Estimating the parameters of the linear compartment model

In this paper we consider the linear compartment model and consider the estimation procedures of the different parameters. We discuss a method to obtain the initial estimators, which can be used for any iterative procedures to obtain the least-squares estimators. Four different types of confidence intervals have been discussed and they have been compared by computer simulations. We propose different methods to estimate the number of components of the linear compartment model. One data set has been used to see how the different methods work in practice.     

Multidimensional Poverty: Measurement,Estimation, and Inference

Calibration in macroeconomics involves choosing fre parameters by matching certain moments of simulted models with those of data. We formally examine this method by treating the process of calibration as an econometric estimator. A numerical version of the Mehra-Prescott (1985) economy is the setting for an evaluation of calibration estimators via Monte Carlo methods. While these estimators sometimes have reasonable finite-sample properties they are not robust to mistakes in setting non-free parameters. In contrast, generalized method-of-moments (GMM) estimators have satisfactory finite-sample characteristics, quick convergence, and informational requirements less stringent than those of calibration estimators. In dynamic equilibrium models in which GMM is infeasible we offer some suggestions for improving estimates based on calibration methodology.     

Semi-Sequential One-Shot Monitoring of Small Disorders With Controlled Type I Error Rate

We propose a simple two-stage monitoring rule for detecting small disorders in a two-sample location problem. The proposed rule is based on ranks and hence is nonparametric in nature. In the first stage, we use a sequential monitoring scheme to decide the necessity of employing a location test at some point of time. If there is urgency, we simply use a two-sample Wilcoxon rank sum test in the second stage. This leads to a semi sequential one-shot monitoring procedure. We study some asymptotic performance of the proposed rule. We also present some numerical findings obtained through Monte Carlo studies. The proposed rule meets the challenge of controlling type I error rate in sequential monitoring of an incoming series of observations.     

Effect of adjustment for sex composition in the measurement of fertility on intrinsic rates

The inconsistencies inherent in the one-sex models created a need for the construction of what are known as marriage functions, especially for the measurement of fertility. But attempts to develop marriage functions have been frustrated by the inability of the proposed functions to meet certain consistency conditions and also by difficulties in empiracally determining function parameters. Among several functions proposed so far, Das Gupta's (1972) "effective population" deserves special mention. He uses both sexes in its formulation, and has shown that the constancy of fertility rates based on such a function together with the constancy of survivorship probabilities of both sexes would result in a stable population. It is suggested in this article that the major source of the problem in a two-sex model seems to be the requirement that the model has to be specific both for sex and age. The ideal of incorporating the relative composition of one sex in the age-specific rates of the other, thereby creating a function dependent on both sexes, is advanced in this article. Such functions, defined explicitly for births, can be easily translated into age-specific birth rates. In addition to simplicity in the definition and form, the conditions leading toward stability can also be established. Interestingly enough, the intrinsic rate for this two-sex model lies in the interval determined by the rates obtained from the two one-sex models.     

Managing ‘American Islam’

Focusing on the case of Islam in post-9/11 United States, this article highlights the particularity of how US secularism is enacted by the state. In much sociological theory, the United States has been understood to be a neutral and non-interfering state with regard to religion, thereby fostering a pluralist religious context of free consumer choice. Some Muslim reformists have argued that this context makes the US highly fertile ground upon which to reform Islam and to improve women's status in Islam. This article argues that, in the context of the US-led ‘war on terror’, the government has drawn on and amplified this discourse in the service of producing a representation of the US as tolerant, while also seeking to promote a concept of ‘true’ Islam and produce patriotic Muslim citizens. At the center of this discourse are contested portrayals of Muslim women as symbolic of a modern and liberated Islam that is uniquely ‘American’ and opposed to other presumably oppressive Islams. While this context may in fact promote the reform of Islam, it does so with state involvement rather than as a result of state neutrality.     

Assessment of hospital services and patient reactions

This paper deals with the study of assessment of hospital services and patient reactions carried out amongst 473 respondents, both in-door (3 wards) and out-door patients in M.L.B. Medical College Hospital, Jhansi (Uttar Pradesh). The patients were from three wards medical, surgical and gynaecological wards and consisted of 373 discharged and 100 out-door patients.     

Bayesian hierarchical model for protein identifications

In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, mass spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a ‘two-step’ procedure of identifying the peptide first followed by the separate protein identification procedure next. In this setup, the interdependence of peptides and proteins is neglected resulting in relatively inaccurate protein identification. In this article, we propose a Markov chain Monte Carlo based Bayesian hierarchical model, a first of its kind in protein identification, which integrates the two steps and performs joint analysis of proteins and peptides using posterior probabilities. We remove the assumption of independence of proteins by using clustering group priors to the proteins based on the assumption that proteins sharing the same biological pathway are likely to be present or absent together and are correlated. The complete conditionals of the proposed joint model being tractable, we propose and implement a Gibbs sampling scheme for full posterior inference that provides the estimation and statistical uncertainties of all relevant parameters. The model has better operational characteristics compared to two existing ‘one-step’ procedures on a range of simulation settings as well as on two well-studied datasets.     

Combining monotonicity and strong equity: construction and representation of orders on infinite utility streams

This paper studies the nature of social welfare orders (SWO) on infinite utility streams, satisfying the efficiency principle known as monotonicity and the consequentialist equity principle known as strong equity. It provides a complete characterization of domain sets for which there exists such a SWO which is in addition representable by a real valued function. It then shows that for those domain sets for which there is no such SWO which is representable, the existence of such a SWO necessarily entails the existence of a non-Ramsey set, a non-constructive object.     

Some partially sequential nonparametric tests for detecting linear trend

In the present study, we develop two nonparametric partially sequential tests for detecting possible presence of linear trend among the incoming series of observations. We assume that a sample of fixed size is available a priori from some unknown univariate continuous population and there is no sign of trend among these historical observations. Our proposed tests can be viewed as the sequential type tests for monitoring structural changes. We use partial sequential sampling schemes based on usual ranks as well as on sequential ranks. We provide detailed discussion on asymptotic studies related to the proposed tests. We compare the two tests under various situations. We also present some numerical results based on simulation studies. Proposed tests are extremely important in profit making in volatile market through Margin Trading. We illustrate the mechanism with a detailed analysis of a stock price data.