An algorithmic approach to construct D-optimal saturated designs for logistic model

In this paper, locally D-optimal saturated designs for a logistic model with one and two continuous input variables have been constructed by modifying the famous Fedorov exchange algorithm. A saturated design not only ensures the minimum number of runs in the design but also simplifies the row exchange computation. The basic idea is to exchange a design point with a point from the design space. The algorithm performs the best row exchange between design points and points form a candidate set representing the design space. Naturally, the resultant designs depend on the candidate set. For gain in precision, intuitively a candidate set with a larger number of points and the low discrepancy is desirable, but it increases the computational cost. Apart from the modification in row exchange computation, we propose implementing the algorithm in two stages. Initially, construct a design with a candidate set of affordable size and then later generate a new candidate set around the points of design searched in the former stage. In order to validate the optimality of constructed designs, we have used the general equivalence theorem. Algorithms for the construction of optimal designs have been implemented by developing suitable codes in R.     

Professionalism in Indian public relations and corporate communications: An empirical analysis

Public relations is evolving in India and the profession is gaining acceptance and recognition. This study is an attempt to measure the extent of professionalism in India and how much consensus, if any, exists regarding professional standards among practitioners in agencies and those in corporations. It also identifies the influence of demographic factors on the standards in the profession.     

Minimally changed run sequences in factorial experiments

Randomization of run sequences in factorial experiments may result in large number of changes in factor levels which will make the experimentation expensive, time-consuming and difficult. Experiments in which it is difficult to change the levels of factor(s) use of minimally changed run sequences may often be preferable to a random run sequence. In the present paper, we have developed method for obtaining minimally changed run sequences for factorial experiments. The general expression of factor-wise number of level changes for the developed minimally changed run sequences has also been obtained. A relationship has been established between the time count effect of a lower order factorial with minimally changed run sequences and that of a higher order factorial with minimally changed run sequences obtained through the lower order minimally changed run sequences. For providing a readymade solution to the end users, a SAS macro has also been developed for generating these minimally changed run sequences along with its parameters.     

Optimal block designs with non additive mixed effects interference

This paper deals with optimality aspects of block designs balanced for interference effects from neighboring units on both sides under a general non additive model along with random block effects. Here, a class of complete, circular block designs strongly balanced for interference effects has been shown to be universally optimal for the estimation of direct effects and interference effects (left and right) of treatments under a non additive mixed effects model.     

DO TERRORIST ATTACKS IMPACT EXCHANGE RATE BEHAVIOR? NEW INTERNATIONAL EVIDENCE

This study examines whether terrorist attacks affect bilateral exchange rates. Using historical 10‐minute exchange rate data for 21 countries' currency vis‐à‐vis the U.S. dollar, we show that exchange rate returns of all countries are statistically significantly affected by terrorist attacks. Some exchange rates appreciate and some depreciate following a terrorist attack, some currencies experience exchange rate reversals while others experience a persistent effect. Generally, the effect declines but persists as terrorist attacks become stale information. (JEL F31, F37)     

Realist Evaluation for Practice

Correspondence to Mansoor A. F. Kazi, Director, Centre for Evaluation Studies, School of Human and Health Sciences, Harold Wilson Building, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK. E-mail: m.a.f.kazi{at}hud.ac.uk Summary This paper outlines the new, emerging realist paradigm in evaluationresearch, and applies it to social work practice. This paradigmhas the potential for a ‘white box’ evaluation thatnot only systematically tracks outcomes, but also the mechanismsthat produce the outcomes, the contexts in which these mechanismsare triggered, and the content of the interventions (or thegenerative mechanisms introduced by a programme). Two examplesare provided, both studies with an extensive use of single-subjectdesigns by practitioners within a realist paradigm. This articleis based on the author's invited keynote address at the OhioState University's Thirteenth National Symposium on DoctoralResearch in Social Work, 6 April 2001.     

A note on circular neighbor balanced designs

ABSTRACT

This paper describes some methods of constructing circular neighbor balanced and circular partially neighbor balanced block designs for estimation of direct and neighbor effects of the treatments. A class of circular neighbor balanced block designs with unequal block sizes is also proposed.     

Testing homogeneity in clustered (longitudinal) count data regression model with over-dispersion

Clustered (longitudinal) count data arise in many bio-statistical practices in which a number of repeated count responses are observed on a number of individuals. The repeated observations may also represent counts over time from a number of individuals. One important problem that arises in practice is to test homogeneity within clusters (individuals) and between clusters (individuals). As data within clusters are observations of repeated responses, the count data may be correlated and/or over-dispersed. For over-dispersed count data with unknown over-dispersion parameter we derive two score tests by assuming a random intercept model within the framework of (i) the negative binomial mixed effects model and (ii) the double extended quasi-likelihood mixed effects model (Lee and Nelder, 2001). These two statistics are much simpler than a statistic derived by Jacqmin-Gadda and Commenges (1995) under the framework of the over-dispersed generalized linear model. The first statistic takes the over-dispersion more directly into the model and therefore is expected to do well when the model assumptions are satisfied and the other statistic is expected to be robust. Simulations show superior level property of the statistics derived under the negative binomial and double extended quasi-likelihood model assumptions. A data set is analyzed and a discussion is given.