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Praise for the Second Edition \"This book should be an essential part of the personal library of every practicing statistician.\"—Technometrics   Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation. Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features: * The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition * New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics * Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics. 

Intelligent transportation system (ITS) has become a crucial section of transportation and traffic management systems in the past decades. As a result, transportation agencies keep improving the quality of transportation infrastructure management information for accessibility and security of transportation networks. The goal of this paper is to evaluate the impact of two competing risks: “natural deterioration” of ITS devices and hurricane-induced failure of the same components. The major devices employed in the architecture of this paper include closed circuit television (CCTV) cameras, automatic vehicle identification (AVI) systems, dynamic message signals (DMS), wireless communication systems and DMS towers. From the findings, it was evident that as ITS infrastructure devices age, the contribution of Hurricane Category 3 as a competing failure risk is higher and significant compared to the natural deterioration of devices. Hurricane Category 3 failure vs. natural deterioration indicated an average hazard ratio of 1.5 for CCTV, AVI and wireless communications systems and an average hazard ratio of 2.3 for DMS, DMS towers and portable DMS. The proportional hazard ratios of the Hurricane Category 1 compared to the devices was estimated as <0.001 and that of Hurricane Category 2 < 0.5, demonstrating the lesser impact of the Hurricane Categories 1 and 2. It is expedient to envisage and forecast the impact of hurricanes on the failure of wireless communication networks, vehicle detection systems and other message signals, in order to prevent vehicle to infrastructure connection disruption, especially for autonomous and connected vehicle systems.

PurposePresent a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.Design/methodology/approachUsing the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.FindingsThe techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.Research limitations/implicationsThe research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.Practical implicationsThese methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.Social implicationsImproved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.Originality/valueBased on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

Reflecting growing emphasis on data analysis and statistical thinking in the information age, mathematics curriculum standards in the U.S. have recently increased expectations for student learning in the domain of statistics and probability. More than 180 teachers in 36 public school districts in Florida applied for a two-week summer institute designed to increase teachers' content and pedagogical content knowledge in statistics and probability. Individual teachers were assigned at random to a treatment or business-as-usual comparison group. The two-week institute increased teachers' knowledge of statistics. Data analyses identified an interaction between years of teaching experience and treatment, indicating that the teachers with more than 10 years of experience had larger knowledge gains than their less-experienced peers. These results underscore the need for professional development for teachers so that they may implement policies emphasizing this branch of the mathematical sciences in the secondary mathematics curriculum. Given the observed lower baseline knowledge scores for teachers with more years of teaching experience, we posit these implications are particularly applicable to teachers who completed their own formal education more than 10 years ago.

Many modern industrial processes are capable of generating rich and complex data records that do not readily permit the use of traditional statistical process-control techniques. For example, a \"single observation\" from a process might consist of n pairs of (x, y) data that can be described as y = f (x) when the process is in control. Such data structures or relationships between y and x are called profiles. Examples of profiles include calibration curves in chemical processing, oxide thickness across wafer surfaces in semiconductor manufacturing, and radar signals of military targets. In this paper, a semiparametric wavelet method is proposed for monitoring for changes in sequences of nonlinear profiles. Based on a likelihood ratio test involving a changepoint model, the method uses the spatial-adaptivity properties of wavelets to accurately detect profile changes taking nearly limitless functional forms. The method is used to differentiate between different radar profiles and its performance is assessed with Monte Carlo simulation. The results presented indicate the method can quickly detect a wide variety of changes from a given, in-control profile.

Karst Flow Model (KFM) simulates transient flow in an unconfined karstic aquifer having a well-developed conduit system. KFM treats the springshed as a two-dimensional porous matrix containing a triangulated irregular network of leaky conduits. The number and location of conduits can be specified arbitrarily, perhaps using field information as a guide, or generated automatically. Conduit networks can be tree-like or braided. Rainwater that has infiltrated down from the surface leaks into the conduits from the adjacent porous matrix at a rate dictated by Darcy’s law, then flows turbulently to the spring via the conduits. KFM is calibrated using the known steady state; geometry and recharge determine the steady fluxes in the conduits, and the head distribution determines conduit gradients and sizes. Spring flow can vary with time due to spatially and temporally variable recharge and due to prescribed variations in the elevation of the spring. KFM is illustrated by four examples run on a test aquifer consisting of 27 nodes, 42 elements, and 26 conduits. Three examples (drought, uniform rainstorm, storm-water input to one element) are simulations, while the fourth uses data from a spring-basin flooding event. The qualitative fit between the predicted and observed spring discharge in the fourth example provides support of the hypothesis that the dynamic behavior of a karst conduit system is an emergent property of a self-organized system, largely independent of the locations and properties of individual conduits.

Praise for the Second Edition \"This book should be an essential part of the personal library of every practicing statistician.\"—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation. Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features: The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.

We consider the problem of online profile monitoring of random functions that admit basis expansions possessing random coefficients for the purpose of out-of-control state detection. Our approach is applicable to a broad class of random functions which feature two sources of variation: additive error and random fluctuations through random coefficients in the basis representation of functions. We focus on a two-phase monitoring problem with a first stage consisting of learning the in-control process and the second stage leveraging the learned process for out-of-control state detection. The foundations of our method are derived under the assumption that the coefficients in the basis expansion are Gaussian random variables, which facilitates the development of scalable and effective monitoring methodology for the observed processes that makes weak functional assumptions on the underlying process. We demonstrate the potential of our method through simulation studies that highlight some of the nuances that emerge in profile monitoring problems with random functions, and through an application.

In Statistical Process Control, control charts are often used to detect undesirable behavior of sequentially observed quality characteristics. Designing a control chart with desirably low False Alarm Rate (FAR) and detection delay (\\(ARL_1\\)) is an important challenge especially when the sampling rate is high and the control chart has an In-Control Average Run Length, called \\(ARL_0\\), of 200 or more, as commonly found in practice. Unfortunately, arbitrary reduction of the FAR typically increases the \\(ARL_1\\). Motivated by eigenvector perturbation theory, we propose the Eigenvector Perturbation Control Chart for computationally fast nonparametric profile monitoring. Our simulation studies show that it outperforms the competition and achieves both \\(ARL_1 1\\) and \\(ARL_0 > 10^6\\).