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General unbiased estimating equations for variance components in linear mixed models
This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations,...
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A Variance Partitioning Multi-level Model for Forest Inventory Data with a Fixed Plot Design
Forest inventories are often carried out with a particular design, consisting of a multi-level structure of observation plots spread over a larger...
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Variance Estimation for Random-Groups Linking in Large-Scale Survey Assessments
The random-groups design is frequently used in equating and linking scores from two tests, in which the linking functions are derived from the test...
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Analysis of Single-Factor Experiments
In this chapter, we will study the introductory model of experimental design, which is the independent single-factor plan. Experiments are a powerful...
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Multifactor Analysis of Variance
This chapter presents the analysis of multifactor ANOVA models. The first three sections deal with the balanced two-way ANOVA model. Section 1...
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Analysis of Accounting Transactions
Accounting transactions are the “raw data” of accounting system, but the idiosyncratic vernacular of accounting, and spotty empirical study of...
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On Mean And/or Variance Mixtures of Normal Distributions
Parametric distributions are an important part of statistics. There is now a voluminous literature on different fascinating formulations of flexible...
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Analysis of Differences
This chapter examines parametric tests and nonparametric alternatives for testing whether statistical differences are observed in data measured on...
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Bayesian Analysis of Multivariate Matched Proportions with Sparse Response
Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals,...
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Real Analysis and Probability Distributions of Vectors and Matrices
The earlier chapters in Part I have been in the area of mathematics generally called “algebra.” It includes the study of the familiar structures such...
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Cluster Validation Based on Fisher’s Linear Discriminant Analysis
Cluster analysis aims to find meaningful groups, called clusters, in data. The objects within a cluster should be similar to each other and...
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Variance Swaps Under Multiscale Stochastic Volatility of Volatility
Many hedge funds and retail investors demand volatility and variance derivatives in order to manage their exposure to volatility and...
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Comparison of variance estimation methods in semiparametric accelerated failure time models for multivariate failure time data
The semiparametric accelerated failure time (AFT) model is a log-linear model of failure times with an unspecified random error term. The rank-based...
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Optimal Mean-Variance Investment-Reinsurance Strategy for a Dependent Risk Model with Ornstein-Uhlenbeck Process
In this paper, we investigate the optimal investment-reinsurance strategy for an insurer with two dependent classes of insurance business, where the...
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Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms
Global sensitivity analysis aims to quantify the importance of model input variables for a model response. We highlight the role sensitivity analysis...
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Variance Propagation for Density Surface Models
Spatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts....
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Multiple Regression Analysis
In the previous chapter we discussed that usually one independent variable is not sufficient to describe the dependent variable. Usually, several...
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A new algorithm for fitting semi-parametric variance regression models
Variance regression allows for heterogeneous variance, or heteroscedasticity, by incorporating a regression model into the variance. This paper uses...
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A Robust Estimation Approach for Mean-Shift and Variance-Inflation Outliers
We consider a classical regression model contaminated by multiple outliers arising simultaneously from mean-shift and variance-inflation...
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Logistic Regression/Meta-Analysis
This section introduces regression analysis using logistic transformation. We utilize the Bernoulli distribution and binomial distribution and, at...