Multilevel Analysis of Determinants of Stunting Prevalence among Children under Age Five in Ethiopia

Background: Stunting is a well-established child health indicator of chronic malnutrition related to environmental and socio-economic circumstances. In Ethiopia, childhood stunting is the most widely prevalent among children under the age of five years. Objective: To estimate the prevalence of stunting and model the determinants of stunting prevalence among children under age five in Ethiopia. Methods: Data were extracted from 2016 EDHS, and samples of 8487 children under age five were used in this study. The sample was selected using a two-stage stratified sampling process, and a multilevel logistic regression model was used to determine the factors associated with childhood stunting in Ethiopia. Results: This study revealed that the prevalence of stunting among children under age five years in Ethiopia was around 39.39%. The multilevel binary logistic regression analysis was performed to investigate the variation of predictor variables of stunting prevalence among children under age five. Accordingly, it has been identified that the ages of the child above 12 months, male gender, children from poor households, and no mother education significantly affect the prevalence of stunting in Ethiopia. It is found that variances related to the random term were statistically significant, implying a variation in the prevalence of stunting across Ethiopia's regional states. Conclusion: The current study confirmed that the prevalence of stunting among children under aged five years in Ethiopia was a severe public health problem. Therefore, governmental or stakeholders should pay attention to all the significant factors mentioned in this study's analysis.

A robust score test of homogeneity for zero-inflated count data

In many applications of zero-inflated models, score tests are often used to evaluate whether the population heterogeneity as implied by these models is consistent with the data. The most frequently cited justification for using score tests is that they only require estimation under the null hypothesis. Because this estimation involves specifying a plausible model consistent with the null hypothesis, the testing procedure could lead to unreliable inferences under model misspecification. In this paper, we propose a score test of homogeneity for zero-inflated models that is robust against certain model misspecifications. Due to the true model being unknown in practical settings, our proposal is developed under a general framework of mixture models for which a layer of randomness is imposed on the model to account for uncertainty in the model specification. We exemplify this approach on the class of zero-inflated Poisson models, where a random term is imposed on the Poisson mean to adjust for relevant covariates missing from the mean model or a misspecified functional form. For this example, we show through simulations that the resulting score test of zero inflation maintains its empirical size at all levels, albeit a loss of power for the well-specified non-random mean model under the null. Frequencies of health promotion activities among young Girl Scouts and dental caries indices among inner-city children are used to illustrate the robustness of the proposed testing procedure.

Lipschitz stability for a semi-linear inverse stochastic transport problem

This paper is addressed to a semi-linear stochastic transport equation with three unknown parameters. It is proved that the initial displacement, the terminal state and the random term in diffusion are uniquely determined by the state on partial boundary and a Lipschitz stability of the inverse problem is established. The main tool we employ is a global Carleman estimate for stochastic transport equations.

Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew-tdistribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing thel1penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.

STOCHASTICALLY EQUIVALENT DYNAMICAL SYSTEM APPROACH TO NONLINEAR DETERMINISTIC PREDICTION

We propose a new prediction method for nonlinear time series based on the paradigm of deterministic chaos. Introducing a stochastically equivalent dynamical system to an original map, a prediction method is derived by minimizing a random term that defines intervals in which a good prediction performance is obtained. The use of the present method is illustrated for some chaotic systems with particular emphasis on issues of choices of variable time steps that are necessary when discretizing the stochastic differential equation. Applying to some systems, it is found that the present method works better than traditional chaotic methods.

Stochastic growth

Environmental variations in food and temperature induce substantial irregular variations in the growth of many living organisms. In fisheries research, stochastic growth has been modelled by adding a random term to deterministic growth functions. This entails large fluctuations in individual growth paths, including spells of negative growth. A different approach to modelling stochastic growth is presented where the anomalous short-term behaviour is avoided. The third moment of length distributions contains valuable information for the formulation of growth models. The size distribution of actual stocks is modified by size-dependent mortality. New methods for estimation of growth functions, taking this effect into account, are presented, requiring less specific assumptions about the properties of growth and stochastic variations than previous methods. Actual length distributions are also affected by genetic variability. The effects of this upon the development of second and third moments of length distributions with age differ depending on whether they are associated with maximum length or growth.