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Circular data arise in many contexts, a particularly rich source being animal orientation experiments. Often, in the analysis of such data, a fundamental question of scientific interest is whether the underlying distribution is reflectively symmetric about some specific axis. In this paper, the situation in which the axis of interest is known to be a median axis is considered and a simple, asymptotically distribution-free test for circular reflective symmetry against skew alternatives is developed The results from a simulation study lead to a testing strategy incorporating the new test and the circular analogue of the modified runs test of Modarres & Gastwirth (1996). The application of the testing strategy is illustrated using circular data arising from two animal orientation experiments. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the robustness properties of likelihood inference with respect to rounding effects. Attention is focused on exponential families and on inference about a scalar parameter of interest, also in the presence of nuisance parameters. A summary value of the influence function of a given statistic, the local-shift sensitivity, is considered. It accounts for small fluctuations in the observations. The main result is that the local-shift sensitivity is bounded for the usual likelihood-based statistics, i.e. the directed likelihood, the Wald and score statistics. It is also bounded for the modified directed likelihood, which is a higher-order adjustment of the directed likelihood. The practical implication is that likelihood inference is expected to be robust with respect to rounding effects. Theoretical analysis is supplemented and confirmed by a number of Monte Carlo studies, performed to assess the coverage probabilities of confidence intervals based on likelihood procedures when data are rounded. In addition, simulations indicate that the directed likelihood is less sensitive to rounding effects than the Wald and score statistics. This provides another criterion for choosing among first-order equivalent likelihood procedures. The modified directed likelihood shows the same robustness as the directed likelihood, so that its gain in inferential accuracy does not come at the price of an increase in instability with respect to rounding. [ABSTRACT FROM AUTHOR]

The selection of an appropriate subset of explanatory variables to use in a linear regression model is an important aspect of a statistical analysis. Classical stepwise regression is often used with this aim but it could be invalidated by a few outlying observations. In this paper, we introduce a robust F-test and a robust stepwise regression procedure based on weighted likelihood in order to achieve robustness against the presence of outliers. The introduced methodology is asymptotically equivalent to the classical one when no contamination is present. Some examples and simulation are presented. [ABSTRACT FROM AUTHOR]

Box & Hunter (1957) recommended a set of orthogonally blocked central composite designs (CCD) when the region of interest is spherical. In order to achieve rotatability along with orthogonal blocking, the block size for those designs becomes unequal and it may not be attractive or practical to use such unequally blocked designs in many practical situations. In this paper, a construction method of orthogonally blocked CCD under the assumption of equal block size is proposed and an index of block orthogonality is introduced. [ABSTRACT FROM AUTHOR]

Rapid technological advances have resulted in continual changes in data acquisition and reporting processes. While such advances have benefited research in these areas, the changing technologies have, at the same time, created difficulty for statistical analysis by generating outdated data which are incompatible with data based on newer technology. Relationships between these incompatible variables are complicated; not only they are stochastic, but also often depend on other variables, rendering even a simple statistical analysis, such as estimation of a population mean, difficult in the presence of mixed data formats. Thus, technological advancement has brought forth, from the statistical perspective, a methodological problem of the analysis of newer data with outdated data. In this paper, we discuss general principles for addressing the statistical issues related to the analysis of incompatible data. The approach taken to the task at hand has three desirable properties, it is readily understood, since it builds upon a linear regression setting, it is flexible to allow for data incompatibility in either the response or covariate, and it is not computationally intensive. In addition, inferences may be made for a latent variable of interest. Our considerations to this problem are motivated by the analysis of delta wave counts, as a surrogate for sleep disorder, in the sleep laboratory of the Department of Psychiatry, University of Pittsburgh Medical Center, where two major changes had occurred in the acquisition of this data, resulting in three mixed formats. By developing appropriate methods for addressing this issue, we provide statistical advancement that is compatible with technological advancement. [ABSTRACT FROM AUTHOR]

In time series analysis, the Box-Cox power transformation is generally used for variance stabilization. In this paper we show that the order and the first step ahead forecast of the transformed model are approximately invariant to those of the original model under certain assumptions on the mean and variance. A small Monte Carlo simulation is performed to support the results. [ABSTRACT FROM AUTHOR]

This paper describes the various stages in building a statistical model to predict temperatures in the core of a reactor, and compares the benefits of this model with those of a physical model. We give a brief background to this study and the applications of the model to rapid online monitoring and safe operation of the reactor. We describe the methods, of correlation and two dimensional spectral analysis, which we use to identify the effects that are incorporated in a spatial regression model for the measured temperatures. These effects are related to the age of the reactor fuel and the spatial geometry of the reactor. A remaining component of the temperature variation is a slowly varying temperature surface modelled by smooth functions with constrained coefficients. We assess the accuracy of the model for interpolating temperatures throughout the reactor, when measurements are available only at a reduced set of spatial locations, as is the case in most reactors. Further possible improvements to the model are discussed. [ABSTRACT FROM AUTHOR]

ABSTRACT Analysing a 2 x 2 table is one of the most frequent problems in applied research (particularly in epidemiology). When the table arises from a 2 x 2 multinomial trial (or the case of double dichotomy), the appropriate test for independence is an unconditional one, like those of Barnard (1947), which, although they date from a long time ago, have not been developed (because of computational problems) until the last ten years. Among the different possible versions, the optimal (Martin Andres & Tapia Garcia, 1999) is Barnard's original one, but the calculation time (even today) is excessive. This paper offers critical region tables for that version, which behave well compared to those of Shuster (1992). The tables are of particular use for researchers wishing to obtain significant results for very small sample sizes (N

This paper proposes two non-parametric statistics that test for a treatment effect in a heterogeneous population. In the model considered, data on two examinations for both a control and a treatment group are needed to perform the test. The model allows for individual (fixed) effects that may be correlated with the choice of treatment. In addition, the model allows for an unspecified, monotonic transformation of the response variable. The techniques are illustrated by testing whether high levels of unemploymentbenefit eligibility affect the consumption patterns of unemployed American workers. [ABSTRACT FROM AUTHOR]

In this paper, it is proposed to modify autoregressive fractionally integrated moving average (ARFIMA) processes by introducing an additional parameter to comply with the criticism of Hauser et al. (1999) that ARFIMA processes are not appropriate for the estimation of persistence, because of the degenerate behavior of their spectral densities at frequency zero. When fitting these modified ARFIMA processes to the US GNP, it turns out that the estimated spectra are very similar to those obtained with conventional ARFIMA models, indicating that, in this special case, the disadvantage of ARFIMA models cited by Hauser et al. (1999) does not seriously aff ect the estimation of persistence. However, according to the results of a goodness-of-fit test applied to the estimated spectra, both the ARFIMA models and the modified ARFIMA models seem to overfit the data in the neighborhood of frequency zero. [ABSTRACT FROM AUTHOR]

A number of recent studies have looked at the coverage probabilities of various common parametric methods of interval estimation of the median effective dose (ED[sub 50]) for a logistic dose-response curve. There has been comparatively little work done on more extreme effective doses. In this paper, the interval estimation of the 90% effective dose (ED[sub 90]) will be of principal interest. We provide a comparison of four parametric methods of interval construction with four methods based on bootstrap resampling. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper highlights distributional connections between directional statistics and shape analysis. In particular, we provide a test of uniformity for highly dispersed shapes, using the standard techniques of directional statistics, We exploit the isometric transformation from triangular shapes to a sphere in three dimensions, to provide a rich class of shape distributions. A link between the Fisher distribution and the complex Bingham distribution is re-examined. Some extensions to higher-dimensional shapes are outlined. [ABSTRACT FROM AUTHOR]

In this paper, a new sampling scheme called the 'conditional double sampling scheme' (CDSS) has been proposed. A compact table is presented for the selection of a CDSS indexed by various combinations of entry parameters. Advantages of the CDSS over the single sampling scheme have been discussed. The basis for the construction of the table is given. [ABSTRACT FROM AUTHOR]

When testing the equality of two population frequencies, one well-known and common situation is that the test is based on two independent samples. In this paper, we consider the other interesting case, in which the comparison is actually within a single population and the test is based on a single sample. [ABSTRACT FROM AUTHOR]

The validity conditions for univariate or multivariate analyses of repeated measures are highly sensitive to the usual assumptions. In cancer experiments, the data are frequently heteroscedastic and strongly correlated with time, and standard analyses do not perform well. Alternative non-parametric approaches can contribute to an analysis of these longitudinal data. This paper describes a method for such situations, using the results from a comparative experiment in which tumour volume is evaluated over time. First, we apply the non-parametric approach proposed by Raz in constructing a randomization Ftest for comparing treatments. A local polynomial fit is conducted to estimate the growth curves and confidence intervals for each treatment. Finally, this technique is used to estimate the velocity of tumour growth. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the eff ect of various baseball play-off configurations on the probability of advancing to the World Series. Play-off games are assumed to be independent. Several paired comparisons models are considered for modeling the probability of a home team winning a single game as a function of the winning percentages of the contestants over the course of the season. The uniform and logistic regression models are both adequate, whereas the Bradley-Terry model (modified for within-pair order eff ects, i.e. the home field advantage) is not. The single-game probabilities are then used to compute the probability of winning the play-off s under various structures. The extra round of play-off s, instituted in 1994, significantly lowers the probability of the team with the best record advancing to the World Series, whereas home field advantage and the diff erent possible play-offdraws have a minimal eff ect. [ABSTRACT FROM AUTHOR]

In finite population sampling, it has long been known that, for small sample sizes, when sampling from a skewed population, the usual frequentist intervals for the population mean cover the true value less often than their stated frequency of coverage. Recently, a non-informative Bayesian approach to some problems in finite population sampling has been developed, which is based on the 'Polya posterior'. For large sample sizes, these methods often closely mimic standard frequentist methods. In this paper, a modification of the 'Polya posterior', which employs the weighted Polya distribution, is shown to give interval estimators with improved coverage properties for problems with skewed populations and small sample sizes. This approach also yields improved tests for hypotheses about the mean of a skewed distribution. [ABSTRACT FROM AUTHOR]

Antedependence modelling has previously been shown to be useful for twogroup discriminant analysis of high-dimensional data. In this paper, the theory of such models is extended to multi-group discriminant analysis and to canonical variate analysis for data display. The application of antedependence models of orders 1, 2 and 3 to spectroscopic analyses of rice samples is described, and the results are compared with those from standard methods based on principal component scores calculated from the data. [ABSTRACT FROM AUTHOR]

Chen and Jernigan proposed a non-parametric, conservative method that involved using a penalized mean to estimate the average concentration of contaminants in soils. The method assumes a random sample obtained from a whole site involved in the US Superfund program. However, in some cases, about 10% of known data are collected from the 'hot spots'. In this paper, two procedures are proposed to use the information from hot spots data or an extreme value to estimate the mean concentration of contaminants. These procedures are evaluated using a data set of chromium concentrations from one of the Environmental Protection Agency's toxic waste sites. The simulation results show that these new procedures are cost-eff ective. [ABSTRACT FROM AUTHOR]

In the context of functional data analysis, we propose new sample tests for homogeneity. Based on some well-known depth measures, we construct four different statistics in order to measure distance between the two samples. A simulation study is performed to check the efficiency of the tests when confronted with shape and magnitude perturbation. Finally, we apply these tools to measure the homogeneity in some samples of real data, and we obtain good results using this new method. [ABSTRACT FROM AUTHOR]

High-yield production processes that involve a low fraction non-conforming are becoming more common, and the limitations of the standard control charting procedures for such processes are well known. This paper examines the control procedures based on the conforming unit run lengths applied to near-zero-defect processes in the presence of serial correlation. Using a correlation binomial model, a few control schemes are investigated and control limits are derived. The results reduce to the traditional case when the measurements are independent. However, it is shown that the false alarm rate cannot be reduced to below the amount of serial correlation present in the process. [ABSTRACT FROM AUTHOR]

Process capability indexes are widely used in the manufacturing industries and by supplier companies in process assessments and in the evaluation of purchasing decisions. One concern about using the process capability indexes is the assumption of the mutual independence of the process data, because, in process industries, process data are often autocorrelated. This paper discusses the use of the process capability indexes Cp and Cpk when the process data are autocorrelated. Interval estimation procedures for Cp and Cpk are proposed and their properties are studied. [ABSTRACT FROM AUTHOR]

Correlated survival data arise frequently in biomedical and epidemiologic research, because each patient may experience multiple events or because there exists clustering of patients or subjects, such that failure times within the cluster are correlated. In this paper, we investigate the appropriateness of the semi-parametric Cox regression and of the generalized estimating equations as models for clustered failure time data that arise from an epidemiologic study in veterinary medicine. The semi-parametric approach is compared with a proposed fully parametric frailty model. The frailty component is assumed to follow a gamma distribution. Estimates of the fixed covariates effects were obtained by maximizing the likelihood function, while an estimate of the variance component ( frailty parameter) was obtained from a profile likelihood construction. [ABSTRACT FROM AUTHOR]

In this paper, a new tightening concept has been incorporated into the single-level continuous sampling plan CSP-1, such that quality degradation will warrant sampling inspection to cease beyond a certain number of sampled items, until new evidence of good quality is established. The expressions of the performance measures for this new plan, such as the operating characteristic, average outgoing quality and average fraction inspected, are derived using a Markov chain model. The advantage of the tightened CSP-1 plan is that it is possible to lower the average outgoing quality limit. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we present an intuitive graphical approach to model validity, which, although to some extent subjective, can be extremely valuable for both presentation and interpretation purposes. In particular, the idea behind such a procedure arises naturally through the generation of a sequence of elements derived from the residuals about a fitted graduating function, based on datum points that are identical or that are relatively close together in a multi-dimensional factor space. [ABSTRACT FROM AUTHOR]

While large models based on a deterministic-reductionist philosophy have an important part to play in environmental research, it is advantageous to consider alternative modelling methodologies which overtly acknowledge the poorly defined and uncertain nature of most environmental systems. The paper discusses this topic and presents an integrated statistical modelling procedure which involves three main methodological tools: uncertainty and sensitivity studies based on Monte Carlo simulation techniques; dominant mode analysis using a new method of combined linearization and model-order reduction; and data-based mechanistic modelling. This novel approach is illustrated by two practical examples: modelling the global carbon cycle in relation to possible climate change; and modelling a horticultural glasshouse for the purposes of automatic climate control system design. [ABSTRACT FROM AUTHOR]

Comments on how scientific publications use statistical analysis. Clarity of language; Exactness of statistical terminology; Software packages; Materials and methods; Code of acceptable symbols and terminology; Guidelines for editors of scientific publications.

Probability distribution functions (PDFs) of atmospheric reentry latitude predictions are shown to be bimodal for spacecraft in low-eccentricity, prograde low Earth orbits at altitudes of 300 km and lower. Using two-line element (TLE) data for initial orbit conditions, coupled with coarse estimates for spacecraft aerodynamic characteristics, parametric simulations produce bimodal distributions that suggest a greater likelihood of reentry near the latitudinal maxima of a given spacecraft's ground track. Various computational measures are used to test for and quantify bimodality in the reentry latitude data sets. Also, a method for approximating bandwidth is introduced for the kernel estimation of reentry latitude probability density. Overall, statistical analysis indicates that actual reentry latitudes are generally within 1-σ of observed hemisphere means as demonstrated by six historical reentry cases. [ABSTRACT FROM AUTHOR]

We investigate the problem of statistical analysis of interval-valued time series data – two nonintersecting real-valued functions, representing lower and upper limits, over a period of time. Specifically, we pay attention to the two concepts of phase (or horizontal) variability and amplitude (or vertical) variability, and propose a phase-amplitude separation method. We view interval-valued time series as elements of a function (Hilbert) space and impose a Riemannian structure on it. We separate phase and amplitude variability in observed interval functions using a metric-based alignment solution. The key idea is to map an interval to a point in R 2 , view interval-valued time series as parameterized curves in R 2 , and borrow ideas from elastic shape analysis of planar curves, including PCA, to perform registration, summarization, analysis, and modeling of multiple series. The proposed phase-amplitude separation provides a new way of PCA and modeling for interval-valued time series, and enables shape clustering of interval-valued time series. We apply this framework to three different applications, including finance, meteorology and physiology, proves the effectiveness of proposed methods, and discovers some underlying patterns in the data. Experimental results on simulated data show that our method applies to the point-valued time series. [ABSTRACT FROM AUTHOR]

In the literature of distribution theory, a vast proportion is acquired by discrete distributions and their applications in real-world phenomena. However, in a rapidly changing technological era, the data generated is becoming increasingly complex day by day, making it difficult for us to capture various aspects of this real data through existing discrete models. In view of this, we propose a new flexible discrete distribution with one parameter. Some statistical and reliability are derived. These properties can be expressed as closed-forms. One of the important virtues of this newly evolved model is that it can model not only over-dispersed, positively skewed and leptokurtic data sets, but it can also be utilized for modeling increasing, decreasing and unimodal failure rate. Various estimation approaches are utilized to estimate the model parameter. A simulation study is carried out to examine the performance of the estimators for different sample size. The flexibility of the new model for analyzing different types of data is explained by utilizing four real data sets in different fields. Finally, the proposed model can serve as an alternative model to other distributions in the existing literature for modeling positive real data in several areas. [ABSTRACT FROM AUTHOR]

The Lee–Carter model is a well-known model in modeling mortality. We aim to compare three probability models (Poisson, negative binomial and binomial) based on the Generalized Linear Model (GLM) framework of the Lee–Carter model. These models are applied to mortality data for 10 selected countries (Japan, United States, United Kingdom, Australia, Sweden, Spain, Belgium, Canada, Netherlands and Bulgaria) and the fit of these models is assessed using the deviance statistics and standardized residuals against fitted value plot. Among these three models, the negative binomial Lee–Carter model gave the best fit based on the deviance statistics and estimates of the log of deaths. [ABSTRACT FROM AUTHOR]

The article discusses the issues that affect the "Journal of Applied Statistics." The journal has undergone changes related to submission of papers and the number of journal pages. Announcements are made regarding the death of editors Gopal Kanji and Julian Besag, who are both made significant contributions to the journal. The author expresses his gratitude for all those who have been involved with the journal.

It is well known that the testing of zero variance components is a non-standard problem since the null hypothesis is on the boundary of the parameter space. The usual asymptotic chi-square distribution of the likelihood ratio and score statistics under the null does not necessarily hold because of this null hypothesis. To circumvent this difficulty in balanced linear growth curve models, we introduce an appropriate test statistic and suggest a permutation procedure to approximate its finite-sample distribution. The proposed test alleviates the necessity of any distributional assumptions for the random effects and errors and can easily be applied for testing multiple variance components. Our simulation studies show that the proposed test has Type I error rate close to the nominal level. The power of the proposed test is also compared with the likelihood ratio test in the simulations. An application on data from an orthodontic study is presented and discussed. [ABSTRACT FROM AUTHOR]

Prediction of demand for professional sports is increasingly drawing the attention of economists. We apply linear mixed models for modeling attendance figures at Spanish professional football. We investigate economic variables, such as the price of the tickets or the size of the market, and sporting variables, such as the quality of a team or the level of competition within the league, as potential predictors of attendance. It turns out that a model with temporally correlated random team effects provides good forecasts of attendance at a time horizon of two seasons. Results from this model agree with economic theory. [ABSTRACT FROM AUTHOR]

This study presents statistical techniques to obtain local approximate query answers for aggregate multivariate materialized views thus eliminating the need for repetitive scanning of the source data. In widely distributed management information systems, detailed data do not necessarily reside in the same physical location as the decision-maker; thus, requiring scanning of the source data as needed by the query demand. Decision-making, business intelligence and data analysis could involve multiple data sources, data diversity, aggregates and large amounts of data. Management often confronts delays in information acquisition from remote sites. Management decisions usually involve analyses that require the most precise summary data available. These summaries are readily available from data warehouses and can be used to estimate or approximate data in exchange for a quicker response. An approach to supporting aggregate materialized view management is proposed that reconstructs data sets locally using posterior parameter estimates based on sufficient statistics in a log-linear model with a multinomial likelihood. [ABSTRACT FROM AUTHOR]

The problem of sample size determination in the context of Bayesian analysis is considered. For the familiar and practically important parameter of a geometric distribution with a beta prior, three different Bayesian approaches based on the highest posterior density intervals are discussed. A computer program handles all computational complexities and is available upon request. [ABSTRACT FROM AUTHOR]

The area of marked-point processes is well developed but simulation is still a challenging problem when mark correlations are to be included. In this paper we propose the use of simulated annealing to incorporate the spatial mark correlation into the simulations of correlated marked-point processes. Such a simulation has wide applications in areas such as inference and goodness-of-fit investigations of proposed models. The technique is applied to a forest dataset for which the results are extremely encouraging. [ABSTRACT FROM AUTHOR]

Winters are a difficult period for the National Health Service (NHS) in the United Kingdom (UK), due to the combination of cold weather and the increased likelihood of respiratory infections, especially influenza. In this article we present a proper statistical time series approach for modelling and analysing weekly hospital admissions in the West Midlands in the UK during the period week 15/1990 to week 14/1999. We consider three variables, namely, hospital admissions, general practitioner consultants, and minimum temperature. The autocorrelations of each series are shown to decay hyperbolically. The correlations of hospital admission and the lag of other series also decay hyperbolically but with different speed and directions. One of the main objectives of this paper is to show that each of the three series can be represented by a Fractional Differenced Autoregressive integrated moving average model, (FDA). Further, the hospital admission winter and summer residuals shows significant interdependency, which may be interpreted as hidden periodicities within the last 10-years time interval. The short-range (8 weeks) forecasting of hospital admission of the FDA model and a fourth-order AutoRegressive AR(4) model are quite similar. However, our results reveal that the long-range forecasting of FDA is more realistic. This implies that, using the FDA approach, the respective authority can plan for winter pressure properly. [ABSTRACT FROM AUTHOR]

Time series regression models have been widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this paper, we study the application of the quasi-least squares method to estimate the parameters in a replicated time series model with errors that follow an autoregressive process of order p. We also discuss two other established methods for estimating the parameters: maximum likelihood assuming normality and the Yule-Walker method. When the number of repeated measurements is bounded and the number of replications n goes to infinity, the regression and the autocorrelation parameters are consistent and asymptotically normal for all three methods of estimation. Basically, the three methods estimate the regression parameter efficiently and differ in how they estimate the autocorrelation. When p=2, for normal data we use simulations to show that the quasi-least squares estimate of the autocorrelation is undoubtedly better than the Yule-Walker estimate. And the former estimate is as good as the maximum likelihood estimate almost over the entire parameter space. [ABSTRACT FROM AUTHOR]

A unit root has important long-run implications for many time series in economics and finance. This paper develops a unit-root test of an ARIMA(p-1, 1, q) with drift null process against a trend-stationary ARMA(p, q) alternative process, where the order of the time series is assumed known through previous statistical testing or relevant theory. This test uses a point-optimal test statistic, but it estimates the null and alternative variance-covariance matrices that are used in the test statistics. Consequently, this test approximates a point-optimal test. Simulations show that its small-sample size is close to the nominal test level for a variety of unit-root processes, that it has a robust power curve against a variety of stationary alternatives, that its combined small-sample size and power properties are highly competitive with previous unit-root tests, and that it is robust to conditional heteroskedasticity. An application to post-Second World War real per capita gross domestic product is provided [ABSTRACT FROM AUTHOR]

The basic premise of running a field trial is that the estimates of treatment effects obtained are representative of how the different treatments will perform in the field. The disparities between the treatment effects observed experimentally, and those that would be observed were the treatments applied to the field, we term ‘representation bias.’ When looking at field trials testing the efficacies of treatment sprays on plant pathogens, representation bias can be caused by positive and negative inter-plot interference. The potential for such effects will be greatest when looking at pathogens that are dispersed by wind. In this paper, a computer simulation that simulates plant disease dispersal under such conditions is described. This program is used to quantify the amount of representation bias occurring in various experimental situations. Through this, the relationships between field design parameters and representation bias are explored, and the importance of plot dimension and spacing, as well as treatment to plot allocation, emphasized. [ABSTRACT FROM AUTHOR]

This paper shows that when series are fractionally integrated, but unit root tests wrongly indicate that they are I(1), Johansen likelihood ratio (LR) tests tend to find too much spurious cointegration, while the Engle-Granger test presents a more robust performance. This result holds asymptotically as well as infinite samples. The different performance of these two methods is due to the fact that they are based on different principles. The Johansen procedure is based on maximizing correlations (canonical correlation) while Engle-Granger minimizes variances (in the spirit of principal components). [ABSTRACT FROM AUTHOR]

Virtually all models for the utility of personnel selection are based on the average criterion score of the predictor selected applicants. This paper indicates how standard results from the theory on order statistics can be used to determine the expected value, the standard error and the sampling distribution of the average criterion score statistic when a finite number of employees is selected. Exact as well as approximate results are derived and it is shown how these results can be used to construct intervals that will contain, with a given probability 1 - α, the average criterion score associated with a particular implementation of the personnel selection. These interval estimates are particularly helpful to the selection practitioner because they can be used to state the confidence level with which the selection payoff will be above a specific value. In addition, for most realistic selection scenarios, it is found that the corresponding utility interval estimate is quite large. For situations in which multiple selections are performed over time, the utility intervals are, however, smaller. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to extend the widely used classical Brownian motion technique for monitoring clinical trial data to a larger class of stochastic processes, i.e. fractional Brownian motion, and compare these results. The beta-blocker heart attack trial is presented as an example to illustrate both methods. [ABSTRACT FROM AUTHOR]

We, the Editor-in-Chief and Publisher of Journal of Applied Statistics have retracted the following article, which was due to appear in a special issue: Muhammad Ijaz, Wali Khan Mashwani, Atilla Göktaş & Yuksel Akay Unvan (2021): A novel alpha power transformed exponential distribution with real-life applications, Journal of Applied Statistics. DOI: . The Editor-in-Chief and the Publisher are cognisant of clear evidence that the findings presented are unreliable. The probability distribution is only valid if α > 1 and numerous mathematical properties in Section 2 have been shown to be incorrect. This has then impacted at least two figures in the article. We are further cognisant that the article contained a number of similarities to previously published papers where some of the findings had been published without proper cross-referencing including: Gupta, R.D. and Kundu, D. (2001), Exponentiated Exponential Family: An Alternative to Gamma and Weibull Distributions. Biom. J., 43: 117–130. . We have been informed in our decision-making by our corrections and editorial policies and the Committee on Publication Ethics (COPE) guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted'. The Editor-in-Chief and the Publisher would like to thank the anonymous reader/s for their comments which alerted JAS to these major errors in the first instance. [ABSTRACT FROM AUTHOR]

An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications. [ABSTRACT FROM AUTHOR]

This paper examines the robustness of control schemes to data conditional heteroscedasticity. Overall, the results show that the control schemes which do not account for heteroscedasticity fail in providing reliable information on the status of the process. Consequently, incorrect conclusions will be drawn by applying these procedures in the presence of data conditional heteroscedasticity. Control charts with time-varying control limits are shown to be useful in that context. [ABSTRACT FROM AUTHOR]

In the literature on unobservable component models , three main statistical instruments have been used for signal extraction: fixed interval smoothing (FIS), which derives from Kalman's seminal work on optimal state-space filter theory in the time domain; Wiener-Kolmogorov-Whittle optimal signal extraction (OSE) theory, which is normally set in the frequency domain and dominates the field of classical statistics; and regularization , which was developed mainly by numerical analysts but is referred to as 'smoothing' in the statistical literature (such as smoothing splines, kernel smoothers and local regression). Although some minor recognition of the interrelationship between these methods can be discerned from the literature, no clear discussion of their equivalence has appeared. This paper exposes clearly the interrelationships between the three methods; highlights important properties of the smoothing filters used in signal extraction; and stresses the advantages of the FIS algorithms as a practical solution to signal extraction and smoothing problems. It also emphasizes the importance of the classical OSE theory as an analytical tool for obtaining a better understanding of the problem of signal extraction. [ABSTRACT FROM AUTHOR]

We consider the 'final' (deciding) set in a tennis match. We examine whether it is true that the chances for both players to win the match are equal at the beginning of the final set, even though they were not equal at the beginning of the match. We also test whether it is easier for an unseeded woman to beat a seeded player than it is for an unseeded man, and whether male players are more closely equal in quality than are females. We examine whether the service dominance decreases in long matches, and whether winning the 'pre-final' set provides an advantage in the final set. We use almost 90 000 points at Wimbledon to test all five hypotheses. [ABSTRACT FROM AUTHOR]

This paper extends our understanding of what determines the length of strikes, by comparing two alternative models of the strike settlement process, while simultaneously allowing for variation in this process as a result of economic conditions and unobserved heterogeneity. Some inconclusive support is found for the view that settlement proceeds by one or both sides presenting terms for acceptance or rejection, rather than by both sides yielding ground toward an intermediate position. Strike duration is found to be shortest at peaks of business cycles, and settlement is not duration dependent. [ABSTRACT FROM AUTHOR]