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This paper introduces an alternating conditional expectation (ACE) algorithm: a non-parametric approach for estimating the transformations that lead to the maximal multiple correlation of a response and a set of independent variables in regression and correlation analysis. These transformations can give the data analyst insight into the relationships between these variables so that this can be best described and non-linear relationships uncovered. Using the Bayesian information criterion (BIC), we show how to find the best closed-form approximations for the optimal ACE transformations. By means of ACE and BIC, the model fit can be considerably improved compared with the conventional linear model as demonstrated in the two simulated and two real datasets in this paper. [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]

Johnson (1992) developed the process loss index Le , which is defined as the ratio of the expected quadratic loss to the square of half specification width. Tsui (1997) expressed the index L e as Le = L pe+ L ot , which provides an uncontaminated separation between information concerning the potential relative expected loss ( L pe ) and the relative off-target squared ( Lot), as the ratio of the process variance and the square of the half specification width, and the square of the ratio of the deviation of mean from the target and the half specification width, respectively. In this paper, we consider these three loss function indices, and investigate the statistical properties of their natural estimators. For the three indices, we obtain their UMVUEs and MLEs, and compare the reliability of the two estimators based on the relative mean squared errors. In addition, we construct 90%, 95%, and 99% upper confidence limits, and the maximum values of L^ e for which the process is capable, 90%, 95%, and 99% of the time. The results obtained in this paper are useful to the practitioners in choosing good estimators and making reliable decisions on judging process capability. [ABSTRACT FROM AUTHOR]

Modern statistical data analysis is predominantly model-driven, seeking to decompose an observed data distribution in terms of major underlying descriptive features modified by some stochastic variation. A large part of data mining is also concerned with this exercise. However, another fundamental part of data mining is concerned with detecting anomalies amongst the vast mass of the data: the small deviations, unusual observations, unexpected clusters of observations, or surprising blips in the data, which the model does not explain. We call such anomalies patterns. For sound reasons, which are outlined in the paper, the data mining community has tended to focus on the algorithmic aspects of pattern discovery, and has not developed any general underlying theoretical base. However, such a base is important for any technology: it helps to steer the direction in which the technology develops, as well as serving to provide a basis from which algorithms can be compared, and to indicate which problems are the important ones waiting to be solved. This paper attempts to provide such a theoretical base, linking the ideas to statistical work in spatial epidemiology, scan statistics, outlier detection, and other areas. One of the striking characteristics of work on pattern discovery is that the ideas have been developed in several theoretical arenas, and also in several application domains, with little apparent awareness of the fundamentally common nature of the problem. Like model building, pattern discovery is fundamentally an inferential activity, and is an area in which statisticians can make very significant contributions. [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]

In this paper, a model has been developed for the x-chart showing the shifts in the process mean and R-chart showing those in the process variability. The model involves such parameters as the sample size, n, the sampling frequency, h, and the range of the control limits, k1 and k2 minimizing the cost function. This is adequate to reach the economic objectives. However, this paper is aimed at meeting the statistical objectives. The statistical objectives include minimizing the Type I error and the enhancing the testing power. Hence, the economic model has been handled as a multi-objective optimization problem involving the statistical objectives. Nonetheless, due to the fact that it is difficult, using classical methods, to solve such economic models that have the nature of a complex function, Genetic Algorithms (GAs) were used. The results are summarized in tables after they have been compared with those in the published literature. It is understood that the results obtained by GA are better than those in the literature and that they are more likely to reach the statistical objectives [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]

The von Mises distribution is widely used for modeling angular data. When such data are seen in a quality control setting, there may be interest in checking whether the values are in statistical control or have gone out of control. A cumulative sum (cusum) control chart has desirable properties for checking whether the distribution has changed from an in-control to an out-of-control setting. This paper develops cusums for a change in the mean direction and concentration of angular data and illustrates some of their properties. [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]

Data from longitudinal studies in which an initiating event and a subsequent event occur in sequence are called 'doubly censored' data if the time of both events is interval-censored. This paper is concerned with using doubly censored data to estimate the distribution function of the so-called 'duration time', i.e. the elapsed time between the originating event and the subsequent event. The paper proposes a generalization of the Gomez and Lagakos two-step method for the case where both the time to the initiating event and the duration time are continuous. This approach is applied to estimate the AIDS-latency time from a haemophiliacs cohort. [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]

SUMMARY In long-term field trials comparing different sequences of crops and husbandry practices, the identification and understanding of trends in productivity over time is an important issue of sustainable crop production. This paper presents a statistical technique for the estimation of time trends in yield variables of a seasonal annual crop under continuous cropping. The estimation procedure incorporates the correlation structure, which is assumed to follow first-order autocorrelation in the errors that arise over time on the same plot. Because large differences in annual rainfall have a major effect on crop performance, rainfall has been allowed for in the estimation of the time trends. Expressions for the number of years (time) required to detect statistically significant time trends have been obtained. Illustrations are based on a 7-year data set of grain and straw yields from a trial in northern Syria. Although agronomic interpretation is not intended in this paper, the barley yield data indicated that a significant time trend can apparently be detected even in a suboptimal data set of 7 years' duration. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide some group sequential procedures for comparing several treatments with a control. These procedures are the generalizations to the sequential sampling setting of the single-step procedure of Dunnett and the step-down procedure of Hochberg and Tamhane. When the responses are independent normal random variables with a common known variance, the critical values for specified alpha-levels are tabulated. The situation where the responses are binary random variables is also discussed. [ABSTRACT FROM AUTHOR]

A relatively newer computational technique adopted by statisticians is known as independent component analysis (ICA) which is used to analyze complex multidimensional data with the objective to separate it into components that are independent to each other. Quite often the main interest for conducting ICA is to identify a small number of significant independent components (ICs) to replace the original complex dimensions with. For this, determining the order of identified ICs is a pre-requisite. The area is not unaddressed but it does deserve a careful revisiting. This is the subject matter of the paper which introduces a new method to order ICs. The proposed method is based upon regression approach. It compares the magnitude of the mixing coefficients and regression coefficients of the regression of the original series on ICs. Their compatibility determines the order. [ABSTRACT FROM PUBLISHER]

The present paper focuses on examining students' attitudes and perception of statistics in Afghanistan universities and the factor structure of the statistical anxiety rating scale (STARS). In total, 209 undergraduate students from different disciplines in different universities in Afghanistan participated in the study. In addition to testing the factor structure of the STARS, a multivariate multilevel analysis that incorporates the correlation in the data was carried out on the aggregated subscales of the STARS scores. Results showed that the original 6-factor structure did not fit the Afghanistan data well. Exploratory factor analysis identified 5-factor constructs to best fit the data and was confirmed by the fit indices as well as a likelihood ratio test. Male students showed more positive attitudes toward statistics and a higher level of statistics anxiety than their female counterparts. Female students experienced higher levels of fear of asking for help and less anxiety in computation. Students who had taken at least a previous statistics course had lower statistics anxiety than those taking the course for the first time. [ABSTRACT FROM PUBLISHER]

Inverse sampling is widely applied in studies with dichotomous outcomes, especially when the subjects arrive sequentially or the response of interest is difficult to obtain. In this paper, we investigate the rate ratio test problem under inverse sampling based on gradient statistic with the asymptotic method and parametric bootstrap technique. The gradient statistic has many advantages, for example, it is simple to calculate and competitive with Wald-type, score and likelihood ratio tests in terms of local power. Numerical studies are carried out to evaluate the performance of our gradient test and the existing tests, namely Wald-type, score and likelihood ratio tests. The simulation results suggest that the gradient test based on the parametric bootstrap method has excellent type I error control and large powers even in small sample design. Two real examples, from a heart disease study and a drug comparison study, are applied to illustrate our methods. [ABSTRACT FROM AUTHOR]

The traditional standardized mortality ratio (SMR) compares the mortality rate of a study population with that of a reference population. In order to measure the performance of a surgeon or a group of surgeons in a hospital performing a particular type of surgical operation, a different SMR is used. This SMR compares the observed number of deaths in a sample with an estimated number of deaths usually calculated based on the average performance of a group of surgeons. The estimated number of deaths involved in the new SMR is not a constant but a random variable. This means that all existing results for the traditional SMR may no longer be valid for the new SMR. In this paper, the asymptotic distribution of the SMR based on an estimated number of deaths is derived. We also use the bootstrap procedure to estimate the finite-sample distribution. A simulation study is used to compare both probabilities of type I error and powers of existing confidence intervals and confidence intervals constructed using the asymptotic and bootstrap distributions of SMR. Our study reveals that, in general, existing confidence intervals are conservative in terms of probability of type I error, and the two new confidence intervals are more accurate. To perform a fair power comparison, the coverage probabilities of existing confidence intervals are recalibrated to match that based on the asymptotic distribution of SMR, and then our study shows that the powers of the asymptotic and bootstrap approaches are lower than existing approaches when the odds ratio of deathQis greater than the odds ratio of death under the null hypothesis,, but higher whenQis smaller than. The effect of patients' risk distribution on the SMR is also investigated. [ABSTRACT FROM PUBLISHER]

In recent years, there has been considerable interest in regression models based on zero-inflated distributions. These models are commonly encountered in many disciplines, such as medicine, public health, and environmental sciences, among others. The zero-inflated Poisson (ZIP) model has been typically considered for these types of problems. However, the ZIP model can fail if the non-zero counts are overdispersed in relation to the Poisson distribution, hence the zero-inflated negative binomial (ZINB) model may be more appropriate. In this paper, we present a Bayesian approach for fitting the ZINB regression model. This model considers that an observed zero may come from a point mass distribution at zero or from the negative binomial model. The likelihood function is utilized to compute not only some Bayesian model selection measures, but also to develop Bayesian case-deletion influence diagnostics based onq-divergence measures. The approach can be easily implemented using standard Bayesian software, such as WinBUGS. The performance of the proposed method is evaluated with a simulation study. Further, a real data set is analyzed, where we show that ZINB regression models seems to fit the data better than the Poisson counterpart. [ABSTRACT FROM PUBLISHER]

Ipsilateral breast tumor relapse (IBTR) often occurs in breast cancer patients after their breast conservation therapy. The IBTR status' classification (true local recurrence versus new ipsilateral primary tumor) is subject to error and there is no widely accepted gold standard. Time to IBTR is likely informative for IBTR classification because new primary tumor tends to have a longer mean time to IBTR and is associated with improved survival as compared with the true local recurrence tumor. Moreover, some patients may die from breast cancer or other causes in a competing risk scenario during the follow-up period. Because the time to death can be correlated to the unobserved true IBTR status and time to IBTR (if relapse occurs), this terminal mechanism is non-ignorable. In this paper, we propose a unified framework that addresses these issues simultaneously by modeling the misclassified binary outcome without a gold standard and the correlated time to IBTR, subject to dependent competing terminal events. We evaluate the proposed framework by a simulation study and apply it to a real data set consisting of 4477 breast cancer patients. The adaptive Gaussian quadrature tools inSASprocedureNLMIXEDcan be conveniently used to fit the proposed model. We expect to see broad applications of our model in other studies with a similar data structure. [ABSTRACT FROM AUTHOR]

Recently, the ensemble learning approaches have been proven to be quite effective for variable selection in linear regression models. In general, a good variable selection ensemble should consist of a diverse collection of strong members. Based on the parallel genetic algorithm (PGA) proposed in [41], in this paper, we propose a novel method RandGA through injecting randomness into PGA with the aim to increase the diversity among ensemble members. Using a number of simulated data sets, we show that the newly proposed method RandGA compares favorably with other variable selection techniques. As a real example, the new method is applied to the diabetes data. [ABSTRACT FROM AUTHOR]

The literature devoted to the export-led growth (ELG) hypothesis, which is of utmost importance for policymaking in emerging countries, provides mixed evidence for the validity of the hypothesis. Recent contributions focus on the time-dependence of the relationship between export and output growth using rolling causality techniques based on vector autoregressive models. These models focus on a short-term view which captures single policy-induced developments. However, long-term structural changes cannot be covered by examinations related to the short-term. This paper hence examines the time-varying validity of the ELG hypothesis for India for the period 1960–2011 using rolling causality techniques for both the short-run and long-run horizon. For the first time, window-wise optimal lag-selection procedures are applied in connection with these techniques. We find that exports long-run caused output growth from 1997 until 2009 which can be seen as a consequence of political reforms of the 1990s that boosted economic growth by generating foreign direct investment opportunities and higher exports. For the short-run, export significantly caused output in the period 1998–2003 which followed a concentration of liberalization measures in 1997. Causality in the reversed direction, from output to exports, only seems to be relevant in the short-run. [ABSTRACT FROM AUTHOR]

The aim of this paper is to define a new approach for the analysis of data collected by means of SERVQUAL questionnaires which is based on the generalized cross entropy (GCE) approach. In this respect, we firstly give a short review about the important role that SERVQUAL plays in the analysis of service quality as well as in the assessment of the competitiveness of public and private organizations. Secondly, we provide a formal definition of GCE approach together with a brief discussion about its features and usefulness. Finally, we show the application of GCE for a SERVQUAL model, based on a patients’ satisfaction case study and we discuss the results obtained by using the proposed GCE-SERVQUAL methodology. [ABSTRACT FROM AUTHOR]

Poverty can be seen as a multidimensional phenomenon described by a set of indicators, the poverty components. A one-dimensional measure of poverty serving as a ranking index can be obtained by combining the component indicators via aggregation techniques. Ranking indices are thought of as supporting political decisions. This paper proposes an alternative to aggregation based on simple concepts of partial order theory and illustrates the pros and cons of this approach taking as case study a multidimensional measure of poverty comprising three components – absolute poverty, relative poverty and income – computed for the European Union regions. The analysis enables one to highlight conflicts across the components with some regions detected as controversial, with, for example, low levels of relative poverty and high levels of monetary poverty. The partial order approach enables one to point to the regions with the most severe data conflicts and to the component indicators that cause these conflicts. [ABSTRACT FROM AUTHOR]

Nowadays, sensory properties of materials are subject to growing attention both in an hedonic point of view and in an utilitarian one. Hence, the formulation of the foundations of an instrumental metrological approach that will allow for the characterization of visual similarities between textures belonging to the same type becomes a challenge of the research activities in the domain of perception. In this paper, our specific objective is to link an instrumental approach of metrology of the assessment of visual textures with a metrology approach based on a softcopy experiment performed by human judges. The experiment consisted in ranking of isochromatic colored textures according to the visual contrast. A fixed effects additive model is considered for the analysis of the rank data collected from the softcopy experiment. The model is fitted to the data using a least-squares criterion. The resulting data analysis gives rise to a sensory scale that shows a non-linear correlation and a monotonic functional relationship with the physical attribute on which the ranking experiment is based. Furthermore, the capacity of the judges to discriminate the textures according to the visual contrast varies according to the color ranges and the textures types. [ABSTRACT FROM AUTHOR]

In this paper, the scheme of the inspection plan, namely the tightened normal tightened (nT,nN;k) is considered and procedures and necessary tables are developed for the selection of the variables sampling scheme, indexed through crossover point (COP). The importance of COP, the properties and advantages of the operating characteristic curve with respect to COP are studied. [ABSTRACT FROM PUBLISHER]

In life-testing and survival analysis, sometimes the components are arranged in series or parallel system and the number of components is initially unknown. Thus, the number of components, sayZ, is considered as random with an appropriate probability mass function. In this paper, we model the survival data with baseline distribution as Weibull and the distribution ofZas generalized Poisson, giving rise to four parameters in the model: increasing, decreasing, bathtub and upside bathtub failure rates. Two examples are provided and the maximum-likelihood estimation of the parameters is studied. Rao's score test is developed to compare the results with the exponential Poisson model studied by Kus [17] and the exponential-generalized Poisson distribution with baseline distribution as exponential and the distribution ofZas generalized Poisson. Simulation studies are carried out to examine the performance of the estimates. [ABSTRACT FROM PUBLISHER]

Under some very reasonable hypotheses, it becomes evident that randomizing the run order of a factorial experiment does not always neutralize the effect of undesirable factors. Yet, these factors do have an influence on the response, depending on the order in which the experiments are conducted. On the other hand, changing the factor levels is many times costly; therefore it is not reasonable to leave to chance the number of changes necessary. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of undesirable factors on the experimentation have been sought. Sequences which are known to produce the desired properties in designs with 8 and 16 experiments can be found in the literature. In this paper, we provide the best possible sequences for designs with 32 experiments, as well as sequences that offer excellent properties for designs with 64 and 128 experiments. The method used to find them is based on a mixture of algorithmic searches and an augmentation of smaller designs. [ABSTRACT FROM PUBLISHER]

In this paper, the exhaustive search principle used in functional trees for classifying densities is shown to select variables with more split points. A new variable selection scheme is proposed to correct this bias. The Pearson chi-squared tests for associated two-way contingency tables are used to select the variables. Through simulation, we show that the new method can control bias and is more powerful in selecting split variable. [ABSTRACT FROM PUBLISHER]

In this paper, a test statistic for testing upper outliers with a slippage alternative, in an exponential sample is proposed. No tables for critical values are required as they can be calculated easily for any sample size. A simulation study is also carried out to compare the performance of the test with the maximum likelihood ratio test and other existing tests. [ABSTRACT FROM PUBLISHER]

Mixture experiments are commonly encountered in many fields including chemical, pharmaceutical and consumer product industries. Due to their wide applications, mixture experiments, a special study of response surface methodology, have been given greater attention in both model building and determination of designs compared with other experimental studies. In this paper, some new approaches are suggested on model building and selection for the analysis of the data in mixture experiments by using a special generalized linear models, logistic regression model, proposed by Chen et al. [7]. Generally, the special mixture models, which do not have a constant term, are highly affected by collinearity in modeling the mixture experiments. For this reason, in order to alleviate the undesired effects of collinearity in the analysis of mixture experiments with logistic regression, a new mixture model is defined with an alternative ratio variable. The deviance analysis table is given for standard mixture polynomial models defined by transformations and special mixture models used as linear predictors. The effects of components on the response in the restricted experimental region are given by using an alternative representation of Cox's direction approach. In addition, odds ratio and the confidence intervals of odds ratio are identified according to the chosen reference and control groups. To compare the suggested models, some model selection criteria, graphical odds ratio and the confidence intervals of the odds ratio are used. The advantage of the suggested approaches is illustrated on tumor incidence data set. [ABSTRACT FROM AUTHOR]

In this paper, a new estimator combined estimator (CE) is proposed for estimating the finite population mean ¯ YN in simple random sampling assuming a long-tailed symmetric super-population model. The efficiency and robustness properties of the CE is compared with the widely used and well-known estimators of the finite population mean ¯ YN by Monte Carlo simulation. The parameter estimators considered in this study are the classical least squares estimator, trimmed mean, winsorized mean, trimmed L-mean, modified maximum-likelihood estimator, Huber estimator (W24) and the non-parametric Hodges-Lehmann estimator. The mean square error criteria are used to compare the performance of the estimators. We show that the CE is overall more efficient than the other estimators. The CE is also shown to be more robust for estimating the finite population mean ¯ YN, since it is insensitive to outliers and to misspecification of the distribution. We give a real life example. [ABSTRACT FROM AUTHOR]

Failure time models are considered when there is a subpopulation of individuals that is immune, or not susceptible, to an event of interest. Such models are of considerable interest in biostatistics. The most common approach is to postulate a proportion p of immunes or long-term survivors and to use a mixture model [5]. This paper introduces the defective inverse Gaussian model as a cure model and examines the use of the Gibbs sampler together with a data augmentation algorithm to study Bayesian inferences both for the cured fraction and the regression parameters. The results of the Bayesian and likelihood approaches are illustrated on two real data sets. [ABSTRACT FROM AUTHOR]

The importance of finding appropriate parameterizations for nonlinear statistical models is highlighted. The purpose of this paper is to explore the principles of reparameterization, using an example from real data. It is shown that stable parameterizations allow likelihood-based confidence intervals to be computed. Further, it is noted that the choice of error distribution may seriously affect the estimates and confidence intervals of quantities of interest. The influence of each observation on the estimation of each parameter is displayed for each error model. Multidimensional likelihood contours may be displayed pairwise using profile likelihood computations. [ABSTRACT FROM AUTHOR]

Automated public health surveillance of disease counts for rapid outbreak, epidemic or bioterrorism detection using conventional control chart methods can be hampered by over-dispersion and background ('in-control') mean counts that vary over time. An adaptive cumulative sum (CUSUM) plan is developed for signalling unusually high incidence in prospectively monitored time series of over-dispersed daily disease counts with a non-homogeneous mean. Negative binomial transitional regression is used to prospectively model background counts and provide 'one-step-ahead' forecasts of the next day's count. A CUSUM plan then accumulates departures of observed counts from an offset (reference value) that is dynamically updated using the modelled forecasts. The CUSUM signals whenever the accumulated departures exceed a threshold. The amount of memory of past observations retained by the CUSUM plan is determined by the offset value; a smaller offset retains more memory and is efficient at detecting smaller shifts. Our approach optimises early outbreak detection by dynamically adjusting the offset value. We demonstrate the practical application of the 'optimal' CUSUM plans to daily counts of laboratory-notified influenza and Ross River virus diagnoses, with particular emphasis on the steady-state situation (i.e. changes that occur after the CUSUM statistic has run through several in-control counts). [ABSTRACT FROM AUTHOR]

Gupta and Shabbir 2 have suggested an alternative form of ratio-type estimators for estimating the population mean. In this paper, we obtained a corrected version for the mean square error (MSE) of the Gupta-Shabbir estimator, up to first order of approximation, and the optimum case is discussed. We expand this estimator to the stratified random sampling and propose general classes for combined and separate estimators. Also an empirical study is carried out to show the properties of the proposed estimators. [ABSTRACT FROM AUTHOR]

The commonly made assumption that all stochastic error terms in the linear regression model share the same variance (homoskedasticity) is oftentimes violated in practical applications, especially when they are based on cross-sectional data. As a precaution, a number of practitioners choose to base inference on the parameters that index the model on tests whose statistics employ asymptotically correct standard errors, i.e. standard errors that are asymptotically valid whether or not the errors are homoskedastic. In this paper, we use numerical integration methods to evaluate the finite-sample performance of tests based on different (alternative) heteroskedasticity-consistent standard errors. Emphasis is placed on a few recently proposed heteroskedasticity-consistent covariance matrix estimators. Overall, the results favor the HC4 and HC5 heteroskedasticity-robust standard errors. We also consider the use of restricted residuals when constructing asymptotically valid standard errors. Our results show that the only test that clearly benefits from such a strategy is the HC0 test. [ABSTRACT FROM AUTHOR]

The aim of this paper is to find an optimal alternative bivariate ranked-set sample for one-sample location model bivariate sign test. Our numerical and theoretical results indicated that the optimal designs for the bivariate sign test are the alternative designs with quantifying order statistics with labels {((r+1)/2, (r+1)/2)}, when the set size r is odd and {(r/2+1, r/2), (r/2, r/2+1)} when the set size r is even. The asymptotic distribution and Pitman efficiencies of these designs are derived. A simulation study is conducted to investigate the power of the proposed optimal designs. Illustration using real data with the Bootstrap algorithm for P-value estimation is used. [ABSTRACT FROM AUTHOR]

This paper provides tables for the construction and selection of tightened-normal-tightened variables sampling scheme of type TNTVSS (n1, n2; k). The method of designing the scheme indexed by (AQL, α) and (LQL, β) is indicated. The TNTVSS (nT, nN; k) is compared with conventional single sampling plans for variables and with TNT (n1, n2; c) scheme for attributes, and it is shown that the TNTVSS is more efficient. [ABSTRACT FROM AUTHOR]

Surles and Padgett recently considered two-parameter Burr Type X distribution by introducing a scale parameter and called it the generalized Rayleigh distribution. It is observed that the generalized Rayleigh and log-normal distributions have many common properties and both distributions can be used quite effectively to analyze skewed data set. In this paper, we mainly compare the Fisher information matrices of the two distributions for complete and censored observations. Although, both distributions may provide similar data fit and are quite similar in nature in many aspects, the corresponding Fisher information matrices can be quite different. We compute the total information measures of the two distributions for different parameter ranges and also compare the loss of information due to censoring. Real data analysis has been performed for illustrative purposes. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a pedagogical explanation of the Le Cam theorem and to illustrate its use, through a practical application, for temporal cluster detection. This theorem focusses on the interval division by randomly chosen points. The aim of the theorem is to characterize the asymptotic behavior of a certain category of sums of functions applied to the length of successive intervals between points. It is not very intuitive and its understanding needs some deepening. After enouncing the theorem, its different aspects are explained and detailed in a way as pedagogical as possible. Theoretical applications are proposed through the proof of two propositions. Then a very concrete application of this theorem for temporal cluster detection is presented, tested by a power study, and compared with other global cluster detection tests. Finally, this approach is applied to the well-known Knox temporal data set. [ABSTRACT FROM AUTHOR]

Pulak and Al-Sultan presented a rectifying inspection plan applying in the determination of optimum process mean. However, they did not point out whether the non-conforming items in the sample of accepted lot are replaced or eliminated from the lot and neglected the quality loss within specification limits. In this paper, we further propose the modified Pulak and Al-Sultan model with quadratic quality loss function. There are four cases considered in the modified model: (1) the non-conforming items in the sample of accepted lot are neither replaced nor eliminated from the lot; (2) the non-conforming items in the sample of accepted lot are not replaced but are eliminated from the lot; (3) the non-conforming items in the sample of accepted lot are replaced by conforming ones; (4) the non-conforming items in the sample of accepted lot are replaced by non-inspected items. The numerical results and sensitivity analysis of parameters show that their solutions are slightly different. [ABSTRACT FROM AUTHOR]

In this paper, we examine deterministic and Bayesian methods for analyzing finite Dirichlet mixtures. The deterministic method is based on the likelihood approach, and the Bayesian approach is implemented using the Gibbs sampler. The selection of the number of clusters for both approaches is based on the Bayesian information criterion, which is equivalent to the minimum description length. Experimental results are presented using simulated data, and a real application for software modules classification is also included. [ABSTRACT FROM AUTHOR]

Variable and model selection problems are fundamental to high-dimensional statistical modeling in diverse fields of sciences. Especially in health studies, many potential factors are usually introduced to determine an outcome variable. This paper deals with the problem of high-dimensional statistical modeling through the analysis of the trauma annual data in Greece for 2005. The data set is divided into the experiment and control sets and consists of 6334 observations and 112 factors that include demographic, transport and intrahospital data used to detect possible risk factors of death. In our study, different model selection techniques are applied to the experiment set and the notion of deviance is used on the control set to assess the fit of the overall selected model. The statistical methods employed in this work were the non-concave penalized likelihood methods, smoothly clipped absolute deviation, least absolute shrinkage and selection operator, and Hard, the generalized linear logistic regression, and the best subset variable selection.The way of identifying the significant variables in large medical data sets along with the performance and the pros and cons of the various statistical techniques used are discussed. The performed analysis reveals the distinct advantages of the non-concave penalized likelihood methods over the traditional model selection techniques. [ABSTRACT FROM AUTHOR]

In this paper, one-sided and two-sided test procedures for comparing several treatments with more than one control with respect to scale parameter are proposed. The proposed test procedures are inverted to obtain the associated simultaneous confidence intervals. The multiple comparisons of test treatments with the best control are also developed. The computation of the critical points, required to implement the proposed procedures, is discussed by taking the normal probability model. Applications of the proposed test procedures to two-parameter exponential probability model are also demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, we consider the estimation of the optimum factor combination in a response surface model. Assuming that the response function is quadratic concave and there is a linear cost constraint on the factor combination, we attempt to find the optimum design using the trace optimality criterion. As the criterion function involves the unknown parameters, we adopt a pseudo-Bayesian approach to resolve the problem. [ABSTRACT FROM AUTHOR]