bluuebunny/crossref_metadata_2025_split huggingface dataset:
Network and Adaptive Sampling with Unequal Probabilities
Authors: Chaudhuri Arijit 
Published: August 2016

We consider two sample survey situations. In one, we have a list of ‘selection units’ (su's) out of which one may suitably draw a sample. But the interest is to estimate the total value of a variable defined on an unknown number of individuals called ‘observational units’ (ou's) which are not directly identifiable but may be contacted through the above su's to “one or more” of which each ‘ou’ is ‘linked’ in a ‘well-defined’ manner. Each collection of ou's so linked is called a ‘network’. Each network is ‘disjoint’ from every other and together they exhaust all the ou's of interest. Through a sample of su's one may observe the variate-values for the ou's in the networks linked to the sampled su's. This approach of reaching samples of ou's through such networks is called ‘network’ sampling. Thompson (1990, 1992) and Thompson and Seber (1996) have given theories of estimation of total or mean and variance estimation when the su's are selected by simple random sampling (SRS) without replacement (WOR) or by stratified SRSWOR methods. Here we extend to general sampling schemes with unequal probabilities. In the second kind of surveys an initial sample is chosen from a list of identifiable units for many of which the value of a variable is ‘zero’ and only for a few ‘unknown and unidentified’ units the values are positive. The purpose is to estimate the population total with a high coverage of the positive-valued units in the sample. A concept of a ‘neighbourhood’ is then well defined such that given a unit with a positive value some of those in its neighbourhood may also hopefully be positive-valued. So, from an initial sample one may go on ‘adaptively’ extending it by adding successively to each positive-valued unit in the sample each unit in its neighbourhood and stopping only on encountering a ‘neighbouring’ unit that is zero-valued. The relevant theory is developed in the above locations cited. But this covers only SRS with and without replacement (WR and WOR) for the initial sample. We extend here to more general sampling schemes. AMS (2000) Subject Classification: 62 D05.
Bayesian Predictive Distribution with Right Invariant Priors
Authors: Komaki Fumiyasu 
Published: August 2016

There exist many examples where Bayesian predictive distributions are more appropriate than plug-in distributions. When we use Bayesian procedure, the choice of prior distributions is a serious problem. Non-informative prior distributions or vague prior distributions are often adopted to construct Bayesian predictive distributions. There exist many studies on the relation between priors and Bayes estimators. Here, we investigate the corresponding relation between priors and Bayesian predictive distributions. We adopt Kullback-Leibler divergence from the true distribution to a predictive distribution as a loss function . We consider invariant predictive distributions. When a model has a group structure, the right invariant measure is often recommended as a noninformative prior. It is shown that Bayesian predictive distributions based on right invariant measures are the best invariant predictive distributions. We touch on shrinkage method of constructing predictive distributions.
Symmetry and Bayesian Function Estimation1
Authors: Angers Jean-Francois , Kim Peter T. 
Published: August 2016

Summary This paper develops Bayesian function estimation on compact Riemannian manifolds. The approach is to combine Bayesian methods along with aspects of spectral geometry associated with the Laplace-Beltrami operator on Riemannian manifolds. Although frequentist nonparametric function estimation in Euclidean space abound, to date, no attempt has been made with respect to Bayesian function estimation on a general Riemannian manifold. The Bayesian approach to function estimation is very natural for manifolds because one can elicit very specific prior information on the possible symmetries in question . One can then establish Bayes estimators that possess built in symmetries. A detailed analysis for the 2–sphere is provided.
Estimation of Residual Effects in Repeated Measurements Designs
Authors: Bose Mausumi , Shah K.R. 
Published: August 2016

Summary Under the standard model and using an approximate design theory approach, we give necessary and sufficient conditions for a repeated measurements design to be universally optimal for residual effects. In exact design theory, it is shown that symmetric optimal designs can be derived from a single linear equation. Examples of optimal designs, which can be obtained from these conditions, are given. Many of the existing optimal designs, together with new ones, can be obtained by this method. Bounds are given for computing the efficiencies of designs for estimating residual effects.
A Perturbation Technique for Sample Moment Matching in Kernel Density Estimation
Authors: Maity Arnab , Sengupta Debapriya 
Published: August 2016

Summary The fundamental idea of kernel smoothing technique can be recognized as one-parameter data perturbation with a smooth density. The usual kernel density estimates might not match arbitrary sample moments calculated from the unsmoothed data. A technique based on two-parameter data perturbation is developed for sample moment matching in kernel density estimation. It is shown that the moments calculated from the resulting tuned kernel density estimate can be made arbitrarily close to the raw sample moments. Moreover, the pointwise rate of MISE of the resulting density estimates remains optimal. Relevant simulation studies are carried out to demonstrate the usefulness and other features of this technique.
Latent Class Analysis of 1997 NSF Survey Data on Science PHDs
Authors: Larsen Michael D. 
Published: August 2016

Summary The field of Information Technology (IT) has provided extraordinary job growth in the United States over the last two decades; however, women and some minority groups are severely underrepresented in IT occupations, especially in management positions. These groups also on average receive lower salaries than their counterparts. The U.S. National Science Foundation's SESTAT database is created from biennial nationally representative surveys of U.S. scientists and engineers. SESTAT provides detailed information, such as employment history, educational background, and demographic characteristics. These data are analyzed here using latent class analysis, which is an exploratory technique that can be used to cluster cases based on categorical variables. The data are from the 1997 Survey of Doctoral Recipients. The subset of respondents received Ph.D.'s between 1990 and 1996 in either than physical or biological sciences or in engineering and work at higher educational institutions. There are a few significant differences between men and women in desired work activities, job search resources, and adequacy of doctoral training. There are many large, significant differences in limitations when searching for a job, work activities, and family and career status. Latent class analysis helped identify important subgroups of females and males based on clustering simultaneously on several categorical variables.
Stimulant-Induced Spatial Temporal Patterns in Pet Data : Illustrations Using SPM, Manacova-CVA and PLS
Authors: Xu Lei , Mazumdar Sati , Greer Philip J. , Price Julie 
Published: August 2016

Summary Functional brain images arc extraordinarily rich data sets that reflect brain function such as cerebral blood flow. These images are widely applied to study brain activity after drug administration or in response to perceptual and cognitive stimuli. Positron Emission Tomography (PET) constitutes one important modality that provides functional brain images. Spatial and temporal patterns in brain activities captured from these images have helped to better understand brain dysfunction in disease and aid in diagnosis and treatment of diseases including ncurogenerative disorders. As PET provides maps consisting of average levels of activity for cuboidal voxels of tissue, it is a substantial analytic challenge to integrate the temporal, spatial and statistical signals making up these data. This paper presents a comparative analysis of three voxel-based methods for characterization of spatial-temporal patterns provided by PET images of cerebral blood flow . The methods are: (i) statistical parametric mapping (SPM) providing a voxel-wise brain map of a selected statistic; (ii) multivariate analysis of covariance jointly with canonical variate analysis (MANCOVA-CVA) identifying regions of the brain that are most responsible for global statistical significance; and (iii) partial least squares (PLS) that uses a path modeling technique with latent variables for dimension reduction. The methods are illustrated using data from sequential [Formula: see text] PET studies in baboons to examine whether amphetamine affects relative cerebral blood flow (rCBF) differentially over time and over different regions of brain. The results are found to be consistent across the three methods.
Classification of Distributions Based on Reversed Hazard Rate
Authors: Chandra Nimai Kumar , Roy Dilip 
Published: August 2016

Summary Growing importance of reversed hazard rate (RHR) and inactivity time, as measures of the life distributions, can be noticed from the recent literature on survival and reliability analysis. Keeping in mind tilis special role of RHR and the mean inactivity time, we have made an attempt to define different classes of distributions and study their implicative relationships. A probability bound has also been proposed for the increasing RHR class of distributions.
A Global Test for the Goodness of Fit of Generalized Linear Models : An Estimating Equation Approach
Authors: Rao R. Prabhakar , Sutradhar B.C. 
Published: August 2016

Summary Generalized linear models are used to analyze a wide variety of discrete and continuous data with possible overdispersion under the assumption that the data follow an exponential family of distributions. The violation of this assumption may have adverse effects on the statistical inferences. The existing goodness of fit tests for checking this assumption are valid only for a standard exponential family of distributions with no overdispersion. In this paper, we develop a global goodness of fit test for the general exponential family of distributions which may or may not contain overdispersion. The proposed statistic has asymptotically standard Gaussian distribution which should be easy to implement.
Improving the Estimation of Eigenvectors Under Quadratic Loss
Authors: Ahmed S. E. , An L. , Nkurunziza S. 
Published: August 2016

Improved estimation of eigenvectors of a covariance matrix is considered under uncertain prior information (UP!) regarding the parameter vector. Like statistical models underlying the statistical inferences to be made, the prior information will be susceptible to uncertainty and the practitioners may be reluctant to impose the additional information regarding parameters in the estimation process. A very large gain in precision may be achieved by judiciously exploiting the information about the parameters which in practice will be available in any realistic problem. Several estimators based on preliminary test and the Stein-type shrinkage rules are constructed. The expressions for the bias and risk of the proposed estimators are derived and compared with the usual estimators. We demonstrate how the classical large sample theory of the conventional estimator can be extended to shrinkage and preliminary test estimators for the eigenvector of a covariance matrix. It is established that shrinkage estimators are asymptotically superior to the usual sample estimators. For illustration purposes, the method is applied to three data sets.
Study on R­Norm Residual Entropy
Authors: Nanda Asok K. , Das Samhita 
Published: August 2016

In this paper a stochastic order and an aging property have been defined based on R-norm residual entropy, and their properties have been studied. A few useful parametric distributions have been characterized with the help of this entropy. A couple of results have been developed for discrete random variables. AMS {2000} Subject Classification: 94AXX, 94A17.
A Natural Goodness­of­Fit Test for the Exponential Growth Curve Model Based on the Relative Growth Rate
Authors: Basu Ayanendranath , Bhattacharjee Amit 
Published: August 2016

This paper develops a natural goodness-of-fit test for the exponential growth curve model (EGCM) based on the property of finite differences. The EGCM has wide applications in various scientific fields such as biology, ecology, demography, population dynamics, finance, econometrics etc. Hill (1968, Biometrics, 4, 192-196) proposed a test for the polynomial growth curve model (PGCM) based on finite differences of the size variable. However, the PGCM has limited practical use and does not have any biologically interpretable para.ffieters. In comparison, the exponential model is more realistic and biologically meaningful; a goodness-of-fit test for the EGCM has substantial practical value. Bhattacharya et. al. (2004) generalized Hill's approach to the case of the EGCM based on appropriate finite differences of functions of the size variable. In this paper we present an alternative test for the EGCM by directly modeling the relative growth rate (rather than the size variable itself) and hence avoid the assumptions necessary in Bhattacharya et al (2004). The performance of the theory developed is illustrated with several sets of real data and through simulation. AMS {2000} Subject Classification: 62F03, 62P10.
Study on Diffusion of Rumours Through Epidemic Models Under Non Identical Set Up
Authors: Chattopadhyay Asis Kumar , Gupta Arindam 
Published: August 2016

The spread of rumour can be modelled easily by using epidemic models because of the similarity in underlying processes. The present paper attempts to study diffusion of rumours through a simple epidemic model, taking into account two possible sources of rumour viz primary and secondary. Since the behavior of different persons involved under the process will be different and also the rate of spread will be time depe~dent the underlying variables will not be independent and identical. The process has been studied through simulation and the process average has been compared with the average obtained from an established theoretical result. The objective of the study is to predict the number of hearers at a particular time point by using the proposed model. AMS {2000} Subject Classification: 62P25.
On Measurement of Export Instability: An Innovative Index
Authors: Chakrabarty Ranajit , Nath Swaraj Kumar 
Published: August 2016

Instability in export performances may be caused by a variety of market forces both short-term and long-term. Fluctuations may be caused by upward or downward turn of export performances or may be due to their combined effect. When such fluctuations upset the export target, it may become a serious concern of the export-planners. But, unfortunately, the present methodologies fail to segregate the downward or negative pulling effect of export performance. The present paper aims at developing a quantitative tool for measuring exportinstability based on upward and downward turn of export performance and which is capable of quantifying instability on a year-to-year basis. AMS {2000) Subject Classification : 91B82
Estimation for Stochastic Differential Equations Driven by Mixed Fractional Brownian Motions
Authors: Rao B. L. S. Prakasa 
Published: August 2016

A linear stochastic differential equation driven by a mixture of two independent fractional Brownian motions possibly with different Hurst indices is considered and an estimator for the trend parameter is obtained based on the observation of the process up to time T. Distributional properties of this estimator are investigated. AMS (2000) Subject Classification : 62M09.
On Generalized Stochastic Orders of Dispersion-Type
Authors: Nanda Asok K. , Kundu Amarjit 
Published: August 2016

In the study of probabilistic orders iterated integrals of survival functions have been used by many authors. Recently, dispersion-typestochastic orders have been defined by Belzunce, Hu and Khaledi (2003) [ Probability in the Engineering and Informational Sciences, Vol. 17, pp. 305–334]. The purpose of this paper is to generalize the dispersion-type stochastic orders using generalized iterated survival functions. The relationships between generalized stochastic orders and the stochastic orders of dispersion-type have been studied. It is shown that under certain condition, generalized down stochastic orders imply generalized stochastic orders of dispersion-type which, in turn, imply generalized stochastic orders. Generalized stochastic orders of dispersion-type of residual random variables are discussed. Some characterizations of generalized stochastic orders of dispersion-type have also been studied. Some results regarding generalized orders are also discussed when the residual time is random. AMS (2000) Subject Classification : 60E15, 60K10.
A New Skew­Normal Distribution and Its Properties
Authors: Sahu Sujit K. , Chai High S. 
Published: August 2016

This article generalizes a multivariate skew-normal distribution and describes its many interesting properties. The univariate version of the new distribution is compared with two other currently used distributions. The use of the new distribution is illustrated with a real data example suitable for regression modeling. The new model provides a better model than its two rivals as evaluated by some suitable Bayesian model selection criteria. AMS (2000) Subject Classification : 62F15, 62J05.
Rao­Hartley­Cochran Sampling with Competitive Estimators
Authors: Bose Arijit Chaudhuri Mausumi , Dihidar Kajal 
Published: August 2016

We study the possibility of using the Horvitz-Thompson (HT) estimator from a sample collected by the Rao-Hartley-Cochran (RHC) scheme. We derive expressions for several alternative unbiased variance estimators of the estimators given by RHC and HT for estimating a population total. For a real life problem, on finding well correlated auxiliary variate values, we consider selecting the first stage units by the RHC scheme and the second stage units by equal probabilities without replacement. Using real data, we compare the relative accuracies in estimation by dint of simulation, using the classical and the new variance estimators. Our criteria for comparison are the actual coverage percentage (ACP) and the average coefficient of variation (ACV). We demonstrate that as against the RHC estimator, the HT estimator is a viable contender in estimation from an RHC sample. AMS (2000) Subject Classification : 62D05.
Extremes, Extreme Spacings and Tail Lengths : An Investigation for Some Important Distributions
Authors: Mudholkar Govind S. , Chaubey Yogendra P. , Tian Lili 
Published: August 2016

Extreme value and extreme spacing distributions are elegant and important artifacts of statistical theory and practice. However, in statistical education, due to the highly technical nature of the subject, they are generally treated as special topics. But, as demonstrated by Freimer et al. (1989), the asymptotic distributions of the extremes and extreme spacings of random samples, and the related theory can be derived and developed by applying elementary methods to the population quantile functions when they are available in closed forms. However, their work excluded the pedagogically important Gaussian and gamma populations. In this paper, using the closed form expression for the quantile function of the Pareto family first, we show how this approach works. We then proceed to demonstrate its use by simple Taylor expansion for the normal and gamma populations, cases where the closed form expressions for the quantile functions are unavailable. In the process, we relate the geometric notion of tail length to the extreme value distribution. We also examine the case of inverse Gaussian (IG) family, which is well known to be strikingly and intriguingly analogous to the Gaussian family. Actually, in the present context we consider not only the IG but the related reciprocal IG (RIG) and the root reciprocal IG (RRIG) families. The extreme value theory for these three families is derived, again using the elementary methods, even though their quantile functions also lack closed form expressions. Interestingly, it is seen that the extreme value theory for the RRIG population, and not of the IG population, is somewhat analogous to the Gaussian distribution. AMS (2000) Subject Classification : 62E20.
Improving the Hansen­Hurwitz Estimator in PPSWR Sampling
Authors: Adhikary Arun Kumar 
Published: August 2016

An attempt has been made to improve upon the Hansen-Hurwitz (1943) estimator based on PPSWR sampling scheme through Rao-Blackwellisation. In order to derive the sampling variance of the improved estimator obtained by Rao-Blackwellisation, it is of interest to derive the probability distribution of the number of distinct units in the sample drawn according to PPSWR sampling scheme, as the improved estimator is based solely on the distinct units in the sample. It has been possible to write down the exact distribution of the number of distinct units in the sample drawn by PPSWR sampling scheme in a closed form. Assuming a super-population model, the model-expected design-variance of the improved estimator is worked out and it is compared with that of the Hansen-Hurwitz estimator under the same super-population model. The percentage gain in efficiency in using the improved estimator over the Hansen-Hurwitz estimator is worked out for certain selected values of the model parameters. AMS (2000) Subject Classification : 62D05.
Classes of Statistics for Testing Exponentiality Versus IFR or NBUE Alternatives
Authors: Mugdadi A.R. , Ahmad Ibrahim A. 
Published: August 2016

Abtsrcat In this investigation, classes of test statistics are given for testing exponentiality against either the class of increasing failure rate (IFR) or the class of new is better than used in expectation (NBUE) life distributions. Our development complete the set of classes of test statistics for all perennial notions of aging. The major advantage of having a class of test statistics for any testing situation is that we can choose the member of that class that enjoys the highest power or efficiency for an alternative at hand and work with it, cf Klar (2000).
Exact Moments of Lower Generalized Order Statistics from Power Function Distribution
Authors: Athar Haseeb , Khan R.U. , Anwar Zaki 
Published: August 2016

Abtsrcat Order statistics, record values and several other model of ordered random variables can be viewed as special case of generalized order statistics ( gos) [Kamps, 1995]. Pawlas and Szynal (2001) introduced the con-cept of lower generalized order statistics ( lgos) to enable a common approach to descending ordered rv's like reversed order statistics and lower record values. The work of Burkschat et al. (2003) may also be seen for dual (lower) generalized order statistics. In this paper simple expressions for single and product moments of lower generalized order statistics from power function distribution have been obtained. Further, some important deductions and computational works are carried out.
A Parametric Quantile Regression Model Useful in Reliability Analysis
Authors: Sankaran P.G. , V. Preetha Kumari P. 
Published: August 2016

Abtsrcat In this paper, we introduce a parametric regression model using Hankin and Lee distribution. The properties of the models are studied. We estimate the parameters of the model using maximum likelihood method. Asymptotic properties of the estimators are discussed. Finally, we apply the proposed model to a real life data.