A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data

Biswas, Jayabrata; Das, Kiranmoy

doi:10.1007/s00180-020-01002-1

Cited by 10 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, there is information on health insurance: (i) health insurance related to employment (Y/N); (ii) government insurance (Medicaid or Medicare) (Y/N); and (iii) other private health insurance (Y/N). For the older individuals the effects of different types of insurance can be assumed to be time-invariant (Biswas and Das 2021).…”

Section: The Health and Retirement Study (Hrs)mentioning

confidence: 99%

A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study

et al. 2021

Self Cite

View full text Add to dashboard Cite

All developed countries are facing the problem of providing affordable and high quality healthcare in recent years. This is due to the combination of an ageing population and the technological advancement in health science which leads to an increased life expectancy. This will affect the future use of hospital inpatient and outpatient services which in turn will place a significant stress on the economy since most medical services for the elderly are apportioned and funded under a national system. Thus, understanding the demand for healthcare and other key factors influencing the demand is crucial to better serve citizens. Hospital admission is considered to be a key proxy of the demand for healthcare, especially in the context of ageing populations as experienced globally. However, modeling hospital admissions, although very important, is often complicated by zero-inflation, by the covariates with time-varying effects, and by the necessity of borrowing information across individuals. Additionally, the rate of hospital admissions might differ between the group of individuals who have been hospitalized before and the group yet to be hospitalized. Also when individuals are clustered based on their baseline self-assessed health status, the distribution of hospital admissions and its relation to predictors may be quite different across and within different groups. In this paper we propose a unified Bayesian dynamic hurdle model which accommodates these features of the data in a semi-parametric approach.We analyze the data collected by the United States Health and Retirement Study in which the rate of hospital admissions varies across different self-assessed health (SAH) groups. Simulation studies are performed for assessing the usefulness of the proposed model.

show abstract

Section: The Health and Retirement Study (Hrs)mentioning

confidence: 99%

A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Next, Zou (2006) [3] gave the proof of the oracle properties of adaptive Lasso in generalized linear models. Biswas and Das (2021) [4] proposed a Bayesian approach of estimating the quantiles of multivariate longitudinal data. Koenker (2004) [5] proposed the L 1 penalty quantile regression based on random effects, which can estimates parameter through weighting random effects information of multiple quantiles.…”

Section: Introductionmentioning

confidence: 99%

Double Penalized Expectile Regression for Linear Mixed Effects Model

et al. 2022

View full text Add to dashboard Cite

This paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojoining double penalized expectile regression. For this model, this paper proposes the iterative Lasso expectile regression algorithm to solve the parameter for this mode, and the Schwarz Information Criterion (SIC) and Generalized Approximate Cross-Validation Criterion (GACV) are used to choose the penalty parameters. Additionally, it establishes the asymptotic normality of the expectile regression coefficient estimators that are suggested. Though simulation studies, we examine the effects of coefficient estimation and the variable selection at varying expectile levels under various conditions, including different signal-to-noise ratios, random effects, and the sparsity of the model. In this work, founding that the proposed method is robust to various error distributions at every expectile levels, and is superior to the double penalized quantile regression method in the robustness of excluding inactive variables. The suggested method may still accurately exclude inactive variables and select important variables with a high probability for high-dimensional data. The usefulness of doubly penalized expectile regression in applications is illustrated through a case study using CD4 cell real data.

show abstract

Bayesian quantile regression models for heavy tailed bounded variables using the No-U-Turn sampler

et al. 2022

View full text Add to dashboard Cite

A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data

Cited by 10 publications

References 34 publications

A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study

A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study

Double Penalized Expectile Regression for Linear Mixed Effects Model

Bayesian quantile regression models for heavy tailed bounded variables using the No-U-Turn sampler

Contact Info

Product

Resources

About