Search
Search Results
-
Empirical estimates for heteroscedastic hierarchical dynamic normal models
The available heteroscedastic hierarchical models perform well for a wide range of real-world data, but for data sets that exhibit a dynamic...
-
Building Predictive Models with Machine Learning
This chapter functions as a practical guide for constructing predictive models using machine learning, focusing on the nuanced process of translating...
-
A Functional approach for constructing dynamic Composite Indicators
This paper contributes to the research on the development of comparable composite indicators by introducing a Functional Weighted Malmquist...
-
An expectation maximization algorithm for the hidden markov models with multiparameter student-t observations
Hidden Markov models are a class of probabilistic graphical models used to describe the evolution of a sequence of unknown variables from a set of...
-
Regularized Latent Trajectory Models for Spatio-temporal Population Dynamics
Climate change impacts ecosystems variably in space and time. Landscape features may confer resistance against environmental stressors, whose...
-
A flexible two-piece normal dynamic linear model
We construct a flexible dynamic linear model for the analysis and prediction of multivariate time series, assuming a two-piece normal initial...
-
A dynamic model for ranking-based conjoint analysis with no-choice options
In ranking data analysis, it is common for all preference ranks to be obtained in each trial. However, if there is a no-choice option in alternatives...
-
Robust test for structural instability in dynamic factor models
In this paper, we consider a robust test for structural breaks in dynamic factor models. The proposed framework considers structural changes when the...
-
Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes
To achieve the goal of providing the best possible care to each individual under their care, physicians need to customize treatments for individuals...
-
A class of transformed joint quantile time series models with applications to health studies
Extensions of quantile regression modeling for time series analysis are extensively employed in medical and health studies. This study introduces a...
-
Expectation Propagation for the Smoothing Distribution in Dynamic Probit
The smoothing distribution of dynamic probit models with Gaussian state dynamics was recently proved to belong to the unified skew-normal family....
-
Temporal Models and Their Applications
Modeling temporal dependency holds significant importance across diverse domains like finance, economics, and resource allocation. By capturing the...
-
Melded Integrated Population Models
Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of...
-
On CUSUM test for dynamic panel models
In this study, we consider the problem of testing for a parameter change in dynamic panel models with fixed effects. As a test, we suggest using the...
-
A Bayesian Approach for Data-Driven Dynamic Equation Discovery
Many real-world scientific and engineering processes are governed by complex nonlinear interactions, and differential equations are commonly used to...
-
Dynamic Bivariate Mortality Modelling
The dependence structure of the life statuses plays an important role in the valuation of life insurance products involving multiple lives. Although...
-
Combination of optimization-free kriging models for high-dimensional problems
Kriging metamodeling (also called Gaussian Process regression) is a popular approach to predict the output of a function based on few observations....
-
Assessing dynamic covariate effects with survival data
Dynamic (or varying) covariate effects often manifest meaningful physiological mechanisms underlying chronic diseases. However, a static view of...
-
Assessing Ecosystem State Space Models: Identifiability and Estimation
Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and...
-
On the statistical analysis of high-dimensional factor models
High-dimensional factor models have received much attention with the rapid development in big data. We make several contributions to the asymptotic...
