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Full-Text Articles in Social and Behavioral Sciences
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
Research Collection School Of Economics
A simple and reliable method of inference for the spatial parameter in spatial autoregressive models is introduced, based on a statistic obtained by centering and rescaling the numerator of the concentrated Gaussian score function. The resulted tests and confidence intervals are robust against the distributional misspecifications and are insensitive to the spatial layouts and the error standard deviation. In contrast, the standard methods based on Gaussian score and information matrix may lead to inconsistent inference when errors are non normal, and can be quite sensitive to the spatial layouts and the error standard deviation even when errors are normally distributed. …
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Journal of Modern Applied Statistical Methods
A robust Root Mean Square Standardized Effect Size (RMSSER) was developed to address the unsatisfactory performance of the Root Mean Square Standardized Effect Size. The coverage performances of the confidence intervals (CI) for RMSSER were investigated. The coverage probabilities of the non-central F distribution-based CI for RMSSER were adequate.
Estimating Internal Consistency Using Bayesian Methods, Miguel A. Padilla, Guili Zhang
Estimating Internal Consistency Using Bayesian Methods, Miguel A. Padilla, Guili Zhang
Journal of Modern Applied Statistical Methods
Bayesian internal consistency and its Bayesian credible interval (BCI) are developed and Bayesian internal consistency and its percentile and normal theory based BCIs were investigated in a simulation study. Results indicate that the Bayesian internal consistency is relatively unbiased under all investigated conditions and the percentile based BCIs yielded better coverage performance.
Accurately Sized Test Statistics With Misspecified Conditional Homoskedasticity, Douglas Steigerwald, Jack Erb
Accurately Sized Test Statistics With Misspecified Conditional Homoskedasticity, Douglas Steigerwald, Jack Erb
Douglas G. Steigerwald
We study the finite-sample performance of test statistics in linear regression models where the error dependence is of unknown form. With an unknown dependence structure there is traditionally a trade-off between the maximum lag over which the correlation is estimated (the bandwidth) and the amount of heterogeneity in the process. When allowing for heterogeneity, through conditional heteroskedasticity, the correlation at far lags is generally omitted and the resultant inflation of the empirical size of test statistics has long been recognized. To allow for correlation at far lags we study test statistics constructed under the possibly misspecified assumption of conditional homoskedasticity. …