Extensions to the Gaussian copula: random recovery and random factor loadings

2005 ◽  
Vol 1 (1) ◽  
pp. 29-70 ◽  
Author(s):  
Leif Andersen ◽  
Jakob Sidenius
Keyword(s):  
Gaussian Copula ◽  
Random Factor ◽  
Factor Loadings

scholarly journals On the Equivalency of Factor and Network Loadings

2020 ◽  
Author(s):  
Alexander P. Christensen ◽  
Hudson Golino
Keyword(s):  
Factor Model ◽  
Network Models ◽  
High Sensitivity ◽  
Factor Loading ◽  
Random Factor ◽  
Factor Loadings ◽  
Network Measure ◽  
Confirmatory Factor ◽  
Similar Information ◽  
Loading Matrix

Recent research has demonstrated that the network measure node strength or sum of a node’s connections is roughly equivalent to confirmatory factor analysis (CFA) loadings. A key finding of this research is that node strength represents a combination of different latent causes. In the present research, we sought to circumvent this issue by formulating a network equivalent of factor loadings, which we call network loadings. In two simulations, we evaluated whether these network loadings could effectively (1) separate the effects of multiple latent causes and (2) estimate the simulated factor loading matrix of factor models. Our findings suggest that the network loadings can effectively do both. In addition, we leveraged the second simulation to derive effect size guidelines for network loadings. In a third simulation, we evaluated the similarities and differences between factor and network loadings when the data were generated from random, factor, and network models. We found sufficient differences between the loadings, which allowed us to develop an algorithm to predict the data generating model called the Loadings Comparison Test (LCT). The LCT had high sensitivity and specificity when predicting the data generating model. In sum, our results suggest that network loadings can provide similar information to factor loadings when the data are generated from a factor model and therefore can be used in a similar way (e.g., item selection, measurement invariance, factor scores).

Pricing CDO tranches with stochastic correlation and random factor loadings in a mixture copula model

2012 ◽  
Vol 219 (6) ◽  
pp. 2909-2916
Author(s):  
Zhe Chen ◽  
Qunfang Bao ◽  
Shenghong Li ◽  
Jianli Chen
Keyword(s):  
Copula Model ◽  
Random Factor ◽  
Factor Loadings ◽  
Stochastic Correlation

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

scholarly journals The state of Spanish /s/ variation in Concepción, Chile: Linguistic and social trends

2020 ◽  
Vol 6 (1) ◽  
pp. 132-153
Author(s):  
Brandon M. A. Rogers
Keyword(s):  
Social Factors ◽  
Central City ◽  
Mixed Effects ◽  
The State ◽  
Mixed Effects Model ◽  
Proportional Odds ◽  
Random Factor ◽  
Social Trends ◽  
Southern Central ◽  
Social Demographics

AbstractThe current study examines /s/ variation in the southern-central city of Concepción, Chile and its relation to a variety of linguistic and social factors. A proportional-odds mixed effects model, with the random factor of “speaker”, was used to treat the categorically coded data on a continuum of acoustical variation ([s] > [h] > ∅). The results presented show that contrary to the previous assertions, heavy sibilant reduction, especially elision, in Concepción, Chile is the rule, rather than the exception, to the extent that it is no longer a marker of certain social demographics as has been reported previously. Furthermore, based on the trends reported, it is likely that this has been the case for several decades. Finally, the overall observed trends are indicative that the rates of /s/ elision will continue to increase across social demographics and different phonetic and phonological contexts in Concepción, Chile.

Prediction of Joint Rain Attenuation Statistics Induced on Earth–Satellite Multiple Site Diversity Systems Using Gaussian Copula

2017 ◽  
Vol 16 ◽  
pp. 95-98 ◽  
Author(s):  
Arsim Kelmendi ◽  
Charilaos Kourogiorgas ◽  
Andrej Hrovat ◽  
Athanasios D. Panagopoulos ◽  
Gorazd Kandus ◽  
...  
Keyword(s):  
Earth Satellite ◽  
Rain Attenuation ◽  
Gaussian Copula ◽  
Multiple Site ◽  
Diversity Systems ◽  
Site Diversity

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

scholarly journals Cox Processes Associated with Spatial Copula Observed through Stratified Sampling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 524
Author(s):  
Walguen Oscar ◽  
Jean Vaillant
Keyword(s):  
Environmental Effects ◽  
Gaussian Copula ◽  
Spatial Dependency ◽  
Spatial Effects ◽  
Doubly Stochastic ◽  
Cox Processes ◽  
Black Leaf Streak ◽  
Spatial Random Field ◽  
Black Leaf Streak Disease ◽  
Spatial Count Data

Cox processes, also called doubly stochastic Poisson processes, are used for describing phenomena for which overdispersion exists, as well as Poisson properties conditional on environmental effects. In this paper, we consider situations where spatial count data are not available for the whole study area but only for sampling units within identified strata. Moreover, we introduce a model of spatial dependency for environmental effects based on a Gaussian copula and gamma-distributed margins. The strength of dependency between spatial effects is related with the distance between stratum centers. Sampling properties are presented taking into account the spatial random field of covariates. Likelihood and Bayesian inference approaches are proposed to estimate the effect parameters and the covariate link function parameters. These techniques are illustrated using Black Leaf Streak Disease (BLSD) data collected in Martinique island.

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