Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels

2004 ◽  
Vol 28 (1) ◽  
pp. 77-94 ◽  
Author(s):  
Yawpo Yang ◽  
Jen-Ning Chang ◽  
Ji-Chyun Liu ◽  
Ching-Hwa Liu
Keyword(s):  
Fading Channels ◽  
Likelihood Function ◽  
Signal Classification ◽  
Log Likelihood

A log-likelihood function-based algorithm for QAM signal classification

1998 ◽  
Vol 70 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Yawpo Yang ◽  
Ching-Hwa Liu ◽  
Ta-Wei Soong
Keyword(s):  
Likelihood Function ◽  
Signal Classification ◽  
Log Likelihood

scholarly journals Evaluating the Observed Log-Likelihood Function in Two-Level Structural Equation Modeling with Missing Data: From Formulas to R Code

Psych ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 197-232
Author(s):  
Yves Rosseel
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Missing At Random ◽  
Equation Modeling ◽  
Computationally Efficient ◽  
Log Likelihood ◽  
Efficient Expression ◽  
Special Case

This paper discusses maximum likelihood estimation for two-level structural equation models when data are missing at random at both levels. Building on existing literature, a computationally efficient expression is derived to evaluate the observed log-likelihood. Unlike previous work, the expression is valid for the special case where the model implied variance–covariance matrix at the between level is singular. Next, the log-likelihood function is translated to R code. A sequence of R scripts is presented, starting from a naive implementation and ending at the final implementation as found in the lavaan package. Along the way, various computational tips and tricks are given.

scholarly journals TCLUST: Trimming Approach of Robust Clustering Method

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Muhamad Alias Md. Jedi ◽  
Robiah Adnan
Keyword(s):  
Clustering Algorithm ◽  
Likelihood Function ◽  
R Package ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Robust Clustering ◽  
Scatter Matrix ◽  
Group Assignment ◽  
Log Likelihood ◽  
Clustering Approach

TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-means clustering algorithm. It is called “crisp” clustering approach because the observation is can be eliminated or assigned to a group. TCLUST strengthen the group assignment by putting constraint to the cluster scatter matrix. The emphasis in this paper is to restrict on the eigenvalues, λ of the scatter matrix. The idea of imposing constraints is to maximize the log-likelihood function of spurious-outlier model. A review of different robust clustering approach is presented as a comparison to TCLUST methods. This paper will discuss the nature of TCLUST algorithm and how to determine the number of cluster or group properly and measure the strength of group assignment. At the end of this paper, R-package on TCLUST implement the types of scatter restriction, making the algorithm to be more flexible for choosing the number of clusters and the trimming proportion.

IR/visible image registration based on EM iteration of log-likelihood function

2011 ◽  
Vol 19 (3) ◽  
pp. 657-663
Author(s):  
聂宏宾 NIE Hong-bin ◽  
侯晴宇 HOU Qing-yu ◽  
赵明 ZHAO Ming ◽  
张伟 ZHANG Wei
Keyword(s):  
Image Registration ◽  
Likelihood Function ◽  
Visible Image ◽  
Log Likelihood

Selection of weights for a weighted regression of tree volume

1986 ◽  
Vol 16 (3) ◽  
pp. 671-673 ◽  
Author(s):  
C. H. Meng ◽  
W. Y. Tsai
Keyword(s):  
Confidence Interval ◽  
Likelihood Function ◽  
Weighted Least Squares ◽  
Tree Height ◽  
Regression Equations ◽  
Tree Diameter ◽  
A Value ◽  
Log Likelihood ◽  
Homogeneity Of Variance ◽  
Selection Of

The construction of regression equations for predicting tree volumes requires the assumption of homogeneity of variance that can be achieved by the method of weighted least squares. Some of the weights have the form (1/DλH)2 or (1/Dλ)2 (where D represents tree diameter at breast height and H represents tree height). Traditionally, λ has been assigned a value of 2. This paper suggests a method to estimate the exponent λ. This is accomplished by finding a maximum log likelihood function for a transformed tree volume regression equation. A proper value of λ is chosen within the confidence interval of λ. The confidence interval is established from the maximum log likelihood function.

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