Skewed data is the main issue in statistical models in healthcare costs. Data transformation is a conventional method to decrease skewness, but there are some disadvantages. Some recent studies have employed generalized linear models (GLMs) and Cox proportional hazard regression as alternative estimators. The aim of this study was to investigate how well these alternative estimators perform in ...

In many studies of health economics, we are interested in the expected total cost over a certain period for a patient with given characteristics. Problems can arise if cost estimation models do not account for distributional aspects of costs. Two such problems are (1) the skewed nature of the data, and (2) censored observations. In this paper we propose an empirical likelihood (EL) method for c...

This paper provides a survey of three families of flexible parametric probability density functions (the skewed generalized t, the exponential generalized beta of the second kind, and the inverse hyperbolic sine distributions) which can be used in modeling a wide variety of econometric problems. A figure, which can facilitate model selection, summarizing the admissible combinations of skewness ...

Unlike majority of current statistical models and methods focusing on mean response for highly skewed longitudinal data, we present a novel model for such data accommodating a partially linear median regression function, a skewed error distribution and within subject association structures. We provide theoretical justifications for our methods including asymptotic properties of the posterior an...

BACKGROUND AND PURPOSE Ischemic stroke lesion volumes have proven difficult to analyze due to the extremely skewed shape of their underlying distribution. We introduce an extension of generalized linear models, beta regression, as a possible method of modeling extremely skewed distributions as evidenced in ischemic stroke lesion volumes. METHODS The NINDS rt-PA clinical trials measured ischem...

We introduce a general perspective on the introduction of skewness into symmetric distributions. Through inverse probability integral transformations we provide a constructive representation of skewed distributions, where the skewing mechanism and the original symmetric distributions are specified separately. We study the effects of the skewing mechanism on e.g. modality, tail behaviour and the...

We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression over SCM principal components. Regression often produces alpha weights with in...