Estimation of the mutagenic potency of environmental chemicals using short-term bioassay

The Ames Salmonella test is a widely used bioassay method for assessing the mutagenic potency of a potential carcinogen. The test is quick and reliable, and exploits the correlation that exists between mutagenic potential and carcinogenic potential. The data for this case study came from an international study involving 20 laboratories in nine countries. The laboratories participated in a designed experiment in which substances (complex chemical mixtures of the type encountered in the environment) were evaluated for mutagenicity using the Ames test. A stringent protocol was followed. The study's principal aim was to investigate intra- and inter-laboratory variation in test results. The data consist of counts of revertant Salmonella colonies at each of six dose levels of a substance. The data were obtained for each of five test substances from each participating laboratory. The bioassays were carried out according to a prescribed factorial experimental design. Three sets of analysts participated in this case study. They were asked to model the dose-response relationship for two substances, to develop an index of the strength of the relationship, and to assess intra- and inter-laboratory variation in bioassay results.     

Ratio and product methods of estimation of population mean in the presence of correlated measurement errors

The properties of the estimators of population mean arising from the ratio and product methods of estimation in the context of sample surveys have been analyzed in this paper when the observations on both the study and auxiliary variables are contaminated with measurement errors. The measurement errors in both the variables are also correlated. The properties of the ratio and product estimators along with the sample mean under the influence of measurement errors are derived and studied. The properties of the estimators in finite samples are studied through Monte-Carlo simulation and its findings are reported.     

An alternative view of the 2001 census and future census taking

Summary.  The 2001 census in the UK asked for a return of people 'usually living at this address'. But this phrase is fuzzy and may have led to undercount. In addition, analysis of the sex ratios in the 2001 census of England and Wales points to a sex bias in the adjustments for net undercount—too few males in relation to females. The Office for National Statistics's abandonment of the method of demographic analysis for the population of working ages has allowed these biases to creep in. The paper presents a demographic account to check on the plausibility of census results. The need to revise preliminary estimates of the national population over a period of years following census day—as experienced in North America and now in the UK—calls into question the feasibility of a one-number census. Looking to the future, the environment for taking a reliable census by conventional methods is deteriorating. The UK Government's proposals for a population register open up the possibility of a Nordic-style administrative record census in the longer term.     

Serial unblocked designs balanced for correlation between neighbours,II

The theory in Part I contained an error that was inferred from the output of a program, written in SAS by Eric P. Smith and David D. Morris. The program produces random BUS designs in accordance with the algorithm of Part I. The theory is here corrected by using a combinatorial argument that involves elementary number theory. The algorithm needs no change but its interpretation is now adjusted.     

Change detection in the mean of a white Gaussian process by the backward standardized sum

A statistical method for detection of a change in the mean of a white Gaussian noise process is developed in this paper. The decision function of the method searches for the maximum of the backward standardized sum in a moving window to detect the change. Statistical properties of the decision function are derived to set the detection threshold. The derivation of the mean delay function and the optimal size of the moving window is also presented. The performance of the proposed method is compared, in terms of the mean delay for the detection, with that of the exponentially weighted moving average (EWMA). The mean delays of the cumulative sum control charts are also compared for benchmarking. The performance comparison is carried out by evaluating the average run length functions and by simulations. The results conclude that the mean detection delay of the proposed method is shorter than that of the standard EWMA for the same Type I error probability.     

An Extension of the Normal/Independent Family for Studies of Robust Estimators

The family of normal scale mixture distributions, also called the Normal/Independent family, has been used for efficient Monte Carlo studies of robust estimators. The distributions in this family are unimodal. The Normal/Independent family is extended by introducing a location mixing in addition to the scale mixing. Distributions in this extension may be nonunimodal. The asymptotic variances of robust estimators of location are compared using the distributions from the extension. A Monte Carlo swindle similar to the one used in the Princeton study is given for the extended family. A small simulation study demonstrates the efficiency of the swindle. The swindle is compared with other swindle methods based on Fisher's score function and regression.     

Estimates of regression coefficients based on the sign covariance matrix

Summary. A new estimator of the regression parameters is introduced in a multivariate multiple-regression model in which both the vector of explanatory variables and the vector of response variables are assumed to be random. The affine equivariant estimate matrix is constructed using the sign covariance matrix (SCM) where the sign concept is based on Oja's criterion function. The influence function and asymptotic theory are developed to consider robustness and limiting efficiencies of the SCM regression estimate. The estimate is shown to be consistent with a limiting multinormal distribution. The influence function, as a function of the length of the contamination vector, is shown to be linear in elliptic cases; for the least squares (LS) estimate it is quadratic. The asymptotic relative efficiencies with respect to the LS estimate are given in the multivariate normal as well as the t -distribution cases. The SCM regression estimate is highly efficient in the multivariate normal case and, for heavy-tailed distributions, it performs better than the LS estimate. Simulations are used to consider finite sample efficiencies with similar results. The theory is illustrated with an example.     

Local Linear Estimation for Time-Dependent Coefficients in Cox's Regression Models

This article develops a local partial likelihood technique to estimate the time-dependent coefficients in Cox's regression model. The basic idea is a simple extension of the local linear fitting technique used in the scatterplot smoothing. The coefficients are estimated locally based on the partial likelihood in a window around each time point. Multiple time-dependent covariates are incorporated in the local partial likelihood procedure. The procedure is useful as a diagnostic tool and can be used in uncovering time-dependencies or departure from the proportional hazards model. The programming involved in the local partial likelihood estimation is relatively simple and it can be modified with few efforts from the existing programs for the proportional hazards model. The asymptotic properties of the resulting estimator are established and compared with those from the local constant fitting. A consistent estimator of the asymptotic variance is also proposed. The approach is illustrated by a real data set from the study of gastric cancer patients and a simulation study is also presented.     

The Impact of Ovarian Cancer on Life Expectancy in Japan

The purpose of this study was to determine how life expectancy is modified by ovarian cancer from 1950-2000. The contributions of ovarian cancer to life expectancy were estimated. The age characteristics of ovarian cancer were detected using the Gompertz relational mortality model. The patterns between years of potential life lost (YPLL) and mortality were obtained by fitting a linear regression equation to the natural logarithm of their ratios. YPLLs are substantially higher in Ireland than in Japan. However, the rates of change were much higher in Japan than in Ireland. YPLLs changed from 0.02 year in 1950 to 0.12 year in 2000. In Japan, there was a sixfold increase in the proportion of YPLLs for death from ovarian cancer relative to those for death from gynaecological cancers during the last half century. The impact of ovarian cancer on life expectancy clearly increased and the age-specific mortality tend to ageing.     

Local influence in multivariate regression

The method of local influence is generalized to the multivariate regression. The scheme of perturbations adopted in multivariate regression is similar in spirit to the perturbation of case-weights in univariate regression case. The method developed here is useful for identifying influential observations in multivariate regression as an exploratory or confirmatory data analysis. An illustrative example is given for the effectiveness of the local influence approach in multivariate regression.     

引力模型的应用：服务贸易国际竞争力度量

服务贸易国际竞争力是进行服务贸易国际比较的重要评价指标,大多数学者利用已有指标对服务贸易国际竞争力进行了测算,但现有的竞争力指标不适合服务贸易国际竞争力的评价。故基于引力模型理论,建立了服务贸易国际竞争力度量的新方法,并通过实证分析得出：总体上中国服务贸易竞争力走强,但现代服务贸易的行业竞争力非常弱的结论。     

Smooth estimation of mean and dispersion function in extended generalized additive models with application to Italian induced abortion data

We analyse data on abortion rate (AR) in Italy with a particular focus on different behaviours in different regions in Italy. The aim is to try to reveal the relationship between the AR and several covariates that describe in some way the modernity of the region and the condition of the women there. The data are mostly underdispersed and the degree of underdispersion also varies with the covariates. To analyse these data, recent techniques for flexible modelling of a mean and dispersion function in a double exponential family framework are further developed now in a generalized additive model context for dealing with the multivariate set-up. The appealing unified framework and approach even allow to semi-parametric modelling of the covariates without any additional efforts. The methodology is illustrated on ozone-level data and leads to interesting findings in the Italian abortion data.     

A New Method for Statistical Multidimensional Unfolding

ABSTRACT

Consider the problem of estimating the positions of a set of targets in a multidimensional Euclidean space from distances reported by a number of observers when the observers do not know their own positions in the space. Each observer reports the distance from the observer to each target plus a random error. This statistical problem is the basic model for the various forms of what is called multidimensional unfolding in the psychometric literature. Multidimensional unfolding methodology as developed in the field of cognitive psychology is basically a statistical estimation problem where the data structure is a set of measures that are monotonic functions of Euclidean distances between a number of observers and targets in a multidimensional space. The new method presented in this article deals with estimating the target locations and the observer positions when the observations are functions of the squared distances between observers and targets observed with an additive random error in a two-dimensional space. The method provides robust estimates of the target locations in a multidimensional space for the parametric structure of the data generating model presented in the article. The method also yields estimates of the orientation of the coordinate system and the mean and variances of the observer locations. The mean and the variances are not estimated by standard unfolding methods which yield targets maps that are invariant to a rotation of the coordinate system. The data is transformed so that the nonlinearity due to the squared observer locations is removed. The sampling properties of the estimates are derived from the asymptotic variances of the additive errors of a maximum likelihood factor analysis of the sample covariance matrix of the transformed data augmented with bootstrapping. The robustness of the new method is tested using artificial data. The method is applied to a 2001 survey data set from Turkey to provide a real data example.     

Modern-day perception of historic footwear and its links to preference

The importance of emotion in consumer preference is explored in the subject of Kansei Engineering. The Kansei methodology has been successfully adopted by many large companies in recent years. Currently, a European Union Fifth framework project called 'Kensys' (Kansei Engineering System) is being implemented to look at the application of Kansei engineering in the field of footwear. The Kensys project is being conducted in collaboration with several SMEs and this paper reports a study that has been carried out with one of the SMEs who designs and makes reproduction historic and specialist footwear. In addition, respondent views on 'real' products from history and reproduction footwear are compared. We report on the views of respondents in general and look at gender differences, the comparison of non-experts' views versus experts' views and we also look at differences due to age. The study was carried out in the UK and in Spain. The views in both counties are compared.     

Multivariate survival mixed models for genetic analysis of longevity traits

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the baseline hazard function will be assumed to be piece-wise constant. The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed. Some key model control techniques are discussed in a supplementary online material.     

中国钢铁产业关联效应及国际比较分析

 本文应用投入产出表计算了中国钢铁产业关联效应及其国际比较,并分析了各项最终需求对钢铁产业生产的诱发作用。研究结果显示,我国钢铁产业对黑色金属矿采选业后向直接关联效应明显高于其它七国;包括中国、巴西等在内的发展中国家和韩国等新兴工业化国家的钢铁产业对建筑业的前向关联效应远远高于发达国家;我国钢铁产业对国民经济的拉动效应低于其它七个产钢大国,且其对我国国民经济的推动作用呈降低趋势;投资需求仍然是诱发我国钢铁产业生产的最重要因素,明显高于日本、美国等钢铁强国,也高于印度和巴西等钢产量很大的发展中国家;出口需求对钢铁产业生产的拉动效应明显低于钢产量远小于中国的日本等OECD国家。     

BUSDM – an algorithm for the bottom-up search of departures from a model

Searching for regions of the input space where a statistical model is inappropriate is useful in many applications. The study proposes an algorithm for finding local departures from a regression-type prediction model. The algorithm returns low-dimensional hypercubes where the average prediction error clearly departs from zero. The study describes the developed algorithm, and shows successful applications on the simulated and real data from the steel plate production. The algorithms that have been originally developed for searching regions of the high-response value from the input space are reviewed and considered as alternative methods for locating model departures. The proposed algorithm succeeds in locating the model departure regions better than the compared alternatives. The algorithm can be utilized in sequential follow-up of a model as time goes along and new data are observed.     

Multiperiod Forecasts of Interest Rates

The accuracy of forecasts of interest rates over different forecast horizons and time periods is examined. The results indicate a deterioration in “absolute” forecast accuracy measured by the mean absolute error and the root mean squared error but no decrease in “relative” accuracy measured by the Theil coefficient with an increase in the forecast span. The results also indicate a decline in accuracy in periods of volatile interest rates. Support is found for the hypothesis that the ratio of the variability of predicted changes to that of actual changes falls with an increase in the forecast horizon.     

Calculating the power or sample size for the logistic and proportional hazards models

An algorithm is presented for calculating the power for the logistic and proportional hazards models in which some of the covariates are discrete and the remainders are multivariate normal. The mean and covariance matrix of the multivariate normal covariates may depend on the discrete covariates.

The algorithm, which finds the power of the Wald test, uses the result that the information matrix can be calculated using univariate numerical integration even when there are several continuous covariates. The algorithm is checked using simulation and in certain situations gives more accurate results than current methods which are based on simple formulae. The algorithm is used to explore properties of these models, in particular, the power gain from a prognostic covariate in the analysis of a clinical trial or observational study. The methods can be extended to determine power for other generalized linear models.