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.     

Simultaneous estimation of location and dispersion in two-level fractional factorial designs

The reduction of variation is one of the obvious goals in quality improvement. The identification of factors aff ecting the dispersion is a step towards this goal. In this paper, the problem of estimating location effects and dispersion eff ects simultaneously in unreplicated factorial experiments is considered. By making a one-to-one transformation of the response variables, the study of the quadratic functions becomes clearer. The transformation also gives a natural motivation to the model of the variances of the original variables. The covariances of the transformed responses appear as parameters in the variances of the original variables. Results of Hadamard products are used for deriving these covariances. The method of estimating dispersion effects is shown in two illustrations. In a 24 factorial design, the essential covariance matrix of the transformed variables is also presented. The method is also illustrated in a 25-1 fractional design with a model which is saturated in this context.     

Demographic problems of military reform in the USSR

The author discusses future reforms in the procedures for drafting personnel into the Soviet army. The analysis is based primarily on the experiences of armies in Western countries. The author also takes into consideration peculiarities of the political and economic structures in the USSR, as well as the attitude of the Soviet people toward military service. The demographic situation at the end of the twentieth and the beginning of the twenty-first century is examined, as is the possibility that a decrease in fertility, worsening life expectancy, and increase in international migration could make the military draft process even more complicated. Data for cohorts born in 1972, 1977, 1982, and 1987 are used as illustrations. The possibility of developing an alternative service is suggested.     

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.     

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.     

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.     

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.     

ON THE DERIVATION AND COMPUTATION OF THE CORNISH-FISHER EXPANSION

The paper gives a simple derivation based on elementary statistics for the Cornish—Fisher expansion. The method simplifies greatly the algebra in working out the polynomials involved both in the normalization and in the inverse expansions. The derivation also leads to recursive formulae which yield powerful algorithms for numerical computation of the expansion.     

Gamma-distribution order statistics,maximal multinomial frequency and randomization designs

The paper discusses the relationship between the gamma-distribution order statistics, the maximal cell frequency in multinomial trials and the distribution arising in a commonly used balanced randomization scheme with several treatments. The latter is used in clinical trials, load balancing in computer files storage, parapsychological experiments, etc., where a randomization design starts with the uniform probability assignment of subjects to treatments. The limiting distributions of the waiting time until a treatment receives the given number of subjects are described. The relationship to classical occupancy problems, in particular, to the Banach match-box problem and to the birthday problem is discussed.     

The concept of spatial order and other problems of regional research in demography

The concept of spatial demographic order is introduced and defined as the preferred spatial distribution of a given demographic variable. The results of a multivariate statistical analysis of demographic trends in rural and urban areas in Poland are presented to illustrate the convergence of such trends in the areas studied. The author concludes that the increasing similarity in demographic processes between rural and urban areas is due mainly to migration.     

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.     

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.     

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.     

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.     

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.     

Quasi-maximum likelihood estimation of GARCH models in the presence of missing values

This work presents a new method to deal with missing values in financial time series. Previous works are generally based in state-space models and Kalman filter and few consider ARCH family models. The traditional approach is to bound the data together and perform the estimation without considering the presence of missing values. The existing methods generally consider missing values in the returns. The proposed method considers the presence of missing values in the price of the assets instead of in the returns. The performance of the method in estimating the parameters and the volatilities is evaluated through a Monte Carlo simulation. Value at risk is also considered in the simulation. An empirical application to NASDAQ 100 Index series is presented.     

Properties of the Cox–Stuart Test for Trend in Application to Hydrological Series: The Simulation Study

The size and power properties of the Cox–Stuart test for detection of a monotonic deterministic trend in hydrological time series are analyzed using the Monte Carlo method. The influence of distribution properties, lengths of series, and trend slopes is studied. Results indicate good size in all cases. The power is high for: length over 60 and strong trend slope, low or medium variation, and medium slope. The power declines if slope and length decrease and if variability increases. The properties are better for skewed distributions than for symmetrical. The test is slightly weaker in comparison to the Mann–Kendall test.     

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.     

Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood

The mixed linear model is a popular method for analysing unbalanced repeated measurement data. The classical statistical tests for parameters in this model are based on asymptotic theory that is unreliable in the small samples that are often encountered in practice. For testing a given fixed effect parameter with a small sample, we develop and investigate refined likelihood ratio (LR) tests. The refinements considered are the Bartlett correction and use of the Cox–Reid adjusted likelihood; these are examined separately and in combination. We illustrate the various LR tests on an actual data set and compare them in two simulation studies. The conventional LR test yields type I error rates that are higher than nominal. The adjusted LR test yields rates that are lower than nominal, with absolute accuracy similar to that of the conventional LR test in the first simulation study and better in the second. The Bartlett correction substantially improves the accuracy of the type I error rates with either the conventional or the adjusted LR test. In many cases, error rates that are very close to nominal are achieved with the refined methods.