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Trends and perspectives in linear st...

(1998 :)

Trends and perspectives in linear statistical inferenceproceedings of the LINSTAT2016 meeting held 22-25 August 2016 in Istanbul, Turkey /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Trends and perspectives in linear statistical inferenceedited by Mujgan Tez, Dietrich von Rosen.
其他題名:	proceedings of the LINSTAT2016 meeting held 22-25 August 2016 in Istanbul, Turkey /
其他題名:	LINSTAT2016
其他作者:	Tez, Mujgan.
團體作者:
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	x, 257 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Linear models (Statistics)
電子資源:	http://dx.doi.org/10.1007/978-3-319-73241-1
ISBN:	9783319732411$q(electronic bk.)

Trends and perspectives in linear statistical inferenceproceedings of the LINSTAT2016 meeting held 22-25 August 2016 in Istanbul, Turkey /
Trends and perspectives in linear statistical inferenceproceedings of the LINSTAT2016 meeting held 22-25 August 2016 in Istanbul, Turkey /[electronic resource] :LINSTAT2016edited by Mujgan Tez, Dietrich von Rosen. - Cham :Springer International Publishing :2018. - x, 257 p. :ill., digital ;24 cm. - Contributions to statistics,1431-1968. - Contributions to statistics..

Foreword -- Comparison of estimation methods for inverse Weibull distribution (F. G. Akgul, 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. O. Akyuz) -- 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 (O. Turksen, M. Tez)

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.

ISBN: 9783319732411$q(electronic bk.)

Standard No.: 10.1007/978-3-319-73241-1doiSubjects--Topical Terms:

181866
Linear models (Statistics)

LC Class. No.: QA279

Dewey Class. No.: 519.5

Trends and perspectives in linear statistical inferenceproceedings of the LINSTAT2016 meeting held 22-25 August 2016 in Istanbul, Turkey /
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520 $a 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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