P. W. West 《统计学通讯:模拟与计算》2017,46(6):4951-4965

Simulations of forest inventory in several populations compared simple random with “quick probability proportional to size” (QPPS) sampling. The latter may be applied in the absence of a list sampling frame and/or prior measurement of the auxiliary variable. The correlation between the auxiliary and target variables required to render QPPS sampling more efficient than simple random sampling varied over the range 0.3–0.6 and was lower when sampling from populations that were skewed to the right. Two possible analytical estimators of the standard error of the estimate of the mean for QPPS sampling were found to be less reliable than bootstrapping.  相似文献   

    

Tanveer A. Tarray  Housila P. Singh 《统计学通讯:理论与方法》2017,46(6):2638-2654

The crux of this article is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Mangat (1991 Mangat, N.S. (1991). An optional randomized response sampling technique using non–stigmatized attribute. Statistica. 51(4):595–602. [Google Scholar]) randomization device by utilizing the Poisson distribution in simple random sampling and stratified sampling. Properties of the proposed randomized response (RR) model have been studied along with recommendations. It is also shown that the proposed model is more efficient than that of Land et al. (2011 Land, M., Singh, S., Sedory, S.A. (2011). Estimation of a rare attribute using Poisson distribution. Statistics. 46(3):351–360.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) in simple random sampling and that of Lee et al. (2013 Lee, G.S., Uhm, D., Kim, J.M. (2013). Estimation of a rare sensitive attribute in stratified sampling using Poisson distribution. Statistics. 47(3):575–589.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) in stratified random sampling when the proportion of persons possessing a rare unrelated attribute is known. Numerical illustrations are also given in support of the present study.  相似文献   

    

Fatima Batool  Javid Shabbir  Zawar Hussain 《统计学通讯:理论与方法》2017,46(6):3070-3079

In this article, we introduce for the first time, the blank card methods for estimation of finite population mean of a sensitive variable. Two generic randomization devices are suggested, and for each device we identify the choices of special models. We introduce additive, multiplicative, and combination of both additive and multiplicative scrambling models that require use of a non sensitive variable. We derive the basic statistical properties of each model. It is interesting to note that various existing estimators can be viewed as the special cases of those presented here. The statistical efficiency of proposed techniques is compared with Greenberg et al. (1971 Greenberg, B.G., Kuebler Jr, R.R., Abernathy, J.R., Horvitz, D.G. (1971). Application of the randomized response technique in obtaining quantitative data. J. Am. Stat. Assoc. 66(334):243–250.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) and modified Perri (2008 Perri, P.F. (2008). Modified randomized devices for Simmons’ model. Model Assisted Stat. Appl. 3(3):233–239. [Google Scholar]) model. The proposed devices can easily be adjusted to achieve the required efficiency level by making suitable choices of different design parameters.  相似文献   

    

Liwen Xu 《统计学通讯:理论与方法》2017,46(7):3308-3320

This paper presents parametric bootstrap (PB) approaches for hypothesis testing and interval estimation of the fixed effects and the variance component in the growth curve models with intraclass correlation structure. The PB pivot variables are proposed based on the sufficient statistics of the parameters. Some simulation results are presented to compare the performance of the proposed approaches with the generalized inferences. Our studies show that the PB approaches perform satisfactorily for various cell sizes and parameter configurations, and tends to outperform the generalized inferences with respect to the coverage probabilities and powers. The PB approaches not only have almost exact coverage probabilities and Type I error rates, but also have the shorter expected lengths and the higher powers. Furthermore, the PB procedure can be simply carried out by a few simulation steps. Finally, the proposed approaches are illustrated by using a real data example.  相似文献   

    

Qing Kang  Christopher I. Vahl 《Pharmaceutical statistics》2017,16(5):361-377

Traditional bioavailability studies assess average bioequivalence (ABE) between the test (T) and reference (R) products under the crossover design with TR and RT sequences. With highly variable (HV) drugs whose intrasubject coefficient of variation in pharmacokinetic measures is 30% or greater, assertion of ABE becomes difficult due to the large sample sizes needed to achieve adequate power. In 2011, the FDA adopted a more relaxed, yet complex, ABE criterion and supplied a procedure to assess this criterion exclusively under TRR‐RTR‐RRT and TRTR‐RTRT designs. However, designs with more than 2 periods are not always feasible. This present work investigates how to evaluate HV drugs under TR‐RT designs. A mixed model with heterogeneous residual variances is used to fit data from TR‐RT designs. Under the assumption of zero subject‐by‐formulation interaction, this basic model is comparable to the FDA‐recommended model for TRR‐RTR‐RRT and TRTR‐RTRT designs, suggesting the conceptual plausibility of our approach. To overcome the distributional dependency among summary statistics of model parameters, we develop statistical tests via the generalized pivotal quantity (GPQ). A real‐world data example is given to illustrate the utility of the resulting procedures. Our simulation study identifies a GPQ‐based testing procedure that evaluates HV drugs under practical TR‐RT designs with desirable type I error rate and reasonable power. In comparison to the FDA's approach, this GPQ‐based procedure gives similar performance when the product's intersubject standard deviation is low (≤0.4) and is most useful when practical considerations restrict the crossover design to 2 periods.  相似文献   

    

《Stat》2017,6(1):131-144

A Bayesian variable selection framework is considered for analyzing image data. For the spatial dependencies to be modelled among the covariates, an Ising prior is assigned to the binary latent vector γ , which indicates whether a covariate should be selected or not. The selection process, that is, the estimation of γ , can be carried out with Gibbs sampler. Although the model has been used in many scientific applications, no theoretical development has been made. In this article, we established theories on the model selection consistency under mild conditions, which is an important theoretical property for high‐dimensional variable selection. Copyright © 2017 John Wiley & Sons, Ltd.  相似文献   

    

Taylor Larkin  Denise McManus 《Statistical Analysis and Data Mining》2020,13(5):451-464

Due to the intricacies surrounding taste profiles, one's view of good wine is subjective. Therefore, it is advantageous to provide a more objective, data‐driven way to assess wine preferences. Motivated by a previous study that modeled wine preferences using machine learning algorithms, this work presents an ensemble approach to predict a wine sample's quality level given its physiochemical properties. Results show the proposed framework out‐performs many sophisticated models including the one recommended by the motivational study. Moreover, the proposed framework offers a simple variable importance strategy to gain insight as to the relevance of the predictor variables and is applied to both simulated and real data. Given the predictive power of using ensembles, especially when they can be interpretable, practitioners can use the following approach to provide an accurate and inferential perspective towards demystifying wine preferences.  相似文献   

    

Yang Wang  Volodymyr Melnykov 《Stat》2020,9(1)

Finite mixture models are widely used for cluster analysis, including clustering matrix data. Nowadays, high‐dimensional matrix observations arise in a variety of fields. It is known that irrelevant variables can severely affect the performance of clustering procedures. Therefore, it is important to develop algorithms capable of excluding irrelevant variables and focusing on informative attributes in order to achieve good clustering results. Several variable selection approaches have been proposed in the multivariate framework. We introduce and study a variable selection procedure that can be applied in the matrix‐variate context. The methodological developments are supported by several simulation studies and application to real‐life data sets, with good results.  相似文献   

    

Takahiro Hoshino  Keisuke Takahata 《Stat》2020,9(1)

We show the identification of the joint distribution of the potential outcomes under various parametric specifications. The key factor of the identification is the nonnormality of the distribution of the observed variables, with which we can obtain information of higher order moments that are not determined only by mean and variance. In particular, we show the identification of the joint distribution of the potential outcomes when it is specified by a normal mixture. Because any continuous distribution can be well approximated by a finite mixture distribution, our result may cover a wide class of distributions. The identification results derived are useful for estimating quantile treatment effects, causal mediation effects, and heterogeneous treatment effects, which cannot be estimated even if the unconfoundedness assumption is satisfied.  相似文献   

    

Fulya Gokalp Yavuz  Olcay Arslan 《Journal of applied statistics》2020,47(11):2025

Variable selection in elliptical Linear Mixed Models (LMMs) with a shrinkage penalty function (SPF) is the main scope of this study. SPFs are applied for parameter estimation and variable selection simultaneously. The smoothly clipped absolute deviation penalty (SCAD) is one of the SPFs and it is adapted into the elliptical LMM in this study. The proposed idea is highly applicable to a variety of models which are set up with different distributions such as normal, student-t, Pearson VII, power exponential and so on. Simulation studies and real data example with one of the elliptical distributions show that if the variable selection is also a concern, it is worthwhile to carry on the variable selection and the parameter estimation simultaneously in the elliptical LMM.  相似文献