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**Search for:**likelihood-functions

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#### Joint likelihood estimation and model order selection for outlier censoring

, Article IET Radar, Sonar and Navigation ; Volume 15, Issue 6 , 2021 , Pages 561-573 ; 17518784 (ISSN) ; Sharif University of Technology
John Wiley and Sons Inc
2021

Abstract

This study deals with the problem of outlier censoring from the secondary data in a radar scenario, where the number of outliers is unknown. To this end, a procedure consisting of joint likelihood estimation and statistical model order selection (MOS) is proposed. Since the maximum likelihood (ML) estimation of the outlier subset requires to solve a combinatorial problem, an approximate ML (AML) method is employed to reduce the complexity. Therefore, to determine the number of outliers, different MOS criteria based on likelihood function are applied. At the analysis stage, the performance of the proposed methods is assessed based on simulated data. The results highlight that the devised...

#### Dependency of codon usage on protein sequence patterns: A statistical study

, Article Theoretical Biology and Medical Modelling ; Vol. 11, issue. 1 , 2014 ; ISSN: 17424682 ; Goliaei, B ; Alishahi, K ; Sadeghi, M ; Sharif University of Technology
Abstract

Background: Codon degeneracy and codon usage by organisms is an interesting and challenging problem. Researchers demonstrated the relation between codon usage and various functions or properties of genes and proteins, such as gene regulation, translation rate, translation efficiency, mRNA stability, splicing, and protein domains. Researchers usually represent segments of proteins responsible for specific functions or structures in a family of proteins as sequence patterns or motifs. We asked the question if organisms use the same codons in pattern segments as compared to the rest of the sequence. Methods. We used the likelihood ratio test, Pearson's chi-squared test, and mutual information...

#### Estimating the step-change time of the location parameter in multistage processes using MLE

, Article Quality and Reliability Engineering International ; Volume 28, Issue 8 , 2012 , Pages 843-855 ; 07488017 (ISSN) ; Niaki, S. T. A ; Sharif University of Technology
2012

Abstract

In this paper, maximum likelihood step-change point estimators of the location parameter, the out-of-control sample and the out-of-control stage are developed for auto-correlated multistage processes. To do this, the multistage process and the concept of change detection are first discussed. Then, a time-series model of the process is presented. Assuming step changes in the location parameter of the process, next, the likelihood functions of different samples before and after receiving out-of-control signal from an X-bar control chart were derived under different conditions. The maximum likelihood estimators were then obtained by maximizing the likelihood functions. Finally, the accuracy and...

#### Change point estimation of location parameter in multistage processes

, Article Proceedings of the World Congress on Engineering 2011, WCE 2011, 6 July 2011 through 8 July 2011 ; Volume 1 , July , 2011 , Pages 622-626 ; 9789881821065 (ISBN) ; Davoodi, M ; Torkamani, E. A ; Sharif University of Technology
2011

Abstract

knowing the time of a process change would simplify the search, identification, and removal of the special causes that disturbed the process. Since, in many real world manufacturing systems, the production of goods comprises several autocorrelated stages; in this paper, the problem of the change point estimation for such processes is addressed. A first order autoregressive model (AR(1)) is used to model a multistage process observations, where a X -chart is established for monitoring its mean. A step change is assumed for the location parameter of the model. After receiving an out-of-control signal, in order to determine the stage and the sample that caused the change (hence finding the time...

#### A hybrid variable neighborhood search and simulated annealing algorithm to estimate the three parameters of the Weibull distribution

, Article Expert Systems with Applications ; Volume 38, Issue 1 , January , 2011 , Pages 700-708 ; 09574174 (ISSN) ; Niaki, S. T. A ; Khalife, M. A ; Faize, Y ; Sharif University of Technology
2011

Abstract

The Weibull distribution plays an important role in failure distribution modeling of reliability research. While there are three parameters in the general form of this distribution, for simplicity, one of its parameters is usually omitted and as a result, the others are estimated easily. However, due to its more flexibility, when the general form of the Weibull distribution is of interest, the estimation procedure is not an easy task anymore. For example, in the maximum likelihood estimation method, the likelihood function that is formed for a three-parameter Weibull distribution is very hard to maximize. In this paper, a new hybrid methodology based on a variable neighborhood search and a...

#### A novel approach to HMM-based speech recognition systems using particle swarm optimization

, Article Mathematical and Computer Modelling ; Volume 52, Issue 11-12 , 2010 , Pages 1910-1920 ; 08957177 (ISSN) ; Razzazi, F ; Sameti, H ; Sharif University of Technology
2010

Abstract

The main core of HMM-based speech recognition systems is Viterbi algorithm. Viterbi algorithm uses dynamic programming to find out the best alignment between the input speech and a given speech model. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization algorithm. The major idea is focused on generating an initial population of segmentation vectors in the solution search space and improving the location of segments by an updating algorithm. Several methods are introduced and evaluated for the representation of particles and their corresponding movement structures. In addition, two segmentation strategies are explored. The first...

#### A new method for assessing domino effect in chemical process industry

, Article Journal of Hazardous Materials ; Volume 182, Issue 1-3 , 2010 , Pages 416-426 ; 03043894 (ISSN) ; Abbasi, T ; Rashtchian, D ; Abbasi, S. A ; Sharif University of Technology
2010

Abstract

A new methodology is presented with which the likely impact of accident in one process unit of an industry on other process units can be forecast and assessed. The methodology is based on Monte Carlo Simulation and overcomes the limitations of analytical methods, used hitherto, which were inherently limited in their ability to handle the uncertainty and the complexity associated with domino effect phenomena. The methodology has been validated and its applicability has been demonstrated with two case studies

#### A distribution-free tracking interval for model selection: application in the strength of brittle materials

, Article Communications in Statistics - Theory and Methods ; 2020 ; Sayyareh, S ; Sharif University of Technology
Bellwether Publishing, Ltd
2020

Abstract

In the literature of model selection, Vuong’s test and Akaike information criterion aim to find the best statistical model. Both of them are related to the expectation of the log-likelihood function of the rival models, but they are not sensitive to the small difference between rival models. The goal of this study is to develop a simple model selection approach which does not assume a true distribution for data. We have introduced a nonparametric tracking interval in the context of model selection. We have shown that this interval is compatibale with the known Vuong’s test results, but here we let the magnitude of the data enhance the performance of statistical inference. Simulation study...

#### Estimating the four parameters of the Burr III distribution using a hybrid method of variable neighborhood search and iterated local search algorithms

, Article Applied Mathematics and Computation ; Volume 218, Issue 19 , 2012 , Pages 9664-9675 ; 00963003 (ISSN) ; Abbasi, B ; Niaki, S. T. A ; Abdi, M ; Sharif University of Technology
2012

Abstract

The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models. The Burr III distribution has four parameters known as location, scale, and two shape parameters. The estimation process of these parameters is controversial. Although the maximum likelihood estimation (MLE) is understood as a straightforward method in parameters estimation, using MLE to estimate the Burr III parameters leads to maximize a complicated function with four unknown variables, where using a conventional optimization such as...