Barnes and Noble The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models at Mall of America® in Bloomington, MN

Marginal Models: For Dependent, Clustered, and Longitudinal Categorical Data

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Marginal Models: For Dependent, Clustered, and Longitudinal Categorical Data

Bayesian Heuristic Approach to Discrete and Global Optimization: Algorithms, Visualization, Software, Applications

Integrated Inferences: Causal Models for Qualitative and Mixed-Method Research

Discrete Stochastic Models and Applications for Reliability Engineering Statistical Quality Control

Approximate Models of Mechanics Composites: An Asymptotic Approach

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

Electrical Conductivity in Polymer-Based Composites: Experiments, Modelling, and Applications

Multivariable Model - Building: A Pragmatic Approach to Regression Anaylsis based on Fractional Polynomials for Modelling Continuous Variables

Asymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems

Scheduling of Power Generation: A Large-Scale Mixed-Variable Model

Conceptual Modeling for Traditional and Spatio-Temporal Applications: The MADS Approach

Language Modeling for Automatic Speech Recognition of Inflective Languages: An Applications-Oriented Approach Using Lexical Data

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The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models

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