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111.
Stuart G. Baker Barnett S. Kramer 《Journal of the Royal Statistical Society. Series C, Applied statistics》2005,54(5):941-954
Summary. When evaluating potential interventions for cancer prevention, it is necessary to compare benefits and harms. With new study designs, new statistical approaches may be needed to facilitate this comparison. A case in point arose in a proposed genetic substudy of a randomized trial of tamoxifen versus placebo in asymptomatic women who were at high risk for breast cancer. Although the randomized trial showed that tamoxifen substantially reduced the risk of breast cancer, the harms from tamoxifen were serious and some were life threaten-ing. In hopes of finding a subset of women with inherited risk genes who derive greater bene-fits from tamoxifen, we proposed a nested case–control study to test some trial subjects for various genes and new statistical methods to extrapolate benefits and harms to the general population. An important design question is whether or not the study should target common low penetrance genes. Our calculations show that useful results are only likely with rare high penetrance genes. 相似文献
112.
Generalized additive models for location, scale and shape 总被引:10,自引:0,他引:10
R. A. Rigby D. M. Stasinopoulos 《Journal of the Royal Statistical Society. Series C, Applied statistics》2005,54(3):507-554
Summary. A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models. 相似文献
113.
Philip L. H. Yu K. F. Lam S. M. Lo 《Journal of the Royal Statistical Society. Series A, (Statistics in Society)》2005,168(3):583-597
Summary. Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs. 相似文献
114.
Christian P. Robert Xiao-Li Meng Jesper Møller Jeffrey S Rosenthal C Jennison M. A Hurn F Al-Awadhi Peter McCullagh Christophe Andrieu Arnaud Doucet Petros Dellaportas Ioulia Papageorgiou Ricardo S Ehlers Elena A Erosheva Stephen E Fienberg Jonathan J Forster Roger C Gill Nial Friel Peter Green David Hastie R King Hans R Künsch N. A. Lazar C Osinski 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2003,65(1):39-55
115.
Cathy W. S. Chen F. C. Liu Mike K. P. So 《Australian & New Zealand Journal of Statistics》2008,50(1):29-51
To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models. 相似文献
116.
Michael S. Rendall Ryan Admiraal Alessandra DeRose Paola DiGiulio Mark S. Handcock Filomena Racioppi 《Statistical Methods and Applications》2008,17(4):519-539
In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument.
For example, in the present application, two sample surveys and a population birth registration system all collect observations
on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum
use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic
regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in
the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors
about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller
survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total
survey sample size. 相似文献
117.
Higher education outcomes, graduate employment and university performance indicators 总被引:1,自引:0,他引:1
Massimiliano Bratti Abigail McKnight Robin Naylor Jeremy Smith 《Journal of the Royal Statistical Society. Series A, (Statistics in Society)》2004,167(3):475-496
Summary. Official employment-related performance indicators in UK higher education are based on the population of students responding to the 'First destination supplement' (FDS). This generates potentially biased performance indicators as this population of students is not necessarily representative of the full population of leavers from each institution. University leavers who do not obtain qualifications and those who do not respond to the FDS are not included within the official analysis. We compare an employment-related performance indicator based on those students who responded to the FDS with alternative approaches which address the potential non-random nature of this subgroup of university leavers. 相似文献
118.
S. Vansteelandt E. Goetghebeur 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2003,65(4):817-835
Summary. We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to overcome these limitations. This is a semiparametric two-stage model which extends the structural mean model to handle non-linear average exposure effects. The first-stage structural model describes the causal effect of received exposure by contrasting the means of observed and potential exposure-free outcomes in exposed subsets of the population. For identification of the structural parameters, a second stage 'nuisance' model is introduced. This takes the form of a classical association model for expected outcomes given observed exposure. Under the model, we derive estimating equations which yield consistent, asymptotically normal and efficient estimators of the structural effects. We examine their robustness to model misspecification and construct robust estimators in the absence of any exposure effect. The double-logistic structural mean model is developed in more detail to estimate the effect of observed exposure on the success of treatment in a randomized controlled blood pressure reduction trial with self-selected non-compliance. 相似文献
119.
Lisa A. Gennetian Greg Duncan Virginia Knox Wanda Vargas Elizabeth Clark‐Kauffman Andrew S. London 《Journal of research on adolescence》2004,14(4):399-423
Using data from 8 random assignment studies and employing meta‐analytic techniques, this article provides systematic evidence that welfare and work policies targeted at low‐income parents have small adverse effects on some school outcomes among adolescents ages 12 to 18 years at follow‐up. These adverse effects were observed mostly for school performance outcomes and occurred in programs that required mothers to work or participate in employment‐related activities and those that encouraged mothers to work voluntarily. The most pronounced negative effects on school outcomes occurred for the group of adolescents who had a younger sibling, possibly because of the increased home and sibling care responsibilities they assumed as their mothers increased their employment. 相似文献
120.
Mohammad Salehi M. George A.F. Seber 《Australian & New Zealand Journal of Statistics》2004,46(3):483-494
Not having a variance estimator is a seriously weak point of a sampling design from a practical perspective. This paper provides unbiased variance estimators for several sampling designs based on inverse sampling, both with and without an adaptive component. It proposes a new design, which is called the general inverse sampling design, that avoids sampling an infeasibly large number of units. The paper provide estimators for this design as well as its adaptive modification. A simple artificial example is used to demonstrate the computations. The adaptive and non‐adaptive designs are compared using simulations based on real data sets. The results indicate that, for appropriate populations, the adaptive version can have a substantial variance reduction compared with the non‐adaptive version. Also, adaptive general inverse sampling with a limitation on the initial sample size has a greater variance reduction than without the limitation. 相似文献