Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine.

Abstract: Herbicide resistance (HR) is a major concern for the agricultural producers as well as environmentalists. Resistance to commonly used herbicides are conferred due to mutation(s) in the genes encoding herbicide target sites/proteins (GETS). Identification of these genes through wet-lab experiments is time consuming and expensive. Thus, a supervised learning-based computational model has been proposed in this study, which is first of its kind for the prediction of seven classes of GETS. The cDNA sequences of the genes were initially transformed into numeric features based on the k-mer compositions and then supplied as input to the support vector machine. In the proposed SVM-based model, the prediction occurs in two stages, where a binary classifier in the first stage discriminates the genes involved in conferring the resistance to herbicides from other genes, followed by a multi-class classifier in the second stage that categorizes the predicted herbicide resistant genes in the first stage into any one of the seven resistant classes. Overall classification accuracies were observed to be ~89% and >97% for binary and multi-class classifications respectively. The proposed model confirmed higher accuracy than the homology-based algorithms viz., BLAST and Hidden Markov Model. Besides, the developed computational model achieved ~87% accuracy, while tested with an independent dataset. An online prediction server HRGPred ( http://cabgrid.res.in:8080/hrgpred ) has also been established to facilitate the prediction of GETS by the scientific community.

Improved estimation of finite population mean in two-phase sampling with subsampling of the nonrespondents

Abstract: Improved chain-ratio estimators for the population mean based on two-phase sampling are proposed when the study variable and two auxiliary variables comprise non-response. Auxiliary information is ...

Gompertz growth model in random environment with time-dependent diffusion

Abstract: The Gompertz nonlinear growth (GNG) model with independently and identically distributed (i.i.d.) errors is often employed for describing growth data. However, the corresponding stochastic differential equation (SDE) variant is more realistic for modeling growth data, as it is capable of taking into account the effect of randomly fluctuating parameters, such as birth and death rates. However, one limitation of this prescription is that the diffusion term is assumed to be time independent. The purpose of this article is to generalize the Gompertz SDE model by taking the diffusion coefficient as timevarying. The resultant model is solved analytically and methodology for estimation of parameters, based on the method of maximum likelihood, is developed. Formulas for optimal predictors and prediction error variances and the linear Gompertz SDE (LGSDE) model and modified Gompertz SDE (MGSDE) model are also derived. Superiority of the proposed MGSDE model is shown over the LGSDE and GNG models for pig growth data.

Mapping changes in district level prevalence of childhood stunting in India 1998-2016: An application of small area estimation techniques.

Abstract: The four rounds of National Family Health Survey (NFHS) conducted during 1992-93, 1998-99, 2005-06 and 2015-16 is main source to track the health and development related indicators including nutritional status of children at national and state level in India Except NFHS-4, first three rounds of NFHS were unable to provides district-level estimates of childhood stunting due to the insufficient sample sizes The small area estimation (SAE) techniques offer a viable solution to overcome the problem of small sample size Therefore, this study uses SAE techniques to derive district level prevalence of childhood stunting corresponding to NFHS-2 (1998-99) Study further estimated GIS maps, univariate Local indicator of spatial autocorrelation (LISA) and Moran's I to understand the trend in district level childhood stunting between NFHS-2 and NFHS-4 Estimates obtained by SAE techniques suggest that prevalence of childhood stunting ranges from 207% (95% CI: 188-227) in South Goa district of Goa to 644% (95%CI: 631-657) in Dhaulpur district of Rajasthan during 1998-99 The diagnostic measures used to validate the reliability of estimates obtained by SAE techniques indicate that the model-based estimates are reliable and representative at district level Results of geospatial analysis indicates substantial reduction in childhood stunting between 1998 and 2016 Out of 640 district,about 81 district experience reduction of more than 50% At the same time 60 district experience less than 10% of reduction between 1998 and 2016 Spatial clustering of childhood stunting remains same over the study period except few additional cluster in Maharashtra, Andhra and Meghalaya in 2016 The district level estimates obtained from this study might be helpful in framing decentralized policies and implementation of vertical programs to enhance the efficacy of various nutrition interventions in priority districts of the country