Chi-square Mixture of Chi-square Distributions

Zaman, Mehwish; Paul, D.; Akhter, Nasreen; ., M.H. Howlader; Kabir, Md. Shaha Nur

doi:10.3923/jas.2006.243.246

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They ascribe negative estimates of the variance of the latent variable to negative error variances. It appears that the conversion of such negative values into zero variances creates the kind of distribution that is to be represented by a mixed chi-square distribution or so-called chi-bar-square distribution (Shapiro, 1985; Zaman, Paul, Akhter, Howlader, & Kabir, 2006). Since the exclusion of outcomes of CFA including a negative estimate of an error variance leads to a distribution, as is expected for variances, we propose to accept a result of CFA as valid only if all estimates of the error variances are positive.…”

Section: Discussionmentioning

confidence: 99%

The EV Scaling Method for Variances of Latent Variables

Schweizer

Troche

2019

Methodology

View full text Add to dashboard Cite

Abstract. The paper describes EV scaling for variances of latent variables included in confirmatory factor models. EV-scaled variances can be achieved in two ways: the estimation of variance parameters based on adjusted factor loadings and alternatively the summation of squared factor loadings obtained under the condition that the variance parameter is set equal to one. By definition, the second procedure yields values that are always positive. EV-scaled variances of latent variables show sizes similar to eigenvalues. The outcome of applying this scaling method is demonstrated in empirical data. The results of a simulation study reveal that the outcomes of the two ways virtually always correspond if the data are generated to include the contribution of a latent source. If there is no such source, the exclusion of solutions with negative error variances virtually always leads to correspondence.

show abstract

Section: Discussionmentioning

confidence: 99%

The EV Scaling Method for Variances of Latent Variables

Schweizer

Troche

2019

Methodology

View full text Add to dashboard Cite

show abstract

Chi-Square Mixture of Transformed/Inverse Transformed Gamma Family

Arcede¹,

Herrera²,

Cubelo³

et al. 2013

IJSP

View full text Add to dashboard Cite

In this study, we define the chi-square mixture of transformed gamma distribution which contained some special submodels namely, the chi-square mixture of gamma, Weibull, and exponential mixture distributions. Also, the chi-square mixture of inverse transformed gamma distribution is defined and a class of submodels are deduced, that is, the chi-square mixture of inverse gamma, inverse Weibull, and inverse exponential mixture distributions. For both classes, statistical properties are investigated, that is, mean, variance, skewness and kurtosis using rth raw moment. The limiting behavior and special cases are also given to established relationships.

show abstract

Chi-square Mixture of Chi-square Distributions

Cited by 2 publications

References 4 publications

The EV Scaling Method for Variances of Latent Variables

The EV Scaling Method for Variances of Latent Variables

Chi-Square Mixture of Transformed/Inverse Transformed Gamma Family

Contact Info

Product

Resources

About