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In the first part of this article, we discuss and generalize the complete convergence introduced by Hsu and Robbins in 1947 to the r-complete convergence introduced by Tartakovsky in 1998. We also establish its relation to the r-quick convergence first introduced by Strassen in 1967 and extensively studied by Lai. Our work is motivated by various statistical problems, mostly in sequential analysis. As we show in the second part, generalizing and studying these convergence modes is important not only in probability theory but also to solve challenging statistical problems in hypothesis testing and changepoint detection for general stochastic non-i.i.d. models. [ABSTRACT FROM AUTHOR]

P-values have played a central role in the advancement of research in virtually all scientific fields; however, there has been significant controversy over their use. "The ASA president's task force statement on statistical significance and replicability" has provided a solid basis for resolving the quarrel, but although the significance part is clearly dealt with, the replicability part raises further discussions. Given the clear statement regarding significance, in this article, we consider the validity of p-value use for statistical inference as de facto. We briefly review the bibliography regarding the relevant controversy in recent years and illustrate how already proposed approaches, or slight adaptations thereof, can be readily implemented to address both significance and reproducibility, adding credibility to empirical study findings. The definitions used for the notions of replicability and reproducibility are also clearly described. We argue that any p-value must be reported along with its corresponding s-value followed by (1 − α) % confidence intervals and the rejection replication index. [ABSTRACT FROM AUTHOR]

This paper deals with a new Bayesian approach to the one-sample test for proportion. More specifically, let x = (x 1 , ... , x n) be an independent random sample of size n from a Bernoulli distribution with an unknown parameter θ. For a fixed value θ 0 , the goal is to test the null hypothesis H 0 : θ = θ 0 against all possible alternatives. The proposed approach is based on using the well-known formula of the Kullback–Leibler divergence between two binomial distributions chosen in a certain way. Then, the difference of the distance from a priori to a posteriori is compared through the relative belief ratio (a measure of evidence). Some theoretical properties of the method are developed. Examples and simulation results are included. [ABSTRACT FROM AUTHOR]

In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates Occam's razor via the multiplicity or trials factor in the look-elsewhere effect. However, if the prior is not known completely, the frequentist hypothesis test via the false-positive rate is a better approach, as it is less sensitive to the prior choice. We argue that when only partial prior information is available, it is best to combine the two methodologies by using the Bayes factor as a test statistic in the frequentist analysis. We show that the standard frequentist maximum likelihood-ratio test statistic corresponds to the Bayes factor with a non-informative Jeffrey's prior. We also show that mixed priors increase the statistical power in frequentist analyses over the maximum likelihood test statistic. We develop an analytic formalism that does not require expensive simulations and generalize Wilks' theorem beyond its usual regime of validity. In specific limits, the formalism reproduces existing expressions, such as the p-value of linear models and periodograms. We apply the formalism to an example of exoplanet transits, where multiplicity can be more than 10 7 . We show that our analytic expressions reproduce the p-values derived from numerical simulations. We offer an interpretation of our formalism based on the statistical mechanics. We introduce the counting of states in a continuous parameter space using the uncertainty volume as the quantum of the state. We show that both the p-value and Bayes factor can be expressed as an energy versus entropy competition. [ABSTRACT FROM AUTHOR]

Road maintenance and cleaning in winter are performed with ploughshares. Due to the fact that the layer of snow and ice that is removed from the road surface contains various hard impurities, ploughshares are exposed to high intensity abrasive wear. This article deals with the resistance to abrasive wear of originally used ploughshare materials and the materials that were designed as a suitable modification of the ploughshare to increase its service life. The chemical composition of materials used to manufacture ploughshare components is unknown. For this reason, they were analyzed with an ARL 4460 spectrometer, which was used to analyze the element content. The main part of the research was focused on the abrasion resistance test, which was performed according to the GOST 23.208-79 standard. Based on the chemical analysis, it was found that the basic body of the ploughshare was made of S355J2G3 steel, and the raking blade material was made of 37MnSi5 steel. The original material (steel S355J2G3) of the ploughshare body as a reference standard was compared to steel HARDOX 450. Furthermore, a sample made of the original material of the raking blade (steel 37MnSi5) was used as a reference standard, the properties of which were compared to the newly designed OK 84.58 and UTP 690 hardfacing materials. The parametric test method of statistical hypotheses was also used to process and evaluate the weight losses of the selected materials. [ABSTRACT FROM AUTHOR]

In this paper, the noise-enhanced detection problem is investigated for the binary hypothesis-testing. The optimal additive noise is determined according to a criterion proposed by DeGroot and Schervish (2011), which aims to minimize the weighted sum of type I and II error probabilities under constraints on type I and II error probabilities. Based on a generic composite hypothesis-testing formulation, the optimal additive noise is obtained. The sufficient conditions are also deduced to verify whether the usage of the additive noise can or cannot improve the detectability of a given detector. In addition, some additional results are obtained according to the specificity of the binary hypothesis-testing, and an algorithm is developed for finding the corresponding optimal noise. Finally, numerical examples are given to verify the theoretical results and proofs of the main theorems are presented in the Appendix. [ABSTRACT FROM AUTHOR]

The upper tail of a flood frequency distribution is always specifically concerned with flood control. However, different model selection criteria often give different optimal distributions when the focus is on the upper tail of distribution. With emphasis on the upper-tail behavior, five distribution selection criteria including two hypothesis tests and three information-based criteria are evaluated in selecting the best fitted distribution from eight widely used distributions by using datasets from Thames River, Wabash River, Beijiang River and Huai River. The performance of the five selection criteria is verified by using a composite criterion with focus on upper tail events. This paper demonstrated an approach for optimally selecting suitable flood frequency distributions. Results illustrate that (1) there are different selections of frequency distributions in the four rivers by using hypothesis tests and information-based criteria approaches. Hypothesis tests are more likely to choose complex, parametric models, and information-based criteria prefer to choose simple, effective models. Different selection criteria have no particular tendency toward the tail of the distribution; (2) The information-based criteria perform better than hypothesis tests in most cases when the focus is on the goodness of predictions of the extreme upper tail events. The distributions selected by information-based criteria are more likely to be close to true values than the distributions selected by hypothesis test methods in the upper tail of the frequency curve; (3) The proposed composite criterion not only can select the optimal distribution, but also can evaluate the error of estimated value, which often plays an important role in the risk assessment and engineering design. In order to decide on a particular distribution to fit the high flow, it would be better to use the composite criterion. [ABSTRACT FROM AUTHOR]

Physical-layer authentication can address physical layer vulnerabilities and security threats in wireless sensor networks, and has been considered as an effective complementary enhancement to existing upper-layer authentication mechanisms. In this paper, to advance the existing research and improve the authentication performance, we propose a novel physical layer assisted authentication scheme for mobile wireless sensor networks. In our proposed scheme, we explore the reciprocity and spatial uncorrelation of the wireless channel to verify the identities of involved transmitting users and decide whether all data frames are from the same sender. In our proposed scheme, a new method is developed for the legitimate users to compare their received signal strength (RSS) records, which avoids the information from being disclosed to the adversary. Our proposed scheme can detect the spoofing attack even in a high dynamic environment. We evaluate our scheme through experiments under indoor and outdoor environments. Experiment results show that our proposed scheme is more efficient and achieves a higher detection rate as well as keeping a lower false alarm rate. [ABSTRACT FROM AUTHOR]

The problem of hypothesis testing in the Neyman-Pearson formulation is considered from a geometric viewpoint. In particular, a concise geometric interpretation of deterministic and random signal detection in the philosophy of information geometry is presented. In such a framework, both hypotheses and detectors can be treated as geometrical objects on the statistical manifold of a parameterized family of probability distributions. Both the detector and detection performance are geometrically elucidated in terms of the Kullback-Leibler divergence. Compared to the likelihood ratio test, the geometric interpretation provides a consistent but more comprehensive means to understand and deal with signal detection problems in a rather convenient manner. Example of the geometry based detector in radar constant false alarm rate (CFAR) detection is presented, which shows its advantage over the classical processing method. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself. [ABSTRACT FROM AUTHOR]

Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning the probabilistic graphical model structure from data. In this paper, we propose the full Bayesian significance test for tests of conditional independence for discrete datasets. The full Bayesian significance test is a powerful Bayesian test for precise hypothesis, as an alternative to the frequentist's significance tests (characterized by the calculation of the p-value). [ABSTRACT FROM AUTHOR]

A generalized maximum entropy estimator is developed for the linear simultaneous equations model. Monte Carlo sampling experiments are used to evaluate the estimator's performance in small and medium sized samples, suggesting contexts in which the current generalized maximum entropy estimator is superior in mean square error to two and three stage least squares. Analytical results are provided relating to asymptotic properties of the estimator and associated hypothesis testing statistics. Monte Carlo experiments are also used to provide evidence on the power and size of test statistics. An empirical application is included to demonstrate the practical implementation of the estimator. [ABSTRACT FROM AUTHOR]

We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the Bayes factor is introduced, along with a review of computational methods and sensitivity related to prior distributions. We demonstrate how Bayesian testing can be practically implemented in several examples, such as the t-test, two-sample comparisons, linear mixed models, and Poisson mixed models by using existing software. Caveats and potential problems associated with Bayesian testing are also discussed. We aim to inform researchers in the many fields where Bayesian testing is not in common use of a well-developed alternative to null hypothesis significance testing and to demonstrate its standard implementation. [ABSTRACT FROM AUTHOR]

Real-time cycle slip detection and repair is one of the key issues in global positioning system (GPS) high precision data processing and application. In particular, when GPS stations are in special environments, such as strong ionospheric disturbance, sea, and high-voltage transmission line interference, cycle slip detection and repair in low elevation GPS observation data are more complicated than those in normal environments. For low elevation GPS undifferenced carrier phase data in different environments, a combined cycle slip detection algorithm is proposed. This method uses the first-order Gauss–Markov stochastic process to model the pseudorange multipath in the wide-lane phase minus narrow-lane pseudorange observation equation, and establishes the state equation of the wide-lane ambiguity with the pseudorange multipath as a parameter, and it uses the Kalman filter for real-time estimation and detects cycle slips based on statistical hypothesis testing with a predicted residual sequence. Meanwhile, considering there are certain correlations among low elevation, observation epoch interval, and ionospheric delay error, a second-order difference geometry-free combination cycle slip test is constructed that takes into account the elevation. By combining the two methods, real-time cycle slip detection for GPS low elevation satellite undifferenced data is achieved. A cycle slip repair method based on spatial search and objective function minimization criterion is further proposed to determine the correct solution of the cycle slips after they are detected. The whole algorithm is experimentally verified using the static and kinematic measured data of low elevation satellites under four different environments: normal condition, high-voltage transmission lines, dynamic condition in the sea, and ionospheric disturbances. The experimental results show that the algorithm can detect and repair cycle slips accurately for low elevation GPS undifferenced data, the difference between the float solution and the true value for the cycle slip does not exceed 0.5 cycle, and the differences obey the normal distribution overall. At the same time, the wide-lane ambiguity and second-order difference GF combination sequence calculated by the algorithm is smoother, which give further evidence that the algorithm for cycle slip detection and repair is feasible and effective, and has the advantage of being immune to the special observation environments. [ABSTRACT FROM AUTHOR]

To perform statistical inference for time series, one should be able to assess if they present deterministic or stochastic trends. For univariate analysis, one way to detect stochastic trends is to test if the series has unit roots, and for multivariate studies it is often relevant to search for stationary linear relationships between the series, or if they cointegrate. The main goal of this article is to briefly review the shortcomings of unit root and cointegration tests proposed by the Bayesian approach of statistical inference and to show how they can be overcome by the Full Bayesian Significance Test (FBST), a procedure designed to test sharp or precise hypothesis. We will compare its performance with the most used frequentist alternatives, namely, the Augmented Dickey–Fuller for unit roots and the maximum eigenvalue test for cointegration. [ABSTRACT FROM AUTHOR]

Taking advantage of the possibility of fuzzy test statistic falling in the rejection region, a statistical hypothesis testing approach for fuzzy data is proposed in this study. In contrast to classical statistical testing, which yields a binary decision to reject or to accept a null hypothesis, the proposed approach is to determine the possibility of accepting a null hypothesis (or alternative hypothesis). When data are crisp, the proposed approach reduces to the classical hypothesis testing approach. [ABSTRACT FROM AUTHOR]

Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher's Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes. [ABSTRACT FROM AUTHOR]

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone. [ABSTRACT FROM AUTHOR]

The device-independent approach to physics is one where conclusions about physical systems (and hence of Nature) are drawn directly and solely from the observed correlations between measurement outcomes. This operational approach to physics arose as a byproduct of Bell's seminal work to distinguish, via a Bell test, quantum correlations from the set of correlations allowed by local-hidden-variable theories. In practice, since one can only perform a finite number of experimental trials, deciding whether an empirical observation is compatible with some class of physical theories will have to be carried out via the task of hypothesis testing. In this paper, we show that the prediction-based-ratio method—initially developed for performing a hypothesis test of local-hidden-variable theories—can equally well be applied to test many other classes of physical theories, such as those constrained only by the nonsignaling principle, and those that are constrained to produce any of the outer approximation to the quantum set of correlations due to Navascués-Pironio-Acín. We numerically simulate Bell tests using hypothetical nonlocal sources of correlations to illustrate the applicability of the method in both the independent and identically distributed (i.i.d.) scenario and the non-i.i.d. scenario. As a further application, we demonstrate how this method allows us to unveil an apparent violation of the nonsignaling conditions in certain experimental data collected in a Bell test. This, in turn, highlights the importance of the randomization of measurement settings, as well as a consistency check of the nonsignaling conditions in a Bell test. [ABSTRACT FROM AUTHOR]