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We attempt to identify the 25 most-cited statistical papers, providing some brief commentary on each paper on our list. This list consists, to a great extent, of papers that are on non-parametric methods, have applications in the life sciences, or deal with the multiple comparisons problem. We also list the most-cited papers published in 1993 or later. In contrast to the overall most-cited papers, these are predominately papers on Bayesian methods and wavelets. We briefly discuss some of the issues involved in the use of citation counts. [ABSTRACT FROM AUTHOR]

Adaptive designs for multi-armed clinical trials have become increasingly popular recently because of their potential to shorten development times and to increase patient response. However, developing response-adaptive designs that offer patient-benefit while ensuring the resulting trial provides a statistically rigorous and unbiased comparison of the different treatments included is highly challenging. In this paper, the theory of Multi-Armed Bandit Problems is used to define near optimal adaptive designs in the context of a clinical trial with a normally distributed endpoint with known variance. We report the operating characteristics (type I error, power, bias) and patient-benefit of these approaches and alternative designs using simulation studies based on an ongoing trial. These results are then compared to those recently published in the context of Bernoulli endpoints. Many limitations and advantages are similar in both cases but there are also important differences, specially with respect to type I error control. This paper proposes a simulation-based testing procedure to correct for the observed type I error inflation that bandit-based and adaptive rules can induce. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide some practical aspects of point and interval estimates of the global maximum of a function using extreme value theory. Consider a real-valued function f:D→ defined on a bounded interval D such that f is either not known analytically or is known analytically but has rather a complicated analytic form. We assume that f possesses a global maximum attained, say, at u*∈D with maximal value x*=max u f(u)≐f(u*). The problem of seeking the optimum of a function which is more or less unknown to the observer has resulted in the development of a large variety of search techniques. In this paper we use the extreme-value approach as appears in Dekkers et al. [A moment estimator for the index of an extreme-value distribution, Ann. Statist. 17 (1989), pp. 1833-1855] and de Haan [Estimation of the minimum of a function using order statistics, J. Amer. Statist. Assoc. 76 (1981), pp. 467-469]. We impose some Lipschitz conditions on the functions being investigated and through repeated simulation-based samplings, we provide various practical interpretations of the parameters involved as well as point and interval estimates for x*. [ABSTRACT FROM AUTHOR]

Consider the regression model . In a variety of situations, an estimate of VAR ( Y | X )?=?? ( X ) is needed. The paper compares the small-sample accuracy of five estimators of ? ( X ). The results suggest that the optimal estimator is a somewhat complex function of the underlying distributions. In terms of mean squared error, one of the estimators, which is based in part on a non-robust version of Cleveland's smoother, performed about as well as a bagged version of the so-called running interval smoother, but the running interval smoother was found to be preferable in terms of bias. A modification of Cleveland's smoother, stemming from Ruppert et al. (1997), achieves its intended goal of reducing bias when the error term is homoscedastic, but under heteroscedasticity, bias can be high, and in terms of mean squared error it does not compete well with the kernel method considered in the paper. When ? has a heavy-tailed distribution, a robust version of Cleveland's smoother performed particularly well except in some situations where X has a heavy-tailed distribution as well. A negative feature of using Cleveland's robust smoother is relatively high bias, and when there is heteroscedasticity and X has a heavy-tailed distribution, a kernel-type method and the running interval smoother give superior results in terms of both mean squared error and bias. [ABSTRACT FROM AUTHOR]

Over 50 years ago, in a 1955 issue of JASA, a paper on a bounded continuous distribution by Topp and Leone [C.W. Topp and F.C. Leone, A family of J-shaped frequency functions, J. Am. Stat. Assoc. 50(269) (1955), pp. 209-219] appeared (the subject was dormant for over 40 years but recently the family was resurrected). Here, we shall investigate the so-called Two-Sided Generalized Topp and Leone (TS-GTL) distributions. This family of distributions is constructed by extending the Generalized Two-Sided Power (GTSP) family to a new two-sided framework of distributions, where the first (second) branch arises from the distribution of the largest (smallest) order statistic. The TS-GTL distribution is generated from this framework by sampling from a slope (reflected slope) distribution for the first (second) branch. The resulting five-parameter TS-GTL family of distributions turns out to be flexible, encompassing the uniform, triangular, GTSP and two-sided slope distributions into a single family. In addition, the probability density functions may have bimodal shapes or admitting shapes with a jump discontinuity at the 'threshold' parameter. We will discuss some properties of the TS-GTL family and describe a maximum likelihood estimation (MLE) procedure. A numerical example of the MLE procedure is provided by means of a bimodal Galaxy M87 data set concerning V-I color indices of 80 globular clusters. A comparison with a Gaussian mixture fit is presented. [ABSTRACT FROM AUTHOR]

Linear mixed effects model (LMEM) is efficient in modeling repeated measures longitudinal data. However, little research has been done in developing goodness-of-fit measures that can evaluate the models, particularly those that can be interpreted in an absolute sense without referencing a null model. This paper proposes three coefficient of determination (R2) as goodness-of-fit measures for LMEM with repeated measures longitudinal data. Theorems are presented describing the properties of R2 and relationships between the R2 statistics. A simulation study was conducted to evaluate and compare the R2 along with other criteria from literature. Finally, we applied the proposed R2 to a real virologic response data of an HIV-patient cohort. We conclude that our proposed R2 statistics have more advantages than other goodness-of-fit measures in the literature, in terms of robustness to sample size, intuitive interpretation, well-defined range, and unnecessary to determine a null model. [ABSTRACT FROM AUTHOR]

While body fat is the most accurate measure of obesity, its measurement requires special equipment that can be costly and time consuming to operate. Attention has thus typically focused on the easier to calculate body mass index (BMI). However, the ability of BMI to accurately identify obesity has been increasingly questioned. This paper focuses attention on whether more general body mass indices are appropriate measures of body fat. Using a data set of body fat, height, and weight measurements, general models are estimated which nest a wide variety of weight-height indices as special cases. In the absence of a race and gender categorisation, the conventional BMI was found to be the appropriate index with which to predict body fat. When such a categorisation was made, however, the BMI was never selected as the appropriate index. In general, predicted female body fat was some 10 kg higher than that of a male of identical build and predicted % body fat was over 11 percentage points higher, but age effects were smaller for females. Considerable racial differences in predicted body fat were found for males, but such differences were less marked for females. The implications of this finding for interpreting recent research on the effect of obesity on health, society, and economic factors are considered. [ABSTRACT FROM AUTHOR]

Count data with excess zeros often occurs in areas such as public health, epidemiology, psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression are useful for modeling such data, but because of hierarchical study design or the data collection procedure, zero-inflation and correlation may occur simultaneously. To overcome these challenges ZIP or ZINB may still be used. In this paper, multilevel ZINB regression is used to overcome these problems. The method of parameter estimation is an expectation-maximization algorithm in conjunction with the penalized likelihood and restricted maximum likelihood estimates for variance components. Alternative modeling strategies, namely the ZIP distribution are also considered. An application of the proposed model is shown on decayed, missing, and filled teeth of children aged 12 years old. [ABSTRACT FROM AUTHOR]

In this paper a new family of test statistics is presented for testing the independence between the binary response Y and an ordered categorical explanatory variable X (doses) against the alternative hypothesis of an increase dose-response relationship between a response variable Y and X (doses). The properties of these test statistics are studied. This new family of test statistics is based on the family of φ-divergence measures and contains as a particular case the likelihood ratio test. We pay special attention to the family of test statistics associated with the power divergence family. A simulation study is included in order to analyze the behavior of the power divergence family of test statistics. [ABSTRACT FROM AUTHOR]

Achieving consistency of growth pattern for commercial yeast fermentation over batches through addition of water, molasses and other chemicals is often very complex in nature due to its bio-chemical reactions in operation. Regression models in statistical methods play a very important role in modeling the underlying mechanism, provided it is known. On the contrary, artificial neural networks provide a wide class of general-purpose, flexible non-linear architectures to explain any complex industrial processes. In this paper, an attempt has been made to find a robust control system for a time varying yeast fermentation process through statistical means, and in comparison to non-parametric neural network techniques. The data used in this context are obtained from an industry producing baker's yeast through a fed-batch fermentation process. The model accuracy for predicting the growth pattern of commercial yeast, when compared among the various techniques used, reveals the best performance capability with the backpropagation neural network. The statistical model used through projection pursuit regression also shows higher prediction accuracy. The models, thus developed, would also help to find an optimum combination of parameters for minimizing the variability of yeast production. [ABSTRACT FROM AUTHOR]

Technical efficiency, which is a measure of production performance of a firm, has been estimated generally using a primal production frontier. Since the estimation is carried out for a given level of inputs, the efficiency measure includes the effect of ‘input-mix’ or ‘input-allocation’ and consequently, the technical efficiency estimate is biased. The objectives of this paper are to gauge the magnitude of ‘input-mix’ bias in technical efficiency estimate and to suggest a method to measure technical efficiency eliminating the bias. The workability of the suggested method is demonstrated through an empirical analysis using agricultural data from India covering the period 1970–1993. [ABSTRACT FROM AUTHOR]

The identification of influential observations has drawn a great deal of attention in regression diagnostics. Most of these identification techniques are based on single case deletion and among them DFFITS has become very popular with the statisticians. But this technique along with all other single case diagnostics may be ineffective in the presence of multiple influential observations. In this paper we develop a generalized version of DFFITS based on group deletion and then propose a new technique to identify multiple influential observations using this. The advantage of using the proposed method in the identification of multiple influential cases is then investigated through several well-referred data sets. [ABSTRACT FROM AUTHOR]

This paper is concerned with the use of regression methods to predict values of a response variable when that variable is naturally ordered. An application to the prediction of student examination performance is provided and it is argued that, although individual scores are unlikely to be well predicted at the extremes of the range using the conditional mean, conditional on covariates, it is possible to usefully predict where an individual is likely to feature in the rank order of performance. [ABSTRACT FROM AUTHOR]

Two approaches have been used for designing spatial surveys to detect a target. The classical approach controls the probability of missing a target that exists; a Bayesian approach controls the probability that a target exists given that none was seen. In both cases, information about the likely size of the target can reduce sampling requirements. In this paper, previous results are summarized and then used to assess the risk that Roman remains could be present at sites scheduled for development in Greater London. [ABSTRACT FROM AUTHOR]

In this paper we present various diagnostic methods for a linear regression model under a logarithmic Birnbaum-Saunders distribution for the errors, which may be applied for accelerated life testing or to compare the median lives of several populations. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are derived, analysed and discussed. We also present a connection between the local influence and generalized leverage methods. A discussion of the computation of the likelihood displacement as well as the normal curvature in the local influence method are presented. Finally, an example with real data is given for illustration. [ABSTRACT FROM AUTHOR]

Pigeon & Raghavarao (1987) introduced control balanced residual treatment effects designs for the situation where one treatment is a control or standard and is to be compared with the v test treatments, and they have also given methods of construction of control balanced residual treatment effects designs and have investigated their efficiencies. In this paper we have developed some new families of control balanced residual treatment effects designs, which are Schur-optimal. [ABSTRACT FROM AUTHOR]

In this paper we introduce a family of test statistics for testing complete symmetry in three-dimensional contingency tables based on phi- divergence families. These test statistics yield the likelihood ratio test and the Pearson test statistics as special cases. Asymptotic distribution for the new test statistics are derived under both the null and the alternative hypotheses. A simulation study is presented to show that some new statistics offer an attractive alternative to the classical Pearson and likelihood ratio test statistics for this problem of complete symmetry. [ABSTRACT FROM AUTHOR]

Compositional data are characterized by values containing relative information, and thus the ratios between the data values are of interest for the analysis. Due to specific features of compositional data, standard statistical methods should be applied to compositions expressed in a proper coordinate system with respect to an orthonormal basis. It is discussed how three-way compositional data can be analyzed with the Parafac model. When data are contaminated by outliers, robust estimates for the Parafac model parameters should be employed. It is demonstrated how robust estimation can be done in the context of compositional data andhowthe results can be interpreted. A real data example from macroeconomics underlines the usefulness of this approach. [ABSTRACT FROM AUTHOR]

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility clustering and asymmetry characterize the logarithmic realized volatility of both the indices. The ARFIMAX model with time-varying conditional heteroskedasticity is the best performing specification and, at least in the case of DAX30, provides statistically superior next trading day's realized volatility forecasts. [ABSTRACT FROM AUTHOR]

Building from the consideration of closeness, we propose the mode quasi-range as an alternative scale parameter. Application of this scale parameter to formulate the population standard deviation is investigated leading to an efficient sample estimator of standard deviation from the point of asymptotic variance. Monte Carlo studies, in terms of finite sample efficiency and robustness of breakdown point, have been performed for the sample mode quasi-range. This study reveals that this closeness consideration-based mode, quasi-range, is satisfactory because these statistical procedures based on it are efficient and are less misleading for drawing conclusion from the sample results. [ABSTRACT FROM AUTHOR]

Empirical-distribution-function (EDF) goodness-of-fit tests are considered for the beta-binomial model. The testing procedures based on EDF statistics are given. A Monte Carlo study is conducted to investigate the accuracy and power of the tests against various alternative distributions. Our method is found to produce considerably greater power than that of Garren et al. (2001) in most cases. The tests are applied to data sets of the foraging behavior of herons and environmental toxicity studies. [ABSTRACT FROM AUTHOR]

Standard methods of estimation for autoregressive models are known to be biased in finite samples, which has implications for estimation, hypothesis testing, confidence interval construction and forecasting. Three methods of bias reduction are considered here: first-order bias correction, FOBC, where the total bias is approximated by the O(T-1) bias; bootstrapping; and recursive mean adjustment, RMA. In addition, we show how first-order bias correction is related to linear bias correction. The practically important case where the AR model includes an unknown linear trend is considered in detail. The fidelity of nominal to actual coverage of confidence intervals is also assessed. A simulation study covers the AR(1) model and a number of extensions based on the empirical AR(p) models fitted by Nelson & Plosser (1982). Overall, which method dominates depends on the criterion adopted: bootstrapping tends to be the best at reducing bias, recursive mean adjustment is best at reducing mean squared error, whilst FOBC does particularly well in maintaining the fidelity of confidence intervals. [ABSTRACT FROM AUTHOR]

A large-sample test for testing the equality of two effect sizes is presented. The null and non-null distributions of the proposed test statistic are derived. Further, the problem of estimating the effect size is considered when it is a priori suspected that two effect sizes may be close to each other. The combined data from all the samples leads to more efficient estimator of the effect size. We propose a basis for optimally combining estimation problems when there is uncertainty concerning the appropriate statistical model-estimator to use in representing the sampling process. The objective here is to produce natural adaptive estimators with some good statistical properties. In the context of two bivariate statistical models, the expressions for the asymptotic mean squared error of the proposed estimators are derived and compared with the parallel expressions for the benchmark estimators. We demonstrate that the suggested preliminary test estimator has superior asymptotic mean squared error performance relative to the benchmark and pooled estimators. A simulation study and application of the methodology to real data are presented. [ABSTRACT FROM AUTHOR]

It is well known that two-phase (or double) sampling is of significant use in practice when the population parameter(s) (say, population mean X¯) of the auxiliary variate x is not known. Keeping this in view, we have suggested a class of ratio-product estimators in two-phase sampling with its properties. The asymptotically optimum estimators (AOEs) in the class are identified in two different cases with their variances. Conditions for the proposed estimator to be more efficient than the two-phase sampling ratio, product and mean per unit estimator are investigated. Comparison with single phase sampling is also discussed. An empirical study is carried out to demonstrate the efficiency of the suggested estimator over conventional estimators. [ABSTRACT FROM AUTHOR]

The comparison of increasing doses of a compound to a zero dose control is of interest in medical and toxicological studies. Assume that the mean dose effects are non-decreasing among the non-zero doses of the compound. A simple procedure that modifies Dunnett's procedure is proposed to construct simultaneous confidence intervals for pairwise comparisons of each dose group with the zero dose control by utilizing the ordering of the means. The simultaneous lower bounds and upper bounds by the new procedure are monotone, which is not the case with Dunnett's procedure. This is useful to categorize dose levels. The expected gains of the new procedure over Dunnett's procedure are studied. The procedure is shown by real data to compare well with its predecessor. [ABSTRACT FROM AUTHOR]

I consider the problem of estimating the Mahalanobis distance between multivariate normal populations when the population covariance matrix satisfies a graphical model. In addition to providing a clear understanding of the dependencies in a multivariate data set, the use of graphical models can reduce the variability of the estimated distances and improve inferences. I derive the asymptotic distribution of the estimated Mahalanobis distance under a general covariance model, which includes graphical models as a special case. Two examples are discussed. [ABSTRACT FROM AUTHOR]

This article reviews and applies saddlepoint approximations to studentized confidence intervals based on robust M-estimates. The latter are known to be very accurate without needing standard theory assumptions. As examples, the classical studentized statistic, the studentized versions of Huber's M-estimate of location, of its initially MAD scaled version and of Huber's proposal 2 are considered. The aim is to know whether the studentized statistics yield robust confidence intervals with coverages close to nominal, with short intervals. The results of an extensive simulation study and the recommendations for practical use given in this article may fill gaps in the current literature and stimulate further discussion and research. [ABSTRACT FROM AUTHOR]

Because it is easy to compute from three common statistics (minimum, maximum, standard deviation) the studentized range is a useful test for non-normality when the original data are unavailable. For samples from symmetric populations, the studentized range allows an assessment of kurtosis with Type I and II error rates similar to those obtained from the moment coefficients. [ABSTRACT FROM AUTHOR]

Introduces a parametric model for the generation of stationary random correlated binary sequences. The parameters of the model; The spacial statistics of such correlated binary sequences; Understanding of such one-dimensional processing as a precondition for understanding in the more interesting two-dimensional case.