scholarly journals Does participation in a social security scheme improve household dietary diversity?

2015 ◽  
Vol 60 (3) ◽  
pp. 347-360 ◽  
Author(s):  
Olajumoke Adenuga ◽  
Raphael Babatunde ◽  
Adewale Adenuga
Keyword(s):  
Maximum Likelihood ◽  
Social Security ◽  
Maximum Likelihood Estimator ◽  
Dietary Diversity ◽  
Cash Transfer ◽  
Likelihood Estimator ◽  
Policy Objective ◽  
Security Scheme ◽  
Social Security Scheme ◽  
Household Dietary Diversity

Social protection in the form of cash transfer is emerging as a policy objective to solve the problem of poverty and food insecurity in developing countries. However, the extent to which this is feasible is yet to be empirically examined. This study was therefore carried out to assess the effect of the Ekiti State Social Security Scheme (ESSSS) on the dietary diversity of beneficiary households in Ekiti State, Nigeria. The study employed a three-stage random sampling technique to collect data from 200 respondents using a structured questionnaire. Descriptive statistics, Household Dietary Diversity Score (HDDS) in terciles and the Poisson Maximum Likelihood Estimator were the main analytical tools employed for the study. The result of the Poisson maximum likelihood estimator at p ? 0.05 showed that access to the Ekiti State Social Security Scheme (ESSSS), years of education and total monthly income were found to significantly influence household dietary diversity. The study concluded that the Ekiti State Social Security Scheme (ESSSS) has a positive effect on household dietary diversity of beneficiaries. It was recommended that the government should endeavour to increase the number of beneficiaries of the programme and organize nutrition-oriented programmes for the elderly people to improve the food substitution knowledge of the households.

The Comparison Between the Bayes Estimator and the Maximum Likelihood Estimator of the Reliability Function for Negative Exponential Distribution

2018 ◽  
pp. 439
Author(s):  
Hazim Mansour Gorgees ◽  
Bushra Abdualrasool Ali ◽  
Raghad Ibrahim Kathum
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Exponential Distribution ◽  
Reliability Function ◽  
Bayes Estimator ◽  
Likelihood Estimator ◽  
Monte Carlo Simulation Technique ◽  
Negative Exponential Distribution ◽  
Negative Exponential ◽  
Better Than

     In this paper, the maximum likelihood estimator and the Bayes estimator of the reliability function for negative exponential distribution has been derived, then a Monte –Carlo simulation technique was employed to compare the performance of such estimators. The integral mean square error (IMSE) was used as a criterion for this comparison. The simulation results displayed that the Bayes estimator performed better than the maximum likelihood estimator for different samples sizes.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

Covariance matrix of the bias-corrected maximum likelihood estimator in generalized linear models

2013 ◽  
Vol 55 (3) ◽  
pp. 643-652
Author(s):  
Gauss M. Cordeiro ◽  
Denise A. Botter ◽  
Alexsandro B. Cavalcanti ◽  
Lúcia P. Barroso
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Covariance Matrix ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Likelihood Estimator

Large deviations and Berry–Esseen inequalities for estimators in nonhomogeneous diffusion driven by fractional Brownian motion

2020 ◽  
Vol 28 (3) ◽  
pp. 183-196
Author(s):  
Kouacou Tanoh ◽  
Modeste N’zi ◽  
Armel Fabrice Yodé
Keyword(s):  
Differential Equation ◽  
Brownian Motion ◽  
Maximum Likelihood ◽  
Large Deviations ◽  
Stochastic Differential Equation ◽  
Fractional Brownian Motion ◽  
Maximum Likelihood Estimator ◽  
Bayes Estimator ◽  
Likelihood Estimator

AbstractWe are interested in bounds on the large deviations probability and Berry–Esseen type inequalities for maximum likelihood estimator and Bayes estimator of the parameter appearing linearly in the drift of nonhomogeneous stochastic differential equation driven by fractional Brownian motion.

Sign in / Sign up

Export Citation Format

Share Document