Browsing by Author "Sanjay Kumar Singh"

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A comparative study of traditional and kullback-leibler divergence of survival functions estimators for the parameter of lindley distribution
(Austrian Statistical Society, 2019) Sultan Parveen; Sanjay Kumar Singh; Umesh Singh; Dinesh Kumar
A new point estimation method based on Kullback-Leibler divergence of survival functions (KLS), measuring the distance between an empirical and prescribed survival functions, has been used to estimate the parameter of Lindley distribution. The simulation studies have been carried out to compare the performance of the proposed estimator with the corresponding Least square (LS), Maximum likelihood (ML) and Maximum product spacing (MPS) methods of estimation. © 2019, Austrian Statistical Society. All rights reserved.
A New Class of Distributions for Modelling Continuous Positively Skewed Data Sets
(Thai Statistical Association, 2025) Nishant Kumar Srivastava; Sanjay Kumar Singh; Vikas Kumar Sharma; Umesh Singh
In this paper, we proposed a new class of distributions by introducing a new constant in the existing model. We discuss general properties of the family such as density function, quantile function and hazard rate function. We then discuss a member of the family considering the exponential distribution as baseline distribution. Various properties of the model such as quantile function, moments, moment generating function, order statistics, stress-strength parameter, and mean residual life function are discussed. We also discussed the mean, variance, skewness and kurtosis of the proposed model numerically. The expression for Rényi and Shannon entropies are also derived. The different methods of estimation such as maximum likelihood estimation, maximum product spacing and least squares estimates are used for the estimation of the unknown parameters of the proposed distribution.. The simulation study is performed to study the behaviour of the estimates based on their mean squared errors. Lastly, we apply our proposed model to two real data sets. © 2025, Thai Statistical Association. All rights reserved.
A new generalized class of Kavya–Manoharan distributions: inferences and applications
(Springer, 2024) Ela Verma; Sanjay Kumar Singh; Suraj Yadav
This article introduces a new method of generating distributions by leveraging the concept of generalization with the hope of achieving more flexibility and greater adaptability. As a baseline distribution, we have considered a one-parameter exponential distribution. Along with studying the behavior of hazard rate, we have explored various statistical characteristics of the proposed distribution. For estimating model parameters we have employed the method of maximum likelihood estimation. To check the empirical validation of estimators obtained, the Monte Carlo simulation technique has been used. To show the model’s flexibility and competency, we have conducted a real data analysis using three real data sets and compared its performance with some widely used existing distributions. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
A new generalized class of Kavya–Manoharan distributions: inferences and applications
(Springer, 2025) Ela Verma; Sanjay Kumar Singh; Suraj Yadav
This article introduces a new method of generating distributions by leveraging the concept of generalization with the hope of achieving more flexibility and greater adaptability. As a baseline distribution, we have considered a one-parameter exponential distribution. Along with studying the behavior of hazard rate, we have explored various statistical characteristics of the proposed distribution. For estimating model parameters we have employed the method of maximum likelihood estimation. To check the empirical validation of estimators obtained, the Monte Carlo simulation technique has been used. To show the model’s flexibility and competency, we have conducted a real data analysis using three real data sets and compared its performance with some widely used existing distributions. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
A new lifetime distribution: Some of its statistical properties and application
(Natural Sciences Publishing, 2018) Dinesh Kumar; Umesh Singh; Sanjay Kumar Singh; Prashant Kumar Chaurasia
In the present paper, a new lifetime distribution has been introduced by the use of Minimum Guarantee transformation as suggested by Kumar et al. (2017). For the purpose, Lindley distribution is considered as a baseline distribution. Some of the statistical properties of this distribution has been studied and classical estimators like maximum likelihood estimator (MLE), least square estimator (LSE) and maximum product of spacing estimator (MPSE) has been obtained and their performance is carried out through simulation study. Further, a real data has been taken to show its application in the real scenario. ©2018 NSP Natural Sciences Publishing Cor.
A new upside-down bathtub shaped hazard rate model for survival data analysis
(Elsevier Inc., 2014) Vikas Kumar Sharma; Sanjay Kumar Singh; Umesh Singh
In medical, engineering besides demography and other applied disciplines, it is pronounced in some applications that the hazard rate of the data initially increased to a pick in the beginning age, declined abruptly till it stabilized. In statistics literature, such hazard rate is known as the upside-down bathtub shaped hazard rate and propound in the various survival studies. In this paper, we proposed a transmuted inverse Rayleigh distribution, which possesses the upside-down bathtub shape for its hazard rate. The fundamental properties such as mean, variance, mean deviation, order statistics, Renyi entropy and stress-strength reliability of the proposed model are explored here. Further, three methods of estimation namely maximum likelihood, least squares and maximum product spacings methods are used for estimating the unknown parameters of the transmuted inverse Rayleigh distribution, and compared through the simulation study. Finally, the applicability of the proposed distribution is shown for a set of real data representing the times between failures of the secondary reactor pumps. © 2014 Elsevier Inc. All rights reserved.
Analysis and prediction of COVID-19 spreading through Bayesian modelling with a case study of Uttar Pradesh, India
(Springer, 2022) Deepmala; Nishant Kumar Srivastava; Sanjay Kumar Singh; Umesh Singh
Predicting the dynamics of COVID-19 cases is imperative to enhance the health care system’s capacity, monitor the effects of policy interventions, and control the transmission. With this view, this paper examines the transmission process of the COVID-19 employing three types of confirmed, deceased, and recovered cases in Uttar Pradesh, India. We demonstrated an approach that has the power to sufficiently predict the number of confirmed, deceased, and recovered cases of COVID-19 in the near future, given the past occurrences. We used the logistic and Gompertz non-linear regression model under the Bayesian setup. In this regard, we built the prior distribution of the model using information obtained from some other states of India, which have already reached the advanced stage of COVID-19. This analysis did not consider any changes in government control measures. © 2022, The Author(s), under exclusive licence to Operational Research Society of India.
Artificial Intelligence-Based Model for Predicting the Minimum Inhibitory Concentration of Antibacterial Peptides Against ESKAPEE Pathogens
(Institute of Electrical and Electronics Engineers Inc., 2024) Ritesh Sharma; Sameer Shrivastava; Sanjay Kumar Singh; Abhinav Kumar; Amit Kumar Singh; Sonal Saxena
In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community. © 2013 IEEE.
Assessing the effect of E-Bayesian inference for Poisson inverse exponential distribution parameters under different loss functions and its application
(Taylor and Francis Ltd., 2022) Anurag Pathak; Manoj Kumar; Sanjay Kumar Singh; Umesh Singh
This paper present E-Bayesian and Bayesian estimators of parameters of Poisson inverse exponential distribution (PIED) under Squared error loss function (SELF), General entropy loss function (GELF) and Linear Exponential loss function (LINEX) for progressive type-II censored data with binomial removals (PT-II CBRs). The E-Bayesian and corresponding Bayesian estimators are compared in terms of their risks based on simulated samples from PIED. The proposed methodology is applied to survival time of multiple myeloma patients data. © 2020 Taylor & Francis Group, LLC.
Automatic Deformity Estimation for Thoracic Section Between Inhale and Exhale Positions
(Springer India, 2016) Ali Imam Abidi; Sanjay Kumar Singh; Lalit M. Aggarwal
Corresponding control point pairs or landmarks in images can be used to define the deformation with respect to time, point of view or modality. Manual definition of the number of control points in an image, enough to define all kinds of deformation is a tedious task. Hence, automatic definition of control points is the way forward taken in this proposed work. The paper proposes an automatic registration process for tracing of the deformity path of the thoracic region based on feature detector speeded up robust feature (SURF) and moving least squares (MLS). The set of control points on the images is defined by its feature set which is obtained by using the SURF detector, which serves as input for MLS algorithm to trace the deformations of the image (thoracic image in this case). The credibility and performance of the above proposed method is demonstrated by its outstanding experimental results. © 2016, The National Academy of Sciences, India.
Bayes Estimator of Generalized-Exponential parameters under Linex loss function using Lindley's approximation
(Ubiquity Press Ltd, 2008) Rahul Singh; Sanjay Kumar Singh; Umesh Singh; Gyan Prakash Singh
In this paper, we have obtained the Bayes Estimator of Generalized- Exponential scale and shape parameter using Lindley 's approximation (L-approximation) under asymmetric loss functions. The proposed estimators have been compared with the corresponding MLE for their risks based on simulated samples from the Generalized-Exponential distribution.
Bayes estimators of the reliability function and parameter of inverted exponential distribution using informative and non-informative priors
(2013) Sanjay Kumar Singh; Umesh Singh; Dinesh Kumar
In this paper, we propose Bayes estimators of the parameter and reliability function of inverted exponential distribution under the general entropy loss function for complete, type I and type II censored samples. The proposed estimators have been compared with the corresponding maximum-likelihood estimators for their simulated risks (average loss over sample space). © 2013 2013 Taylor & Francis.
Bayesian analysis for Type-II hybrid censored sample from inverse Weibull distribution
(Springer, 2013) Sanjay Kumar Singh; Umesh Singh; Vikas Kumar Sharma
In this paper, we have discussed the Bayesian procedure for the estimation of the parameters of inverse Weibull distribution under Type-II hybrid censoring scheme. The highest posterior density credible intervals for the parameters have also been constructed. The performance of the Bayes estimators of the model parameters have been compared with maximum likelihood estimators through the Monte Carlo Markov chain techniques. Finally, two real data sets have been analysed for illustration purpose. © 2013 The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.
Bayesian estimation and prediction for flexible Weibull model under type-II censoring scheme
(2013) Sanjay Kumar Singh; Umesh Singh; Vikas Kumar Sharma
We have developed the Bayesian estimation procedure for flexible Weibull distribution under Type-II censoring scheme assuming Jeffrey's scale invariant (noninformative) and Gamma (informative) priors for the model parameters. The interval estimation for the model parameters has been performed through normal approximation, bootstrap, and highest posterior density (HPD) procedures. Further, we have also derived the predictive posteriors and the corresponding predictive survival functions for the future observations based on Type-II censored data from the flexible Weibull distribution. Since the predictive posteriors are not in the closed form, we proposed to use the Monte Carlo Markov chain (MCMC) methods to approximate the posteriors of interest. The performance of the Bayes estimators has also been compared with the classical estimators of the model parameters through the Monte Carlo simulation study. A real data set representing the time between failures of secondary reactor pumps has been analysed for illustration purpose. © 2013 Sanjay Kumar Singh et al.
Bayesian estimation and prediction for the generalized Lindley distribution under asymmetric loss function
(Hacettepe University, 2014) Sanjay Kumar Singh; Umesh Singh; Vikas Kumar Sharma
The paper develops the Bayesian estimation procedure for the generalized Lindley distribution under squared error and general entropy loss functions in case of complete sample of observations. For obtaining the Bayes estimates, both non-informative and informative priors are used. Monte Carlo simulation is performed to compare the behaviour of the proposed estimators with the maximum likelihood estimators in terms of their estimated risks. Discussion is further extended to Bayesian prediction problem based on an informative sample where an attempt is made to derive the prediction intervals for future observations. Numerical illustrations are provided based on a real data example. © 2014, Hacettepe University. All rights reserved.
Bayesian Estimation for Poisson-exponential Model under Progressive Type-II Censoring Data with Binomial Removal and Its Application to Ovarian Cancer Data
(Taylor and Francis Inc., 2016) Sanjay Kumar Singh; Umesh Singh; Manoj Kumar
In this article, we propose Maximum likelihood estimators (MLEs) and Bayes estimators of parameters of Poisson-exponential distribution (PED) under General entropy loss function (GELF) and Squared error loss function (SELF) for Progressive type-II censored data with binomial removals (PT-II CBRs). The MLEs and corresponding Bayes estimators are compared in terms of their risks based on simulated samples from PED. The proposed methodology is illustrated on a real dataset of ovarian cancer. © 2016, Copyright © Taylor & Francis Group, LLC.
Bayesian estimation of inverted exponential distribution based on record values
(ISOSS Publications, 2017) Pradeep Kr. Vishwakarma; Umesh Singh; Sanjay Kumar Singh
In the present study, we propose Bayes estimators of parameter, reliability and hazard function for inverted exponential distribution (IED) under different loss functions based on record values. For this purpose, we have used both informative and non-informative priors. A Monte Carlo simulation study is carried out to compare the performance of proposed estimators with corresponding maximum likelihood estimators (MLEs) in terms of their simulated risks. © ISOSS Publications 2017.
Bayesian estimation of Marshall-Olkin extended exponential parameters under various approximation techniques
(Hacettepe University, 2014) Sanjay Kumar Singh; Umesh Singh; Abhimanyu Singh Yadav
In this paper, we propse Bayes estimators of the parameters of Marshall Olkin extended exponential distribution (MOEED) introduced by Marshall-Olkin [2] for complete sample under squared error loss function (SELF). We have used different approximation techniques to obtain the Bayes estimate of the parameters. A Monte Carlo simulation study is carried out to compare the performance of proposed estimators with the corresponding maximum likelihood estimator (MLE's) on the basis of their simulated risk. A real data set has been considered for illustrative purpose of the study. © 2014, Hacettepe University. All rights reserved.
Bayesian estimation of parameters of inverse Weibull distribution
(2013) Sanjay Kumar Singh; Umesh Singh; Dinesh Kumar
The present paper describes the Bayes estimators of parameters of inverse Weibull distribution for complete, type I and type II censored samples under general entropy and squared error loss functions. The proposed estimators have been compared on the basis of their simulated risks (average loss over sample space). A real-life data set is used to illustrate the results. © 2013 Copyright Taylor and Francis Group, LLC.
Bayesian estimation of the exponentiated gamma parameter and reliability function under asymmetric loss function
(National Statistical Institute, 2011) Sanjay Kumar Singh; Umesh Singh; Dinesh Kumar
In this paper, we propose Bayes estimators of the parameter of the exponentiated gamma distribution and associated reliability function under General Entropy loss function for a censored sample. The proposed estimators have been compared with the corresponding Bayes estimators obtained under squared error loss function and maximum likelihood estimators through their simulated risks (average loss over sample space).