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Modern perspectives on statistics for spatio‐temporal data Early View

  • Journal: WIREs Computational Statistics
  • Authors: Christopher K. Wikle
  • Published Date: Nov 20, 2014

Spatio‐temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially explicit processes that evolve over time....

The role of simulations in econometrics pedagogy Early View

  • Journal: WIREs Computational Statistics
  • Authors: Anton Bekkerman
  • Published Date: Dec 02, 2014

This article assesses the role of simulation methods in econometrics pedagogy. Technological advances have increased researchers' abilities to use simulation methods and have contributed...

Variable selection in the presence of missing data: imputation‐based methods Early View

  • Journal: WIREs Computational Statistics
  • Authors: Yize Zhao, Qi Long
  • Published Date: May 24, 2017

Variable selection plays an essential role in regression analysis as it identifies important variables that are associated with outcomes and is known to improve predictive accuracy of...

A survey on theoretical advances of community detection in networks Early View

  • Journal: WIREs Computational Statistics
  • Authors: Yunpeng Zhao
  • Published Date: Jun 14, 2017

Real‐world networks usually have community structure, that is, nodes are grouped into densely connected communities. Community detection is one of the most popular and best‐studied...

Developments in pseudo‐random number generators Early View

  • Journal: WIREs Computational Statistics
  • Authors: Lih‐Yuan Deng, Dale Bowman
  • Published Date: Aug 08, 2017

Monte Carlo simulations have become a common practice to evaluate a proposed statistical procedure, particularly when it is analytically intractable. Validity of any simulation study...

Hierarchical clustering for histogram data Early View

  • Journal: WIREs Computational Statistics
  • Authors: L. Billard, Jaejik Kim
  • Published Date: Aug 10, 2017

Clustering methods for classical data are well established, though the associated algorithms primarily focus on partitioning methods and agglomerative hierarchical methods. With the advent...

Bayesian multiple comparisons and model selection Early View

  • Journal: WIREs Computational Statistics
  • Authors: Andrew A. Neath, Javier E. Flores, Joseph E. Cavanaugh
  • Published Date: Nov 17, 2017

The testing of multiple hypotheses is an important consideration in many statistical analyses. A theme for multiple comparisons problems under a frequentist paradigm is the need for an...

Sieve maximum likelihood estimation in generalized linear models with an unknown link function Early View

  • Journal: WIREs Computational Statistics
  • Authors: Mengdie Yuan, Guoqing Diao
  • Published Date: Dec 22, 2017

Standard generalized linear models (GLMs) consist of three components: random component referring to a distribution of the response variable that belongs to the exponential family;...

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