Approximate percentiles for ratios of f-variates

The need for percentiles of ratios of independent F-variates arises in many types of applications. Some tabulated values are available mostly for equal or nearly equal degrees-of-freedom cases, but it is not possible to provide comprehensive tables for the numerous combinations of possible degrees-of-freedom and probability levels. A simple approximation for the percentiles based on the Cornish-Fisher expansion technique is provided which is adequate for larger degrees-of-freedom and a more complicated approximation is provided which is suitable for all cases. The accuracy of the approximation is shown over a wide range of typical cases     

The Invisible Unattended: Low‐wage Chinese Immigrant Workers,Health Care,and Social Capital in Southern California’s San Gabriel Valley*

This study investigates the factors affecting the availability of health insurance, the accessibility of health care, and the dissemination of the relevant information among low‐wage Chinese immigrants in Southern California by relying on the concepts of social and cultural capital. Using community‐based research and in‐depth interviews, our study suggests that a severe shortage in health care coverage among low‐wage Chinese immigrants is influenced by the lack of employment with employer‐provided health insurance within the Chinese “ethnoburb” community. Although the valuable social capital generated by Chinese immigrant networks seems to be sufficient enough to provide them with certain practical resources, the lack of cultural capital renders the social network rather ineffective in providing critical health care information from mainstream American society.     

LOSS OF INFORMATION ASSOCIATED WITH THE ORDER STATISTICS AND RELATED ESTIMATORS IN THE DOUBLE EXPONENTIAL DISTRIBUTION CASE

Fisher (1934) derived the loss of information of the maximum likelihood estimator (MLE) of the location parameter in the case of the double exponential distribution. Takeuchi & Akahira (1976) showed that the MLE is not second order asymptotically efficient. This paper extends these results by obtaining the (asymptotic) losses of information of order statistics and related estimators, and by comparing them via their asymptotic distributions up to the second order.