1,720,988 research outputs found
Inference for quantiles of a finite population: asymptotic versus resampling results.
No full textThe aim of the paper is to study the problem of estimating the quantile function of a finite population. Attention is first focused on point estimation, and asymptotic results are obtained. Confidence intervals are then constructed, based on both the following: (i) asymptotic results and (ii) a resampling technique based on rescaling the ‘usual’ bootstrap. A simulation study to compare asymptotic and resampling-based results, as well as an application to a real population, is finally performed
Measuring uncertainty in statistical matching
No full textAn important feature of statistical matching is that the underlying joint distribution of the variables of interest is not identifiable. This produces a form on “uncertainty” on the statistical model. A measure to evaluate such an uncertainty is proposed. The effect of prior information in the form of constraints is exploited. Finally, the estimation of the proposed measure of uncertainty is studied
Uncertainty Analysis for statistical matching of ordered categorical variables
No full textThe aim is to analyze the uncertainty in statistical matching for ordered categorical variables. Uncertainty in statistical matching consists in estimating a joint distribution by observing only samples from its marginals. Unless very restrictive conditions are met, observeddatadonotidentifythejointdistributiontobeestimated,andthisisthereason of uncertainty. The notion of uncertainty is first formally introduced, and a measure of uncertainty is then proposed. Moreover, the reduction of uncertainty in the statistical model due to the introduction of logical constraints is investigated and evaluated via simulation
Uncertainty Analysis in Statistical Matching
No full textAmong the goals of statistical matching, a very important one is the estimation of the joint distribution of variables not jointly observed in a sample survey but separately available from independent sample surveys. The absence of joint information on the variables of interest leads to uncertainty about the data generating model. The present article reviews the concept of uncertainty in statistical matching and how to measure it by providing a unified framework for the parametric and nonparametric setting. Furthermore, the reduction of uncertainty due to the introduction of logical constraints is investigated and a simulation experiment is performed
Efficient unequal probability resampling from finite populations
Full text linkA resampling technique for probability-proportional-to size sampling designs is proposed. It is essentially based on a special form of variable probability, without replacement sampling applied directly to the sample data, yet according to the pseudo-population approach. From a theoretical point of view, it is asymptotically correct: as both the sample size and the population size increase, under mild regularity conditions the proposed resampling design tends to coincide with the original sampling design under which sample data were collected. From a computational point of view, the proposed methodology is easy to be implemented and efficient, because it neither requires the actual construction of the pseudo-population nor any form of randomization to ensure integer weights and sizes. Empirical evidence based on a simulation study1 indicates that the proposed resampling technique outperforms its two main competitors for confidence interval construction of various population parameters including quantiles. (c) 2021 Published by Elsevier B.V
On the Matching Noise of some Nonparametric Imputation Procedures
No full textThe aim of the paper is to evaluate the matching noise produced by nonparametric imputation techniques referring to the kNN
method, both with fixed and variable number of donors k. The matching noise is evaluated formally and via a simulation
Long-range dependence and performance in telecom networks
No full textTelecommunications systems have recently undergone significant innovations. These call for suitable statistical models that can properly describe the behaviour of the input traffic in a network. Here we use fractional Brownian motion (FBM) to model cumulative traffic network, thus taking into account the possible presence of long-range dependence in the data. A Bayesian approach is devised in such a way that we are able to: (a) estimate the Hurst parameter H of the FBM; (b) estimate the overflow probability which is a parameter measuring the quality of service of a network: (c) develop a test for comparing the null
hypothesis of long-range dependence in the data versus the alternative of short-range dependence. In order to achieve these inferential results, we elaborate an MCMC sampling scheme whose output enables us to obtain an approximation of the quantities of interest. An application to three real datasets, corresponding to three different levels of traffic, is finally considered
Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators
No full textA new matching procedure based on imputing missing data by means of a local linear
estimator of the underlying population regression function (that is assumed not necessarily
linear) is introduced. Such a procedure is compared to other traditional approaches, more
precisely hot deck methods as well as methods based on kNN estimators. The relationship
between the variables of interest is assumed not necessarily linear. Performance is
measured by the matching noise given by the discrepancy between the distribution
generating genuine data and the distribution generating imputed values
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