scholarly journals Performance Analysis of Maximum Likelihood Estimation for Transmit Power Based on Signal Strength Model

2018 ◽  
Vol 7 (3) ◽  
pp. 38 ◽  
Author(s):  
Xiao-Li Hu ◽  
Pin-Han Ho ◽  
Limei Peng
Keyword(s):  
Maximum Likelihood ◽  
Performance Analysis ◽  
Theoretical Analysis ◽  
Numerical Experiments ◽  
Likelihood Estimation ◽  
Signal Strength ◽  
Strength Model ◽  
Transmit Power ◽  
Ml Estimation ◽  
Theoretical Performance

We study theoretical performance of Maximum Likelihood (ML) estimation for transmit power of a primary node in a wireless network with cooperative receiver nodes. The condition that the consistence of an ML estimation via cooperative sensing can be guaranteed is firstly defined. Theoretical analysis is conducted on the feasibility of the consistence condition regarding an ML function generated by independent yet not identically distributed random variables. Numerical experiments justify our theoretical discoveries.

scholarly journals Performance Analysis of Maximum Likelihood Estimation for Multiple Transmitter Locations

2021 ◽  
Vol 1 (2) ◽  
pp. 60-66
Keyword(s):  
Maximum Likelihood ◽  
Performance Analysis ◽  
Algebraic Curve ◽  
Numerical Experiments ◽  
Likelihood Estimation ◽  
Sufficient Condition ◽  
Limit Set ◽  
Excitation Condition ◽  
Ml Estimation ◽  
Persistent Excitation

The paper considers the consistence condition of Maximum Likelihood (ML) estimation for multiple transmitter locations in a wireless network with cooperative receiver nodes. It is found that the location set of receiver nodes should not locate (or asymptotically in some sense) merely in an algebraic curve of order 2M −1 if there are totally M transmitters. A sufficient condition for consistence of the ML estimation for M transmitters is that the limit set of locations contains a subset, comprised of (2M2 −M +2) points, which is non-C-2M-co-curved, a notion given by Definition IV-B. This condition can be compared to the persistent excitation condition used to guarantee the convergence of least squares algorithm. Numerical experiments are designed to demonstrate the theoretical discoveries in both positive and negative aspects.

scholarly journals Quantifying the strength of general factors in psychopathology: A comparison of CFA with maximum likelihood estimation, BSEM and ESEM/EFA bi-factor approaches

2018 ◽  
Author(s):  
Aja Louise Murray ◽  
Tom Booth ◽  
Manuel Eisner ◽  
Ingrid Obsuth ◽  
Denis Ribeaud
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Factor Models ◽  
General Factor ◽  
Normative Sample ◽  
Ml Estimation ◽  
Co Morbidity ◽  
Simulation Results ◽  
P Factor

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p-factor) in classifying, researching, diagnosing and treating psychiatric disorders depends (amongst other issues) on the extent to which co-morbidity is symptom-general rather than staying largely within the confines of narrower trans-diagnostic factors such as internalising and externalising. In this study we compared three methods of estimating p-factor strength. We compared omega hierarchical and ECV calculated from CFA bi-factor models with maximum likelihood (ML) estimation, from ESEM/EFA models with a bifactor rotation, and from BSEM bi-factor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings may be the preferred option. However, CFA with ML also performed well provided secondary loadings were modelled We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n=1286) and University counselling sample (n= 359).

scholarly journals A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome

2020 ◽  
Vol 44 (5) ◽  
pp. 447-457
Author(s):  
Su-Young Kim ◽  
David Huh ◽  
Zhengyang Zhou ◽  
Eun-Young Mun
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Structural Equation ◽  
Growth Models ◽  
Likelihood Estimation ◽  
Equation Modeling ◽  
Latent Growth ◽  
Ml Estimation ◽  
Parameter Recovery ◽  
Latent Growth Models

Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a lack of research providing guidance on when Bayesian estimation may be preferable to ML or vice versa. This study compared the performance of Bayesian versus ML estimators for LGMs by evaluating their accuracy via Monte Carlo (MC) simulations. For the MC study, longitudinal data sets were generated and estimated using LGM via both ML and Bayesian estimation with three different priors, and parameter recovery across the two estimators was evaluated to determine their relative performance. The findings suggest that ML estimation is a reasonable choice for most LGMs, unless it fails to converge, which can occur with limiting data situations (i.e., just a few time points, no covariate or outcome, modest sample sizes). When models do not converge using ML, we recommend Bayesian estimation with one caveat that the influence of the priors on estimation may have to be carefully examined, per recent recommendations on Bayesian modeling for applied researchers.

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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