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Jayant Singh

Publications and source records attributed to Jayant Singh.

9 recordsLinked to original sources
arXiv2015

Stability of Discrete time Recurrent Neural Networks and Nonlinear optimization problems

We consider the method of Reduction of Dissipativity Domain to prove global Lyapunov stability of Discrete Time Recurrent Neural Networks. The standard and advanced criteria for Absolute Stability of these essentially nonlinear systems produce rather weak results. The method mentioned above is proved to be more powerful. It involves a multi-step procedure with maximization of special nonconvex functions over polytopes on every step. We derive conditions which guarantee an existence of at most one point of local maximum for such functions over every hyperplane. This nontrivial result is valid for wide range of neuron transfer functions.

math.OC↗
arXiv2013

A family of estimators for estimating the population mean in simple random sampling under measurement errors

In this article we have suggested an improved estimator for estimating the population mean in simple random sampling using auxiliary information under the presence of measurement errors. The mean square error (MSE) of the proposed estimator has been derived under large sample approximation. Besides, considering the minimum case of the MSE equation, the efficient conditions between the proposed and existing estimators are obtained. These theoretical findings are supported by a numerical example.

stat.AP↗
arXiv2013

Use of Auxiliary Information in Variance Estimation

This paper proposes a class of ratio type estimators of finite population variance, when the population variance of an auxiliary character is known. Asymptotic expression for mean square error (MSE) is derived and compared with the mean square errors of some existing estimators. An empirical study is carried out to illustrate the performance of the constructed estimator over others.

math.ST↗
arXiv2013

A General Family of Estimators for Estimating Population Mean in Systematic Sampling Using Auxiliary Information in the Presence of Missing Observations

This paper proposes a general family of estimators for estimating the population mean in systematic sampling in the presence of non-response adapting the family of estimators proposed by Khoshnevisan et al. (2007). In this paper we have discussed the general properties of the proposed family including optimum property. The results have been illustrated numerically by taking an empirical population considered in the literature.

math.ST↗
arXiv2012

Unbiased Ratio-Type Estimator Using Transformed Auxiliary Variable In Negative Correlation Case

The objective of this paper is to propose an unbiased ratio-type estimator for finite population mean when the variables are negatively correlated. Hartley and Ross[2] and Singh and Singh [6] estimators are identified as particular cases of the proposed unbiased estimator. The variance expression of the proposed estimator to the first degree of approximation has been obtained. An empirical study is carried out to demonstrate the performance of the proposed estimator over, Robson [5] estimator and Singh and Singh [6] estimator.

stat.ME↗
arXiv2009

District Level Analysis of Urbanization from Rural-to-Urban Migration in the Rajasthan State

Migration has various dimensions; urbanization due to migration is one of them. In Rajasthan State, district level analysis of urbanization due to migrants shows trend invariably for all districts of the state, though the contribution in urbanization by migrants varies from district to district. In some districts the share of migrants moving to urban areas is very impressive, in others it is not that much high. The migrants' contribution is on the raising over the decades. In this paper, the district level migration in the Rajasthan State is examined in relation to total urbanization and urbanization due to migration.

math.GM↗
arXiv2009

Optimum Statistical Test Procedure

In this paper we obtain a test which minimizes the sum of the two error probabilities irrespective of whether $σ^2$ is known or unknown.

math.GM↗
arXiv2008

A Note on Testing of Hypothesis

In this paper, a problem of testing is discussed when the samples have been drawn from the normal distribution. The study of hypothesis testing is also extended to Baye's set up.

math.GM↗