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Efficiency Bound Under Identifiability Constraints in Semiparametric Models
The purpose of this work is to define an adequate efficiency bound in some models presenting some identification problems. We show how it is possible...
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Linear Models
This chapter introduces the fundamental concepts and terminology of the text. It includes a discussion of identifiability and, most crucially, a...
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Imposing unsupervised constraints to the Benefit-of-the-Doubt (BoD) model
Policymakers are in growing need of metrics which will assist them in ranking and assessing entities on different topics. Composite indicators,...
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Linear hypothesis testing in ultra high dimensional generalized linear mixed models
This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the...
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Bayesian analysis of testing general hypotheses in linear models with spherically symmetric errors
We consider Bayesian analysis for testing the general linear hypotheses in linear models with spherically symmetric errors. These error distributions...
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Linear Programming Subject to Max-Product Fuzzy Relation Inequalities with Discrete Variables
In this paper, we introduced the linear programming subject to max-product fuzzy relation inequalities with discrete variables to denote the...
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Deep support vector quantile regression with non-crossing constraints
We propose a new nonparametric regression approach that combines deep neural networks with support vector quantile regression models. The nature of...
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Automatic piecewise linear regression
Regression modelling often presents a trade-off between predictiveness and interpretability. Highly predictive and popular tree-based algorithms such...
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Poisson Model/Log-Linear Model
This section introduces regression analysis using exponential transformation. Analyze counting data using the Poisson distribution. At the end of the...
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Constraints on Quantification
In Chap. 1, we compared typical data analysis with quantification analysis, and witnessed how much more information of data one can retrieve from...
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Identifiability of latent-variable and structural-equation models: from linear to nonlinear
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable. In factor analysis, an orthogonal rotation of the...
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A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models
Diagnostic classification models (DCMs) are restricted latent class models with a set of cross-class equality constraints and additional monotonicity...
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Inferences for extended partially linear single-index models
In partially linear single-index models, there are two different covariate matrices in the model for the linear part and nonlinear part. All...
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Degrees of freedom for regularized regression with Huber loss and linear constraints
The ordinary least squares estimate for linear regression is sensitive to errors with large variance. It is not robust to heavy-tailed errors or...
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Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation
In this paper, we consider conditional selective inference (SI) for a linear model estimated after outliers are removed from the data. To apply the...
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Hierarchical Linear Model
In the experimental designs already covered, we assumed a normal distribution with a mean of zero as the prior distribution for parameters’ random...
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Partial-linear single-index transformation models with censored data
In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to...
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Taylor’s power law and reduced-rank vector generalized linear models
Taylor’s power law (TPL) from empirical ecological theory has had many explanations proposed for its widespread observation in data. We show that the...
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Linear Hypotheses
Many testing problems concernLinear model the means of normal distributions and are special cases of the following general univariate linear...
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Matrix-variate generalized linear model with measurement error
Matrix-variate generalized linear model (mvGLM) has been investigated successfully under the framework of tensor generalized linear model, because...
