Optimal orthogonal-array-based latin hypercubes

The use of optimal orthogonal array latin hypercube designs is proposed. Orthogonal arrays were proposed for constructing latin hypercube designs by Tang (1993). Such designs generally have better space filling properties than random latin hypercube designs. Even so, these designs do not necessarily fill the space particularly well. As a result, we consider orthogonal-array-based latin hypercube designs that try to achieve optimality in some sense. Optimization is performed by adapting strategies found in Morris & Mitchell (1995) and Ye et al. (2000). The strategies here search only orthogonal-array-based latin hypercube designs and, as a result, optimal designs are found in a more efficient fashion. The designs found are in general agreement with existing optimal designs reported elsewhere.     

Market orientation research: a qualitative synthesis and future research agenda

The prominence of market orientation as a core organizational concept, as well as recent discussions about its contributions and shortcomings, suggest that it is time to assess the large and expanding body of research surrounding the construct. Our research takes a systematic qualitative approach to assessing extant research on market orientation, identifying problems, patterns, and paradoxes in the literature. Specifically, we provide an integration of the burgeoning empirical research on market orientation, based on a thorough synthesis of articles published over more than 25 years of inquiry. Bringing together the different strands of inquiry that have guided knowledge accumulation on market orientation, we are able to identify major theoretical gaps and omissions in the literature. We also find that while market orientation research has made considerable strides over the years, much work still needs to be done as voids remain that need to be addressed in future investigations.

     

ENTERTAINING MALTHUS: BREAD,CIRCUSES, AND ECONOMIC GROWTH

Motivated by the basic adage that man does not live by bread alone, we offer a theory of historical economic growth and population dynamics where human beings need food to survive, but enjoy other things, too. Our model imposes a Malthusian constraint on food, but introduces a second good to the analysis that affects living standards without affecting population growth. We show that technological change does a good job explaining historical consumption patterns and population dynamics, including the Neolithic Revolution, the Industrial Revolution, and the Great Divergence. Our theory stands in contrast to models that assume a single composite good and a Malthusian constraint. These models generate negligible growth prior to the Industrial Revolution. However, recent revisions to historical data show that historical living standards—though obviously much lower than today's—varied over time and space much more than previously thought. These revisions include updates to Maddison's dataset, which served as the basis for many papers taking long‐run stagnation as a point of departure. This new evidence suggests that the assumption of long‐run stagnation is problematic. Our model shows that when we give theoretical accounting of these new observations the Industrial Revolution is much less puzzling. (JEL B10, I31, J1, N1, O30)     

Optimum covariate designs in partially balanced incomplete block (PBIB) design set-ups

The use of covariates in block designs is necessary when the covariates cannot be controlled like the blocking factor in the experiment. In this paper, we consider the situation where there is some flexibility for selection in the values of the covariates. The choice of values of the covariates for a given block design attaining minimum variance for estimation of each of the parameters has attracted attention in recent times. Optimum covariate designs in simple set-ups such as completely randomised design (CRD), randomised block design (RBD) and some series of balanced incomplete block design (BIBD) have already been considered. In this paper, optimum covariate designs have been considered for the more complex set-ups of different partially balanced incomplete block (PBIB) designs, which are popular among practitioners. The optimum covariate designs depend much on the methods of construction of the basic PBIB designs. Different combinatorial arrangements and tools such as orthogonal arrays, Hadamard matrices and different kinds of products of matrices viz. Khatri–Rao product, Kronecker product have been conveniently used to construct optimum covariate designs with as many covariates as possible.     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.     

100 key research questions for the post‐2015 development agenda

The Sustainable Development Goals (SDGs) herald a new phase for international development. This article presents the results of a consultative exercise to collaboratively identify 100 research questions of critical importance for the post‐2015 international development agenda. The final shortlist is grouped into nine thematic areas and was selected by 21 representatives of international and non‐governmental organisations and consultancies, and 14 academics with diverse disciplinary expertise from an initial pool of 704 questions submitted by 110 organisations based in 34 countries. The shortlist includes questions addressing long‐standing problems, new challenges and broader issues related to development policies, practices and institutions. Collectively, these questions are relevant for future development‐related research priorities of governmental and non‐governmental organisations worldwide and could act as focal points for transdisciplinary research collaborations.     

Tests of symmetry with one-sided alternatives in three-way contingency tables

For a two-dimensional contingency table of probabilities, the concept of symmetry around the main diagonal is well defined. Statistical hypothesis test of symmetry versus positive bias have also been explored. For tables of higher (three or more) dimensions, however, different concepts of symmetry are available. In this study, we consider statistical inference procedures of symmetry in partial tables versus various biases in three-dimensional tables. We find the maximum likelihood estimates of the cell probabilities and the asymptotic distribution of the likelihood ratio test statistic in each case. Simulation studies are used to investigate the sizes and powers of the tests. The methodologies developed are applied on real data sets.     

Pointwise and uniform convergence of multivariate kernel density estimators using random bandwidths

We obtain the rates of pointwise and uniform convergence of multivariate kernel density estimators using a random bandwidth vector obtained by some data-based algorithm. We are able to obtain faster rate for pointwise convergence. The uniform convergence rate is obtained under some moment condition on the marginal distribution. The rates are obtained under i.i.d. and strongly mixing type dependence assumptions.     

Testing for ordered failure rates under general progressive censoring

For exponentially distributed failure times under general progressive censoring schemes, testing procedures for ordered failure rates are proposed using the likelihood ratio principle. Constrained maximum likelihood estimators of the failure rates are found. The asymptotic distributions of the test statistics are shown to be mixtures of chi-square distributions. When testing the equality of the failure rates, a simulation study shows that the proposed test with restricted alternative has improved power over the usual chi-square statistic with an unrestricted alternative. The proposed methods are illustrated using data of survival times of patients with squamous carcinoma of the oropharynx.