Estimation of Sine Inverse Exponential Model under Censored Schemes by M. Shrahili, I. Elbatal, Waleed Almutiry, Mohammed Elgarhy

“…We investigated some fundamental statistical properties such as quantile function (QFu), moments (Mo), moment generating function (MGFu), incomplete moments (ICMo), conditional moments (CMo), and the SIE distribution parameters were estimated using the maximum likelihood (ML) method for estimation under censored samples (CS). …”
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A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO by Hamid Amiriara, Mohammadreza Zahabi

“…The proposed TLBO detector, in a limited time, achieves a bit error rate (BER) 10^(-5) in the average signal-to-noise ratio of 12 dB, which is approximately equal to the optimal detector performance, and maximum likelihood. As a result, the proposed detector is very efficient for use in massive MIMO systems.…”
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A New Truncated Muth Generated Family of Distributions with Applications by Abdullah M. Almarashi, Farrukh Jamal, Christophe Chesneau, Mohammed Elgarhy

“…In an applied part, the parameters are calculated using the maximum likelihood procedure. We use a comprehensive simulation analysis to demonstrate the accuracy of the derived estimates. …”
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Marshall–Olkin Extended Gumbel Type-II Distribution: Properties and Applications by Farwa Willayat, Naz Saud, Muhammad Ijaz, Anita Silvianita, Mahmoud El-Morshedy

“…Estimation of the unknown parameters is carried out by the method of maximum likelihood. A simulation study is conducted using three different iterative methods with different samples of sizes n. …”
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Sufficient statistics for the Pareto distribution parameter by I. S. Pulkin, A. V. Tatarintsev

“…On the basis of the maximum likelihood method the distribution degree indicator is estimated. …”
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Competing Risks in Accelerated Life Testing: A Study on Step-Stress Models with Tampered Random Variables by Hanan Haj Ahmad, Ehab M. Almetwally, Dina A. Ramadan

“…This study employs the TRV model to integrate constant tampering coefficients for each failure cause within step-stress data analysis. Maximum-likelihood estimates for model parameters and tampering coefficients are derived from SSLT data, and some confidence intervals are presented based on the Type-II censoring scheme. …”
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Nonlinear Programming to Determine Best Weighted Coefficient of Balanced LINEX Loss Function Based on Lower Record Values by Fuad S. Al-Duais, Mohammed Alhagyan

“…Comparisons are made between Bayesian estimators (SE, BSE, LINEX, and BLINEX) and maximum likelihood estimator via Monte Carlo simulation. …”
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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data. by Lulu Pan, Qian Gao, Kecheng Wei, Yongfu Yu, Guoyou Qin, Tong Wang

“…In the oracle case with known transferable source datasets, a transfer learning algorithm based on penalized maximum likelihood and expectation-maximization algorithm is established. …”
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The Arcsine Exponentiated-X Family: Validation and Insurance Application by Wenjing He, Zubair Ahmad, Ahmed Z. Afify, Hafida Goual

“…Based on the proposed approach, a three-parameter extension of the Weibull distribution called the arcsine exponentiated-Weibull (ASE-W) distribution is studied in detail. Maximum likelihood is used to estimate the model parameters, and its performance is evaluated by two simulation studies. …”
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A Bounded Lifetime Distribution Specified by a Trigonometric Function: Properties, Regression Model, and Applications by Simon A. Ogumeyo, Festus C. Opone, Abdul Ghaniyyu Abubakari, Jacob C. Ehiwario

“…Seven methods of parameter estimation, including maximum likelihood, ordinary least squares, weighted least squares, moment product spacing, Anderson–Darling, and Cramér–von Mises estimators are used to estimate the parameters of the new distribution. …”
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Bayesian inference for the parameters of the generalized logistic distribution under a combined framework of generalized type-I and type-II hybrid censoring schemes with applicatio... by Mustafa M. Hasaballah, Oluwafemi Samson Balogun, M. E. Bakr

“…Key objectives include the development of maximum likelihood estimators and asymptotic confidence intervals, alongside Bayesian estimation techniques using Markov chain Monte Carlo methods. …”
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Modelling seasonal influenza in Israel by Oren Barnea, Rami Yaari, Guy Katriel, Lewi Stone

“…The results were corroborated by implementing a stochastic model and using a maximum likelihood approach. The paper discusses the difficulties in estimating these important parameters especially when the reporting rate of influenza cases might only be known with limited accuracy, as is generally the case. …”
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Mathematical probit and logistic mortality models of the Khapra beetle fumigated with plant essential oils by Alhadi E. Alamir, Gomah E. Nenaah, Mohamed A. Hafiz

“…A generalized inverse matrix technique was used to estimate the mortality model parameters instead of the usual statistical iterative maximum likelihood estimation. As this technique needs to perturb the observed mortality proportions if the proportions include 0 or 1, the optimal perturbation in terms of minimum least squares ($L_2$) error was also determined. …”
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Morphology and multigene phylogeny revealed four new species of Geastrum (Geastrales, Basidiomycota) from China by Xin Yang, Yonggao Zhu, Songjing Duan, Xingxing Wu, Changlin Zhao

“…Sequences of the internal transcribed spacers (ITS), large subunit (nrLSU), the largest subunit of ribosomal polymerase II (RPB1), and subunit 6 of ATP synthase (ATP6) of the nuclear ribosomal DNA (rDNA) markers of the studied samples were generated, and the phylogenetic analyses were performed with maximum likelihood, maximum parsimony and Bayesian inference methods. …”
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Human Settlements Interactions and Deforestation in Gambari Forest Reserve located in Oluyole Local Government Area (LGA) of Oyo State, Nigeria by E.O. Toyinbo, R.A. Fasasi, C.F. Agbor, C.O. Fakorede

“…Landsat images of 1984 and 2019 were used to extract land cover types using maximum likelihood classifier in Idrisi environment. The level of attractiveness and Interactions of the communities with the reserve were determined employing gravitational model. …”
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Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors by Jinyi Qi

“…It has been shown that the effect of a Gaussian prior can be approximated by linear filtering a maximum likelihood (ML) reconstruction. As a result, sharp edges in reconstructed images are not preserved. …”
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The Reliability Model for Bike-Sharing Dispatch Based on Hotspot Detection and Hypothesis Test: A Case Study in Beijing by Chao Sun, Jian Lu

“…By adopting different distribution forms, including Poisson distribution, Rayleigh distribution, exponential distribution, normal distribution, and gamma distribution, we examine the distribution characteristics of OD differences. The maximum likelihood estimation (MLE) technique is applied for model calibration, and chi-squared statistics are used to identify the acceptance of the null hypothesis. …”
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Upper Bound on the Bit Error Probability of Systematic Binary Linear Codes via Their Weight Spectra by Jia Liu, Mingyu Zhang, Chaoyong Wang, Rongjun Chen, Xiaofeng An, Yufei Wang

“…Numerical results show that the proposed bound on the bit error probability matches well with the maximum-likelihood (ML) decoding simulation approach especially in the high signal-to-noise ratio (SNR) region, which is better than the recently proposed Ma bound.…”
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Application of Harmony Search to Design Storm Estimation from Probability Distribution Models by Sukmin Yoon, Changsam Jeong, Taesam Lee

“…Generally, estimated parameters for PDMs are provided based on the method of moments, probability weighted moments, and maximum likelihood (ML). The results using ML are more reliable than the other methods. …”
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Contribution to the Molecular Phylogeny of Anthribidae (Coleoptera: Curculioniodea) Inferred from COI Sequences by Ali Nafiz Ekiz, Polen Döngel

“…The phylogenetic trees were inferred by using Neighbor-Joining (NJ) and Maximum Likelihood (ML) methods. Both method provided quite similar evolutionary histories. …”
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