Faculty

Institution

Unit

Venue

Nitis Mukhopadhyay

Professor

University of Connecticut

Affiliations

College of Liberal Arts and Sciences

Statistics

Nitis Mukhopadhyay is currently serving as a College Of Liberal Arts And Sciences Professor at University of Connecticut.

Honors

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Experiences

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Education

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Nitis Mukhopadhyay

University of Connecticut

Professor

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statistics applied statistics statistical methodology estimation theory applied mathematics statistical theory statistical inference data science sampling probability theory mathematical analysis health sciences sequential estimation econometrics normal mean probabilistic analysis analytics normal distribution machine learning research multivariate analysis statistical analysis second-order asymptotics computational statistics statistical science exponential distribution bayesian analysis parameter estimation sequential point estimation two-stage procedures sequential methodologies sequence analysis density estimation mathematical statistics second-order properties normal means linear function asymptotic normality statistical averaging sequential learning negative exponential populations two-stage estimation mean vector two-stage sampling mathematical sciences location estimation exponential algorithm statistical modeling parameter identification partial evaluation robust statistics theoretical analysis sampling (statistics) horticulture research applied entomology plant science agronomy livestock science nonlinear analysis combinatorial optimization historical review symptom management psychological medicine psychiatric research continual improvement process method engineering methodological development agricultural sciences crop improvement plant reproduction crop science phenology bibliometrics population protocol population studies analytic number theory approximation methods biostatistics behavioral statistic survey methodology

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

A Personal Celebration of Dr. D. Basu With Emphasis on Examples-Counterexamples-Clarifications. Sankhya A (2024)

Nitis Mukhopadhyay

anthropology

Constrained Bayesian Method for Testing Composite Hypotheses Concerning Normal Distribution With Equal Parameters. Sequential Analysis (2024)

K. J. KachiashviliN. MukhopadhyayJ. K. Kachiashvili

normal distributiondata sciencemathematical analysiscomposite hypothesesequal parametersstatistics

Fine-Tuned Parallel Piecewise Sequential Confidence Region and Point Estimation Strategies for the Regression Parameters in a Linear Model: Big Data Context. Calcutta Statistical Association Bulletin (2024)

Nitis MukhopadhyayChen Zhang

big data contextlinear modelbiostatisticsdata sciencestatistical theorypoint estimation strategiesparallel computingparallel programminghigh-dimensional statisticsapplied statisticslarge-scale datasetsbig dataregression parametersstatistics

Multi-Stage Minimum Risk Point Estimation (Mrpe) of a Function of Unknown $$\Varvec{\Beta }$$ in a Gamma($$\Varvec{\Alpha,\Beta }$$) Model With $$\Varvec{\Alpha }$$ Known: Related Problems and Illustrations With Analysis From Simulations and Cancer Data. Japanese Journal of Statistics and Data Science (2024)

Nitis MukhopadhyayJing Li

econometricsprobability theorymathematical financeanalyticssurvival analysisstatistical analysisstatistics

Multi-Stage Minimum Risk Point Estimation Strategies for Comparing the Locations From Two Negative Exponential Models and Second-Order Asymptotics: Illustrations With Simulated Data and Bone Marrow Transplant Data. Journal of Statistical Theory and Practice (2024)

Nitis MukhopadhyayAnhar S. Aloufi

statistical sciencesimulated datastatistical methodologybiostatisticssecond-order asymptoticsdata sciencestatistical theorystatistical inferencenegative exponential modelssamplingsurvival analysisapplied statisticsmodel comparisonstatistics

Theory and Practice of Second-Order Expansions for Moments of 100 ρ % Accelerated Sequential Stopping Times in Parametric and Nonparametric Estimation With Arbitrary Fractional Ρ. Sequential Analysis (2024)

Nitis MukhopadhyayBoyi Zhang

theoretical analysisnonparametric estimationstatistical methodologymathematical analysisapplied statisticssecond-order expansionsprobabilistic analysisstatistics

Second-Order (S.O.) Multi-Stage Fixed-Width Confidence Interval (Fwci) Estimation Strategies for Comparing Location Parameters From Two Negative Exponential (Ne) Populations: Illustrations With Cancer Data. Metrika (2023)

Nitis MukhopadhyayAnhar Aloufi

estimation strategiesbenchmark studiesbiostatisticsmachine learningcancer datalocation parametersstatistics

Theory of New Second-Order Expansions for the Moments of $${100\Rho \%}$$ Accelerated Sequential Stopping Times in Normal Mean Estimation Problems When $${0 \Rho 1}$$ is Arbitrary. Japanese Journal of Statistics and Data Science (2023)

Nitis MukhopadhyayBoyi Zhang

data scienceeconometricsstatistical theorystatistics

A New Formulation of Minimum Risk Fixed-Width Confidence Interval (Mrfwci) Estimation Problems for a Normal Mean With Illustrations and Simulations: Applications to Air Quality Data. Sequential Analysis (2022)

Nitis MukhopadhyaySwathi Venkatesan

estimation problemsapplied mathematicsair quality datanormal meandata scienceair qualitymathematical analysisapplied statisticsestimation theorystatistics

An Optimal Purely Sequential Strategy With Asymptotic Second-Order Properties: Applications From Statistical Inference and Data Analysis. Sequential Analysis (2022)

Srawan Kumar BishnoiNitis Mukhopadhyay

asymptotic analysisapplied mathematicsdata analysisasymptotic second-order propertiesstatistics

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Nitis Mukhopadhyay

Professor

University of Connecticut

Affiliations

College of Liberal Arts and Sciences

Statistics

Nitis Mukhopadhyay is currently serving as a College Of Liberal Arts And Sciences Professor at University of Connecticut.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Nitis Mukhopadhyay

University of Connecticut

Professor

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

A Personal Celebration of Dr. D. Basu With Emphasis on Examples-Counterexamples-Clarifications. Sankhya A (2024)

Nitis Mukhopadhyay

anthropology

Constrained Bayesian Method for Testing Composite Hypotheses Concerning Normal Distribution With Equal Parameters. Sequential Analysis (2024)

K. J. KachiashviliN. MukhopadhyayJ. K. Kachiashvili

normal distributiondata sciencemathematical analysiscomposite hypothesesequal parametersstatistics

Nitis MukhopadhyayChen Zhang

Nitis MukhopadhyayJing Li

econometricsprobability theorymathematical financeanalyticssurvival analysisstatistical analysisstatistics

Nitis MukhopadhyayAnhar S. Aloufi

Nitis MukhopadhyayBoyi Zhang

theoretical analysisnonparametric estimationstatistical methodologymathematical analysisapplied statisticssecond-order expansionsprobabilistic analysisstatistics

Nitis MukhopadhyayAnhar Aloufi

estimation strategiesbenchmark studiesbiostatisticsmachine learningcancer datalocation parametersstatistics

Nitis MukhopadhyayBoyi Zhang

data scienceeconometricsstatistical theorystatistics

Nitis MukhopadhyaySwathi Venkatesan

estimation problemsapplied mathematicsair quality datanormal meandata scienceair qualitymathematical analysisapplied statisticsestimation theorystatistics

An Optimal Purely Sequential Strategy With Asymptotic Second-Order Properties: Applications From Statistical Inference and Data Analysis. Sequential Analysis (2022)

Srawan Kumar BishnoiNitis Mukhopadhyay

asymptotic analysisapplied mathematicsdata analysisasymptotic second-order propertiesstatistics

1
2
3
4
5
26
10 / page

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bayesian statistic

empirical likelihood

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Texas Tech University

spectral theory

projection pursuit

Soumendra Lahiri

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Washington University in St. Louis

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high-dimensional method

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big data

spatio-temporal stream processing

S Jammalamadaka

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statistics

applied statistics

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biological computation

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