Comput Methods Programs Biomed

Techniques for conducting hypothesis testing on the median and other quantiles of two or more subgroups under complex survey design are limited. In this paper, we introduce programs in both SAS and R to perform such a test. A detailed illustration of the computations, macro variable definitions, input and output for the SAS and R programs are also included in the text. Urinary iodine data from National Health and Nutrition Examination Survey (NHANES) are used as examples for comparing medians between females and males as well as comparing the 75th percentiles among three salt consumption groups.CC999999/Intramural CDC HHS/United States2017-11-30T00:00:00Z25123100PMC570817

Statistical models to study the BMI of under five children in Ethopia.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Maternal and child malnutrition has long and short-term consequences on the health status of the people and on the country’s economy. It is among the major public health problems in Ethiopia. Worldwide, maternal and child malnutrition is an underlying cause for more than 3.5 million deaths each year. About 35% of the global disease burden is in under five children. Such a heavy burden requires an understanding of the nutritional status of the people, especially children under the age of five years and associated factors. Therefore, this study attempted to use possible statistical methods to estimate the effects of the risks related to the nutritional status of children. It also tried to identify the socio-economic and demographic factors that are associated with the BMI of under five children in Ethiopia. The study employed the 2016 Ethiopian Demographic and Health Survey data. A nationally representative sample of children under the age of five years was used to get information on weight and height measures of under five children. The BMI of children under five years of age was used as a response variable to fit weighted quantile regression. The covariates, age of a child, sex and other relevant socio-economic and demographic factors were used in the study. Following the quantile regression, the generalized linear models such as logistic regression model was applied after categorizing the response variable, BMI of under five children, into two categories namely normal and malnourished. Following binary logistic regression, an attempt to fit ordinal logistic regression was made. That means nutritional status was considered as ordinal outcome with four categories namely underweight, normal, overweight and obese. The findings and comparison of estimates using these different statistical methods with and without complex survey design were presented. The results revealed that methods that take into account the complex nature of the design, perform better than those that do not take this into account. It has also been found that age of a child, weight of child at birth, mother’s BMI, educational attainment of mother, region and wealth index were significantly associated with under five children’s nutritional status. Furthermore, the results are discussed and then a conclusion is made in the context of policy implication for Ethiopia.Refer to page i for two articles that were published from this thesis

How Best to Target the Poor? An operational targeting of the poor using indicator-based proxy means tests

This paper seeks to answer an operational development question: how best to target the poor? In their endeavor, policy makers, program managers, and development practitioners face the daily challenge of targeting policies, projects, and services at the poorer strata of the population. This is also the case for microfinance institutions that seek to estimate the poverty outreach among their clients. This paper addresses these challenges. Using household survey data from Uganda, we estimate four alternative models for improving the identification of the poor in the country. Furthermore, we analyze the model sensitivity to different poverty lines and test their validity using bootstrapped simulation methods. While there is bound to be some errors, no indicator being perfectly correlated with poverty, the models developed achieve fairly accurate out-of-sample predictions of absolute poverty. Furthermore, findings suggest that the estimation method is not relevant for developing a fairly accurate model for targeting the poor. The models developed are potentially useful tools for the development community in Uganda. This research can also be applied in other developing countries.Uganda, poverty assessment, targeting, proxy means tests, validations, bootstrap, Food Security and Poverty,

Do Income Constraints Inhibit Spending on Fruits and Vegetables Among Low-Income Households?

This study assesses whether income constraints inhibit spending on fruits and vegetables among low-income households. If this is the case, then it is hypothesized that the distribution of expenditures on fruits and vegetables by low-income households should be stochastically dominated by the distribution of expenditures on these same food items by other households. Moreover, it must be the case that low-income households would increase their spending on fruits and vegetables in response to an increase in their income. Using household data from the 2000 Consumer Expenditure Survey, a test of stochastic dominance is performed. Censored quantile regressions are also estimated at selected points of the conditional expenditure distribution. Low income households are found to spend less on fruits and vegetables than other households, but they are not responsive to changes in income.censored least absolute deviations, consumption, fruits and vegetables, low-income households, nutrition, sample design, stochastic dominance, Consumer/Household Economics,

On the use of robust regression in econometrics

The use of robust regression estimators has gained popularity among applied econometricians. The main argument invoked to justify the use of the robust estimators is that they provide efficiency gains in the presence of outliers or non-normal errors. Unfortunately, most practitioners seem to be unaware of the fact that heteroskedastic and skewed errors can dramatically affect the properties of these estimators. In this paper we reconsider the interpretation of the specific robust estimator that has become popular in applied econometrics, and conclude that its use in this context cannot be generally recommended.

The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators

The R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the headcount ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of datadriven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria