Screening opportunity bias in case-control studies of cancer screening

In case-control evaluations of cancer screening, subjects who have died from the cancer in question (cases) are compared with those who have not (controls) with respect to screening histories. This method is subject to a rather subtle bias, among others, whereby the cases have greater opportunity to have been screened than the controls. In this paper, we propose a method of correction for this bias. We demonstrate its use on two case-control studies of mammographic screening for breast cancer.     

我国货币政策与财政政策的协调配合

 近年来我国经济成功地实现“软着陆”后,又面临着前所未有的下滑困境,突出表现在企业普遍开工不足、产品积压、市场滞销、工人失业,特别是通货膨胀、经济增长率持续下滑。为了抑制这些问题的负效应,我国的宏观货币政策和财政政策先后启用,并在刺激有效需求、促进经济回升方面具有一定的成效,但远远没有达到预期的目标。表现在投资需求增幅下降、消费启而不动、通货紧缩日益蔓延。那么,如何看待我国货币财政政策的有效性问题呢？ 简单地从表面上看,确实存在着实效的问题,但如果我们从政策所依托的大环境入手。深入到货币财政政策的内部机制,具体地分析其效力,可能会得出不同的结论。     

食品标签信息对消费者购买决策的影响研究——以婴幼儿食品为例

基于Hicks消费者剩余的CV形式,构建信息关注度、信息源信任度、信息充分度的Logit食品标签信息影响决策模型.通过在北京市发放问卷并回收、统计数据及分析,研究发现:消费者在购买婴幼儿食品时,对食品标签上的食品生产日期和保质期、营养成分、配料清单、食用指导注意等信息的关注度高,影响显著;对食品品牌、生产厂家及厂址、使用方法及贮藏、安全标识等信息关注度低,影响不显著;消费者对政府机构、婴幼儿医师和学者、亲朋及名人代言等信息源的信任度高,对购买决策影响显著;消费者对非政府机构和食品企业等信息源的信任度低,对购买决策影响不显著;食品标签信息充分度对消费者获得高质量信息、降低风险和提高决策准确度等起到的作用很大;消费者的性别、年龄、收入、受教育程度等对购买决策影响显著;相对于女性而言,食品标签信息对男性消费者的购买决策更有影响.     

对外直接投资缓解贸易失衡的实证研究 ——以20世纪80年代日美贸易失衡为例

 当前中国正面临金融危机导致的新一轮贸易摩擦,其产生根源是中国对主要出口国不断加剧的贸易失衡。而传统财政货币和汇率政策对缓解贸易失衡均有一定局限性。许多国际经济学经典模型都指出对外直接投资可能具有减小投资国贸易顺差的作用,同时,一个基于协整技术的贸易差额决定模型证明,日本20世纪80年代中期后的对外直接投资对减小日美贸易顺差具有显著作用。我国可以利用当前人民币升值、外汇储备大量累积和世界范围内的相对宽松货币政策等机遇加快对外直接投资,在促进企业国际化的同时适当缓解贸易失衡以应对贸易摩擦。     

Neighbor-Balanced Row-column Designs

In this article, row-column designs incorporating directional neighbor effects have been studied. A row-column design is said to be neighbor balanced if every treatment has all other treatments appearing as a neighbor a constant number of times. We considered here three different situations under row-column setup incorporating neighbor effects viz., row-column design with one-sided neighbor effect, two-sided neighbor effect, and four-sided neighbor effect. The information matrices for all the situations for estimating the direct and neighbor effects of treatments have been derived. Methods of constructing neighbor-balanced row-column designs have been developed and its characterization properties have been studied.     

A glimpse of the impact of pál erd#ous on probability and statistics

The title of this article notwithstanding, it is the author's aspiration here to provide a bit more than merely a glimpse of some of Erdõs's contributions per se to probability‐statistics. He hopes to have succeeded in providing a guided tour of, and whenever it has appeared feasible, an introduction to, a few selected areas that have been strongly influenced by the work of Erdõs. The author also hopes to have succeeded in facilitating a glimpse of the impact of these contributions by presenting them in their historical context.     

基于状态空间模型的货币流动性与股价关系的实证研究

货币流动性与股价波动之间相互影响的关系一直是学术界争论的热点。利用变参数状态空间模型,研究货币流动性与股价之间的动态关系。研究结果表明：M2增长率／GDP增长率对股价的影响不大;M2与股价的波动从长期和短期来看均存在单向因果关系,而利率与股价波动只是短期存在单向因果关系,但相对于货币供应量而言,利率对股价的波动更为敏感。     

Catalogue of group structures for two-level fractional factorial designs

SUMMARY Taguchi introduced the concept of split-unit design to sort factors into different groups with respect to difficulties involved in changing the levels of factors. Li et al. have developed all possible group structures for eight factors in an L16 orthogonal array for resolution IV with split-plot design. Chen et al. have searched for a best design, according to the various criteria for two-level fractional factorial design and have presented a catalogue. In this paper, we have developed an algorithm for generating group structure and possible allocations for various 2n- k fractional factorial designs that correspond to the designs given by Chen et al.     

A family of models for uniform and serial dependence in repeated measurements studies

Data arising from a randomized double-masked clinical trial for multiple sclerosis have provided particularly variable longitudinal repeated measurements responses. Specific models for such data, other than those based on the multivariate normal distribution, would be a valuable addition to the applied statistician's toolbox. A useful family of multivariate distributions can be generated by substituting the integrated intensity of one distribution into a second (outer) distribution. The parameters in the second distribution are then used to create a dependence structure among observations on a unit. These may either be a form of serial dependence for longitudinal data or of uniform dependence within clusters. These are respectively analogous to the Kalman filter of state space models and to copulas, but they have the major advantage that they do not require any explicit integration. One useful outer distribution for constructing such multivariate distributions is the Pareto distribution. Certain special models based on it have previously been used in event history analysis, but those considered here have much wider application.     

Simulation study of confidence regions for canonical variate analysis

Canonical variate analysis often involves the construction of confidence regions round points representing group means in a 2-dimensional plot. Traditionally circles have always been constructed, but some authors have recently advocated ellipses as being more appropriate. This paper describes a Monte Carlo study investigating the effect of a range of factors on the inclusion rates of true population means within both types of region for normal data. The traditional circles do not perform too badly within a restricted range, but they are nearly always under-included. The ellipses usually have higher inclusion rates, and so are often closer to the nominal rate, but are sometimes over-included.     

Generalized Markov-Polya urn models with predetermined strategies

By using combinatorial methods involving lattice path combinatorics, three generalized probability models dependent on predetermined strategies have been obtained with the help of urn models.The models have been developed with the help of a sampling scheme which unifies both, the binomial and the inverse binomial sampling schemes. These models generate a number of important discrete probability distributions both as particular cases and as limiting cases. Recurrence relations among the moments of the models have also been obtained.     

Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment

In the past, many clinical trials have withdrawn subjects from the study when they prematurely stopped their randomised treatment and have therefore only collected ‘on‐treatment’ data. Thus, analyses addressing a treatment policy estimand have been restricted to imputing missing data under assumptions drawn from these data only. Many confirmatory trials are now continuing to collect data from subjects in a study even after they have prematurely discontinued study treatment as this event is irrelevant for the purposes of a treatment policy estimand. However, despite efforts to keep subjects in a trial, some will still choose to withdraw. Recent publications for sensitivity analyses of recurrent event data have focused on the reference‐based imputation methods commonly applied to continuous outcomes, where imputation for the missing data for one treatment arm is based on the observed outcomes in another arm. However, the existence of data from subjects who have prematurely discontinued treatment but remained in the study has now raised the opportunity to use this ‘off‐treatment’ data to impute the missing data for subjects who withdraw, potentially allowing more plausible assumptions for the missing post‐study‐withdrawal data than reference‐based approaches. In this paper, we introduce a new imputation method for recurrent event data in which the missing post‐study‐withdrawal event rate for a particular subject is assumed to reflect that observed from subjects during the off‐treatment period. The method is illustrated in a trial in chronic obstructive pulmonary disease (COPD) where the primary endpoint was the rate of exacerbations, analysed using a negative binomial model.     

Structural identification and variable selection in high-dimensional varying-coefficient models

Varying-coefficient models have been widely used to investigate the possible time-dependent effects of covariates when the response variable comes from normal distribution. Much progress has been made for inference and variable selection in the framework of such models. However, the identification of model structure, that is how to identify which covariates have time-varying effects and which have fixed effects, remains a challenging and unsolved problem especially when the dimension of covariates is much larger than the sample size. In this article, we consider the structural identification and variable selection problems in varying-coefficient models for high-dimensional data. Using a modified basis expansion approach and group variable selection methods, we propose a unified procedure to simultaneously identify the model structure, select important variables and estimate the coefficient curves. The unique feature of the proposed approach is that we do not have to specify the model structure in advance, therefore, it is more realistic and appropriate for real data analysis. Asymptotic properties of the proposed estimators have been derived under regular conditions. Furthermore, we evaluate the finite sample performance of the proposed methods with Monte Carlo simulation studies and a real data analysis.     

Improved point and confidence interval estimators of mean response in simulation when control variates are used

In discrete event simulation, the method of control variates is often used to reduce the variance of estimation for the mean of the output response. In the present paper, it is shown that when three or more control variates are used, the usual linear regression estimator of the mean response is one of a large class of unbiased estimators, many of which have smaller variance than the usual estimator. In simulation studies using control variates, a confidence interval for the mean response is typically reported as well. Intervals with shorter width have been proposed using control variates in the literature. The present paper however develops confidence intervals which not only have shorter width but also have higher coverage probability than the usual confidence interval     

Bias correction in logistic regression with missing categorical covariates

Logistic regression plays an important role in many fields. In practice, we often encounter missing covariates in different applied sectors, particularly in biomedical sciences. Ibrahim (1990) proposed a method to handle missing covariates in generalized linear model (GLM) setup. It is well known that logistic regression estimates using small or medium sized missing data are biased. Considering the missing data that are missing at random, in this paper we have reduced the bias by two methods; first we have derived a closed form bias expression using Cox and Snell (1968), and second we have used likelihood based modification similar to Firth (1993). Here we have analytically shown that the Firth type likelihood modification in Ibrahim led to the second order bias reduction. The proposed methods are simple to apply on an existing method, need no analytical work, with the exception of a little change in the optimization function. We have carried out extensive simulation studies comparing the methods, and our simulation results are also supported by a real world data.     

Estimation of population mean in two-occasion successive sampling

In the present article, we have proposed some classes of estimators based on transformed auxiliary variable. The biases and mean squared errors (MSEs) of the proposed estimators have been obtained. The proposed estimators have been compared with simple mean estimator when there is no matching and the optimum estimator, which is a combination of the means of the matched and unmatched portion of the sample at the second occasion. Optimum replacement policy and the efficiency of the proposed estimators have been discussed. Theoretical results are well supported with an empirical study.     

Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

A catalogue of designs for partial profiles in paired comparison experiments with three groups of factors

A common strategy for avoiding information overload in multi-factor paired comparison experiments is to employ pairs of options which have different levels for only some of the factors in a study. For the practically important case where the factors fall into three groups such that all factors within a group have the same number of levels and where one is only interested in estimating the main effects, a comprehensive catalogue of D-optimal approximate designs is presented. These optimal designs use at most three different types of pairs and have a block diagonal information matrix.     

Estimating the common hazard rate of several exponential distributions

Abstract

In the present communication, we consider the estimation of the common hazard rate of several exponential distributions with unknown and unequal location parameters with a common scale parameter under a general class of bowl-shaped scale invariant loss functions. We have shown that the best affine equivariant estimator (BAEE) is inadmissible by deriving a non smooth improved estimator. Further, we have obtained a smooth estimator which improves upon the BAEE. As an application, we have obtained explicit expressions of improved estimators for special loss functions. Finally, a simulation study is carried out for numerically comparing the risk performance of various estimators.     

On estimation of the exponentiated Pareto distribution under different sample schemes

Bayes and classical estimators have been obtained for a two-parameter exponentiated Pareto distribution for when samples are available from complete, type I and type II censoring schemes. Bayes estimators have been developed under a squared error loss function as well as under a LINEX loss function using priors of non-informative type for the parameters. It has been seen that the estimators obtained are not available in nice closed forms, although they can be easily evaluated for a given sample by using suitable numerical methods. The performances of the proposed estimators have been compared on the basis of their simulated risks obtained under squared error as well as under LINEX loss functions.