scholarly journals Fast maximum-likelihood estimation methods for scintillation cameras and other optical sensors

2007 ◽  
Author(s):  
L. R. Furenlid ◽  
J. Y. Hesterman ◽  
H. H. Barrett
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Optical Sensors ◽  
Likelihood Estimation ◽  
Estimation Methods

Jaya algorithm in estimation of P[X > Y] for two parameter Weibull distribution

2022 ◽  
Vol 7 (2) ◽  
pp. 2820-2839
Author(s):  
Saurabh L. Raikar ◽  
◽  
Dr. Rajesh S. Prabhu Gaonkar ◽  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Real Life ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Jaya Algorithm ◽  
Two Parameter

<abstract> <p>Jaya algorithm is a highly effective recent metaheuristic technique. This article presents a simple, precise, and faster method to estimate stress strength reliability for a two-parameter, Weibull distribution with common scale parameters but different shape parameters. The three most widely used estimation methods, namely the maximum likelihood estimation, least squares, and weighted least squares have been used, and their comparative analysis in estimating reliability has been presented. The simulation studies are carried out with different parameters and sample sizes to validate the proposed methodology. The technique is also applied to real-life data to demonstrate its implementation. The results show that the proposed methodology's reliability estimates are close to the actual values and proceeds closer as the sample size increases for all estimation methods. Jaya algorithm with maximum likelihood estimation outperforms the other methods regarding the bias and mean squared error.</p> </abstract>

The Estimated Probability of Dizygotic Twins: A Comparison of Two Methods

2009 ◽  
Vol 12 (1) ◽  
pp. 79-85 ◽  
Author(s):  
Jill Hardin ◽  
Steve Selvin ◽  
Suzan L. Carmichael ◽  
Gary M. Shaw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Data Sources ◽  
Maximum Likelihood Estimates ◽  
Estimation Methods ◽  
Dizygotic Twin ◽  
Twin Data ◽  
Dizygotic Twins ◽  
The Relationship

AbstractThis study presents a general model of two binary variables and applies it to twin sex pairing data from 21 twin data sources to estimate the frequency of dizygotic twins. The purpose of this study is to clarify the relationship between maximum likelihood and Weinberg's differential rule zygosity estimation methods. We explore the accuracy of these zygosity estimation measures in relation to twin ascertainment methods and the probability of a male. Twin sex pairing data from 21 twin data sources representing 15 countries was collected for use in this study. Maximum likelihood estimation of the probability of dizygotic twins is applied to describe the variation in the frequency of dizygotic twin births. The differences between maximum likelihood and Weinberg's differential rule zygosity estimation methods are presented as a function of twin data ascertainment method and the probability of a male. Maximum likelihood estimation of the probability of dizygotic twins ranges from 0.083 (95% approximate CI: 0.082, 0.085) to 0.750 (95% approximate CI: 0.749, 0.752) for voluntary ascertainment data sources and from 0.374 (95% approximate CI: 0.373, 0.375) to 0.987 (95% approximate CI: 0.959, 1.016) for active ascertainment data sources. In 17 of the 21 twin data sources differences of 0.01 or less occur between maximum likelihood and Weinberg zygosity estimation methods. The Weinberg and maximum likelihood estimates are negligibly different in most applications. Using the above general maximum likelihood estimate, the probability of a dizygotic twin is subject to substantial variation that is largely a function of twin data ascertainment method.

Finite Mixture Model: A Comparison of Maximum Likelihood Estimation and Bayesian Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 63-63
Author(s):  
Seuk Yen Phoong ◽  
Seuk Wai Phoong
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Mixture Model ◽  
Likelihood Estimation ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Skewed Distribution ◽  
Estimation Methods ◽  
Series Data ◽  
Number Of Components

The mixture model is known as model-based clustering that is used to model a mixture of unknown distributions. The clustering of mixture model is based on four important criteria, including the number of components in the mixture model, clustering kernel (such as Gaussian mixture models, Dirichlet, etc.), estimation methods, and dimensionality (Lai et al., 2019). Finite mixture model is a finite dimensional of a hierarchical model. It is useful in modeling the data with outliers, non-normal distributed or heavy tails. Furthermore, finite mixture model is flexible when fitted with the models that have multiple modes or skewed distribution. The flexibility depends on the increasing number of parameters with the existence of a number of components. The finite mixture model is a flexible model family and widely applied for large heterogeneous datasets. In addition, the finite mixture model is a probabilistic model that is used to examine the presence of unobserved situations or groups and to measure the distinct parameters or distribution. The situations, such as trend, seasoning, crisis time, normal situation, etc., might affect the number of components that exist for a probabilistic distribution. Furthermore, the finite mixture model is essential for time series data because these data exhibit nonlinearity properties and may have missing data or a jump-diffusion situation (Gensler, 2017; McLachlan and Lee, 2019). Keywords: Bayesian method; Finite Mixture Model; Maximum Likelihood Estimation; Prior distribution; Likelihood Function.

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