Statistics

We study a multivariate ultrastructural measurement error (MUME) model with more than one response variable. This model is a synthesis of multivariate functional and structural models. Three consistent estimators of regression coefficients, satisfying the exact linear restrictions have been proposed. Their asymptotic distributions are derived under the assumption of a non-normal measurement error and random error components. A simulation study is carried out to investigate the small sample properties of the estimators. The effect of departure from normality of the measurement errors on the estimators is assessed.

A replicated ultrastructural measurement error regression model is considered where both predictor and response variables are observed with error. Availability of some prior information regarding regression coefficients in the form of stochastic linear restrictions is assumed. Using this prior information, three classes of consistent estimators of regression coefficients are proposed. A two-stage procedure is discussed to obtain feasible version of these Stochastically Restricted estimators. The asymptotic properties of the proposed estimators are studied. No distributional assumption is imposed on any random component of the model. Monte Carlo simulations study is performed to assess the effect of sample size, replicates and non-normality on the estimators. The methods are illustrated using real economic data.

Consider the problem of comparing variances of k populations with the variance of a control population. When the experimenter has a prior expectation that the variances of k populations are not less than the variance of a control population, one-sided simultaneous confidence intervals provide more inferential sensitivity than two-sided simultaneous confidence intervals. But the two-sided simultaneous confidence intervals have advantages over the one-sided simultaneous confidence intervals as they provide both lower and upper bounds for the parameters of interest. In this article, a new multiple comparison procedure is developed for comparing several variances with a control variance, which provides two-sided simultaneous confidence intervals for the ratios of variances with the control variance and maintains the inferential sensitivity of one-sided simultaneous confidence intervals. Computation of the critical constants for the implementation of the proposed procedure is discussed and the selected critical constants are tabulated.

In the present article, a new nonparametric estimator of quantile density function is defined and its asymptotic properties are studied. The comparison of the proposed estimator has been made with estimators given by Jones (1992), graphically and in terms of mean square errors for the uncensored and censored case.

Background: An m-health application has been developed and implemented with community health workers to improve their counseling in a rural area of India. The ultimate aim was to generate demand and improve utilization of key maternal, neonatal, and child health services. The present study aims to assess the impact and cost-effectiveness of this project. Methods/design: A preÁpost quasi-experimental design with a control group will be used to undertake difference in differences analysis for assessing the impact of intervention. The Annual Health Survey (2011) will provide pre-intervention data, and a household survey will be carried out to provide post-intervention data. Two community development blocks where the intervention was introduced will be treated as intervention blocks while two controls blocks are selected after matching with intervention blocks on three indicators: average number of antenatal care checkups, percentage of women receiving three or more antenatal checkups, and percentage of institutional deliveries. Two categories of beneficiaries will be interviewed in both areas: women with a child between 29 days and 6 months and women with a child between 12 and 23 months. Propensity score matched samples from intervention and control areas in preÁpost periods will be analyzed using the difference in differences method to estimate the impact of intervention in utilization of key services. Bottom-up costing methods will be used to assess the cost of implementing intervention. A decision model will estimate long-term effects of improved health services utilization on mortality, morbidity, and disability. Cost-effectiveness will be assessed in terms of incremental cost per disability-adjusted life year averted and cost per unit increase in composite service coverage in intervention versus control groups. Conclusions: The study will generate significant evidence on impact of the m-health intervention for maternal, neonatal, and child services and on the cost of scaling up m-health technology for accredited social health activists in India.

An m-health application has been developed and implemented with community health workers to improve their counseling in a rural area of India. The ultimate aim was to generate demand and improve utilization of key maternal, neonatal, and child health services. The present study aims to assess the impact and cost-effectiveness of this project. A pre-post quasi-experimental design with a control group will be used to undertake difference in differences analysis for assessing the impact of intervention. The Annual Health Survey (2011) will provide pre-intervention data, and a household survey will be carried out to provide post-intervention data.Two community development blocks where the intervention was introduced will be treated as intervention blocks while two controls blocks are selected after matching with intervention blocks on three indicators: average number of antenatal care checkups, percentage of women receiving three or more antenatal checkups, and percentage of institution...

To raise the quality of counselling by community health volunteers resulting in improved uptake of maternal, neonatal and child health services (MNCH), an m-health application was introduced under a project named 'Reducing Maternal and Newborn Deaths (ReMiND)' in district Kaushambi in India. We report the impact of this project on coverage of key MNCH services. A pre- and post-quasi-experimental design was undertaken to assess the impact of intervention. This project was introduced in two community development blocks in Kaushambi district in 2012. Two other blocks from the same district were selected as controls after matching for coverage of two indicators at baseline - antenatal care and institutional deliveries. The Annual Health Survey conducted by the Ministry of Health and Family Welfare in 2011 served as pre-intervention data, whereas a household survey in four blocks of Kaushambi district in 2015 provided post-intervention coverage of key services. Propensity score m...

A variety of mobile-based health technologies (mHealth) have been developed for use by community health workers to augment their performance. One such mHealth intervention-ReMiND program, was implemented in a poor performing district of India. Despite some research on the extent of its effectiveness, there is significant dearth of evidence on cost-effectiveness of such mHealth interventions. In this paper we evaluated the incremental cost per disability adjusted life year (DALY) averted as a result of ReMiND intervention as compared to routine maternal and child health programs without ReMiND. A decision tree was parameterized on MS-Excel spreadsheet to estimate the change in DALYs and cost as a result of implementing ReMiND intervention compared with routine care, from both health system and societal perspective. A time horizon of 10 years starting from base year of 2011 was considered appropriate to cover all costs and effects comprehensively. All costs, including those during sta...

Background: The main intervention under ReMiND program consisted of a mobile health application which was used by community health volunteers, called ASHAs, for counselling pregnant women and nursing mothers. This program was implemented in two rural blocks in Uttar Pradesh state of India with an overall aim to increase quality of health care, thereby increasing utilization of maternal & child health services. The aim of the study was to assess annual & unit cost of ReMiND program and its scale up in UP state. Method and materials: Economic costing was done from the health system and patient's perspectives. All resources used during designing & planning phase i.e., development of application; and implementation of the intervention, were quantified and valued. Capital costs were annualised, after assessing their average number of years for which a product could be used and accounting for its depreciation. Shared or joint costs were apportioned for the time value a resource was utilized under intervention. Annual cost of implementing ReMiND in two blocks of UP along and unit cost per pregnant woman were estimated. Scale-up cost for implementing the intervention in entire state was calculated under two scenariosfirst, if no extra human resource were employed; and second, if the state government adopted the same pattern of human resource as employed under this program. Results: The annual cost for rolling out ReMiND in two blocks of district Kaushambi was INR 12.1 million (US $ 191,894). The annualised start-up cost constituted 9% of overall cost while rest of cost was attributed to implementation of the intervention. The health system program costs in ReMiND were estimated to be INR 31.4 (US $ 0.49) per capita per year and INR 1294 (US $ 20.5) per registered women. The per capita incremental cost of scale up of intervention in UP state was estimated to be INR 4.39 (US $ 0.07) when no additional supervisory staffs were added. Conclusion: The cost of scale up of ReMiND in Uttar Pradesh is 6% of annual budget for 'reproductive and child health' line item under state budget, and hence appears to be financially sustainable.

objective To raise the quality of counselling by community health volunteers resulting in improved uptake of maternal, neonatal and child health services (MNCH), an m-health application was introduced under a project named ‘Reducing Maternal and Newborn Deaths (ReMiND)’ in district Kaushambi in India. We report the impact of this project on coverage of key MNCH services. methods A preand post-quasi-experimental design was undertaken to assess the impact of intervention. This project was introduced in two community development blocks in Kaushambi district in 2012. Two other blocks from the same district were selected as controls after matching for coverage of two indicators at baseline – antenatal care and institutional deliveries. The Annual Health Survey conducted by the Ministry of Health and Family Welfare in 2011 served as preintervention data, whereas a household survey in four blocks of Kaushambi district in 2015 provided post-intervention coverage of key services. Propensity ...

Three nonparametric tests for several sample scale problem against umbrella alternative, when the peak of the umbrella is known, are proposed. The proposed statistics have the advantage of not requiring the several distribution functions to have a common median, but rather any common quantile of order α, 0 ≤ α ≤ 1, (not necessarily 1/2) which is assumed to be known. The proposed tests are compared with each other in the Pitman asymptotic relative efficiency sense for different distributions.

Haar Wavelets has become important tool for solving number of problems of science and engineering. In this paper a computational scheme is implemented using Haar matrices to find the numerical solution of differential equations with known initial and boundary conditions. We also presented exact solution, numerical solution and absolute error. Numerical experiments presented here are comparable with the available data. The algorithm used in this is very simple and easy to implement.

Gini index, Bonferroni index, and Absolute Lorenz index are some popular indices of inequality showing different features of inequality measurement. In general simple random sampling procedure is commonly used to estimate the inequality indices and their related inference. The key condition that the samples must be drawn via simple random sampling procedure though makes calculations much simpler but this assumption is often violated in practice as the data does not always yield simple random sample. Nonsimple random samples like Ranked set sampling or stratified sampling are gaining popularity for estimating these indices. The purpose of the present paper is to compare the efficiency of simple random sample estimates of inequality indices with their nonsimple random counterparts. Monte Carlo simulation technique is applied to get the results for some specific distributions.

Bayesian estimators of Gini index and a Poverty measure are obtained in case of Pareto distribution under censored and complete setup. The said estimators are obtained using two noninformative priors, namely, uniform prior and Jeffreys’ prior, and one conjugate prior under the assumption of Linear Exponential (LINEX) loss function. Using simulation techniques, the relative efficiency of proposed estimators using different priors and loss functions is obtained. The performances of the proposed estimators have been compared on the basis of their simulated risks obtained under LINEX loss function.

The paper develops Bayesian estimators and HPD intervals for the stress strength reliability of generalised inverted exponential distribution using upper record values. For prior distribution, informative prior as well as non-informative prior both are considered. The Bayes estimators are obtained under both symmetric and asymmetric loss functions. A simulation study is conducted to obtain the Bayes estimates of stress strength reliability. Simulated data sets are also considered here for illustration purpose.

Bayesian and semi-Bayesian estimators of parameters of the generalized inverse Weibull distribution are obtained using Jeffreys' prior and informative prior under specific assumptions of loss function. Using simulation, the relative efficiency of the proposed estimators is obtained under different setups. A real life example is also given.

A class of nonparametric tests for testing homogeneity of variances against ordered alternatives based on subsample extrema Summary-A class of nonparametric test statistics for several sample scale problem is proposed by considering extrema of subsamples of size c. The proposed class of statistics has the advantage of not requiring the several distribution functions to have a common median, but rather any common quantile of order α, 0 α 1, (not necessarily 1 / 2) which is assumed to be known. The proposed class of tests is compared with other known tests already existing in the literature in the Pitman asymptotic relative efficiency sense for different distributions. Some results of Monte Carlo power study are also presented.