuerksen, M. Tez).
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linear statistical models;linear statistical inference;estimators;model selection;theoretical and applied statistics;high-dimensional statistical analysis;multivariate model;variance components;prediction and testing;linear experiments;mixed linear model