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Trends and Perspectives in Linear Statistical Inference: LinStat, Istanbul, August 2016 de Müjgan Tez

Trends and Perspectives in Linear Statistical Inference: LinStat, Istanbul, August 2016: Contributions to Statistics

Editat de Müjgan Tez, Dietrich von Rosen
en Limba Engleză Hardback – 2 feb 2018
This volume features selected contributions on a variety of topics related to linear statistical inference. The peer-reviewed papers from the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat 2016) held in Istanbul, Turkey, 22-25 August 2016, cover topics in both theoretical and applied statistics, such as linear models, high-dimensional statistics, computational statistics, the design of experiments, and multivariate analysis. The book is intended for statisticians, Ph.D. students, and professionals who are interested in statistical inference. 
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Specificații

ISBN-13: 9783319732404
ISBN-10: 3319732404
Pagini: 256
Ilustrații: X, 257 p. 60 illus., 26 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.55 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Contributions to Statistics

Locul publicării:Cham, Switzerland

Cuprins

Foreword.- Comparison of estimation methods for inverse Weibull distribution (F. G. Akgül, B. Şenoğlu).- 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. Eğrioğlu).- A weighted ensemble learning by SVM for longitudinal data: Turkish bank bankruptcy (B. E. Erdogan, S. Ö. Akyüz).- 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, İ. 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 (Ö. Türkşen, M. Tez).

Notă biografică

Müjgan Tez is a professor at the Department of Mathematics of the Marmara University in Istanbul, Turkey. Her research interests include nonlinear models, measurement error of nonlinear models, geometry of statistical models, variance and covariance analysis, mixed models and meta-analysis.
Dietrich von Rosen graduated in mathematical statistics at Stockholm University, Sweden and is currently a professor at the Department of Energy and Technology of the Swedish University of Agricultural Sciences. His main research interest is multivariate analysis and its extensions, including repeated measurements analysis and high-dimensional analysis. 



Textul de pe ultima copertă

This volume features selected contributions on a variety of topics related to linear statistical inference. The peer-reviewed papers from the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat 2016) held in Istanbul, Turkey, 22-25 August 2016, cover topics in both theoretical and applied statistics, such as linear models, high-dimensional statistics, computational statistics, the design of experiments, and multivariate analysis. The book is intended for statisticians, Ph.D. students, and professionals who are interested in statistical inference. 

Caracteristici

Presents selected and peer-reviewed contributions on linear statistical inference Covers a wide range of topics in both theoretical and applied statistics Includes contributions on linear models and high-dimensional statistical analysis