© 2018

Trends and Perspectives in Linear Statistical Inference

Proceedings of the LINSTAT2016 meeting held 22-25 August 2016 in Istanbul, Turkey

  • Müjgan Tez
  • Dietrich von Rosen
  • 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

Conference proceedings

Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Table of contents

  1. Front Matter
    Pages i-x
  2. Fatma Gül Akgül, Birdal Şenoğlu
    Pages 1-22
  3. Huruy Debessay Asfha, Betul Kan Kilinc
    Pages 41-55
  4. Ali Zafer Dalar, Erol Eğrioğlu
    Pages 69-87
  5. Birsen Eygi Erdogan, Süreyya Özöğür Akyüz
    Pages 89-103
  6. Jarkko Isotalo, Augustyn Markiewicz, Simo Puntanen
    Pages 111-129
  7. Timothy E. O’Brien
    Pages 165-180
  8. Back Matter
    Pages 257-257

About these proceedings


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. 


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

Editors and affiliations

  • Müjgan Tez
    • 1
  • Dietrich von Rosen
    • 2
  1. 1.Department of Statistics, Arts and Sciences FacultyMarmara UniversityIstanbulTurkey
  2. 2.Department of Energy and TechnologySwedish University of Agricultural SciencesUppsalaSweden

About the editors

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. 

Bibliographic information

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