The Experts below are selected from a list of 49575 Experts worldwide ranked by ideXlab platform
Xiaojun Zhu - One of the best experts on this subject based on the ideXlab platform.
-
on the existence and uniqueness of the Maximum Likelihood Estimates of parameters of laplace birnbaum saunders distribution based on type i type ii and hybrid censored samples
Metrika, 2019Co-Authors: Xiaojun Zhu, N Balakrishnan, Helton SauloAbstract:In this paper, we discuss the existence and uniqueness of the Maximum Likelihood Estimates (MLEs) of the parameters of Laplace Birnbaum–Saunders distribution based on Type-I, Type-II and hybrid censored samples. We first derive the relationship between the MLEs of the two parameters and then discuss the monotonicity property of the profile Likelihood function. Numerical iterative procedure is then discussed for determining the MLEs of the parameters. Finally, for illustrative purpose, we analyze one real data from the literature and present some graphical illustrations of the approach.
-
birnbaum saunders distribution based on laplace kernel and some properties and inferential issues
Statistics & Probability Letters, 2015Co-Authors: Xiaojun ZhuAbstract:Abstract For the Birnbaum–Saunders distribution based on Laplace kernel, we discuss the shape characteristics of density and hazard functions. We show the existence and uniqueness of Maximum Likelihood Estimates. Simple modified moment estimators are proposed and compared with Maximum Likelihood Estimates.
-
on the existence and uniqueness of the Maximum Likelihood Estimates of the parameters of birnbaum saunders distribution based on type i type ii and hybrid censored samples
Statistics, 2014Co-Authors: N Balakrishnan, Xiaojun ZhuAbstract:The Birnbaum–Saunders (BS) distribution is a positively skewed distribution and is a common model for analysing lifetime data. In this paper, we discuss the existence and uniqueness of the Maximum Likelihood Estimates (MLEs) of the parameters of BS distribution based on Type-I, Type-II and hybrid censored samples. The line of proof is based on the monotonicity property of the Likelihood function. We then describe the numerical iterative procedure for determining the MLEs of the parameters, and point out briefly some recently developed simple methods of estimation in the case of Type-II censoring. Some graphical illustrations of the approach are given for three real data from the reliability literature. Finally, for illustrative purpose, we also present an example in which the MLEs do not exist.
Xiaohua Zhou - One of the best experts on this subject based on the ideXlab platform.
-
semi parametric Maximum Likelihood Estimates for roc curves of continuous scale tests
Statistics in Medicine, 2008Co-Authors: Xiaohua ZhouAbstract:In this paper, we propose a new semi-parametric Maximum Likelihood (ML) estimate of a receiver operating characteristic (ROC) curve that satisfies the property of invariance of the ROC curve and is easy to compute. We show that our new estimator is √n-consistent and has an asymptotically normal distribution. Our extensive simulation studies show that the proposed method is efficient and robust. Finally, we illustrate the application of the proposed estimator in a real data set.
Frantisek Matus - One of the best experts on this subject based on the ideXlab platform.
-
generalized Maximum Likelihood Estimates for exponential families
Probability Theory and Related Fields, 2008Co-Authors: Ivan Csiszar, Frantisek MatusAbstract:For a standard full exponential family on $$\mathbb R^d$$ , or its canonically convex subfamily, the generalized Maximum Likelihood estimator is an extension of the mapping that assigns to the mean $$a\in\mathbb R^d$$ of a sample for which a maximizer $$\vartheta^*$$ of a corresponding Likelihood function exists, the member of the family parameterized by $$\vartheta^*$$ . This extension assigns to each $$a\in\mathbb R^d$$ with the Likelihood function bounded above, a member of the closure of the family in variation distance. Its detailed description, complete characterization of domain and range, and additional results are presented, not imposing any regularity assumptions. In addition to basic convex analysis tools, the authors’ prior results on convex cores of measures and closures of exponential families are used.
-
generalized Maximum Likelihood Estimates for exponential families
International Symposium on Information Theory, 2006Co-Authors: Ivan Csiszar, Frantisek MatusAbstract:For a standard full exponential family on Ropfd, or its canonically convex subfamily, the generalized Maximum Likelihood estimator is an extension of the mapping that assigns to the mean alpha isin Ropfd of a sample for which a maximizer v* of the corresponding Likelihood function exists, the member of the family parameterized by v*. This extension assigns to each alpha; isin Ropfd with the Likelihood function bounded above, a member of the closure of the family in variation distance. Its detailed description, complete characterization of domain and range, and additional results are presented, in a general setting. In addition to basic convex analysis tools, the authors' prior results on convex cores of measures and closures of exponential families are used
D. Kececioglu - One of the best experts on this subject based on the ideXlab platform.
-
Maximum Likelihood Estimates, from censored data, for mixed-Weibull distributions
IEEE Transactions on Reliability, 1992Co-Authors: S. Jiang, D. KececiogluAbstract:An algorithm for estimating the parameters of mixed-Weibull distributions from censored data is presented. The algorithm follows the principle of the MLE (Maximum Likelihood estimate) through the EM (expectation and maximization) algorithm, and it is derived for both postmortem and non-postmortem time-to-failure data. The MLEs of the nonpostmortem data are obtained for mixed-Weibull distributions with up to 14 parameters in a five-subpopulation mixed-Weibull distribution. Numerical examples indicate that some of the log-Likelihood functions of the mixed-Weibull distributions have multiple local maxima; therefore the algorithm should start at several initial guesses of the parameters set. It is shown that the EM algorithm is very efficient. On the average for two-Weibull mixtures with a sample size of 200, the CPU time (on a VAX 8650) is 0.13 s/iteration. The number of iterations depends on the characteristics of the mixture. The number of iterations is small if the subpopulations in the mixture are well separated. Generally, the algorithm is not sensitive to the initial guesses of the parameters.
Andrew R Francis - One of the best experts on this subject based on the ideXlab platform.
-
Maximum Likelihood Estimates of pairwise rearrangement distances
Journal of Theoretical Biology, 2017Co-Authors: Stuart Serdoz, Attila Egrinagy, Jeremy G Sumner, Barbara R Holland, P D Jarvis, Mark M Tanaka, Andrew R FrancisAbstract:Accurate estimation of evolutionary distances between taxa is important for many phylogenetic reconstruction methods. Distances can be estimated using a range of different evolutionary models, from single nucleotide polymorphisms to large-scale genome rearrangements. Corresponding corrections for genome rearrangement distances fall into 3 categories: Empirical computational studies, Bayesian/MCMC approaches, and combinatorial approaches. Here, we introduce a Maximum Likelihood estimator for the inversion distance between a pair of genomes, using a group-theoretic approach to modelling inversions introduced recently. This MLE functions as a corrected distance: in particular, we show that because of the way sequences of inversions interact with each other, it is quite possible for minimal distance and MLE distance to differently order the distances of two genomes from a third. The second aspect tackles the problem of accounting for the symmetries of circular arrangements. While, generally, a frame of reference is locked, and all computation made accordingly, this work incorporates the action of the dihedral group so that distance Estimates are free from any a priori frame of reference. The philosophy of accounting for symmetries can be applied to any existing correction method, for which examples are offered.
-
Maximum Likelihood Estimates of pairwise rearrangement distances
arXiv: Populations and Evolution, 2016Co-Authors: Stuart Serdoz, Attila Egrinagy, Jeremy G Sumner, Barbara R Holland, P D Jarvis, Mark M Tanaka, Andrew R FrancisAbstract:Accurate estimation of evolutionary distances between taxa is important for many phylogenetic reconstruction methods. In the case of bacteria, distances can be estimated using a range of different evolutionary models, from single nucleotide polymorphisms to large-scale genome rearrangements. In the case of sequence evolution models (such as the Jukes-Cantor model and associated metric) have been used to correct pairwise distances. Similar correction methods for genome rearrangement processes are required to improve inference. Current attempts at correction fall into 3 categories: Empirical computational studies, Bayesian/MCMC approaches, and combinatorial approaches. Here we introduce a Maximum Likelihood estimator for the inversion distance between a pair of genomes, using the group-theoretic approach to modelling inversions introduced recently. This MLE functions as a corrected distance: in particular, we show that because of the way sequences of inversions interact with each other, it is quite possible for minimal distance and MLE distance to differently order the distances of two genomes from a third. This has obvious implications for the use of minimal distance in phylogeny reconstruction. The work also tackles the above problem allowing free rotation of the genome. Generally a frame of reference is locked, and all computation made accordingly. This work incorporates the action of the dihedral group so that distance Estimates are free from any a priori frame of reference.
