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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

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TLDR
The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Abstract
Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary

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Stochastic degradation models with several accelerating variables

TL;DR: New accelerated test models are developed based on a generalized cumulative damage approach with a stochastic process characterizing a degradation phenomenon that motivates the need for developing general accelerated test Models with several accelerating variables for inference based on both observed failure values, and degradation measurements.
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Model averaging, missing data and multiple imputation: a case study for behavioural ecology

TL;DR: This paper employs an example from behavioural ecology to illustrate how missing data can affect the conclusions drawn from model selection or based on hypothesis testing, and shows how missing observations can be recovered to give accurate estimates for IT-related indices and parameters by utilizing ‘multiple imputation’.
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MOTMOT: models of trait macroevolution on trees

TL;DR: “Models of trait macroevolution on trees (MOTMOT) is a new software package that tests for variation in the tempo and mode of continuous character evolution on phylogenetic trees.
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Seabirds as indicators of marine food supplies: Cairns revisited

TL;DR: Testing data collected at colonies of common murre Uria aalge and black-legged kittiwake Rissa tridactyla in Cook Inlet, Alaska found similarities and some differences in how species responded to variability in prey density.