Trends and Perspectives in Linear Statistical Inference

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

• Department of Statistics, Arts and Sciences Faculty, Marmara University, Istanbul, Turkey

  Müjgan Tez

• Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden

  Dietrich von Rosen

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. 

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