scholarly journals “Childhood Anemia in India: an application of a Bayesian geo-additive model”

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Holendro Singh Chungkham ◽  
Strong P. Marbaniang ◽  
Pralip Kumar Narzary
Keyword(s):  
Logistic Regression ◽  
Child Health ◽  
Additive Model ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Geographical Differences ◽  
Wealth Status ◽  
Linear Effect ◽  
Continuous Covariates ◽  
Non Linear

Abstract Background The geographical differences that cause anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections. The studies failed to explain the non-linear effect of the continuous covariates on childhood anaemia. The present paper aims to investigate the risk factors of childhood anaemia in India with focus on geographical spatial effect. Methods Geo-additive logistic regression models were fitted to the data to understand fixed as well as spatial effects of childhood anaemia. Logistic regression was fitted for the categorical variable with outcomes (anaemia (Hb < 11) and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized spline and spatial effects were smoothed by the two-dimensional spline. Results At 95% posterior credible interval, the influence of unobserved factors on childhood anaemia is very strong in the Northern and Central part of India. However, most of the states in North Eastern part of India showed negative spatial effects. A U-shape non-linear relationship was observed between childhood anaemia and mother’s age. This indicates that mothers of young and old ages are more likely to have anaemic children; in particular mothers aged 15 years to about 25 years. Then the risk of childhood anaemia starts declining after the age of 25 years and it continues till the age of around 37 years, thereafter again starts increasing. Further, the non-linear effects of duration of breastfeeding on childhood anaemia show that the risk of childhood anaemia decreases till 29 months thereafter increases. Conclusion Strong evidence of residual spatial effect to childhood anaemia in India is observed. Government child health programme should gear up in treating childhood anaemia by focusing on known measurable factors such as mother’s education, mother’s anaemia status, family wealth status, child health (fever), stunting, underweight, and wasting which have been found to be significant in this study. Attention should also be given to effects of unknown or unmeasured factors to childhood anaemia at the community level. Special attention to unmeasurable factors should be focused in the states of central and northern India which have shown significant positive spatial effects.

scholarly journals A Bayesian Geo-Additive Modeling of Childhood Anemia in India

2021 ◽  
Author(s):  
Holendro Singh Chungkham ◽  
STRONG P MARBANIANG ◽  
Pralip Kumar Narzary
Keyword(s):  
Logistic Regression ◽  
Credible Interval ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Geographical Differences ◽  
Wealth Status ◽  
Linear Effect ◽  
Northern India ◽  
Continuous Covariates ◽  
Non Linear

Abstract Background The geographical differences that caused anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections. The studies failed to explain the non-linear effect of the continuous covariates on childhood anaemia. The present paper aimed to investigate the risk factors of childhood anaemia in India with focus on geographical spatial effect. Methods Geo-additive logistic regression models were fitted to the data to understand fixed as well as spatial effects of childhood anaemia. Logistic regression was fitted for the categorical variable with outcomes (anaemia (Hb < 11) and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized spline and spatial effects were smoothed by the two-dimensional spline. Results At 95% posterior credible interval, the influence of unobserved factors on childhood anaemia is very strong in the Northern and Central part of India. However, most of the states in North Eastern part of India showed negative spatial effects. A U-shape non-linear relationship was observed between childhood anaemia and mother’s age. This indicates that mothers of young and old ages are more likely to have children who are anaemic; in particular mothers aged 15 years to about 25 years. Then the risk of childhood anaemia starts declining after the age of 25 years and it continues till the age of around 37 years, thereafter again starts increasing. Further, the non-linear effects of duration of breastfeeding on childhood anaemia show that the risk of childhood anaemia decreases till 29 months thereafter increases. Conclusion Strong evidence of residual spatial effect to childhood anaemia in India. Government child health programme should gear up in treating childhood anaemia by focusing on known measurable factors such as mother’s education, mother’s anaemia status, family wealth status, child fever, stunting, underweight, and wasting which have been found to be significant in this study, attention should also be given to effects of unknown or unmeasured factors to childhood anaemia at the community level. Special attention to these unmeasurable factors should be focused in the states of central and northern India which have shown significant positive spatial effects.

scholarly journals Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1448
Author(s):  
Xuan Liu ◽  
Jianbao Chen
Keyword(s):  
Variable Selection ◽  
Autoregressive Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Autoregressive Model ◽  
Monte Carlo Studies ◽  
Spatial Autoregressive ◽  
Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

scholarly journals Non-linear effect of different humidity types on scrub typhus occurrence in endemic provinces, Thailand

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06095
Author(s):  
Bhophkrit Bhopdhornangkul ◽  
Aronrag Cooper Meeyai ◽  
Waranya Wongwit ◽  
Yanin Limpanont ◽  
Sopon Iamsirithaworn ◽  
...  
Keyword(s):  
Scrub Typhus ◽  
Linear Effect ◽  
Non Linear Effect ◽  
Non Linear

scholarly journals Non-linear effect mitigation for FMCW radar system

2018 ◽  
Author(s):  
Nizar Bouhlel ◽  
Stephane Meric ◽  
Claude Moullec ◽  
Christian Brousseau
Keyword(s):  
Radar System ◽  
Linear Effect ◽  
Fmcw Radar ◽  
Non Linear Effect ◽  
Non Linear

scholarly journals Non-linear effect of sea ice: Spectacled Eider survival declines at both extremes of the ice spectrum

2018 ◽  
Vol 8 (23) ◽  
pp. 11808-11818
Author(s):  
Katherine S. Christie ◽  
Tuula E. Hollmen ◽  
Paul Flint ◽  
David Douglas
Keyword(s):  
Sea Ice ◽  
Linear Effect ◽  
Non Linear Effect ◽  
Non Linear

scholarly journals Foreign Ownership Effect to Stock Market Liquidity in Indonesia

MANAJERIAL ◽  
2021 ◽  
Vol 8 (01) ◽  
pp. 01
Author(s):  
Annisa Yasmin
Keyword(s):  
Stock Market ◽  
Financial Institutions ◽  
Capital Market ◽  
Foreign Ownership ◽  
Market Liquidity ◽  
Linear Effect ◽  
Non Linear Effect ◽  
Non Linear ◽  
Stock Market Liquidity ◽  
Research Period

Background – One of economic indicators of a country is the capital market. Liquid capital market can attract investors, both foreign and domestic investors, to invest their ownership in that country, which in turn can improve the country’s economic growth. Aim – This research aims to examine the influence foreign ownership on stock market liquidity in Indonesia. Design / methodology / approach – This research splits foreign ownership into two groups, the first one is foreign ownership by financial institutions, and the second one is foreign ownership by non-financial corporations. The type of data used is panel data using fixed effect model (FEM). The technique for examining the influence of foreign ownership on liquidity used multiple regression analysis. Findings – The result found that foreign ownership by financial institutions and non-financial corporations negatively affect liquidity.  The study also found a positively non-linear effect between foreign ownership by financial institutions to liquidity and a negatively non-linear effect between foreign ownership by non-financial institutions to liquidity. Research implication – This research can assist investors in determining investment in the Indonesian capital market by pay attention to variables such as foreign ownership, return, turnover, market capitalization and standard deviation. Limitation – The research period was short, which was only 21 months due to limited data and the research period that has passed too long, that is January 2012 to September 2013.

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