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Hereditarily homogeneous C-spaces
Invariant metrics on current Lie algebras
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Benchmarking the Observed Best Predictor
Calcutta Statistical Association Bulletin, Ahead of Print.
We propose two methods for benchmarking the observed best predictor (OBP; Jiang et al.[]) in small area estimation under the Fay-Herriot model. Furthermore, we propose two methods for estimating the mean squared prediction error (MSPE) of the benchmarked OBP. Theoretical and empirical properties of the benchmarked OBP as well as its MSPE estimators are studied. A real-data example is considered.AMS 2000 subject classification: 62J05
We propose two methods for benchmarking the observed best predictor (OBP; Jiang et al.[]) in small area estimation under the Fay-Herriot model. Furthermore, we propose two methods for estimating the mean squared prediction error (MSPE) of the benchmarked OBP. Theoretical and empirical properties of the benchmarked OBP as well as its MSPE estimators are studied. A real-data example is considered.AMS 2000 subject classification: 62J05
Comparison of two coefficients of variation: a new Bayesian approach
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Asymptotic UMVUE: Asymptotic Moments Matching the UMVUE under the Ewens Sampling Formula
Calcutta Statistical Association Bulletin, Ahead of Print.
The Ewens sampling formula is a distribution related to the random partition of a positive integer. In this study, we investigate the issue of non-existent solutions in parameter estimation under the distribution. We derive the first and second moments matching estimators to the uniformly minimum variance unbiased estimator (UMVUE) in the asymptotic sense (hereafter referred to as Asymptotic UMUVE) using the Ewens sampling formula. A Monte Carlo simulation study is performed to evaluate the efficiency of the resulting estimators.AMS 2000 subject classification: 62F12, 62P25
The Ewens sampling formula is a distribution related to the random partition of a positive integer. In this study, we investigate the issue of non-existent solutions in parameter estimation under the distribution. We derive the first and second moments matching estimators to the uniformly minimum variance unbiased estimator (UMVUE) in the asymptotic sense (hereafter referred to as Asymptotic UMUVE) using the Ewens sampling formula. A Monte Carlo simulation study is performed to evaluate the efficiency of the resulting estimators.AMS 2000 subject classification: 62F12, 62P25
Dynamic response analysis of fractional order RLCα circuit and its order dependent oscillation criterion
Exact and explicit analytical solutions for optimal pension management with general utilities
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Sensitivity analysis of Bayesian nonparametric spatial crash frequency models
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Separation of the refined estimator of the measure for symmetry in square contingency tables
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