The Experts below are selected from a list of 222 Experts worldwide ranked by ideXlab platform
B. K. Shivanna - One of the best experts on this subject based on the ideXlab platform.
-
Weibull-Bayesian Estimation Based on Maximum Ranked Set Sampling with Unequal Samples
Open Journal of Statistics, 2016Co-Authors: B. S. Biradar, B. K. ShivannaAbstract:A modification of ranked set sampling (RSS) called maximum ranked set sampling with unequal sample (MRSSU) is considered for the Bayesian estimation of scale parameter α of the Weibull distribution. Under this method, we use Linex loss function, conjugate and Jeffreys prior distributions to derive the Bayesian Estimate of α. In order to measure the efficiency of the obtained Bayesian Estimates with respect to the Bayesian Estimates of simple random sampling (SRS), we compute the bias, mean squared error (MSE) and asymptotic relative efficiency of the obtained Bayesian Estimates using simulation. It is shown that the proposed Estimates are found to be more efficient than the corresponding one based on SRS
B. S. Biradar - One of the best experts on this subject based on the ideXlab platform.
-
Weibull-Bayesian Estimation Based on Maximum Ranked Set Sampling with Unequal Samples
Open Journal of Statistics, 2016Co-Authors: B. S. Biradar, B. K. ShivannaAbstract:A modification of ranked set sampling (RSS) called maximum ranked set sampling with unequal sample (MRSSU) is considered for the Bayesian estimation of scale parameter α of the Weibull distribution. Under this method, we use Linex loss function, conjugate and Jeffreys prior distributions to derive the Bayesian Estimate of α. In order to measure the efficiency of the obtained Bayesian Estimates with respect to the Bayesian Estimates of simple random sampling (SRS), we compute the bias, mean squared error (MSE) and asymptotic relative efficiency of the obtained Bayesian Estimates using simulation. It is shown that the proposed Estimates are found to be more efficient than the corresponding one based on SRS
Petr Zeman - One of the best experts on this subject based on the ideXlab platform.
-
A Bayesian Estimate of the Risk of Tick-Borne Diseases
Applications of Mathematics, 2004Co-Authors: Marek Jiruše, Josef Machek, Viktor Beneš, Petr ZemanAbstract:The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS. The methodology covers the choice of those covariates which influence the risk of infection most. Generalized linear models are used and AIC criterion yields the decision. Further, an empirical Bayesian approach is used to Estimate the parameters of the risk model. Statistical properties of the estimators are investigated. Finally, a comparison with earlier results is discussed from the point of view of statistical disease mapping.
-
A Bayesian Estimate OF THE RISK OF TICK-BORNE DISEASES*
Applications of Mathematics, 2004Co-Authors: Marek Jiruše, Josef Machek, Viktor Beneš, Petr ZemanAbstract:The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS.
Arne Ø. Mooers - One of the best experts on this subject based on the ideXlab platform.
-
The phylogeny of the subgroups within the melanogaster species group: likelihood tests on COI and COII sequences and a Bayesian Estimate of phylogeny.
Molecular phylogenetics and evolution, 2005Co-Authors: Rebecca L. Lewis, Andrew T. Beckenbach, Arne Ø. MooersAbstract:The relationships among the majority of the subgroups in the Drosophila melanogaster species group remain unresolved. We present a 2223 basepair dataset for mitochondrial cytochrome oxidase I and cytochrome oxidase II for 43 species (including new data from 11 species), sampled to include the major subgroups. After a brief review of competing hypotheses for the ananassae, montium, suzukii, and takahashii subgroups, we combine the two genes based on a new use of the SH test and present KH and SH likelihood comparisons (Kishino and Hasegawa, 1989. J. Mol. Evol. 29, 170–179; Shimodaira and Hasegawa, 1999) to test the monophyly and placement of these subgroups within the larger species group. Although we Wnd insigniWcant diVerences between the two suggested placements for the ananassae subgroup, the ananassae is sister to the rest of the subgroups in the melanogaster species group in every investigation. For the takahashii subgroup, although we cannot reject monophyly, the species are so closely related to the suzukii subgroup for these data that the two subgroups often form one clade. Finally, we present a Bayesian Estimate of the phylogeny for both genes combined, utilizing a recently published method that allows for diVerent models of evolution for diVerent sites. 2005 Published by Elsevier Inc.
Marek Jiruše - One of the best experts on this subject based on the ideXlab platform.
-
A Bayesian Estimate of the Risk of Tick-Borne Diseases
Applications of Mathematics, 2004Co-Authors: Marek Jiruše, Josef Machek, Viktor Beneš, Petr ZemanAbstract:The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS. The methodology covers the choice of those covariates which influence the risk of infection most. Generalized linear models are used and AIC criterion yields the decision. Further, an empirical Bayesian approach is used to Estimate the parameters of the risk model. Statistical properties of the estimators are investigated. Finally, a comparison with earlier results is discussed from the point of view of statistical disease mapping.
-
A Bayesian Estimate OF THE RISK OF TICK-BORNE DISEASES*
Applications of Mathematics, 2004Co-Authors: Marek Jiruše, Josef Machek, Viktor Beneš, Petr ZemanAbstract:The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS.
