A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples

A new test of hypothesis for classifying stationary time series based on the bias-adjusted estimators of the fitted autoregressive model is proposed. It is shown theoretically that the proposed test has desirable properties. Simulation results show that when time series are short, the size and power estimates of the proposed test are reasonably good, and thus this test is reliable in discriminating between short-length time series. As the length of the time series increases, the performance of the proposed test improves, but the benefit of bias-adjustment reduces. The proposed hypothesis test is applied to two real data sets: the annual real GDP per capita of six European countries, and quarterly real GDP per capita of five European countries. The application results demonstrate that the proposed test displays reasonably good performance in classifying relatively short time series.

Polarization of forecast densities : a new approach to time series classification

Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.

The effects of rurality on mental and physical health

The effects of rurality on physical and mental health are examined in analyses of a national dataset, the Community Tracking Survey, 2000-2001, that includes individual level observations from household interviews. We merge it with county level data reflecting community resources and use econometric methods to analyze this multi-level data. The statistical analysis of the impact of the choice of definition on outcomes and on the estimates and significance of explanatory variables in the model is presented using modern econometric methods, and differences in results for mental health and physical health are evaluated. © 2010 Springer Science+Business Media, LLC.

Shortages of rural mental health professionals

This study presents a conceptual model of the supply and demand for mental health professionals. It uses national data to profile differences in the supply of mental health professionals in different types of rural and urban areas. It contrasts the availability of general health and mental health professionals. It examines shortage areas identified in 2000 and their related community characteristics. Because of the absence of data on a national level to describe many types of mental health professionals state licensure data for one state were used to show the volume and distribution of these practitioners. To improve rural mental health service delivery it will be necessary to implement system changes to promote the increased availability, competency, and support of rural health professionals. Copyright 2003, Elsevier Science (USA). All rights reserved.

New estimates for CRNA vacancies

A national survey to estimate vacancy rates of Certified Registered Nurse Anesthetists (CRNAs) in hospitals and ambulatory surgical centers was conducted in 2007. Poisson regression methods were used to improve the precision of the estimates. A significant increase in the estimated vacancy rate was reported for hospitals relative to an earlier study from 2002, although it is important to note that there were some methodological differences between the 2 surveys explaining part of the increase. Results from this study found the vacancy rate was higher in rural hospitals than in nonrural hospitals, and it was lower in ambulatory surgical centers. A number of simulations were run to predict the effects of relevant changes in the market for surgeries and number of CRNAs, which were compared to the predictions from the previous survey. The remarkable factor since the last survey was the unusually large rate of new CRNAs entering the market, yet the vacancy rates remain relatively high.