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Abstract This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semicontinuous responses. In this paper, we present a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum‐of‐tree models. Our simulation results demonstrate that sharing of information across related model components is often very beneficial, particularly in sparse high‐dimensional problems in which variable selection must be conducted. We illustrate our methodology by analyzing medical expenditure data from the Medical Expenditure Panel Survey (MEPS). To facilitate the Bayesian nonparametric regression analysis, we develop two novel models for analyzing the MEPS data using Bayesian additive regression trees—a heteroskedastic log‐normal hurdle model with a “shrink‐toward‐homoskedasticity” prior and a gamma hurdle model.
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Bayesian synthetic prediction of state level poverty using Indian Household Consumer Expenditure Survey Data1
Goal 1 of the 2030 Agenda for Sustainable Development, adopted by all United Nations member States in 2015, is to end poverty in all forms everywhere. The major indicator to monitor the goal is the so-called headcount ratio or poverty rate, i.e., proportion or percentage of people under poverty. In India, where nearly a quarter of population still live below the poverty line, monitoring of poverty needs greater attention, more frequently at shorter intervals (e.g., every year) to evaluate the effectiveness of planning, programs and actions taken by the governments to eradicate poverty. Poverty rate computation for India depends on two basic ingredients – rural and urban poverty lines for different states and union territories and average Monthly Per-capita Consumer Expenditure (MPCE). While MPCE can be obtained every year, usually from the Consumer Expenditure Survey on shorter schedules with a few exceptions where the information is obtained from another survey, determination of poverty lines is a highly complex, costly and time-consuming process. Poverty lines are essentially determined by a panel of experts who draws their conclusions partly based on their subjective opinions and partly based on data from multiple sources. The main data source the panel uses is the Consumer Expenditure Survey data with a detailed schedule, which are usually available every five years or so. In this paper, we undertake a feasibility study to explore if estimates of headcount ratios or Poverty Ratios in intervening years can be provided in absence of poverty lines by relating poverty ratios with average MPCE through a statistical model. Then we can use the fitted model to predict poverty rates for intervening years based on average MPCE. We explore a few in this work models using Bayesian methodology. The reason behind calling this ‘synthetic prediction’ rests on the synthetic assumption of model invariance over years, often used in the small area literature. While the data-based assessment of our Bayesian synthetic prediction procedure is encouraging, there is a great potential for improvements on the models presented in this paper, e.g., by incorporating more auxiliary data as they become available. In any case, we expect our preliminary work in this important area will encourage researchers to think about statistical modeling as a possible way to at least partially solve a problem for which no objective solution is currently available.
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- Award ID(s):
- 1758808
- PAR ID:
- 10396343
- Date Published:
- Journal Name:
- Statistical Journal of the IAOS
- Volume:
- 38
- Issue:
- 4
- ISSN:
- 1874-7655
- Page Range / eLocation ID:
- 1325 to 1335
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3233/SJI-220965
