Clustering of Microarray data via Clique Partitioning

Microarrays are repositories of gene expression data that hold tremendous potential for new understanding, leading to advances in functional genomics and molecular biology. Cluster analysis (CA) is an early step in the exploration of such data that is useful for purposes of data reduction, exposing hidden patterns, and the generation of hypotheses regarding the relationship between genes and phenotypes. In this paper we present a new model for the clique partitioning problem and illustrate how it can be used to perform cluster analysis in this setting.     

Relationship between the weighted distributions and some inequality measures

In this paper, we study the relationships between the weighted distributions and the parent distributions in the context of Lorenz curve, Lorenz ordering and inequality measures. These relationships depend on the nature of the weight functions and give rise to interesting connections. The properties of weighted distributions for general weight functions are also investigated. It is shown how to derive and to determine characterizations related to Lorenz curve and other inequality measures for the cases weight functions are increasing or decreasing. Some of the results are applied for special cases of the weighted distributions. We represent the reliability measures of weighted distributions by the inequality measures to obtain some results. Length-biased and equilibrium distributions have been discussed as weighted distributions in the reliability context by concentration curves. We also review and extend the problem of stochastic orderings and aging classes under weighting. Finally, the relationships between the weighted distribution and transformations are discussed.     

A non-nested test of level-differenced versus log-differenced stationary models

This paper considersthe applicationof the simulated Cox test procedure developed in Pesaran and Pesaran (1993) to test linear versus log-linear models. The test procedure can also be applied to other generalized linear regression models such as level-difference stationary models versus the log-difference stationary models. In order to compare the small sample performanceof the proposed test with other tests extant in the literature, the paper also reports the resultsof a numberof Monte Carlo experiments using the experimental framework of Godfrey et al. (1988). The Monte Carlo results provide strong support for a simplified version of the simulatedCox test over the PE and the BM tests, but suggest that there is little to choose between the simulated Cox test and the DL test.     

An Algorithm for Enhancing Spreadsheet Regression with Out-of-Sample Statistics

An innovative algorithm is developed for obtaining spreadsheet regression measures used in computing out-of-sample statistics. This algorithm alleviates the leave-one-out computational simulation complexity and memory size problems perceived in computing these statistics. Hence, the purpose of this article is to describe a computationally enhanced algorithm that gives spreadsheet users advanced regression capabilities thereby adding a new dimension to spreadsheet regression operations. These statistics include diagonals of the hat matrix, legitimate forecasting intervals, and PRESS residuals. These computational innovations promote learning while eliminating spreadsheet inadequacies thereby making spreadsheet regression attractive to academicians in teaching and practitioners in acquiring further application competence.     

A note on the permutationally convex games

If marginal worth vector of a game for an ordering is in the core, the game does not have to be a p.c. game.     

Fuzzy process capability indices for simple linear profile

Process capability indices are numerical tools that quantify how well a process can meet customer requirements, specifications or engineering tolerances. Fuzzy logic is incorporated to deal imprecise, incomplete data along with uncertainty. This paper develops two fuzzy methods for measuring the process capability in simple linear profiles for the circumstances in which lower and upper specification limits are imprecise. To guide practitioners, numerical example is provided.     

An entropic structure in capability indices

Abstract

Analysis capability indices for symmetric process in normal case is obtained via maximum entropy approach of distribution function of the data. In view of it, we have perused on production processes to be in statistical control. Generally a process is capable based on capability indices when its reasonable index was more than a known threshold value. Thus by conditioning on indices, the most general distribution is found out whose parameters can be approximated by using the data of process. Also analysis via Kullback-Leibler information measure based on the above arguments is obtained in the last part of the paper.     

A FRAMEWORK FOR MACHINE SCHEDULING PROBLEMS WITH CONTROLLABLE PROCESSING TIMES

This is a study of single and parallel machine scheduling problems with controllable processing time for each job. The processing time for job j depends on the position of the job in the schedule and is a function of the number of resource units allocated to its processing. Processing time functions and processing cost functions are allowed to be nonlinear. The scheduling problems considered here have important applications in industry and include many of the existing scheduling models as special cases. For the single machine problem, the objective is minimization of total compression costs plus a scheduling measure. The scheduling measures include makespan, total flow time, total differences in completion times, total differences in waiting times, and total earliness and tardiness with a common due date for all jobs. Except when the total earliness and tardiness measure is involved, each case the problem is solved efficiently. Under an assumption typically satisfied in just-in-time systems, the problem with total earliness and tardiness measure is also solved efficiently. Finally, for a large class of processing time functions; parallel machine problems with total flow time and total earliness and tardiness measures are solved efficiently. In each case we reduce the problem to a transportation problem.