Comparative Analysis Of Healthcare Performance In West And South Regions Of Ukraine

This study examines variations in efficiency among hospitals located in western and southern Ukraine. We estimated efficiency using a nonparemetric modeling technique known as data envelopment analysis (DEA). DEA is a very powerful tool to compare relative efficiency among several economic units of study, known as decision making units (DMUS). In our current and previous research we have focused attention on comparative efficiency among hospitals and then explained variation in efficiency based on historical and cultural differences that influence managerial behavior at the hospital level of decision making. This study using current data and an expanded geographic territory provides further support to our findings from previous studies

Combination of statistical process control (SPC) methods and classification strategies for situation assessment of batch processes

Postprint (published version

PEMILIHAN UJI TERBAIK NON-PARAMETRIK UNTUK DUA SAMPEL BEBAS MENGGUNAKAN METODE SIMULASI

Telah tersedia beberapa statistik uji non-parametrik, seperti uji Cramer-von Mises, uji Kolmogorov-Smirnov, uji Wilcoxon, uji peringkat baru yang dapat digunakan untuk menentukan apakah dua sampel bebas berasal dari populasi yang sama atau tidak. Secara analitik keempat uji tersebut tidak dapat dibandingkan , namun demikian dalam makalah ini secara simulasi dapat dibandingkan dalam rangka untuk mencari uji terbaik diantara uji-uji tersebut. Berdasarkan hasil simulasi menunjukkan bahwa uji peringkat baru mempunyai kepekaan tertinggi didalam menolak Ho bila ternyata Ho salah. Ini berarti uji peringkat baru merupakan uji terbaik diantara uji-uji tersebut

CCA: An R Package to Extend Canonical Correlation Analysis

Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.

Comparison of methods in the analysis of dependent ordered catagorical data

Rating scales for outcome variables produce categorical data which are often ordered and measurements from rating scales are not standardized. The purpose of this study is to apply commonly used and novel methods for paired ordered categorical data to two data sets with different properties and to compare the results and the conditions for use of these models. The two applications consist of a data set of inter-rater reliability and a data set from a follow-up evaluation of patients. Standard measures of agreement and measures of association are used. Various loglinear models for paired categorical data using properties of quasi-independence and quasi-symmetry as well as logit models with a marginal modelling approach are used. A nonparametric method for ranking and analyzing paired ordered categorical data is also used. We show that a deeper insight when it comes to disagreement and change patterns may be reached using the nonparametric method and illustrate some problems with standard measures as well as parametric loglinear and logit models. In addition, the merits of the nonparametric method are illustrated.Agreement:ordinal data; ranking; reliability.rating scales

On the Beta-Nakagami Distribution

This study is focused on combining Nakagami distribution and beta distribution with a view to obtaining a distribution that is better than each of them individually in terms of the estimate of their characteristics and parsimonious in their parameters using the logit of beta (the link function of the Beta generalized distribution by Jones (2004)). The resulting model, beta Nakagami distribution is better in terms of its flexibility and shape. The statistical properties of the proposed distribution such as moments, moment generating function, the asymptotic behavior among others were investigated. Our findings showed that beta Nakagami apart from being flexible, has better representation of data than Nakagami distribution.  It therefore describes situations better than the Nakagami distribution