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Heterogeneity in general multinomial choice models
Different voters behave differently at the polls, different students make different university choices, or different countries choose different...
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Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome
This paper develops a Bayesian model with a flexible link function connecting a binary treatment response to a linear combination of covariates and a...
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Clustered Sparse Structural Equation Modeling for Heterogeneous Data
Joint analysis with clustering and structural equation modeling is one of the most popular approaches to analyzing heterogeneous data. The methods...
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Bayesian Multiple Change-Points Detection in a Normal Model with Heterogeneous Variances
This study considers the problem of multiple change-points detection. For this problem, we develop an objective Bayesian multiple change-points...
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Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model
The comparison of coefficients of logit models obtained for different groups is widely considered as problematic because of possible heterogeneity of...
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A Bayesian method for multinomial probit model
The independence of irrelevant alternatives (IIA) property states that the ratio of any two choice probabilities in a set of alternatives is...
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A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures
Relational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models...
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Comparison of extreme order statistics from two sets of heterogeneous dependent random variables under random shocks
In this paper, we consider two k -out-of- n systems comprising heterogeneous dependent components under random shocks, with an Archimedean copula. We...
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Model averaging for estimating treatment effects
The estimation of treatment effects on the response variable is often a primary goal in empirical investigations in disciplines such as medicine,...
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Robust Multivariate Modelling for Heterogeneous Data Sets with Mixtures of Multivariate Skew Laplace Normal Distributions
Modelling multivariate heterogeneous data with taking into account skewness and thick-tailedness is a challenging problem. Finite mixture model of...
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A dynamic network model to measure exposure concentration in the Austrian interbank market
Motivated by an original financial network dataset, we develop a statistical methodology to study non-negatively weighted temporal networks. We focus...
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Testing for trend in two-way crossed effects model under heteroscedasticity
In this paper, a two-way ANOVA model is considered when interactions between two factors are present and errors are normally distributed with...
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Bayesian Prediction and Model Checking
Aspects of Bayesian prediction have been addressed in previous chapters. In particular, Chaps. 7 and...
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Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests
Estimating the individualized treatment effect has become one of the most popular topics in statistics and machine learning communities in recent...
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Realized Stochastic Volatility Model
In this chapter, we further extend the SV model by incorporating a model-free volatility estimator called realized volatility. The realized...
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Estimation and testing of kink regression model with endogenous regressors
Kink regression model which assumes continuity at the threshold point has wide applications in statistics and economics. Existing estimation methods...
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Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets
Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of the variables. A clustering algorithm should be able,...
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Selective inference for false discovery proportion in a hidden Markov model
We address the multiple testing problem under the assumption that the true/false hypotheses are driven by a hidden Markov model (HMM), which is...
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A semiparametric dynamic higher-order spatial autoregressive model
Conventional higher-order spatial autoregressive models assume that all regression coefficients are constant, which ignores dynamic feature that may...
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Clustering by deep latent position model with graph convolutional network
With the significant increase of interactions between individuals through numeric means, clustering of nodes in graphs has become a fundamental...
