Related Experiment Video
Updated: Jun 29, 2025
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
3.3K
Income, education, and other poverty-related variables: A journey through Bayesian hierarchical models
Irving Gómez-Méndez1, Chainarong Amornbunchornvej1
1National Electronics and Computer Technology Center (NECTEC), Thailand.
Heliyon
|March 28, 2024
Summary
Bayesian hierarchical models offer a flexible approach to poverty analysis, outperforming single or custom policies. Education significantly boosts household income across all Thai regions, potentially mediating regional income disparities.
Area of Science:
- Econometrics
- Statistical Modeling
- Development Economics
Background:
- Poverty alleviation requires tailored policies, but one-size-fits-all approaches are ineffective.
- Area-specific policies face resource limitations and ignore inter-regional dependencies.
Purpose of the Study:
- To apply Bayesian hierarchical models to analyze poverty-related variables in Thailand.
- To compare the performance of Bayesian hierarchical models against complete pooling and no pooling methods.
Main Methods:
- Development and evaluation of Bayesian hierarchical models with increasing complexity.
- Incorporation of variables like household income, education level, and regional data.
- Performance assessment based on variable explanation and model complexity.
Main Results:
- Bayesian hierarchical models demonstrated superior performance compared to complete pooling and no pooling.
- The inclusion of years of education significantly enhanced the model's explanatory power.
- Higher education levels were strongly correlated with increased household income across all regions.
Conclusions:
- Bayesian hierarchical models provide a robust framework for understanding regional poverty dynamics.
- Education plays a crucial role, potentially mediating the impact of region on household income.
- This approach offers valuable insights for policy design in Thailand and other nations.
Related Concept Videos
01:08Outliers and Influential Points
4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
39
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
39
01:26Distributions to Estimate Population Parameter
4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
01:29Econometric Views (EViews)
143
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
143
01:14Parametric Survival Analysis: Weibull and Exponential Methods
424
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
424
01:17Probability Histograms
11.4K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.4K