Power Xgamma distribution: properties, estimation, regression, simulation and applications by Okechukwu Obulezi, Precious Ibeakuzie, Ifeanyi Anabike, Chinyere Igbokwe, Harrison Etaga

“…The estimation of the parameters was done using the maximum likelihood method. The study’s uniqueness is in developing a parametric regression model capable of competing with the classical regression model and also useful in the face of censored data. …”
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A New Residual Life Prediction Method for Complex Systems Based on Wiener Process and Evidential Reasoning by Xin Zhang, Shuaiwen Tang, Taoyuan Liu, Bangcheng Zhang

“…For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. …”
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A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model by Jing Zhao, Yiwen Wang

“…First, a three-step estimation method is proposed to improve the parameters of the linear-effects model, avoiding the complicated iterative steps of maximum likelihood estimation. Second, we perform spectral clustering based on test data on the basis of defining data attributes and basic evaluation rules. …”
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The Redox System in C. elegans, a Phylogenetic Approach by Andrew D. Johnston, Paul R. Ebert

“…We use protein sequences from central redox systems in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae to query Genbank for homologous proteins in C. elegans. We then use maximum likelihood phylogenetic analysis to compare protein families between C. elegans and the other organisms to facilitate future research into the genetics of redox biology.…”
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The first complete mitochondrial genome sequence of the common Baya weaverbird (Ploceus philippinus) from southern India by Venkatesh Nagarajan-Radha, Subanithi-Purnima Murugan, Paramanantha Swami Doss Devaraj

“…The circular mitochondrial genome of 16,867 bp contains 13 protein-coding genes, 22 transfer RNAs, two ribosomal RNAs (12S and 16S subunits), and a non-coding control region. A maximum-likelihood phylogenetic tree analysis placed P. philippinus and P. nigricollis weaverbirds in a separate clade among other bird species. …”
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An Approximation of Minimum Initial Capital of Investment Discrete Time Surplus Process with Weibull Distribution in a Reinsurance Company by Soontorn Boonta, Somchit Boonthiem

“…We compare the parameter estimation using a direct search method with other frequently used methods, such as the least squares method, the maximum likelihood estimation, and the method of moments. …”
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Properties and Applications of the Modified Kies–Lomax Distribution with Estimation Methods by Abdelaziz Alsubie

“…The results show that the maximum product of spacings and maximum likelihood approaches are recommended to estimate the MKL parameters. …”
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AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System by Saleh O. Al-Jazzar, Sami Aldalahmeh

“…These estimates can be used as initializations for other MIMO radar methods, such as the maximum likelihood algorithm. Simulation results show significantly low root mean square error (RMSE) for AOAs and complex propagation factors. …”
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The Structure of an Automatic Decision System for a Large Number of Independent Particle Detectors by Andreea Rodica Sterian

“…Based on this analytical model of Poisson type, the structure of an automatic decision system based on the decision criterion of maximum a posteriori probability (MAP) or the maximum likelihood (ML) criterion is proposed. The purpose of the system is to analyze the exit from the measurement process and to decode the message transmitted, taking into account the presence of the noise which generates errors in the decoder. …”
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EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution by Sangseob Leem, Taesung Park

“…Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). …”
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On using a non-probability sample for the estimation of population parameters by Ieva Burakauskaitė, Andrius Čiginas

“…We consider two modeling scenarios: with an assumption that the willingness to participate in the voluntary survey does not depend on the survey variable itself and that such a variable does contribute to whether the individual responds or not. The maximum likelihood method is applied in both scenarios to estimate the propensity scores. …”
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Long Short-Term Memory Recurrent Neural Network for Predicting the Return of Rate Underframe the Fama-French 5 Factor by Bui Thanh Khoa, Tran Trong Huynh

“…Two approaches are employed in the Fama-French model: Long Short Term Memory Recurrent Neural Network (LSTM-RNN) and Maximum Likelihood Estimation (MLE). From January 1, 2010, through March 3, 2022, the stock market in Ho Chi Minh City was experimentally researched. …”
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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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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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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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