RANK-BASED INFERENCE FOR SURVEY SAMPLING DATA

For regression models where data are obtained from sampling surveies, the statistical analysis is often based on approaches that are either non-robust or inefficient. The handling of survey data requires more appropriate techniques, as the classical methods usually result in biased and inefficient estimates of the underlying model parameters. This article is concerned with the development of a new approach of obtaining robust and efficient estimates of regression model parameters when dealing with survey sampling data. Asymptotic properties of such estimators are established under mild regularity conditions. To demonstrate the performance of the proposed method, Monte Carlo simulation experiments are carried out and show that the estimators obtained from the proposed methodology are robust and more efficient than many of those obtained from existing approaches, mainly if the survey data tend to result in residuals with heavy-tailed or skewed distributions and/or when there are few gross outliers. Finally, the proposed approach is illustrated with a real data example

Home Exercise Adherence in an Underserved Ecuadorian Community

Purpose: Physical therapy service learning projects and volunteer experiences in foreign countries are becoming more commonplace. Patients in underserved regions are not likely to receive therapy services regularly; therefore, adherence to a home exercise program (HEP) is critical. The primary purpose of this study was to observe home exercise adherence rates between the 1st and 2nd visits in an underserved population. The secondary purpose of this study was to determine specific factors that affect HEP adherence in this population. Methods: Consecutive patients seen in Santo Domingo, Ecuador were considered for participation in this observational study. All patients were recruited from one clinic or during home visits in the surrounding community over a 5 -month period by one physical therapist. To be included in the study, patients were required to display sufficient cognitive ability by stating their name, the date, their location, and their reason for being at that location, were at least 19 years of age, and had an impairment or functional limitation that was included in the physical therapy scope of practice. Patient demographics, medical history, and answers to questionnaires were collected on the initial visit. Immediately after the initial evaluation, patients were issued 5 home exercises . On the subsequent follow-up visit, adherence was measured with the Medical Outcomes Study General Adherence Items (MOSGAI). Adherence percentage, defined by the frequency in which the patient performed all the exercises as prescribed, was calculated. In order to evaluate potential factors affecting HEP adherence, separate Kruskal-Wallis tests were performed on the categorical variables (gender, marital status, education, employment, duration of symptoms, and comorbidities) and separate Spearman correlation tests were performed on the continuous data (age, pain level, and sport injury rehabilitation adherence scale - SIRAS). Alpha was set at p ≤.05 a priori. Results and Conclusion: A total of 40 patients satisfied the eligibility criteria and agreed to participate, of which 29 (mean age 55, SD 14) were seen for a second visit. Of the patients who returned for a second visit, the median (interquartile range) MOSGAI score was 24 (21-29) and the average adherence percentage was 73%. Age was negatively correlated with the MOSGAI (p = 0.008, r = - 0.60), while the SIRAS was positively correlated with the MOSGAI (p = 0.002, r = 0.52 ). Exercise adherence in this population was similar to previously reported data, but in areas where access to health care is limited, it may be even more important to im prove adherence. It is possible that both age and the level of adherence observed by the physical therapist during the first visit helped predict HEP adherence in this population. Innovation: Volunteer physical therapists serving in this community should proactively explore strategies to increase adherence in patients with these characteristics

On the Consistency of a Class of Nonlinear Regression Estimators

In this paper, we study conditions sufficient for strong consistency of a class of estimators of parameters of nonlinear regression models. The study considers continuous functions depending on a vector of parameters and a set of random regressors. The estimators chosen are minimizers of a generalized form of the signed-rank norm. The generalization allows us to make consistency statements about minimizers of a wide variety of norms including the L1 and L2 norms. By implementing trimming, it is shown that high breakdown estimates can be obtained based on the proposed dispersion function

The Exponentiated Half-logistic Odd Burr III-G: Model, Properties and Applications: The Exponentiated Half-logistic Odd Burr III-G

A new family of distributions called exponentiated half-logistic Odd Burr III-G (EHL-OBIII-G) is developed and studied. Mathematical and statistical properties such as the hazard function, quantile function, moments, probability weighted moments, Renyi entropy and stochastic orders are derived. The model parameters are estimated based on the maximum likelihood estimation method. The usefulness of the proposed family of distributions is demonstrated via extensive simulation studies. Finally the proposed model and its special case is applied to real data sets to illustrate its best fit and flexibility