Estimating the distribution function using k-tuple ranked set samples

The basic assumption underlying the concept of ranked set sampling is that actual measurement of units is expensive, whereas ranking is cheap. This may not be true in reality in certain cases where ranking may be moderately expensive. In such situations, based on total cost considerations, k-tuple ranked set sampling is known to be a viable alternative, where one selects k units (instead of one) from each ranked set. In this article, we consider estimation of the distribution function based on k-tuple ranked set samples when the cost of selecting and ranking units is not ignorable. We investigate estimation both in the balanced and unbalanced data case. Properties of the estimation procedure in the presence of ranking error are also investigated. Results of simulation studies as well as an application to a real data set are presented to illustrate some of the theoretical findings.     

Bargaining with set-valued disagreement

It is arguable that in many-two person bargaining situations disagreement leads to a set of possible payoffs with no probabilities attached to the elements of the set. Axioms are developed for bargaining games of this kind and solution concepts are derived from these axioms. Particular attention is paid to what are here called the max-max and rectangular general solutions. The latter can be applied to an important sub-class of bargaining games where the disagreement set is equal to the feasible set.A part of this work was done while I was a visitor at the University of Stockholm. For valuable discussions and comments I am grateful to T.C.A. Anant, Salvador Barbera, Bhaskar Dutta, Efe Ok, Jorgen Weibull and an anonymous referee of this journal. The paper also benefited from a presentation at the Indian Statistical Institute, New Delhi.     

Analysis of microtubule dynamics using growth curve models

Microtubules are part of the structural network within a cell's cytoplasm, providing structural support as well as taking part in many of the cellular processes. A large body of data provide evidence that dynamics of microtubules in a cell is reponsible for the performance of many critical cellular functions such as cell division. In this article, we study the effect of four different isoforms of a protein tau on microtubule dynamics using growth curve models. The results show that a linear growth curve model is sufficient to explain the data. Moreover, we find that a mutated version of a 3-repeat tau protein has a similar effect as a 4-repeat tau protein on microtubule dynamics. The latter findings conform with the biological understanding of the effect of the protein tau on microtubule dynamics.     

Bayesian inference for the parameters of Weibull distribution under progressive Type-I interval censored data with beta-binomial removals

This article considers the problem of estimating the parameters of Weibull distribution under progressive Type-I interval censoring scheme with beta-binomial removals. Classical as well as the Bayesian procedures for the estimation of unknown model parameters have been developed. The Bayes estimators are obtained under SELF and GELF using MCMC technique. The performance of the estimators, has been discussed in terms of their MSEs. Further, expression for the expected number of total failures has been obtained. A real dataset of the survival times for patients with plasma cell myeloma is used to illustrate the suitability of the proposed methodology.     

A Semiparametric Bayesian Model for Circular-Linear Regression

Circular data are observations that are represented as points on a unit circle. Times of day and directions of wind are two such examples. In this work, we present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is useful especially when the likelihood surface is ill behaved. Markov chain Monte Carlo techniques are used to fit the proposed model and to generate predictions. The method is illustrated using an environmental data set.     

The rise of Trump and an agenda for regulatory reform

The arrival of Trump in the White House does not augur well for the long-run prospects of the US economy. The protectionism that is gathering steam under his presidency may yield some short-term gains, but at the detriment of the long-run. Examining the discontent among ordinary voters that brought Trump to power, this paper argues that this should be treated as an opportunity for a major regulatory reform. With the advance of digital technology, we have increasing returns to scale, and that, in turn, is making our antitrust laws backfire and hurt the well-being of ordinary people, thereby sowing discontent. The paper builds a simple model to illustrate this and makes the case for revoking the older antitrust laws and replacing them with some simple profit-sharing rules at the level of the firm, and the economy.     

Experiments with the Traveler��s Dilemma: welfare, strategic choice and implicit collusion

This article investigates behavior in the Traveler??s Dilemma game and isolates deviations from textbook predictions caused by differences in welfare perceptions and strategic miscalculations. It presents the results of an experimental analysis based on a 2?×?2 design where the own and the other subject??s bonus?Cpenalty parameters are changed independently. We find that the change in own bonus?Cpenalty alone entirely explains the effect on claims of a simultaneous change in one??s own and the other??s bonus?Cpenalty. An increase in the other subject??s bonus?Cpenalty has a significant negative effect on claims when the own bonus?Cpenalty is low, whereas it does not have a significant effect when the own bonus?Cpenalty is high. We also find that expected claims are inconsistent with actual claims in the asymmetric treatments. Focusing on reported strategies, we document substantial heterogeneity and show that changes in choices across treatments are largely explained by risk aversion.     

Diversity in Canada: Predictors of the perceptions of social interactions

ABSTRACT

Language is an important characteristic of culture. Importantly, with the ever-present migration of persons across countries and cultures, physical boundaries of language have been blurred creating a robust area of study: language brokering in the context of globalization. Migration and immigration often necessitate the learning of a new language and in immigrant families, open an opportunity for language skill differences and language brokering. Language brokering is a process by which a bilingual individual, often the children of immigrants, assists in communication between two parties speaking distinct languages. This common practice has generated an increased focus and body of research on the current and aftereffects of language brokering on individuals who broker. The present article provides a brief review of the literature on language brokering including identified antecedents and select outcomes such as family, academic, and psychological outcomes. Overall, the literature shows the associations among language brokering, the contexts in which it may occur, and the outcomes (family, academic, and psychological) for language brokers remain less than clearly understood, although inroads are being made as researchers delve more deeply into specific areas such as parentification, age, child development, and family dynamics.     

Semiparametric Bayesian Techniques for Problems in Circular Data

In this paper, we consider the problems of prediction and tests of hypotheses for directional data in a semiparametric Bayesian set-up. Observations are assumed to be independently drawn from the von Mises distribution and uncertainty in the location parameter is modelled by a Dirichlet process. For the prediction problem, we present a method to obtain the predictive density of a future observation, and, for the testing problem, we present a method of computing the Bayes factor by obtaining the posterior probabilities of the hypotheses under consideration. The semiparametric model is seen to be flexible and robust against prior misspecifications. While analytical expressions are intractable, the methods are easily implemented using the Gibbs sampler. We illustrate the methods with data from two real-life examples.     

A new and easy-to-use measure of literacy,its axiomatic properties and an application

It can be argued that just as there are different kinds of literacy, there are different kinds of illiteracy. A “proximate illiterate,” i.e. an illiterate who has easy access to a literate person, is clearly better off than someone without such access. The existing literature that takes account of these differences (1) defines an illiterate person to be a proximate illiterate if he or she lives in a household with at least one literate person and (2) derives new measures of literacy which typically exceed the standard literacy rate. The latter risks generating policy complacency. The aim of this paper is to suggest a measure of literacy that is not limited by (1) and (2). The measure is axiomatically characterized and its use is illustrated with a numerical exercise for the provinces of South Africa. We wish to thank James Foster and Tapan Mitra for discussions and comments. Any remaining errors are our own.