Shikhar Tyagi

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Shikhar Tyagi


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https://researchid.co/shikhar1724
24

Scopus Publications

141

Scholar Citations

7

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Ensemble and Hybrid Machine Learning Models for Seasonal Water Consumption Forecasting Under Climate Variability
    Aruna Rajballie, Vrijesh Tripathi, Shikhar Tyagi, Amarnath Chinchamee
    Civil Engineering Journal Iran, 2026
    The objective of this paper is to improve the forecasting of monthly water consumption under climate variability by combining ensemble and hybrid modelling with a season-aware design. Monthly consumption and meteorological data from 2003 to 2024 were utilized in this study. Four models were evaluated: (i) a stacking ensemble with STL-trend plus residual learning; (ii) a hybrid machine-learning–physics model with differentially-evolved weights; and (iii–iv) season-specific stacked models for wet and dry periods. Robustness was assessed with time-aware validation and residual diagnostics (Shapiro–Wilk, Breusch–Pagan, Durbin–Watson, Ljung–Box). The findings indicate that across models, ensembles captured nonlinear climate–demand variations while maintaining linear structure. The ensemble and hybrid model achieved strong accuracy with low errors while the season-specific models attained high fit (wet R²≈0.998; dry R²≈0.991) with stable residual behavior. Sensitivity to temperature and humidity aligns with expected physical behavior. Precipitation shows a diminishing-returns effect on water use, where moderate rainfall leads to higher consumption, while heavy rainfall tends to reduce demand. The framework innovatively combines decomposition-assisted stacking, physics-informed hybridization, and seasonal ensemble modelling. Overall, the approach provides highly accurate, interpretable, and climate-aware water demand forecasts for tropical regions, offering a practical basis for utility-scale implementation.
  • Bayesian survival modeling with mixtures of inverse Gaussian frailties
    Gilbert Kiprotich, Shikhar Tyagi, Pedro L. Ramos
    Journal of Applied Statistics, 2026
    We introduce a Bayesian framework for survival analysis that integrates frailty and mixture modeling. In our approach, a mixture of two inverse Gaussian (MIG) distributions is used as the frailty variable for bivariate failure times. The parameterization of the mixture directly specifies the mixing weights, and the Laplace transform is obtained in closed form, which facilitates efficient computation. Flexible baseline distributions are modeled using the generalized Weibull and generalized log-logistic families. Parameter estimation is performed in a fully Bayesian setting using Markov chain Monte Carlo (MCMC) algorithms, allowing for uncertainty quantification. The proposed methodology is illustrated through an analysis of a kidney dataset, where the use of MIG frailties results in improved model fit and predictive performance relative to conventional approaches.
  • A flexible discrete logarithmic-transformed exponential model for count data analysis
    Abhishek Tyagi, Shikhar Tyagi, Kartik Waliya, Alka Chaudhary, Vrijesh Tripathi
    Life Cycle Reliability and Safety Engineering, 2026
  • Bayesian and Frequentist Estimation of Stress-Strength Reliability from a New Extended Burr XII Distribution
    Agiwal, Varun, Tyagi, Shikhar, Chesneau, Christophe
    Revstat Statistical Journal, 2025
    In this article, we propose and study a new three-parameter heavy-tailed distribution that unifes the Burr type XII and power inverted Topp-Leone distributions in an original manner. This unification is made through the use of a simple 'shift parameter'. Among its interesting functionalities, it exhibits possibly decreasing and unimodal probability density and hazard rate functions. We examine its quantile function, stochastic dominance, ordinary moments, weighted moments, incomplete moments, and stress-strength reliability cofficient. Then, the classical and Bayesian approaches are developed to estimate the model and stress strength reliability parameters. Bayes estimates are obtained under the squared error and entropy loss functions. Simulated data are considered to point out the performance of the derived estimates based on the mean squared error. In the final part, the potential of the new model is exemplified by the analysis of two engineering data sets, showing that it is preferable to other reputable and comparable models.
  • A Study on Bivariate Inverse Topp-Leone Model to Counter Heterogeneous Data: Properties, Dependence Studies, Classical and Bayesian Estimation
    Thailand Statistician, 2025
  • Modified Topp-Leone Distribution: Properties, Classical and Bayesian Estimation with Application to COVID-19 and Reliability Data
    Thailand Statistician, 2025
  • A study on comparisons of additive regression frailty models to counter heterogeneity: Bayesian strategies and case study
    Shikhar Tyagi, Arvind Pandey, David D. Hanagal, Christophe Chesneau
    Communications in Statistics Simulation and Computation, 2025
    Historically, the primary goal of conventional survival study methods has been to reduce the frequency of failures over time. If the associated observed and unobserved variables are not known when studying such events, this can have detrimental effects. Frailty models offer a tempting solution for investigating the impact of unknown variables in such a case. In this article, we assume that frailty affects the hazard rate. We find that the weighted Lindley frailty models, which use general versions of the Weibull and log-logistic type II distributions as the baseline distributions, are a reliable method for ensuring the influence of endogenous variability. The parameters involved are estimated according to different loss functions using the Bayesian structure as the basis of Markov Chain Monte Carlo. Bayesian evaluation strategies are then implemented to evaluate the models. The results are demonstrated on known data of kidney infections. It is shown that the novel models outperform those based on the inverse Gaussian and gamma frailty distributions.
  • On bivariate Teissier model using Copula: dependence properties, and case studies
    Shikhar Tyagi
    International Journal of System Assurance Engineering and Management, 2024
  • Modelling Climate, COVID-19, and Reliability Data: A New Continuous Lifetime Model under Different Methods of Estimation
    Statistics and Applications, 2024
  • Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study
    Pragya Gupta, Arvind Pandey, David D. Hanagal, Shikhar Tyagi
    Springer Series in Reliability Engineering, 2024
  • Theory and practice of a bivariate trigonometric Burr XII distribution
    Shikhar Tyagi, Varun Agiwal, Sumit Kumar, Christophe Chesneau
    Afrika Matematika, 2023
  • On Bivariate Inverse Lindley Distribution Derived From Copula
    Thailand Statistician, 2023
  • Generalised Lindley shared additive frailty regression model for bivariate survival data
    Arvind Pandey, David D. Hanagal, Shikhar Tyagi
    Statistics in Transition New Series, 2022
  • Weighted Lindley Shared Regression Model for Bivariate Left Censored Data
    Shikhar Tyagi, Arvind Pandey, Christophe Chesneau
    Sankhya B, 2022
  • Identifying the Effects of Observed and Unobserved Risk Factors Using Weighted Lindley Shared Regression Model
    Shikhar Tyagi, Arvind Pandey, Christophe Chesneau
    Journal of Statistical Theory and Practice, 2022
  • Parametric confidence intervals of generalized process capability index and its applications
    Sumit Kumar, Mahendra Saha, Shikhar Tyagi
    Life Cycle Reliability and Safety Engineering, 2022
  • Generalized Lindley Shared Frailty Based on Reversed Hazard Rate
    Arvind Pandey, David D. Hanagal, Shikhar Tyagi, Pragya Gupta
    International Journal of Reliability Quality and Safety Engineering, 2022
  • ON A BIVARIATE XGAMMA DISTRIBUTION DERIVED FROM COPULA
    Mohammed Abulebda, Ashok Kumar Pathak, Arvind Pandey, Shikhar Tyagi
    Statistica, 2022
  • Modeling Australian Twin Data Using Generalized Lindley Shared Frailty Models
    Arvind Pandey, David D. Hanagal, Shikhar Tyagi, Pragya Gupta
    Springer Proceedings in Mathematics and Statistics, 2022
  • Comparison of Multiplicative Frailty Models Under Weibull Baseline Distribution
    Arvind Pandey, Shikhar Tyagi
    Lobachevskii Journal of Mathematics, 2021
  • Weighted Lindley multiplicative regression frailty models under random censored data
    Shikhar Tyagi, Arvind Pandey, Varun Agiwal, Christophe Chesneau
    Computational and Applied Mathematics, 2021
  • Generalized Lindley Shared Frailty Models
    Statistics and Applications, 2021
  • Analysis of bivariate survival data using shared inverse Gaussian frailty models: A Bayesian approach
    Predictive Analytics Using Statistics and Big Data Concepts and Modeling, 2020
  • Can the aging influence cold environment mediated cancer risk in the USA female population?
    Shreetama Bandyopadhayaya, Rashmi Bundel, Shikhar Tyagi, Arvind Pandey, Chandi C. Mandal
    Journal of Thermal Biology, 2020

RECENT SCHOLAR PUBLICATIONS

  • ArvindSt: Five Novel Stochastic Regression Models with Arvind-Distributed Errors and Effects. R package version 1.0.0
    S Tyagi, A Pandey
    https://cran.r-project.org/package=ArvindSt , 2026
    2026
  • A flexible discrete logarithmic-transformed exponential model for count data analysis
    A Tyagi, S Tyagi, K Waliya, A Chaudhary, V Tripathi
    Life Cycle Reliability and Safety Engineering, 1-17 , 2026
    2026
  • Ensemble and Hybrid Machine Learning Models for Seasonal Water Consumption Forecasting Under Climate Variability
    A Rajballie, V Tripathi, S Tyagi, A Chinchamee
    Civil Engineering Journal 12 (2), 743-762 , 2026
    2026
  • A study on comparisons of additive regression frailty models to counter heterogeneity: Bayesian strategies and case study
    S Tyagi, A Pandey, DD Hanagal, C Chesneau
    Communications in Statistics-Simulation and Computation 54 (11), 4690-4711 , 2025
    2025
    Citations: 1
  • Bayesian survival modeling with mixtures of inverse Gaussian frailties
    G Kiprotich, S Tyagi, PL Ramos
    Journal of Applied Statistics, 1-27 , 2025
    2025
  • Bayesian and frequentist estimation of stress-strength reliability from a new extended Burr XII distribution
    V Agiwal, S Tyagi, C Chesneau
    REVSTAT-Statistical Journal 23 (1), 117-138 , 2025
    2025
    Citations: 5
  • A Study on Bivariate Inverse Topp-Leone Model to Counter Heterogeneous Data: Properties, Dependence Studies, Classical and Bayesian Estimation
    S Tyagi
    Thailand Statistician 23 (1), 181-198 , 2025
    2025
    Citations: 1
  • Modified Topp-Leone distribution: properties, classical and Bayesian estimation with application to COVID-19 and reliability data
    B Singh, S Tyagi, RP Singh, A Tyagi
    Thailand Statistician 23 (1), 72-96 , 2025
    2025
    Citations: 10
  • On bivariate Teissier model using Copula: dependence properties, and case studies
    S Tyagi
    International Journal of System Assurance Engineering and Management 15 (6 … , 2024
    2024
    Citations: 5
  • Exploring the impact of latent and obscure factors on left-censored data: Bayesian approaches and case study
    P Gupta, A Pandey, DD Hanagal, S Tyagi
    Reliability engineering for industrial processes: An analytics perspective … , 2024
    2024
    Citations: 1
  • Data: Bayesian Approaches and Case Study
    P Gupta, A Pandey, DD Hanagal, S Tyagi
    Reliability Engineering for Industrial Processes: An Analytics Perspective, 293 , 2024
    2024
  • Importance of Frailty Regression Models in Modern Data Analysis Domain
    S Tyagi
    https://medium.com/@shikhartyagi_93772/importance-of-frailty-regression … , 2024
    2024
  • Modelling climate, COVID-19, and reliability data: A new continuous lifetime model under different methods of estimation
    A Pandey, RP Singh, S Tyagi, A Tyagi
    Stat. Appl. 22 (2) , 2024
    2024
    Citations: 1
  • Theory and practice of a bivariate trigonometric Burr XII distribution
    S Tyagi, V Agiwal, S Kumar, C Chesneau
    Afrika Matematika 34 (3), 49 , 2023
    2023
    Citations: 2
  • On bivariate inverse Lindley distribution derived from Copula
    M Abulebda, A Pandey, S Tyagi
    Thailand Statistician 21 (2), 291-304 , 2023
    2023
    Citations: 9
  • Shared Frailty Models Based on Cancer Data
    A Pandey, DD Hanagal, S Tyagi
    International Journal of Statistics and Reliability Engineering 9 (3), 461-474 , 2023
    2023
  • Weighted Lindley shared regression model for bivariate left censored data
    S Tyagi, A Pandey, C Chesneau
    Sankhya B 84 (2), 655-682 , 2022
    2022
    Citations: 5
  • On a bivariate XGamma distribution derived from Copula
    M Abulebda, AK Pathak, A Pandey, S Tyagi
    Statistica 82 (1), 15-40 , 2022
    2022
    Citations: 17
  • Power xgamma distribution: Properties and its applications to cancer data
    S Tyagi, S Kumar, A Pandey, M Saha, H Bagariya
    International Journal of Statistics and Reliability Engineering 9 (1), 51-60 , 2022
    2022
    Citations: 6
  • Bivariate Inverse Topp-Leone Model to Counter Heterogeneous Data
    S Tyagi
    arXiv preprint arXiv:2206.05798 , 2022
    2022
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • On a bivariate XGamma distribution derived from Copula
    M Abulebda, AK Pathak, A Pandey, S Tyagi
    Statistica 82 (1), 15-40 , 2022
    2022
    Citations: 17
  • Weighted Lindley multiplicative regression frailty models under random censored data
    S Tyagi, A Pandey, V Agiwal, C Chesneau
    Computational and Applied Mathematics 40 (8), 265 , 2021
    2021
    Citations: 11
  • Modified Topp-Leone distribution: properties, classical and Bayesian estimation with application to COVID-19 and reliability data
    B Singh, S Tyagi, RP Singh, A Tyagi
    Thailand Statistician 23 (1), 72-96 , 2025
    2025
    Citations: 10
  • Analysis of Bivariate Survival Data using Shared Inverse Gaussian Frailty Models: A Bayesian Approach
    A Pandey, S Bhushan, R Lalpawimawha, S Tyagi
    Predictive Analytics Using Statistics and Big Data: Concepts and Modeling 1 … , 2020
    2020
    Citations: 10
  • On bivariate inverse Lindley distribution derived from Copula
    M Abulebda, A Pandey, S Tyagi
    Thailand Statistician 21 (2), 291-304 , 2023
    2023
    Citations: 9
  • Analysis of Australian Twin Data Using Generalized Inverse Gaussian Shared Frailty Models Based on Reversed Hazard Rate
    A Pandey, DD Hanagal, P Gupta, S Tyagi
    International Journal of Statistics and Reliability Engineering 7 (2), 219-235 , 2020
    2020
    Citations: 8
  • Generalized Lindley shared frailty based on reversed hazard rate
    A Pandey, DD Hanagal, S Tyagi, P Gupta
    International Journal of Reliability, Quality and Safety Engineering 29 (01 … , 2022
    2022
    Citations: 7
  • Bayesian Inferences in Generalized Lindley Shared Frailty Model with Left Censored Bivariate Data
    S Tyagi, A Pandey, DD Hanagal, P Gupta
    Advance Research Trends in Statistics and Data Science, 137-157 , 2021
    2021
    Citations: 7
  • Can the aging influence cold environment mediated cancer risk in the USA female population?
    S Bandyopadhayaya, R Bundel, S Tyagi, A Pandey, CC Mandal
    Journal of thermal biology 92, 102676 , 2020
    2020
    Citations: 7
  • Power xgamma distribution: Properties and its applications to cancer data
    S Tyagi, S Kumar, A Pandey, M Saha, H Bagariya
    International Journal of Statistics and Reliability Engineering 9 (1), 51-60 , 2022
    2022
    Citations: 6
  • Generalized Lindley Shared Frailty Models
    A Pandey, DD Hanagal, S Tyagi
    Statistics and Applications 19 (2), 41-62 , 2021
    2021
    Citations: 6
  • Bayesian and frequentist estimation of stress-strength reliability from a new extended Burr XII distribution
    V Agiwal, S Tyagi, C Chesneau
    REVSTAT-Statistical Journal 23 (1), 117-138 , 2025
    2025
    Citations: 5
  • On bivariate Teissier model using Copula: dependence properties, and case studies
    S Tyagi
    International Journal of System Assurance Engineering and Management 15 (6 … , 2024
    2024
    Citations: 5
  • Weighted Lindley shared regression model for bivariate left censored data
    S Tyagi, A Pandey, C Chesneau
    Sankhya B 84 (2), 655-682 , 2022
    2022
    Citations: 5
  • Identifying the effects of observed and unobserved risk factors using weighted lindley shared regression model
    S Tyagi, A Pandey, C Chesneau
    Journal of Statistical Theory and Practice 16 (2), 16 , 2022
    2022
    Citations: 5
  • Comparison of Multiplicative Frailty Models under Generalized Log-Logistic-II Baseline Distribution for Counter Heterogeneous Left Censored Data
    P Gupta, A Pandey, S Tyagi
    Statistical Techniques for Interdisciplinary Research 1, 97-114 , 2022
    2022
    Citations: 4
  • Comparison of Multiplicative Frailty Models Under Weibull Baseline Distribution
    A Pandey, S Tyagi
    Lobachevskii Journal of Mathematics 42 (13), 3184–3195 , 2022
    2022
    Citations: 4
  • Theory and practice of a bivariate trigonometric Burr XII distribution
    S Tyagi, V Agiwal, S Kumar, C Chesneau
    Afrika Matematika 34 (3), 49 , 2023
    2023
    Citations: 2
  • Parametric confidence intervals of generalized process capability index and its applications
    S Kumar, M Saha, S Tyagi
    Life Cycle Reliability and Safety Engineering 11 (2), 177-187 , 2022
    2022
    Citations: 2
  • Applied Statistical Methods: ISGES 2020, Pune, India, January 2-4
    DD Hanagal, RV Latpate, G Chandra
    Springer , 2022
    2022
    Citations: 2