Detectability constraints on meso-scale structure in complex networks. by Rudy Arthur

“…We then derive a general equivalence between optimising block modularity and maximum likelihood estimation of the parameters of the degree corrected Stochastic Block Model. …”
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Improved Estimation of the Initial Number of Susceptible Individuals in the General Stochastic Epidemic Model Using Penalized Likelihood by Changhyuck Oh

“…This short note describes how to improve the maximum likelihood estimators of the infection rate and the initial number of susceptible individuals and provides their approximate Hessian matrix for the general stochastic epidemic model by using the concept of the penalized likelihood function. …”
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Parameter Estimation on a Stochastic SIR Model with Media Coverage by Changguo Li, Yongzhen Pei, Meixia Zhu, Yue Deng

“…In order to reduce the computational load, the Newton-Raphson algorithm and Markov Chain Monte Carlo (MCMC) technique are incorporated with maximum likelihood estimation. Simulations validate our estimation results and the necessity of a model with media coverage when modeling the contagious diseases.…”
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Inference of Process Capability Index Cpy for 3-Burr-XII Distribution Based on Progressive Type-II Censoring by Rashad M. EL-Sagheer, Mustafa M. Hasaballah

“…In this paper, we discussed the estimation of the index Cpy for a 3-Burr-XII distribution based on Progressive Type-II censoring. The maximum likelihood and Bayes method have been used to obtain the estimating of the index Cpy. …”
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Harmonic Mixture Fréchet Distribution: Properties and Applications to Lifetime Data by Selasi Kwaku Ocloo, Lewis Brew, Suleman Nasiru, Benjamin Odoi

“…Estimators for the parameters of the harmonic mixture Fréchet distribution are derived using the estimation techniques such as the maximum-likelihood estimation, the ordinary least-squares estimation, the weighted least-squares estimation, the Cramér–von Mises estimation, and the Anderson–Darling estimation. …”
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EM-based blind LDPC identification in multipath channels by Yu LIU, Fanggang WANG, Jingwen ZHANG, Bo AI, Zhangdui ZHONG

“…As the advent of cognitive radios,blind encoder identification has attracted increasingly attentions since it plays an important role.The existing works mainly focus on additive white Gaussian noise (AWGN) channel,while the blind identification in multipath scenarios has not been sufficiently investigated.Considering the blind low density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel,a likelihood-based classifier was proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters.Then,an average log-likelihood ratio (LLR) estimator was adopted to classify the unknown encoder.Numerical results show that the proposed algorithm provides promising identification performance in multipath channels,especially in the low signal-to-noise ratio region.…”
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Retinal thickness and vascular density changes in Keratoconus: A systematic review and meta-analysis by Hadi Vahedi, Mirsaeed Abdollahi, Reza Moshfeghinia, Shima Emami, Navid Sobhi, Rana Sorkhabi, Ali Jafarizadeh

“…We analyzed the data using STATA software 17 and the random effects model with the restricted maximum likelihood (REML) or maximum likelihood (ML) method to pool the standardized mean difference (SMD) and the weighted mean difference (WMD) of retinal thickness and vascular density compared to non-KC eyes. …”
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A Semiparametric Approach for Modeling Partially Linear Autoregressive Model with Skew Normal Innovations by Leila Sakhabakhsh, Rahman Farnoosh, Afshin Fallah, Mohammadhassan Behzadi

“…Then, the conditional maximum-likelihood approach is used to estimate the unknown parameters through the expectation-maximization (EM) algorithm. …”
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Improved Inference for Moving Average Disturbances in Nonlinear Regression Models by Pierre Nguimkeu

“…Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. …”
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Neural network decoding of the block Markov superposition transmission by Qianfan WANG, Sheng BI, Zengzhe CHEN, Li CHEN, Xiao MA

“…A neural network (NN)-based decoding algorithm of block Markov superposition transmission (BMST) was researched.The decoders of the basic code with different network structures and representations of training data were implemented using NN.Integrating the NN-based decoder of the basic code in an iterative manner,a sliding window decoding algorithm was presented.To analyze the bit error rate (BER) performance,the genie-aided (GA) lower bounds were presented.The NN-based decoding algorithm of the BMST provides a possible way to apply NN to decode long codes.That means the part of the conventional decoder could be replaced by the NN.Numerical results show that the NN-based decoder of basic code can achieve the BER performance of the maximum likelihood (ML) decoder.For the BMST codes,BER performance of the NN-based decoding algorithm matches well with the GA lower bound and exhibits an extra coding gain.…”
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Estimation of Concentrations Parameters in the Model of Mixture with Varying Concentrations by Rostyslav Maiboroda, Vitaliy Miroshnychenko, Olena Sugakova

“…The Least Squares (LS) estimator is based on fitting the distribution functions of the observations. The Empirical Maximum Likelihood estimator (EML) utilizes some empirical version of the likelihood function. …”
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Pilot Position Optimization Design of WLAN System Based on Experiment by Jue Lan, Yunzhou Li, Xin Su, Shidong Zhou

“…Considering the frame structure of IEEE 802.11ac with “leading preamble plus comb-like pilot”，the performance evaluation formula of the correction scheme based on maximum likelihood estimation was derived，according to which the effect of pilot location，channel fading and subcarrier correlation was studied.Furthermore，a signalling exchange method was proposed，which ensured pilot position synchronization on both end，and a low complexity algorithm of pilot optimization according to the frequency channel amplitude information.In this way，the pilot overhead can be reduced without loss of correction performance.The experiment and simulation result shows that for ideal Rayleigh channel data，correction performance improves by 1.85 dB and 7.15 dB for unknown channel amplitude information in frequency domain and known cases respectively.For field experiment channel data，correction performance improves by 1.67 dB and 2.68 dB respectively.…”
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Blind audio watermarking mechanism based on variational Bayesian learning by Xin TANG, Zhao-feng MA, Xin-xin NIU, Yi-xian YANG

“…In order to improve the performance of audio watermarking detection,a blind audio watermarking mechanism using the statistical characteristics based on MFCC features of audio frames was proposed.The spread spectrum watermarking was embedded in the DCT coefficients of audio frames.MFCC features extracted from watermarked audio frames as well as un-watermarked ones were trained to establish their Gaussian mixture models and to estimate the parameters by vatiational Bayesian learning method respectively.The watermarking was detected according to the maximum likelihood principle.The experimental results show that our method can lower the false detection rate compared with the method using EM algorithm when the audio signal was under noise and malicious attacks.Also,the experiments show that the proposed method achieves better performance in handling insufficient training data as well as getting rid of over-fitting problem.…”
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Differential spatial modulation scheme based on orthogonal space-time block coded by Zhi-cheng WANG, Zhi-gang CHEN, Lei WANG

“…Focusing on the problem that differential spatial modulation (DSM) couldn’t obtain transmit diversity and has high decoding complexity,a new differential spatial modulation scheme based on the orthogonal space-time block code was proposed and the proposed scheme is called OSTBC-DSM.There were two matrices in this scheme:the spatial modulation matrix and the symbol matrix.The former was aimed to activate different transmit antennas by setting the position of nonzero elements,and the latter structured symbolic matrix by using orthogonal space-time block codes (OSTBC) as the basic code block.The proposed scheme could obtain full transmit diversity and higher spectral efficiency compared with the conventional DSM schemes.Moreover,the OSTBC-DSM supported linear maximum likelihood (ML) decoding.The simulation results show that under different spectral efficiencies,the proposed OSTBC-DSM scheme has better bit error rate (BER) performance than other schemes.…”
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Parsimonious mixture of mean-mixture of normal distributions with missing data by Farzane Hashemi, Saeed Darijani

“…The EM-type algorithms are carried out to determine maximum likelihood of parameters estimations. We analyzed the real data sets and conducted simulation studies to demonstrate the superiority of the proposed methodology.…”
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The Extended Inverse Weibull Distribution: Properties and Applications by Said Alkarni, Ahmed Z. Afify, I. Elbatal, M. Elgarhy

“…Four estimation methods, namely, the maximum likelihood, least squares, weighted least squares, and Cramér–von Mises methods, are utilized to estimate the TIHLIW parameters. …”
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New Lindley Generator Based on T-X Family of Distributions: Properties and Reliability Applications by Fatehi Yahya Eissa, Chhaya Dhanraj Sonar

“…To estimate the parameters of the new family of distributions, we utilize a range of methods, including maximum likelihood, maximum product spacing, least squares, Cramer-von Mises, and Anderson–Darling estimations. …”
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Bayesian Estimations under the Weighted LINEX Loss Function Based on Upper Record Values by Fuad S. Al-Duais

“…Next, we compared the performance of the proposed method (WLINEX) in this work with Bayesian estimation using the LINEX loss function, Bayesian estimation using the squared-error (SEL) loss function, and maximum likelihood estimation (MLE). The evaluation depended on the difference between the estimated parameters and the parameters of completed data. …”
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Shrinkage Methods for Estimating the Shape Parameter of the Generalized Pareto Distribution by Wilhemina Adoma Pels, Atinuke O. Adebanji, Sampson Twumasi-Ankrah, Richard Minkah

“…The performance of the proposed estimators is compared with the existing estimators (i.e., maximum likelihood, likelihood moment estimators, etc.) for the shape parameter of the generalized Pareto distribution in a simulation study. …”
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The Moderating Effect of the Sector's Level of Concentration on the Relationship Between Balance Sheet Composition and the Firm's Competitive Advantage by Luiz Claudio Louzada, Márcio Augusto Gonçalves

“…We based the tests on the hierarchical model approach with repeated measures involving serial and nested regressions, estimated by maximum likelihood. The test results suggest that (i) the firm’s idiosyncratic features have greater explanatory capability for the firm’s performance than the industry features; (ii) the relation between firm idiosyncratic resources and firm performance are sensitive to industry characteristics.…”
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