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31.
Terence C. Mills 《Journal of the Royal Statistical Society. Series A, (Statistics in Society)》2009,172(1):107-117
Summary. Forecasts of trends in obesity in England for 2010 are produced by treating the available data, which contain the proportions of the population, categorized by age and sex, falling into different body mass index ranges, as compositional data sets, so that the implicit simplex restrictions are automatically satisfied. Forecasts are calculated by using linear trend models for the log-ratio transformations and are accompanied by prediction regions. The advantages of treating data on proportions compositionally are emphasized and compared with forecasts that have been obtained by ignoring this restriction. 相似文献
32.
Randomized response techniques are widely employed in surveys dealing with sensitive questions to ensure interviewee anonymity
and reduce nonrespondents rates and biased responses. Since Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering work, many
ingenious devices have been suggested to increase respondent’s privacy protection and to better estimate the proportion of
people, π
A
, bearing a sensitive attribute. In spite of the massive use of auxiliary information in the estimation of non-sensitive parameters,
very few attempts have been made to improve randomization strategy performance when auxiliary variables are available. Moving
from Zaizai’s (Model Assist Stat Appl 1:125–130, 2006) recent work, in this paper we provide a class of estimators for π
A
, for a generic randomization scheme, when the mean of a supplementary non-sensitive variable is known. The minimum attainable
variance bound of the class is obtained and the best estimator is also identified. We prove that the best estimator acts as
a regression-type estimator which is at least as efficient as the corresponding estimator evaluated without allowing for the
auxiliary variable. The general results are then applied to Warner and Simmons’ model. 相似文献
33.
Daniela M. Witten Robert Tibshirani 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2009,71(3):615-636
Summary. We propose covariance-regularized regression, a family of methods for prediction in high dimensional settings that uses a shrunken estimate of the inverse covariance matrix of the features to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing the log-likelihood of the data, under a multivariate normal model, subject to a penalty; it is then used to estimate coefficients for the regression of the response onto the features. We show that ridge regression, the lasso and the elastic net are special cases of covariance-regularized regression, and we demonstrate that certain previously unexplored forms of covariance-regularized regression can outperform existing methods in a range of situations. The covariance-regularized regression framework is extended to generalized linear models and linear discriminant analysis, and is used to analyse gene expression data sets with multiple class and survival outcomes. 相似文献
34.
For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose
a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models
and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second
order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values.
The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison
the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions
of pseudo-value regression equations. 相似文献
35.
Ning Hao 《The American statistician》2017,71(4):291-297
The problem of interaction selection in high-dimensional data analysis has recently received much attention. This note aims to address and clarify several fundamental issues in interaction selection for linear regression models, especially when the input dimension p is much larger than the sample size n. We first discuss how to give a formal definition of “importance” for main and interaction effects. Then we focus on two-stage methods, which are computationally attractive for high-dimensional data analysis but thus far have been regarded as heuristic. We revisit the counterexample of Turlach and provide new insight to justify two-stage methods from the theoretical perspective. In the end, we suggest new strategies for interaction selection under the marginality principle and provide some simulation results. 相似文献
36.
37.
This paper compares the performance between regression analysis and a clustering based neural network approach when the data deviates from the homoscedasticity assumption of regression. Heteroskedasticity is a problem that arises in linear regression due to the unequal error variances. One of the methods to deal heteroskedasticity in classical regression theory is weighted least-square regression (WLS). In order to deal the problem of heteroskedasticity, backpropagation neural network is applied. In this context, an algorithm is proposed which is based on robust estimates of location and dispersion matrix that helps in preserving the error assumption of the linear regression. Analysis is carried out with appropriate designs using simulated data and the results are presented. 相似文献
38.
Previously, a method was proposed for calculating a reconstructed coefficient of determination in the case of right-censored regression using the expectation–maximization (EM) algorithm. This measure is assessed via simulation study for the purpose of evaluating the utility of model fit. Further, several reconstructed adjusted coefficients of determination are proposed and compared via simulation study for the purpose of model selection. The application of these proposed measures is illustrated on a real dataset. 相似文献
39.
40.
The rescue and relief operations triggered by the September 11, 2001 attacks on the World Trade Center in New York City demanded collaboration among hundreds of organisations. To shed light on the response to the September 11, 2001 attacks and help to plan and prepare the response to future disasters, we study the inter-organisational network that emerged in response to the attacks. Studying the inter-organisational network can help to shed light on (1) whether some organisations dominated the inter-organisational network and facilitated communication and coordination of the disaster response; (2) whether the dominating organisations were supposed to coordinate disaster response or emerged as coordinators in the wake of the disaster; and (3) the degree of network redundancy and sensitivity of the inter-organisational network to disturbances following the initial disaster. We introduce a Bayesian framework which can answer the substantive questions of interest while being as simple and parsimonious as possible. The framework allows organisations to have varying propensities to collaborate, while taking covariates into account, and allows to assess whether the inter-organisational network had network redundancy—in the form of transitivity—by using a test which may be regarded as a Bayesian score test. We discuss implications in terms of disaster management. 相似文献