Adaptation to climate change in coastal saline area of south-western region of Bangladesh

Abstract

An adaptation research under Food and Agriculture Organization (FAO) funded LACC-II project started at coastal saline area of Laudove, Dakope upzila in Khulna district of Bangladesh through on-farm research division of Bangladesh Agricultural Research Institute (BARI), Khulna Bangladesh during May-June 2008 to May-June 2009. It aimed to find out the appropriate adaptation measures against salinity problem. After selection of adaptation options through three focus group discussions (FGD), homestead vegetable production started immediately with saline/excess soil moisture tolerant vegetables for coastal area. In coastal area several homestead vegetables were successfully produced through scientific management like making ridge and furrowing of bed. There was large participation of women in all the activities of home gardening from land preparation to marketing at Laudove. Through utilization of different niches of homestead farm family succeeded to increase their vegetable consumption three to five folds more from the bench mark, though intake was below recommended level. Economically homestead vegetable production was quite lucrative. Also social relationship of the farm family was improved with neighbors and relatives through free distribution of vegetables. Short duration T. aman rice variety (cv. BINAdhan 4) was tested at Laudove for facilitating timely planting of rabi crops. It gave better yield and one month shortening of field duration was possible. Farmers kept most of the produced seed for next year cultivation. In post-rainy season (Rabi) different field crops were tested, such as relaying (for timely planting and avoiding of increased soil salinity) of mustard, wheat, cowpea and later on creeping crops like watermelon and sweet gourd. Among them cowpea, water melon and sweet gourd proved to be promising.

Keywords

Adaptation, Salinity, FGD, Vegetable and Short duration.

How to cite

Yusuf Ali, Shah AL-Emran, M. B. Islam, and E. Raheem (2014). Adaptation to climate change in coastal saline area of south-western region of Bangladesh. Int. J. Sustain. Agril. Tech. 10(4): 09-16, April 2014

Shrinkage and absolute penalty estimation in linear regression models

Abstract

In predicting a response variable using multiple linear regression model, several candidate models may be available which are subsets of the full model. Shrinkage estimators borrow information from the full model and provides a hybrid estimate of the regression parameters by shrinking the full model estimates toward the candidate submodel. The process introduces bias in the estimation but reduces the overall prediction error that offsets the bias. In this article, we give an overview of shrinkage estimators and their asymptotic properties. A real data example is given and a Monte Carlo simulation study is carried out to evaluate the performance of shrinkage estimators compared to the absolute penalty estimators such as least absolute shrinkage and selection operator (LASSO), adaptive LASSO and smoothly clipped absolute deviation (SCAD) based on prediction errors criterion in a multiple linear regression setup. WIREs Comput Stat 2012, 4:541–553. DOI: 10.1002/wics.1232

Keywords
shrinkage estimation; absolute penalty estimation; LASSO; adaptive LASSO; SCAD

How to cite

S. Ejaz Ahmed, and S. E. Raheem, (2012). Shrinkage and absolute penalty estimation in linear modelsWIREs Computational StatisticsVolume 4, Issue 6, pages 541–553, November/December 2012.

Absolute penalty and shrinkage estimation in partially linear models

Abstract
In the context of a partially linear regression model, shrinkage semiparametric estimation is considered based on the Stein-rule. In this framework, the coefficient vector is partitioned into two sub-vectors: the first sub-vector gives the coefficients of interest, i.e., main effects (for example, treatment effects), and the second sub-vector is for variables that may or may not need to be controlled. When estimating the first sub-vector, the best estimate may be obtained using either the full model that includes both sub-vectors, or the reduced model which leaves out the second sub-vector. It is demonstrated that shrinkage estimators which combine two semiparametric estimators computed for the full model and the reduced model outperform the semiparametric estimator for the full model. Using the semiparametric estimate for the reduced model is best when the second sub-vector is the null vector, but this estimator suffers seriously from bias otherwise. The relative dominance picture of suggested estimators is investigated. In particular, suitability of estimating the nonparametric component based on the B-spline basis function is explored. Further, the performance of the proposed estimators is compared with an absolute penalty estimator through Monte Carlo simulation. Lasso and adaptive lasso were implemented for simultaneous model selection and parameter estimation. A real data example is given to compare the proposed estimators with lasso and adaptive lasso estimators.

Keywords
Partially linear model; James–Stein estimator; Absolute penalty estimation; Lasso; Adaptive lasso; B-spline approximation; Semiparametric model; Monte Carlo simulation

How to cite

Raheem, S. E, Ahmed S. E., Doksum K. A. (2012). Absolute penalty and shrinkage estimation in partially linear models. Computational Statistics and Data Analysis. 56(4):874-891.