On the Alpha Power Transformed Power Lindley Distribution by Amal S. Hassan, M. Elgarhy, Rokaya E. Mohamd, Sharifah Alrajhi

“…Various properties of the APTPL distribution including moments, incomplete moments, quantiles, entropy, and stochastic ordering are obtained. Maximum likelihood, maximum products of spacings, and ordinary and weighted least squares methods of estimation are utilized to obtain the estimators of the population parameters. …”
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ANALYSIS WITH NESTED MULTINOMIAL LOGIT MODEL OF DEMAND FOR HEALTHCARE: AN APPLICATION IN KAYSERI PROVINCE by Zeynep Öztürk Yaprak, Eyyup Ecevit

“…Within the scope of the study, Nested Logit Modelwas implemented to the data set using Full Information Maximum Likelihood(FIML) technique which estimates both decision levels simultaneously. …”
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Bayesian Inference of Elevation to Reduce Large Interpolation Errors in 2-d Road Features Draped Over Digital Elevation Models by Crispin H. V. Cooper

“…This paper introduces a Bayesian maximum likelihood approach to correcting interpolated heights, by combining a DEM with prior expectations of feature gradient. …”
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Three new asexual Kirschsteiniothelia species from Guizhou Province, China by Xing-Juan Xiao, Ning-Guo Liu, Jian Ma, Li-Juan Zhang, Dan-Feng Bao, Song Bai, Fatimah Al-Otibi, Kevin D. Hyde, Yong-Zhong Lu

“…Phylogenetic analyses of ITS, LSU, and SSU sequences, performed using Maximum Likelihood and Bayesian Inference methods, confirmed that these isolates belong to Kirschsteiniothelia. …”
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A Stochastic Model of Stress Evolution in a Bolted Structure in the Presence of a Joint Elastic Piece: Modeling and Parameter Inference by Mohammed Haiek, Youness El Ansari, Nabil Ben Said Amrani, Driss Sarsri

“…Next, we validate statistically our proposed stochastic model, and we use the maximum likelihood estimation method based on Euler–Maruyama scheme to estimate the parameters of this model. …”
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Dynamique du couvert végétal et implications socio-environnementales à la périphérie du parc W/Burkina Faso by Raogo Noël Gansaonré

“…Landsat images of 30 meters of resolution from 1984 and 2015 were classified using the maximum likelihood method. The results show that 48.26% of the area of Diapaga were affected by vegetation degradation, while 40.38% and 11.36% were in a state of stability and improvement respectively. …”
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Exponentiated Gull Alpha Exponential Distribution with Application to COVID-19 Data by Hazar A. Khogeer, Amani Alrumayh, M. M. Abd El-Raouf, Mutua Kilai, Ramy Aldallal

“…The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. …”
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Increased Statistical Efficiency in a Lognormal Mean Model by Grant H. Skrepnek, Ashok Sahai

“…Results of an empirical simulation study across varying sample sizes and population standard deviations indicated relative improvements in efficiency of up to 129.47 percent compared to the usual maximum likelihood estimator and up to 21.33 absolute percentage points above the efficient estimator presented by Shen and colleagues (2006). …”
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Alpha Power Transformed Inverse Lomax Distribution with Different Methods of Estimation and Applications by Ramadan A. ZeinEldin, Muhammad Ahsan ul Haq, Sharqa Hashmi, Mahmoud Elsehety

“…The model parameters are estimated using eight estimation methods including maximum likelihood, least squares, weighted least squares, percentile, Cramer–von Mises, maximum product of spacing, Anderson–Darling, and right-tail Anderson–Darling. …”
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Degradation and reliability assessment of accuracy life of RV reducers by XU Hang, NIE Yixuan, WEN Dongjie, REN Jihua, HONG Zhihui

“…Combined with the performance degradation data of the reducer transmission accuracy, the model parameters were estimated based on the matrix method and the maximum likelihood estimation method. A Gaussian process regression model optimized by genetic algorithm was established using vibration characteristic data to optimize the prediction of transmission accuracy.ResultsThe results show that the prediction accuracy based on Gaussian process regression model is significantly better than that of traditional regression model. …”
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Interval shrinkage estimation of two-parameter exponential distribution with random censored data by Ali Soori, Parviz Nasiri, Mehdi Jabbari Nooghabi, Farshin Hormozinejad, Mohammadreza Ghalani

“…Considering the importance of the model, its parameter estimation is discussed using the method of moment, maximum likelihood and shrinkage estimation. To present the interval shrinkage estimator, it is first proved that the moment estimators are asymptotically unbiased and the interval shrinkage estimator performs better compared to other estimators. …”
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A Logistic Trigonometric Generalized Class of Distribution Characteristics, Applications, and Simulations by Zafar Mahmood, Hazar A. Khogeer, Eslam Hussam Hafez, Md. Moyazzem Hossain

“…For parametric estimation, the maximum likelihood approach is used, and simulation analysis is performed to ensure that the estimates are asymptotic. …”
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Analysis of Type-II Censored Competing Risks’ Data under Reduced New Modified Weibull Distribution by Saad J. Almalki, Tahani A. Abushal, M. D. Alsulami, G. A. Abd-Elmougod

“…The model parameters under the type-II censoring scheme are estimated with the maximum likelihood method with the corresponding asymptotic confidence intervals. …”
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The Half-Logistic Generalized Weibull Distribution by Masood Anwar, Amna Bibi

“…The parameters involved in the model are estimated using the method of maximum likelihood estimation. The asymptotic distribution of the estimators is also investigated via Fisher’s information matrix. …”
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Confirmatory Factor Analysis of the Personal Growth Initiative Scale-II in Indonesian Women Leaders by Dhini Rama Dhania, Fendy Suhariadi, Fajrianthi Fajrianthi

“…The results showed that the maximum likelihood estimation (MLE) matched scores with different ranges. …”
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High-Speed Wireline Links—Part I: Modeling by Hossein Shakiba, Davide Tonietto, Ali Sheikholeslami

“…In a wireline link, we wish to model a wide variety of architectures and optimize their parameters, such as the feedforward equalizer and decision feedback equalizer tap coefficients, continuous-time linear equalizer frequency response, termination impedances, and possibly maximum-likelihood sequence estimation parameters, for a given channel and within a given set of constraints as dictated by the application requirements so as to minimize the link’s bit error rate. …”
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Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity by Autcha Araveeporn

“…Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate normal distribution in order to classify binary dependent variables. …”
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Markov Switching Model Analysis of Implied Volatility for Market Indexes with Applications to S&P 500 and DAX by Luca Di Persio, Samuele Vettori

“…We will take into account two different approaches for inference on Markov switching models, namely, the classical approach based on the maximum likelihood techniques and the Bayesian inference method realized through a Gibbs sampling procedure. …”
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Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications by Salifu Nanga, Shei Baba Sayibu, Irene Dekomwine Angbing, Mubarika Alhassan, Abdul-Majeed Benson, Abdul Ghaniyyu Abubakari, Suleman Nasiru

“…Five special cases of the family of distributions are presented, and their flexibility is shown by the varying degrees of skewness and kurtosis and nonmonotonic hazard rates. The maximum likelihood estimation method is used to obtain estimators of the family of distributions. …”
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SAGE-Based Algorithm for Direction-of-Arrival Estimation and Array Calibration by Kunlai Xiong, Zheng Liu, Wenli Jiang

“…Based on this model, the Space Alternating Generalized Expectation-Maximization (SAGE) algorithm is applied to jointly estimate the DOA and array perturbation parameters, which simplifies the multidimensional search procedure required for finding maximum likelihood (ML) estimates. The proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. …”
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