Search results for "variance"
showing 10 items of 2030 documents
Parameter orthogonality and conditional profile likelihood: the exponential power function case
1999
Orthogonality, according to Fisher’s metrics, between the parameters of a probability density function, as well as giving rise to a series of statistical implications, makes it possible to express a function of conditional profile likelihood with better properties than the ordinary profile likelihood function. In the present paper the parameters of exponential power function are made orthogonal and the conditional profile likelihood of the shape parameter p is determined in order to study its properties with reference to p estimation. Moreover, by means of a simulation plan, a comparison is made between the estimates of p obtained from the conditional profile log-likelihood and those obtain…
Distribucion final de referencia para el problema de Fieller-Creasy
1982
The problem of making inferences about the ratio of two normal populations is usually known as the Fieller-Creasy problem, and it gave rise to a controversy among fiducialists and confidence-intervalists. A Bayesian solution to such a problem when the two normal populations have the same unknown variance was presented by Bernardo (1977) using reference non-informative prior distributions. The solution to the case in which the variances are not assumed equal is obtained here. Some numerical results for artificial populations are given
The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression.
2020
This paper focuses on hypothesis testing in lasso regression, when one is interested in judging statistical significance for the regression coefficients in the regression equation involving a lot of covariates. To get reliable p-values, we propose a new lasso-type estimator relying on the idea of induced smoothing which allows to obtain appropriate covariance matrix and Wald statistic relatively easily. Some simulation experiments reveal that our approach exhibits good performance when contrasted with the recent inferential tools in the lasso framework. Two real data analyses are presented to illustrate the proposed framework in practice.
Tests and estimates of shape based on spatial signs and ranks
2009
Nonparametric procedures for testing and estimation of the shape matrix in the case of multivariate elliptic distribution are considered. Testing for sphericity is an important special case. The tests and estimates are based on the spatial sign and rank covariance matrices. The estimates based on the spatial sign covariance matrix and symmetrized spatial sign covariance matrix are Tyler's [A distribution-free M-estimator of multivariate scatter, Ann. Statist. 15 (1987), pp. 234–251] shape matrix and and Dümbgen's [On Tyler's M-functional of scatter in high dimension, Ann. Inst. Statist. Math. 50 (1998), pp. 471–491] shape matrix, respectively. The test based on the spatial sign covariance m…
On the stability and ergodicity of adaptive scaling Metropolis algorithms
2011
The stability and ergodicity properties of two adaptive random walk Metropolis algorithms are considered. The both algorithms adjust the scaling of the proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of the algorithms, the adapted scaling parameter is not constrained within a predefined compact interval. The first algorithm is based on scale adaptation only, while the second one incorporates also covariance adaptation. A strong law of large numbers is shown to hold assuming that the target density is smooth enough and has either compact support or super-exponentially decaying tails.
Asymptotic efficiency of the calibration estimator in a high-dimensional data setting
2022
Abstract In a finite population sampling survey, auxiliary information is commonly used to improve the Horvitz-Thompson estimators and calibration has been extensively used by national statistical agencies over the last decades for that purpose. This method enables to make estimators consistent with known totals of auxiliary variables and to reduce variance if the calibration variables are explanatory for the variable of interest. Nowadays, it is not unusual anymore to have high-dimensional auxiliary data sets and adding too much additional calibration variables may increase the variance of calibration estimators. We study in this paper the asymptotic efficiency of the calibration estimator…
Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved
2019
International audience; Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, k…
Multivariate Nonparametric Tests
2004
Multivariate nonparametric statistical tests of hypotheses are described for the one-sample location problem, the several-sample location problem and the problem of testing independence between pairs of vectors. These methods are based on affine-invariant spatial sign and spatial rank vectors. They provide affine-invariant multivariate generalizations of the univariate sign test, signed-rank test, Wilcoxon rank sum test, Kruskal–Wallis test, and the Kendall and Spearman correlation tests. While the emphasis is on tests of hypotheses, certain references to associated affine-equivariant estimators are included. Pitman asymptotic efficiencies demonstrate the excellent performance of these meth…
Efficient Estimation of Non-Linear Finite Population Parameters by Using Non-Parametrics
2013
Summary Currently, high precision estimation of non-linear parameters such as Gini indices, low income proportions or other measures of inequality is particularly crucial. We propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a non-parametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any non-linear parameter that is associated with any study variable of the survey, using a plug-in principle. Based on a rigorous functional approach and a linearization principle, the asymptotic variance of the estimators propose…
A Random Field Approach to Transect Counts of Wildlife Populations
1991
Line transect counting of a wildlife population is considered a sampling from a planar marked point process, where the marks describe the detectability of the animals. Sampling properties of transect counts and a new density estimator are derived from a counting process, which is a shot-noise field induced by the marked point process. A general formula for the sampling variance of a transect is derived and applied to compare five common types of transects. Some stereological connections of transect sampling and density estimators are shown.