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Quasi Maximum Likelihood Estimation of Structural Equation Models With Multiple Interaction and Quadratic Effects
"... The development of statistically efficient and computationally practicable estimation methods for the analysis of structural equation models with multiple nonlinear effects has been called for by substantive researchers in psychology, marketing research, and sociology. But the development of efficie ..."
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The development of statistically efficient and computationally practicable estimation methods for the analysis of structural equation models with multiple nonlinear effects has been called for by substantive researchers in psychology, marketing research, and sociology. But the development of efficient methods is complicated by the fact that a nonlinear model structure implies specifically nonnormal multivariate distributions for the indicator variables. In this paper, nonlinear structural equation models with quadratic forms are introduced and a new QuasiMaximum Likelihood method for simultaneous estimation of model parameters is developed with the focus on statistical efficiency and computational practicability. The QuasiML method is based on an approximation of the nonnormal density function of the joint indicator vector by a product of a normal and a conditionally normal density. The results of MonteCarlo studies for the new QuasiML method indicate that the parameter estimation is almost as efficient as ML estimation, whereas ML estimation is only computationally practical for elementary models. Also, the QuasiML method outperforms other currently available methods with respect to efficiency. It is demonstrated in a MonteCarlo study that the QuasiML method permits computationally feasible and very efficient analysis of models with multiple latent nonlinear effects. Finally, the applicability of the QuasiML method is illustrated by an empirical example of an aging study in psychology. Key words: structural equation modeling, quadratic form of normal variates, latent interaction effect, moderator effect, QuasiML estimation, variance function model. 1 1.
Spectrum Based Fraud Detection in Social Networks (Extended Abstract)
, 2010
"... Social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this paper we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates ..."
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Social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this paper we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack the regular members of the network. We show that RLA attackers can be filtered by using their spectral coordinate characteristics, which are hard to hide even after the efforts by the attackers of resembling as much as possible the rest of the network. Experimental results show that our technique is very effective in detecting those attackers and outperforms techniques previously published.
Night Vision and Electronic Sensors Directorate AMSRDCERNVTRC258 Expectation Maximization and Its Application In Modeling, Segmentation and Anomaly Detection Approved for Public Release; Distribution Unlimited
"... hpccl,'ioo Maximizmioo and ils ApplicatiOJl ln Modding, Scgme>1ta!ion aOO ..."
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hpccl,'ioo Maximizmioo and ils ApplicatiOJl ln Modding, Scgme>1ta!ion aOO
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"... ABsTRACT The distribution of a symmetric statistic T = g(xl~x2 ~ •• • ~xn)~ for a random sample from a mixed population with density f(x) = pf l (x)+qf ..."
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ABsTRACT The distribution of a symmetric statistic T = g(xl~x2 ~ •• • ~xn)~ for a random sample from a mixed population with density f(x) = pf l (x)+qf
Measuring the Reliability of the Average Estimated Variance
"... In this dissertation an attempt is made to provide tools for evaluating the reliability of decisions based on the AEV for model selection in the general linear model setting. criterion The traditional distribution theory approach to such an evaluation is shown to be intractable due to the complex na ..."
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In this dissertation an attempt is made to provide tools for evaluating the reliability of decisions based on the AEV for model selection in the general linear model setting. criterion The traditional distribution theory approach to such an evaluation is shown to be intractable due to the complex nature of the joint distribution of the AEV's. A perturbation/conditional risk approach is developed which utilizes the idea of perturbing the observed data and determining the proportion of decisions based on perturbed data which differ from the decision based on the original data. The,proportion of changed decisions is shown to be a conditional risk function for an appropriately defined loss function.
Some Statistical Procedures Based on Distances
"... A criterion is proposed for classifying multivariate "observations" according to their populations of origin when the observable data are the distances between pairs of "observations, " with these distances themselves subject to further variation, such as measurement error. The s ..."
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A criterion is proposed for classifying multivariate "observations" according to their populations of origin when the observable data are the distances between pairs of "observations, " with these distances themselves subject to further variation, such as measurement error. The same basIc problem is investigated under s'everalassumptions on the underlying normal distributions. In each case, the criterion is shown to be a particular quadratic form in normal variables. In the. simplest case considered, a computational form for the distribution is given. An asymptotic expansion is developed which provides an·approximation to the distribution in other cases. The accuracy of the approximation is investigated numerically. The related problem of estimation of the noncentrality parameter of a noncentral chisquared random variable is also investigated. An estimator is proposed which is based on the twosample Wilcoxon statistic, using independent samples from the central and noncentral chisquared distributions. The estimator has the property that it is invariant under monotonic transformations of the observed data. Further properties of the estimator are derived and its asymptotic relative efficiency with respect to the maximum likelihood estimator is investigated numerically.