Nadiminti Nagamani

@vitap.ac.in

Assistant professor and Department of Mathematics
VIT AP University



              

https://researchid.co/nadimintinagamani

RESEARCH INTERESTS

Estimation Theory, Statistical Inference.

6

Scopus Publications

31

Scholar Citations

5

Scholar h-index

Scopus Publications

  • Adaptive estimation: Fuzzy data-driven gamma distribution via Bayesian and maximum likelihood approaches
    Abbarapu Ashok and Nadiminti Nagamani
    American Institute of Mathematical Sciences (AIMS)
    <p>Integrating fuzzy concepts into statistical estimation offers considerable advantages by enhancing both the accuracy and reliability of parameter estimations, irrespective of the sample size and technique used. This study specifically examined the improvement of parameter estimation accuracy when dealing with fuzzy data, with a focus on the gamma distribution. We explored and evaluated a variety of estimation techniques for determining the scale parameter $ \\eta $ and shape parameter $ \\rho $ of the gamma distribution, employing both maximum likelihood (ML) and Bayesian methods. In the case of ML estimates, the expectation-maximization (EM) algorithm and the Newton-Raphson (NR) method were applied, with confidence intervals constructed using the Fisher information matrix. Additionally, the highest posterior density (HPD) intervals were derived through Gibbs sampling. For Bayesian estimates, the Tierney and Kadane (TK) approximation and Gibbs sampling were used to enhance the estimation process. A thorough performance comparison was undertaken using a simulated fuzzy dataset of the lifetimes of rechargeable batteries to assess the effectiveness of these methods. The methods were evaluated by comparing the estimated parameters to their true values using mean squared error (MSE) as a metric. Our findings demonstrate that the Bayesian approach, particularly when combined with the TK method, consistently produces more accurate and reliable parameter estimates compared to traditional methods. These results underscore the potential of Bayesian techniques in addressing fuzzy data and enhancing precision in statistical analyses.</p>


  • ESTIMATING COMMON PARAMETERS OF DIFFERENT CONTINUOUS DISTRIBUTIONS
    Abbarapu Ashok and Nadiminti Nagamani
    Faculty of Engineering, University of Kragujevac

  • Improved estimation of quantiles of two normal populations with common mean and ordered variances
    Nadiminti Nagamani and Manas Ranjan Tripathy
    Informa UK Limited
    Abstract Estimation of quantiles from two normal populations is considered under the assumption of common mean and ordered variances. Several new estimators have been proposed using certain estimators of the common mean, including the plug-in type restricted MLE. A sufficient condition for improving equivariant estimators is proved and as a result improved estimators are derived. The percentage of risk improvements for each of the improved estimators have been computed numerically, which are quite significant. All the improved estimators have been compared numerically using Monte-Carlo simulation method. Finally, recommendations have been made for the use of estimators in practice.

  • Estimating Common Scale Parameter of Two Logistic Populations: A Bayesian Study
    Nadiminti Nagamani, Manas Ranjan Tripathy, and Somesh Kumar
    Informa UK Limited
    Abstract Estimation under equality restrictions is an age old problem and has been considered by several researchers in the past due to practical applications and theoretical challenges involved in it. Particularly, the problem has been extensively studied from classical as well as decision theoretic point of view when the underlying distribution is normal. In this paper, we consider the problem when the underlying distribution is non-normal, say, logistic. Specifically, estimation of the common scale parameter of two logistic populations has been considered when the location parameters are unknown. It is observed that closed forms of the maximum likelihood estimators (MLEs) for the associated parameters do not exist. Using certain numerical techniques the MLEs have been derived. The asymptotic confidence intervals have been derived numerically too, as these also depend on the MLEs. Approximate Bayes estimators are proposed using non-informative as well as conjugate priors with respect to the squared error (SE) and the LINEX loss functions. A simulation study has been conducted to evaluate the proposed estimators and compare their performances through mean squared error (MSE) and bias. Finally, two real life examples have been considered in order to show the potential applications of the proposed model and illustrate the method of estimation.

  • Estimating Common Scale Parameter of Two Gamma Populations: A Simulation Study
    Nadiminti Nagamani and Manas Ranjan Tripathy
    Informa UK Limited
    SYNOPTIC ABSTRACT The problem of estimating the common scale parameter of two gamma populations has been considered when the shape parameters are unknown and possibly different. The performance of an estimator is evaluated using both bias and mean squared error. The maximum likelihood estimator (MLE) has been obtained numerically using the Monte-Carlo simulation, as the closed form does not exist. The Fisher information matrix has been obtained for our model and, consequently, asymptotic confidence intervals have been constructed. Certain Bayes estimators, using various priors, have been obtained. Like the MLE, the closed form of these Bayes estimators does not exist. An approximation due to Lindley (1980) has been used to obtain these Bayes estimators approximately. Finally, the bias and the mean squared error of all these estimators have been numerically compared through the Monte-Carlo simulation method.

RECENT SCHOLAR PUBLICATIONS

  • ENHANCING LINDLEY DISTRIBUTION PARAMETER ESTIMATION WITH HYBRID BAYESIAN AVERAGE MODEL FOR FUZZY DATA
    A Ashok, N Nagamani
    Reliability: Theory & Applications 20 (1 (82)), 528-542 2025

  • Adaptive estimation: Fuzzy data-driven gamma distribution via Bayesian and maximum likelihood approaches
    A Ashok, N Nagamani
    AIMS Mathematics 10 (1), 438-459 2025

  • Estimating Common Parameters of Different Continuous Distributions
    A Ashok, N Nagamani
    Proceedings on Engineering Sciences 6 (3), 1273-1286 2024

  • Estimating common scale parameter of two logistic populations: a Bayesian study
    N Nagamani, MR Tripathy, S Kumar
    American Journal of Mathematical and Management Sciences 40 (1), 44-67 2020

  • Improved estimation of quantiles of two normal populations with common mean and ordered variances
    N Nagamani, M Ranjan Tripathy
    Communications in Statistics-Theory and Methods 49 (19), 4669-4692 2020

  • Estimating Quantiles and Common Parameter in Certain Stochastic Models
    N Nagamani
     2019

  • Estimating common dispersion parameter of several inverse Gaussian populations: A simulation study
    N Nagamani, MR Tripathy
    Journal of Statistics and Management Systems 21 (7), 1357-1389 2018

  • Estimating common scale parameter of two gamma populations: a simulation study
    N Nagamani, MR Tripathy
    American Journal of Mathematical and Management Sciences 36 (4), 346-362 2017

  • Estimating common shape parameter of two gamma populations: A simulation study
    MR Tripathy, N Nagamani
    Journal of Statistics and Management Systems 20 (3), 369-398 2017

MOST CITED SCHOLAR PUBLICATIONS

  • Estimating common scale parameter of two gamma populations: a simulation study
    N Nagamani, MR Tripathy
    American Journal of Mathematical and Management Sciences 36 (4), 346-362 2017
    Citations: 7

  • Estimating common shape parameter of two gamma populations: A simulation study
    MR Tripathy, N Nagamani
    Journal of Statistics and Management Systems 20 (3), 369-398 2017
    Citations: 7

  • Estimating common scale parameter of two logistic populations: a Bayesian study
    N Nagamani, MR Tripathy, S Kumar
    American Journal of Mathematical and Management Sciences 40 (1), 44-67 2020
    Citations: 6

  • Improved estimation of quantiles of two normal populations with common mean and ordered variances
    N Nagamani, M Ranjan Tripathy
    Communications in Statistics-Theory and Methods 49 (19), 4669-4692 2020
    Citations: 6

  • Estimating common dispersion parameter of several inverse Gaussian populations: A simulation study
    N Nagamani, MR Tripathy
    Journal of Statistics and Management Systems 21 (7), 1357-1389 2018
    Citations: 5