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PSYCHOLOGY IS in a bit of trouble these days. It has made headlines for questionable interpretations of statistics and for wellknown studies that can't be replicated. Other quantitative social and [...]

The problem of testing the mean vector in a high-dimensional setting is considered. Up to date, most high-dimensional tests for the mean vector only make use of the marginal information from the variables, and do not incorporate the correlation information into the test statistics. A new testing procedure is proposed, which makes use of the covariance information between the variables. The new approach is novel in that it can select important variables that contain evidence against the null hypothesis and reduce the impact of noise accumulation. Simulations and real data analysis demonstrate that the new test has higher power than some competing methods proposed in the literature. [ABSTRACT FROM AUTHOR]

A class of goodness-of-fit tests whose test statistic is an L 2 norm of the difference of the empirical characteristic function of the sample and a parametric estimate of the characteristic function in the null hypothesis, is considered. The null distribution is usually estimated through a parametric bootstrap. Although very easy to implement, the parametric bootstrap can become very computationally expensive as the sample size, the number of parameters or the dimension of the data increase. It is proposed to approximate the null distribution through a weighted bootstrap. The method is studied both theoretically and numerically. It provides a consistent estimator of the null distribution. In the numerical examples carried out, the estimated type I errors are close to the nominal values. The asymptotic properties are similar to those of the parametric bootstrap but, from a computational point of view, it is more efficient. [ABSTRACT FROM AUTHOR]

This paper proposes a chi-square method (CSM) to obtain a priority vector for group decision making (GDM) problems where decision-makers’ (DMs’) assessment on alternatives is furnished as incomplete reciprocal preference relations with missing values. Relevant theorems and an iterative algorithm about CSM are proposed. Saaty’s consistency ratio concept is adapted to judge whether an incomplete reciprocal preference relation provided by a DM is of acceptable consistency. If its consistency is unacceptable, an algorithm is proposed to repair it until its consistency ratio reaches a satisfactory threshold. The repairing algorithm aims to rectify an inconsistent incomplete reciprocal preference relation to one with acceptable consistency in addition to preserving the initial preference information as much as possible. Finally, four examples are examined to illustrate the applicability and validity of the proposed method, and comparative analyses are provided to show its advantages over existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, the noise enhanced system performance in a binary hypothesis testing problem is investigated when the additive noise is a convex combination of the optimal noise probability density functions (PDFs) obtained in two limit cases, which are the minimization of false-alarm probability ( P FA ) without decreasing detection probability ( P D ) and the maximization of P D without increasing P FA , respectively. Existing algorithms do not fully consider the relationship between the two limit cases and the optimal noise is often deduced according to only one limit case or Bayes criterion. We propose a new optimal noise framework which utilizes the two limit cases and deduce the PDFs of the new optimal noise. Furthermore, the sufficient conditions are derived to determine whether the performance of the detector can be improved or not via the new noise. In addition, the effects of the new noise are analyzed according to Bayes criterion. Rather than adjusting the additive noise again as shown in other algorithms, we just tune one parameter of the new optimal noise PDF to meet the different requirements under the Bayes criterion. Finally, an illustrative example is presented to study the theoretical results. [ABSTRACT FROM AUTHOR]

Frequent episode discovery is one of the methods used for temporal pattern discovery in sequential data. An episode is a partially ordered set of nodes with each node associated with an event type. For more than a decade, algorithms existed for episode discovery only when the associated partial order is total (serial episode) or trivial (parallel episode). Recently, the literature has seen algorithms for discovering episodes with general partial orders. In frequent pattern mining, the threshold beyond which a pattern is inferred to be interesting is typically user-defined and arbitrary. One way of addressing this issue in the pattern mining literature has been based on the framework of statistical hypothesis testing. This paper presents a method of assessing statistical significance of episode patterns with general partial orders. A method is proposed to calculate thresholds, on the non-overlapped frequency, beyond which an episode pattern would be inferred to be statistically significant. The method is first explained for the case of injective episodes with general partial orders. An injective episode is one where event-types are not allowed to repeat. Later it is pointed out how the method can be extended to the class of all episodes. The significance threshold calculations for general partial order episodes proposed here also generalize the existing significance results for serial episodes. Through simulations studies, the usefulness of these statistical thresholds in pruning uninteresting patterns is illustrated. [ABSTRACT FROM AUTHOR]

The article discusses the growing realization that statistically significant claims in scientific publications are routinely mistaken. The authors explain researchers' use of the p-value (probability) to express the confidence of their data against a null hypothesis and discuss how to test a hypothesis and research by Michael Peterson and colleagues who claimed to have found a statistical association, expressed as a p-value, between arm strength and socioeconomic status.

The class of composite likelihood functions provides a flexible and powerful toolkit to carry out approximate inference for complex statistical models when the full likelihood is either impossible to specify or unfeasible to compute. However, the strength of the composite likelihood approach is dimmed when considering hypothesis testing about a multidimensional parameter because the finite sample behavior of likelihood ratio, Wald, and score-type test statistics is tied to the Godambe information matrix. Consequently, inaccurate estimates of the Godambe information translate in inaccurate p-values. The approach based on a fully nonparametric saddlepoint test statistic derived from the composite score functions is shown to achieve accurate inference. The proposed statistic is asymptotically chi-squared distributed up to a relative error of second order and does not depend on the Godambe information. The validity of the method is demonstrated through simulation studies. [ABSTRACT FROM AUTHOR]

Multiple-testing problems have received much attention. Different strategies have been considered in order to deal with this problem. The false discovery rate (FDR) is, probably, the most studied criterion. On the other hand, the sequential goodness of fit (SGoF), is a recently proposed approach. Most of the developed procedures are based on the independence among the involved tests; however, in spite of being a reasonable proviso in some frameworks, independence is not realistic for a number of practical cases. Therefore, one of the main problems in order to develop appropriate methods is, precisely, the effect of the dependence among the different tests on decisions making. The consequences of the correlation on the z-values distribution in the general multitesting problem are explored. Some different algorithms are provided in order to approximate the distribution of the expected rejection proportions. The performance of the proposed methods is evaluated in a simulation study in which, for comparison purposes, the Benjamini and Hochberg method to control the FDR, the Lehmann and Romano procedure to control the tail probability of the proportion of false positives (TPPFP), and the Beta--Binomial SGoF procedure are considered. Three different dependence structures are considered. As usual, for a better understanding of the problem, several practical cases are also studied. [ABSTRACT FROM AUTHOR]

The article focuses on the use of multi-linescan light-field camera in computational imaging. Topics discussed include light-field photography, construction of all-in-focus images with extended depth of field (DoF), and criterion definition for slope hypothesis testing through block matching (BM) in the image spatial domain. Also mentioned are short exposure time, epipolar plane images (EPIs), and structure tensor.

Abstract: In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation results show that both PHD filter implementations successfully track multiple targets using only Doppler shift measurements. Moreover, as a proof-of-concept, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Experimental study results reveal that it is possible to track multiple ground targets using acoustic Doppler shift measurements in a passive multi-static scenario. We observed that the GM-PHD is more effective, efficient and easy to implement than the SMC-PHD filter. [Copyright &y& Elsevier]

Abstract: A selection procedure with no optimization step called LOLA, for Learning Out of Leaders with Adaptation is proposed. LOLA is an auto-driven algorithm with two thresholding steps. The consistency of the LOL procedure (the non adaptive version of LOLA) is proved under sparsity conditions and simulations are conducted to illustrate the practical good performances of LOLA. The behavior of the algorithm is studied when instrumental variables are artificially added without a priori significant connection to the model. Finally, the problem of empirically verifying the conditional convergence hypothesis used in economics concerning the growth rate is studied. To avoid unnecessary discussion about the choice and the pertinence of instrumental variables, the model is embedded in a very high dimensional setting. Using the LOLA algorithm, a solution for modeling the link between the growth rate and the initial level of the gross domestic product is provided and the convergence hypothesis is empirically proved. [Copyright &y& Elsevier]

Abstract: A recently proposed Bayesian model selection technique, stochastic model specification search, is carried out to discriminate between two trend generation hypotheses. The first is the trend-stationary hypothesis, for which the trend is a deterministic function of time and the short run dynamics are represented by a stationary autoregressive process. The second is the difference-stationary hypothesis, according to which the trend results from the cumulation of the effects of random disturbances. A difference-stationary process may originate in two ways: from an unobserved components process adding up an integrated trend and an orthogonal transitory component, or implicitly from an autoregressive process with roots on the unit circle. The different trend generation hypotheses are nested within an encompassing linear state space model. After a reparameterisation in non-centred form, the empirical evidence supporting a particular hypothesis is obtained by performing variable selection on the model components, using a suitably designed Gibbs sampling scheme. The methodology is illustrated with reference to a set of US macroeconomic time series which includes the traditional Nelson and Plosser dataset. The conclusion is that most series are better represented by autoregressive models with time-invariant intercept and slope and coefficients that are close to boundary of the stationarity region. The posterior distribution of the autoregressive parameters provides useful insight on quasi-integrated nature of the specifications selected. [Copyright &y& Elsevier]

Abstract: A new method is proposed for testing multiple hypotheses with equality and inequality constraints on the parameters of interest. The method is based on the fractional Bayes factor with a modification that the updated prior is centered on the boundary of the constrained parameter space under investigation. The resulting prior adjusted default Bayes factors work as an “Ockham’s razor” when testing inequality constrained hypotheses, which is not the case for the fractional Bayes factor. Two different types of prior adjusted default Bayes factors are considered. In the first type, the updated prior is based on imaginary training data. Analytical and numerical examples show that this criterion converges fastest to a true inequality constrained hypothesis. In the second type, the updated prior is based on empirical training data. This second criterion only outperforms the fractional Bayes factor in the case of small samples. [Copyright &y& Elsevier]

Abstract: Noise enhanced hypothesis-testing is studied according to the restricted Neyman–Pearson (NP) criterion. First, a problem formulation is presented for obtaining the optimal probability distribution of additive noise in the restricted NP framework. Then, sufficient conditions for improvability and nonimprovability are derived in order to specify if additive noise can or cannot improve detection performance over scenarios in which no additive noise is employed. Also, for the special case of a finite number of possible parameter values under each hypothesis, it is shown that the optimal additive noise can be represented by a discrete random variable with a certain number of point masses. In addition, particular improvability conditions are derived for that special case. Finally, theoretical results are provided for a numerical example and improvements via additive noise are illustrated. [Copyright &y& Elsevier]

Abstract: An inferential model is developed for large-scale simultaneous hypothesis testing. Starting with a simple hypothesis testing problem, the inferential model produces a probability triplet on an assertion of the null or alternative hypothesis. The probabilities p and q are for and against the truth of the assertion, whereas is the remaining probability called the probability of “donʼt know”. For a large set of hypotheses, a sequence of assertions concerning the total number of true alternative hypotheses are proposed. The inferential model provides levels of belief without a prior for the sequence of assertions and offers a new multiple comparison procedure (MCP). The proposed method is obtained by improving Fisherʼs fiducial and the Dempster–Shafer theory of belief functions so that it produces probabilistic inferential results with desirable frequency properties. The new multiple comparison procedure is shown to have a comparable performance with other existing MCPs and is favorable in terms of probabilistic interpretation. The proposed method is applied in identifying differentially expressed genes in microarray data analysis. [Copyright &y& Elsevier]

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality. [ABSTRACT FROM PUBLISHER]

Abstract: The NDARMA model is a discrete counterpart to the usual ARMA model, which can be applied to purely categorical processes. NDARMA processes are shown to be -mixing, so it is possible to find asymptotic expressions for the distribution of several types of statistics. Such asymptotic properties are useful for hypothesis testing or other inferential procedures. This is exemplified by considering the Gini index and the entropy as measures of marginal dispersion, the Pearson statistic for checking the goodness-of-fit with regard to a hypothetical marginal distribution, and several measures of signed or unsigned serial dependence. For each of these cases, the obtained asymptotic approximations are also compared to the empirically observed behavior in time series of finite length. Practical applications are illustrated by a real-data example. [Copyright &y& Elsevier]

The Lorenz regression procedure quantifies the inequality of a response explained by a set of covariates. Formally, it gives a weight to each covariate to maximize the concentration index between the response and a weighted average of the covariates. The obtained index is called the explained Gini coefficient. Unlike methods based on decompositions of inequality measures, the procedure does not assume a linear relationship between the response and the covariates. Inference can be performed by noticing a similarity with the monotone rank estimator, introduced in the context of the single-index model. A continuity correction is presented in the presence of discrete covariates. The Lorenz- R 2 is a goodness-of-fit measure evaluating the proportion of explained inequality and is used to build a test of joint significance of several covariates. Monte-Carlo simulations and a real-data example are presented. [ABSTRACT FROM AUTHOR]

• Bayesian hypothesis tests present an alternative to NHST and p-values. • By now, calibration of the power and error rates of Bayesian tests is challenging. • This paper demonstrates how to balance errors and power for nonparametric Bayesian two-sample tests. • The dependence of the prior modelling and influence of sample size is investigated. • Bayesian nonparametric two-sample tests can be calibrated via the presented results. Hypothesis testing is a central statistical method in the biomedical sciences. The ongoing debate about the concept of statistical significance and the reliability of null hypothesis significance tests (NHST) and p-values has brought the advent of various Bayesian hypothesis tests as possible alternatives, which often employ the Bayes factor. However, careful calibration of the prior parameters is necessary for the type I error rates or power of these alternatives to be any better. Also, the availability of various Bayesian tests for the same statistical problem leads to the question which test to choose based on which criteria. Recently proposed Bayesian nonparametric two-sample tests are analyzed with regard to their type I error rates and power to detect an effect. Results show that approaches vary substantially in their ability to control the type I and II errors, and it is shown how to select the prior parameters to balance power and type I error control. This allows for prior elicitation and power analyses based on objective criteria like type I and II error rates even when conducting a Bayesian nonparametric two-sample test. Also, it is shown that existing nonparametric Bayesian two-sample tests are adequate only to test for location-shifts. Together, the results provide guidance how to perform a nonparametric Bayesian two-sample test while simultaneously improving the reliability of research. [ABSTRACT FROM AUTHOR]

Scholars of social networks often rely on summary statistics to measure and compare the structures of their networks of interest. However, measuring the uncertainty inherent in these summaries can be challenging, thus making hypothesis testing for network summaries difficult. Computational and nonparametric procedures can overcome these difficulties by allowing researchers to generate reference distributions for comparison directly from their data. In this research, I demonstrate the use of nonparametric hypothesis testing in networks using the popular network summary statistic network modularity. I provide a method based on permutation testing for assessing whether a particular network modularity score is larger than a researcher might expect due to random chance. I then create a simulation study of network modularity and its simulated reference distribution that I propose. Finally, I provide an empirical example of this technique using cosponsorship networks from U.S. state legislatures. [ABSTRACT FROM PUBLISHER]

Abstract: In longitudinal cluster randomized clinical trials (cluster-RCT), subjects are nested within a higher level unit such as clinics and are evaluated for outcome repeatedly over the study period. This study design results in a three level hierarchical data structure. When the primary goal is to test the hypothesis that an intervention has an effect on the rate of change in the outcome over time and the between-subject variation in slopes is substantial, the subject-specific slopes are often modeled as random coefficients in a mixed-effects linear model. In this paper, we propose approaches for determining the samples size for each level of a 3-level hierarchical trial design based on ordinary least squares (OLS) estimates for detecting a difference in mean slopes between two intervention groups when the slopes are modeled as random. Notably, the sample size is not a function of the variances of either the second or the third level random intercepts and depends on the number of second and third level data units only through their product. Simulation results indicate that the OLS-based power and sample sizes are virtually identical to the empirical maximum likelihood based estimates even with varying cluster sizes. Sample sizes for random versus fixed slope models are also compared. The effects of the variance of the random slope on the sample size determinations are shown to be enormous. Therefore, when between-subject variations in outcome trends are anticipated to be significant, sample size determinations based on a fixed slope model can result in a seriously underpowered study. [Copyright &y& Elsevier]

Abstract: We consider the problem of testing for perfect rankings in ranked set sampling (RSS). By using a new algorithm for computing the probability that specified independent random variables have a particular ordering, we find most powerful rank tests of the null hypothesis of perfect rankings against fully specified alternatives. We compare the power of these most powerful rank tests to that of existing rank tests in the literature, and we find that the existing tests are surprisingly close to optimal over a wide range of alternatives to perfect rankings. This finding holds both for balanced RSS and for unbalanced RSS cases where the different ranks are not equally represented in the sample. We find that the best of the existing tests is the test that rejects when the null probability of the observed ranks is small, and we provide a new, more efficient R function for computing the test statistic. [Copyright &y& Elsevier]

Abstract: A new test of hypothesis for classifying stationary time series based on the bias-adjusted estimators of the fitted autoregressive model is proposed. It is shown theoretically that the proposed test has desirable properties. Simulation results show that when time series are short, the size and power estimates of the proposed test are reasonably good, and thus this test is reliable in discriminating between short-length time series. As the length of the time series increases, the performance of the proposed test improves, but the benefit of bias-adjustment reduces. The proposed hypothesis test is applied to two real data sets: the annual real GDP per capita of six European countries, and quarterly real GDP per capita of five European countries. The application results demonstrate that the proposed test displays reasonably good performance in classifying relatively short time series. [Copyright &y& Elsevier]

Abstract: This paper discusses nonparametric comparison of survival functions when one observes only interval-censored failure time data (Peto and Peto, 1972; Sun, 2006; Zhao et al., 2008). For the problem, a few procedures have been proposed in the literature. However, most of the existing test procedures determine the test results or -values based on ad hoc methods or the permutation approach. Furthermore for the test procedures whose asymptotic distributions have been derived, the results are only for the null hypothesis. In other words, no nonparametric test procedure exists that has a known asymptotic distribution under the alternative hypothesis and thus can be employed to carry out the power and test size calculation. In this paper, a new class of generalized log-rank tests is proposed and their asymptotic distributions are derived under both null and alternative hypotheses. A simulation study is conducted to assess their performance for finite sample situations and an illustrative example is provided. [Copyright &y& Elsevier]

The state prediction based on the unscented Kalman filter (UKF) for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval (Cl) is proposed to overcome the problem. The advantages of Cl are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the Cl of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Abstract: Fuzzy pattern trees (FPTs) have recently been introduced as a novel model class for machine learning. In this paper, we consider the problem of learning fuzzy pattern trees for binary classification from data streams. Apart from its practical relevance, this problem is also interesting from a methodological point of view. First, the aspect of efficiency plays an important role in the context of data streams, since learning has to be accomplished under hard time (and memory) constraints. Moreover, a learning algorithm should be adaptive in the sense that an up-to-date model is offered at any time, taking new data items into consideration as soon as they arrive and perhaps forgetting old ones that have become obsolete due to a change of the underlying data generating process. To meet these requirements, we develop an evolving version of fuzzy pattern tree learning, in which model adaptation is realized by anticipating possible local changes of the current model, and confirming these changes through statistical hypothesis testing. In experimental studies, we compare our method to a state-of-the-art tree-based classifier for learning from data streams, showing that evolving pattern trees are competitive in terms of performance while typically producing smaller and more compact models. [Copyright &y& Elsevier]

Abstract: Storey’s method for multiple hypothesis testing “the Optimal Discovery Procedure” (ODP) minimising the false discovery rate (FDR) and giving p-values and q-values (estimates of FDR) for each test, was extended by iteration to enforce consistency between the p-values of the tests and the binary parameters defining which data points contribute to the fitted null hypothesis. These parameters arise when the null hypothesis has to be estimated from the data. The ODP as previously described, is only optimal for fixed values of these parameters. The extension proposed here requires the introduction of a cut-off parameter for the p-values. Motivated by using this method to analyse a set of pairs of frequencies representing gene expression for a set of genes in two libraries, from which it was desired to select those that are most likely to be not following the null hypothesis that the frequency ratio is a fixed unknown number, this method was tested by analysing many similar simulated datasets. The results showed that the ODP modified by iteration could be improved sometimes greatly by a suitable choice of the cut-off parameter, but varying this parameter alone may not lead to the globally optimal solution because statistical testing based on the binomial distribution is more efficient than using a form of the ODP when the number of non-null hypotheses in the data is small, but the reverse is true when it is large. This may be an effect of using discrete data. Efficiency here is defined in terms of the expected proportion of errors that occur (q-value) when a given proportion of the data is declared “significant” (i.e. the null hypothesis is believed not to hold for them). An improved version of the ODP along these lines is likely to have numerous applications such as in the optimised search for candidate genes that show unusual expression patterns for example when more than two experimental conditions are simultaneously compared and to cases when additional categorical variables or a time series is present in the experimental design. [Copyright &y& Elsevier]

Abstract: We study the problem of fitting ellipses to observed points in the context of Errors-In-Variables regression analysis. The accuracy of fitting methods is characterized by their variances and biases. The variance has a theoretical lower bound (the KCR bound), and many practical fits attend it, so they are optimal in this sense. There is no lower bound on the bias, though, and in fact our higher order error analysis (developed just recently) shows that it can be eliminated, to the leading order. Kanatani and Rangarajan recently constructed an algebraic ellipse fit that has no bias, but its variance exceeds the KCR bound; so their method is optimal only relative to the bias. We present here a novel ellipse fit that enjoys both optimal features: the theoretically minimal variance and zero bias (both to the leading order). Our numerical tests confirm the superiority of the proposed fit over the existing fits. [Copyright &y& Elsevier]

Abstract: We study the problems of hypothesis testing and point estimation for the correlation coefficient between the disturbances in the system of two seemingly unrelated regression equations. An objective Bayesian solution to each problem is proposed based on combined use of the invariant loss function and the objective prior distribution for the unknown model parameters. It is shown that this new solution possesses an invariance property under monotonic reparameterization of the quantity of interest. The performance of the proposed solution is examined through a simulation study. Furthermore, the solution is illustrated by an application to the real annual data for analyzing the investment model. [Copyright &y& Elsevier]

Abstract: A natural methodology for discriminating functional data is based on the distances from the observation or its derivatives to group representative functions (usually the mean) or their derivatives. It is proposed to use a combination of these distances for supervised classification. Simulation studies show that this procedure performs very well, resulting in smaller testing classification errors. Applications to real data show that this technique behaves as well as–and in some cases better than–existing supervised classification methods for functions. [Copyright &y& Elsevier]

Abstract: In this article, we introduce a new method of image pixel classification. Our method is a nonparametric classification method which uses combined evidence from the multiple hypothesis testings and minimum distance to carry out the classification. Our work is motivated by the test-based classification introduced by . We focus on binary and multiclass classification of image pixels taking into account both equal and unequal prior probability of classes. Experiments show that our method works better in classifying image pixels in comparison with some of the standard classification methods such as linear discriminant analysis, quadratic discriminant analysis, classification tree, the polyclass method, and the Liao and Akritas method. We apply our classifier to perform image segmentation. Experiments show that our test-based segmentation has excellent edge detection and texture preservation property for both gray scale and color images. [Copyright &y& Elsevier]

Abstract: This paper tests the null hypothesis of stationarity against the alternative of changes in persistence for sequences in the domain of attraction of a stable law. The proposed moving ratio test is valid for multiple changes in persistence while the previous residual based ratio tests are designed for processes displaying only a single change. We show that the new test is consistent whether the process changes from to or vice versa. And it is easy to identify the direction of detected change points. In particular, a bootstrap approximation method is proposed to determine the critical values for the null distribution of the test statistic containing unknown tail index. We also propose a two step approach to estimate the change points. Numerical evidence suggests that our test performs well in finite samples. In addition, we show that our test is still powerful for changes between short and long memory, and displays no tendency to spuriously over-reject null in favor of a persistence change if the process is actually throughout. Finally, we illustrate our test using the US inflation rate data and a set of high frequency stock closing price data. [Copyright &y& Elsevier]

Abstract: The paper addresses a common problem in the analysis of high-dimensional high-throughput “omics” data, which is parameter estimation across multiple variables in a set of data where the number of variables is much larger than the sample size. Among the problems posed by this type of data are that variable-specific estimators of variances are not reliable and variable-wise tests statistics have low power, both due to a lack of degrees of freedom. In addition, it has been observed in this type of data that the variance increases as a function of the mean. We introduce a non-parametric adaptive regularization procedure that is innovative in that (i) it employs a novel “similarity statistic”-based clustering technique to generate local-pooled or regularized shrinkage estimators of population parameters, (ii) the regularization is done jointly on population moments, benefiting from C. Stein’s result on inadmissibility, which implies that usual sample variance estimator is improved by a shrinkage estimator using information contained in the sample mean. From these joint regularized shrinkage estimators, we derived regularized t-like statistics and show in simulation studies that they offer more statistical power in hypothesis testing than their standard sample counterparts, or regular common value-shrinkage estimators, or when the information contained in the sample mean is simply ignored. Finally, we show that these estimators feature interesting properties of variance stabilization and normalization that can be used for preprocessing high-dimensional multivariate data. The method is available as an R package, called ‘MVR’ (‘Mean–Variance Regularization’), downloadable from the CRAN website. [Copyright &y& Elsevier]

Abstract: For the purpose of comparison between several independent populations, many procedures exist for testing equality of means or medians among the groups. However, the mean or the median do not determine the entire distribution. This paper addresses the problem of testing the equality of quantiles of several normal distributions. We propose an approximate test based on large sample method and an exact procedure based on a generalized -value. An extensive simulation study was conducted to evaluate the size and powers of these two tests. Simulation results show that the generalized -value approach performs very satisfactorily even for small samples while the approximate method exhibits poor Type I error control. A robustness study was done for power-exponential distribution and -distribution. Finally, the proposed methods are applied to two real examples. [Copyright &y& Elsevier]

Abstract: Statistical procedures for the equality of two and univariate distributions based on samples of dependent observations are proposed in this work. The test statistics are distances of standard empirical and characteristic function processes. The -values of the tests are obtained from a version of the multiplier central limit theorem whose asymptotic validity is established. Simple formulas for the test statistics and their multiplier versions in terms of multiplication of matrices are provided. Simulations under many patterns of dependence characterized by copulas show the good behavior of the tests in small samples, both in terms of their power and of their ability to keep their nominal level under the null hypothesis. [Copyright &y& Elsevier]

Abstract: The construction of a multivariate two sample test is considered. An attractive approach to this problem, for instance when the data contain missing values or the number of variables is large, is to form an overall test by combining the componentwise test statistics. This can be done via their -values or some other transformation. An important problem is how to perform the combination, as the relative power of a given combination will depend on the unknown true alternative. Recently, an approach has been proposed that makes use of the data to identify an appropriate combination. The method forms a pool of potential combinations of the componentwise -values, setting the overall test statistic to the minimum -value across the pool. One drawback of the approach, however, is that it does not utilize dependence between the componentwise tests, and thus potentially ignores valuable information. This issue is addressed, and two approaches are described that make use of the data to (1) determine which tests to combine; and (2) how best to utilize the between test statistic dependence. Simulations show that the proposed methods can lead to a substantial increase in power. An application to a dietary intervention study is given. [Copyright &y& Elsevier]

Abstract: Multiple testing analysis and clustering methodologies are usually applied in microarray data analysis. A combination of both methods to deal with multiple comparisons among groups obtained from microarray expressions of genes is proposed. Assuming normal data, a statistic which depends on sample means and sample variances, distributed as a non-central -distribution is defined. As multiple comparisons among groups are considered, a mixture of non-central -distributions is derived. The estimation of the components of mixtures is obtained via a Bayesian approach, and the model is applied in a multiple comparison problem from a microarray experiment obtained from gorilla, bonobo and human cultured fibroblasts. [Copyright &y& Elsevier]

Abstract: This paper uses Bayesian tree models for statistical benchmarking in data sets with awkward marginals and complicated dependence structures. The method is applied to a very large database on corporate performance over the last four decades. The results of this study provide a formal basis for making cross-peer-group comparisons among companies in very different industries and operating environments. This is done by using models for Bayesian multiple hypothesis testing to determine which firms, if any, have systematically out-performed their peer groups over time. We conclude that systematic out-performance, while it seems to exist, is quite rare worldwide. [Copyright &y& Elsevier]

Abstract: This paper studies the relations which exist between Dempster–Shafer Theory and one of its extensions which considers frames of discernment with non-exclusive hypotheses. More precisely, we use propositional logic to show that this extension can be reformulated in the classical framework of Dempster–Shafer theory, showing that, even if it allows to manipulate more compact expressions, it is not more expressive. Finally, we believe that using propositional logic as the basics for modelling concepts in Dempster–Shafer Theory can help logicians to understand this theory and its extension to non-exclusive hypotheses. [Copyright &y& Elsevier]

Abstract: Testing non-inferiority in active-controlled clinical trials examines whether a new procedure is, to a pre-specified amount, no worse than an existing procedure. To assess non-inferiority between two procedures using clustered matched-pair binary data, two new statistical tests are systematically compared to existing tests. The calculation of corresponding confidence interval is also proposed. None of the tests considered requires structural within-cluster correlation or distributional assumptions. The results of an extensive Monte Carlo simulation study illustrate that the performance of the statistics depends on several factors including the number of clusters, cluster size, probability of success in the test procedure, the homogeneity of the probability of success across clusters, and the intra-cluster correlation coefficient (ICC). In evaluating non-inferiority for a clustered matched-pair study, one should consider all of these issues when choosing an appropriate test statistic. The ICC-adjusted test statistic is generally recommended to effectively control the nominal level when there is constant or small variability of cluster sizes. For a greater number of clusters, the other test statistics maintain the nominal level reasonably well and have higher power. Therefore, with the carefully designed clustered matched-pair study, a combination of the statistics investigated may serve best in data analysis. Finally, to illustrate the practical application of the recommendations, a real clustered matched-pair collection of data is used to illustrate testing non-inferiority. [Copyright &y& Elsevier]

Abstract: The positive and negative predictive values of a binary diagnostic test are measures of the clinical accuracy of the diagnostic test, which depend on the sensitivity and specificity of the diagnostic test and the disease prevalence, and therefore they are two interdependent parameters. The comparisons of predictive values in paired designs do not consider the dependence between predictive values. A global hypothesis test has been studied in order to simultaneously compare the predictive values of two or more binary diagnostic tests when the binary tests and the gold standard are applied to all of the individuals in a random sample. This global hypothesis test is an asymptotic hypothesis test based on the chi-square distribution. Simulation experiments have been carried out in order to study the type I error and the power of the global hypothesis test when comparing the predictive values of two and three binary diagnostic tests, respectively. From the results of the simulation experiments, a method has been proposed to simultaneously compare the predictive values of two or more binary diagnostic tests. The results have been applied to the diagnosis of coronary disease. [Copyright &y& Elsevier]

Abstract: Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman–Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results. [Copyright &y& Elsevier]

Abstract: This work shows that two well-known spatial and temporal mobility metrics for wireless multi-hop networks have limitations, possibly resulting in misleading results. Based on the concept of spatial dependence among nodes including transient periods of no movement, we propose mobility metrics able to promptly capture spatial and temporal dependence among mobile nodes. Through simulation, we compared the metrics over an extensive set of synthetic mobility models. The results revealed that our spatial metric can accurately capture spatial dependence in scenarios having different levels of node pause time. Our temporal metric also demonstrated to be better suited for capturing different levels of temporal dependence, without being biased by node speed. Moreover, we also proposed and validated a regression model for predicting our spatial mobility metric with a 94.8% goodness-of-fit. [Copyright &y& Elsevier]

We study minimalism in sensing and control by considering a multi-agent system in which each agent moves like a Dubins car and has a limited sensor that reports only the presence of another agent within some sector of its windshield. Using a simple quantized control law with three values, each agent tracks another agent (its target) assigned to it by maintaining that agent within this windshield sector. We use Lyapunov analysis to show that by acting autonomously in this way, the agents will achieve rendezvous given a connected initial assignment graph and the assumption that an agent and its target will merge into a single agent when they are sufficiently close. We then proceed to show that, by making the quantized control law slightly stronger, a connected initial assignment graph is not required and the sensing model can be weakened further. A distinguishing feature of our approach is that it does not involve any estimation procedure aimed at reconstructing coordinate information. Our scenario thus provides an example in which an interesting task is performed with extremely coarse sensing and control, and without state estimation. The system was implemented in computer simulation, accessible through the Web, of which the results are presented in the paper. [ABSTRACT FROM AUTHOR]

The article explores the viability of the field of study known as Freakonomics, which was created by economist Steven D. Levitt and journalist Stephen J. Dubner to explore social problems with statistics in their books "Freakonomics," and "SuperFreakonomics," as well as other media venues. Criticisms are provided for several examples in Levitt and Dubner's works, including statistical calculation errors, a reliance on unexamined assumptions, and not listening to outside opinions in fields in which they had little expertise. Recommendations are given for how to keep the field of popular statistics accessible without committing these errors and oversimplifications.

Abstract: In image processing, image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image. Commonly used measures, such as the mean squared error (MSE) and peak signal to noise ratio (PSNR), ignore the spatial information (e.g. redundancy) contained in natural images, which can lead to an inconsistent similarity evaluation from the human visual perception. Recently, a structural similarity measure (SSIM), that quantifies image fidelity through estimation of local correlations scaled by local brightness and contrast comparisons, was introduced by . This correlation-based SSIM outperforms MSE in the similarity assessment of natural images. However, as correlation only measures linear dependence, distortions from multiple sources or nonlinear image processing such as nonlinear filtering can cause SSIM to under- or overestimate the true structural similarity. In this article, we propose a new similarity measure that replaces the correlation and contrast comparisons of SSIM by a term obtained from a nonparametric test that has superior power to capture general dependence, including linear and nonlinear dependence in the conditional mean regression function as a special case. The new similarity measure applied to images from noise contamination, filtering, and watermarking, provides a more consistent image structural fidelity measure than commonly used measures. [Copyright &y& Elsevier]

Abstract: Although there are several proposals in the instance selection field, none of them consistently outperforms the others over a wide range of domains. In recent years many authors have come to the conclusion that data must be characterized in order to apply the most suitable selection criterion in each case. In light of this hypothesis, herein we propose a set of measures to characterize databases. These measures were used in decision rules which, given their values for a database, select from some pre-selected methods, the method, or combination of methods, that is expected to produce the best results. The rules were extracted based on an empirical analysis of the behaviors of several methods on several data sets, then integrated into an algorithm which was experimentally evaluated over 20 databases and with six different learning paradigms. The results were compared with those of five well-known state-of-the-art methods. [Copyright &y& Elsevier]