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Influence of socio-demographic factors on the breastfeeding period of women in Bangladesh: a polytomous logistic regression model

100%

Islam M. , Afroja S. , Biswas A. , Khan M. S. , Khandker S.

Family Medicine & Primary Care Review

2019

nr 3

223-229

Background. In Bangladesh, terrible degradation in the breastfeeding period has occurred with rapid urbanization in recent years that is causing a shortage of child nourishment. Identifying the risk factors of breastfeeding duration is important for planning nutritional programs and strategies. Objectives. This study tries to identify influential demographic and socio-economic factors that affect the breastfeeding period for reducing child nutrition deficiency. Material and methods. The study attempts to proceed with data collected from an observational study entitled the Bangladesh Demographic and Health Survey (BDHS) 2014. The breastfeeding period (Ordinal exogenous variable) is classified into three groups: 0–5-months, 6–23 months and at least 24 months. Gamma, chi-square and linear-by-linear statistics are used to identify the associated factors that have an impact on the breastfeeding period. A test of parallelism is conducted to evaluate the proportional odds. The polytomous logistic regression (PLR) model and the proportional odds (PO) model are used to find the marginal effect of demographic and socio-economic predictors that affect the breastfeeding period. Results. Parental educational attainment, wealth index, division, religion, mother’s BMI, drinking water source, household members, amenorrhea and abstaining, respectively, are the most significant factors that influence the breastfeeding period. The PLR model is also more precise than the PO model for indicating the marginal effect among those vital factors for the breastfeeding period. Conclusions. PLR is an appropriate model to recognize the effect of predictors of breastfeeding duration instead of the PO model and other measures.

On background driving distribution functions (BDDF) for some selfdecomposable variables

59%

Jurek Z. J.

2021

tom Vol. 49, no. 2

85--109

Many classical variables (statistics) are selfdecomposable. They admit the random integral representations via Levy processes. In this note are given formulas for their background driving distribution functions (BDDF). This may be used for a simulation of those variables. Among the examples discussed are: gamma variables, hyperbolic characteristic functions, Student t-distributions, stochastic area under planar Brownian motions, inverse Gaussian variable, logistic distributions, non-central chi-square, Bessel densities and Fisher z-distributions. Found representations might be of use in statistical applications.

Wiele klasycznych modeli probabilistycznych opiera sie o zmienne losowe samorozkładalne. Maja one losowe reprezentacje całkowe oparte o procesy Lévy’ego. W tej notatce podano wzory dla ich kierujących (generujących) dystrybuant. Takich reprezentacji można używać do symulacji tych zmiennych. Wśród omawianych przykładów są: rozkłady t-Studenta, pole stochastyczne pod planarnymi ruchami Browna, odwrotny rozkład Gaussa, rozkłady logistyczne, niecentralny rozkład chi-kwadrat, rozkład Bessela i rozkłady statystyk Z-Fishera. Podane reprezentację mogą być przydatne w statystyce.