Search

This research was conducted with the aim of analyzing two of the main metrological characteristics of any measurement system: Repeatability and Reproducibility. Both of these features play an important role in the analysis of the measurements and they can give us a lot of information about who and what influences any measuring system. The analysis of Repeatability and Reproducibility is generally carried out through the use of the study Gage R&R . This study permits to understand which are the decisive factors in a measurement system, and, definitively, if the process is stable, that is under statistical control or out of statistical control. [ABSTRACT FROM AUTHOR]

Particle filters are of great concern in a large variety of engineering fields such as robotics, statistics or automatics. Recently, it has developed among Prognostics and Health Management (PHM) applications for diagnostics and prognostics. According to some authors, it has ever become a state-of-the-art technique for prognostics. Nowadays, around 50 papers dealing with prognostics based on particle filters can be found in the literature. However, no comprehensive review has been proposed on the subject until now. This paper aims at analyzing the way particle filters are used in that context. The development of the tool in the prognostics׳ field is discussed before entering the details of its practical use and implementation. Current issues are identified, analyzed and some solutions or work trails are proposed. All this aims at highlighting future perspectives as well as helping new users to start with particle filters in the goal of prognostics. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to examine the prevalence of current smoking in male patients with schizophrenia in China.Method: A systematic literature search was conducted from database inception until June 20, 2015 without language restrictions in PubMed, EMBASE, China National Knowledge Infrastructure (CNKI) and WanFang Database. Studies fulfilling the following criteria were included: (a) data available in male schizophrenia patients and (b) data available on current smoking status. Statistical analyses were performed with the Comprehensive Meta-Analysis program.Results: A total of 23 studies met eligibility criteria for the meta-analysis. The pooled prevalence of current smoking was 59.1% (95% Confidence interval [CI]: 53.3-64.7%). Current smoking was significantly more frequent in inpatients than in outpatients (61.3% vs. 48.2%, Q=7.5, P=0.006), and higher in chronic compared to first-episode patients (74.5% vs. 45.1%, Q=32.3, P=0.0001). Furthermore, using a median split, patients aged 38.2 years or older smoked more often than those aged below 38.2 years (65.8% vs. 52.3%, Q=6.4, P=0.01). There were no significant associations between prevalence of current smoking and definitions of smoking, study publication year, sample size and illness duration.Conclusions: The pooled prevalence of current smoking of male patients with schizophrenia in China is lower compared to Western and other Asian countries. Possible relationships between lower prevalence of current smoking and psychopathology in patients with schizophrenia require further investigation. [ABSTRACT FROM AUTHOR]

Motivated by the requirement of controlling the number of false discoveries that arises in several application fields, we study the behaviour of diagnostic procedures obtained from popular highbreakdown regression estimators when no outlier is present in the data. We find that the empirical error rates for many of the available techniques are surprisingly far from the prescribed nominal level. Therefore, we propose a simulation-based approach to correct the liberal diagnostics and reach reliable inferences. We provide evidence that our approach performs well in a wide range of settings of practical interest and for a variety of robust regression techniques, thus showing general appeal.We also evaluate the loss of power that can be expected from our corrections under different contamination schemes and show that this loss is often not dramatic. Finally, we detail some possible extensions that may further enhance the applicability of the method. [ABSTRACT FROM AUTHOR]

The paper considers a version of the question of how to define treatment and control groups in a dynamic setting where treatments can occur at any time (but only once). The version considered presupposes that treatments as well as outcomes can be conceptualised as events occurring in temporal locations of a discrete time axis. It is proposed to think of effects as being dependent on both the time when and the time since the treatment occurred. The paper develops corresponding definitions of treatment and control groups, and proposes a notion of 'comprehensive treatment effect' that takes into account how treatment and control groups are generated. Based on this notion, the paper discusses causal interpretations that do not pre-suppose a potential outcomes framework. [ABSTRACT FROM AUTHOR]

Most classification models have presented an imbalanced learning state when dealing with the imbalanced datasets. This article proposes a novel approach for learning from imbalanced datasets, which based on an improved SMOTE (synthetic Minority Over-sampling technique) algorithm. By organically combining the over-sampling and the under-sampling method, this approach aims to choose neighbors targetedly and synthesize samples with different strategy. Experiments show that most classifiers have achieved an ideal performance on the classification problem of the positive and negative class after dealing imbalanced datasets with our algorithm. [ABSTRACT FROM AUTHOR]

LetT1: n⩽T2: n⩽ ⋅⋅⋅ ⩽Tn: nbe ordered lifetimes of components of a parallel system. In this article, the α-quantile past lifetime from the failure of the component with lifetimeTr: nprovided that the system has failed at or before timethas been introduced. Then, some properties of this measure have been studied. [ABSTRACT FROM AUTHOR]

We report measurements of the near-wall flow field in turbulent Rayleigh-Bénard convection in air (Pr = 0.7) using particle image velocimetry. The measurements were performed in a thin, rectangular sample at fixed Rayleigh number Ra = 1.45 × 1010. In particular, we focus on the evolution of the boundary layer that a single convection roll generates along its path at the lower horizontal plate. We identify three specific flow regions along this path: (i) a region of wall-normal impingement of the down flow close to one corner of the sample, (ii) a region where a shear layer with almost constant thickness evolves, and (iii) a region in which this boundary layer grows and eventually detaches from the plate surface at the opposite corner of the sample. Our measurements with a spatial resolution better than 1/500 of the total thickness of the boundary layer show that the typical velocity field as well as its statistics qualitatively varies between the three flow regions. In particular, it could be verified that the shear layer region covering about 75% of the total area of the plate is in transition to turbulence at the Rayleigh number as low as investigated in the present work. [ABSTRACT FROM AUTHOR]

Demonstrations that analyses of social media content can align with measurement from sample surveys have raised the question of whether survey research can be supplemented or even replaced with less costly and burdensome data mining of already-existing or "found" social media content. But just how trustworthy such measurement can be--say, to replace official statistics--is unknown. Survey researchers and data scientists approach key questions from starting assumptions and analytic traditions that differ on, for example, the need for representative samples drawn from frames that fully cover the population. New conversations between these scholarly communities are needed to understand the potential points of alignment and non-alignment. Across these approaches, there are major differences in (a) how participants (survey respondents and social media posters) understand the activity they are engaged in; (b) the nature of the data produced by survey responses and social media posts, and the inferences that are legitimate given the data; and (c) practical and ethical considerations surrounding the use of the data. Estimates are likely to align to differing degrees depending on the research topic and the populations under consideration, the particular features of the surveys and social media sites involved, and the analytic techniques for extracting opinions and experiences from social media. Traditional population coverage may not be required for social media content to effectively predict social phenomena to the extent that social media content distills or summarizes broader conversations that are also measured by surveys. [ABSTRACT FROM AUTHOR]

One of the most common applications in statistical process monitoring is the use of control charts to monitor a process mean. In practice, this is often performed with a Shewhart chart along with a Shewhart R (or an S) chart. Thus, two charts are typically used together, as a scheme, each using the 3-sigma limits. Moreover, the process mean and standard deviation are often unknown and need to be estimated before monitoring can begin. We show that there are three major issues with this monitoring scheme described in most textbooks. The first issue is not accounting for the effects of parameter estimation, which is known to degrade chart performance. The second issue is the implicit assumption that the charting statistics are both normally distributed and, accordingly, using the 3-sigma limits. The third issue is multiple charting, because two charts are used, in this scheme, at the same time. We illustrate the deleterious effects of these issues on the in-control properties of the charting scheme and present a method for finding the correct charting constants taking proper account of these issues. Tables of the new charting constants are provided for some commonly used nominal in-control average run length values and different sample sizes. This will aid in implementing the charting scheme correctly in practice. Examples are given along with a summary and some conclusions. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper will consider a number of issues. Section 1 will introduce the paper, establishing why reliable internationally comparable statistics on cultural participation are needed. Section 2 will address the major and various difficulties that arise in comparing national surveys, both over time and to each other. Section 3 will consider the problems that persist even with cross-national surveys for comparative purposes. Section 5 will conclude the paper with some policy recommendations. [ABSTRACT FROM PUBLISHER]

Suppose estimating a model on each of a small number of potentially heterogeneous clusters yields approximately independent, unbiased, and Gaussian parameter estimators. We make two contributions in this setup. First, we showhowto compare a scalar parameter of interest between treatment and control units using a two-sample t-statistic, extending previous results for the one-sample t-statistic. Second, we develop a test for the appropriate level of clustering; it tests the null hypothesis that clustered standard errors from a much finer partition are correct. We illustrate the approach by revisiting empirical studies involving clustered, time series, and spatially correlated data. [ABSTRACT FROM AUTHOR]

Existing channel-aware scheduling work has mainly focused on scheduling in small timescales, that is, tens to hundreds of seconds. We propose to use long-term user profiles to provide useful statistical information on future network conditions in large timescales. We design scheduling algorithms based on Markov decision theory. We collect and use a large set of real-life traces from the general public. Extensive trace-driven evaluations show that many real mobile users can benefit from our framework. In addition, we compare our framework against state-of-the-art algorithms and observe significant performance differences because the existing algorithms were not designed for the large timescale scenario. [ABSTRACT FROM AUTHOR]

The article presents theoretical aspects related to the QR code, stages of appearance and evolution of QR code, its structure, features and benefits. The methodology of creating a QR code and the stages of work for accessing legal information by using the QR codes, as well as mobile applications, are presented along with highlighting the many advantages presented by using the QR code. Statistical research regarding the usefulness of accessing legal information encoded by QR codes addressed to legal advisers within the College of Legal Advisers from Brasov (Romania) was carried out in order to evaluate the usefulness of developing a collection of laws codified QR, the degree of ease regarding the implementation of the application, but also the level of interest in using this collection by legal advisors in their professional activity. [ABSTRACT FROM AUTHOR]

This paper is devoted to testing a classical periodic autoregressive against a periodic threshold autoregressive modelwith specified threshold. The local asymptotic normality property is shown via the adapted sufficient conditions due to Swensen (1985). Using this result we consider the case where the innovation density is specified, and we obtain a parametric local asymptotic “most stringent” test. [ABSTRACT FROM AUTHOR]

It is crucial to test the goodness of fit of a model before it is used to make statistical inferences. However, no satisfactory goodness of fit test is available for the case of categorical multilevel data which occur when categorical data are clustered or hierarchical in nature. Hence the aim of this paper is to develop a new goodness of fit test for multilevel binary data based on Hosmer and Lemeshow and Lipsitz et.al. In order to identify the properties of the developed test, simulation studies were carried out to assess the Type I error and the power. [ABSTRACT FROM PUBLISHER]

This article compares four methods used to approximate value at risk (VaR) from the first four moments of a probability distribution: Cornish–Fisher, Edgeworth, Gram–Charlier, and Johnson distributions. Increasing rearrangements are applied to the first three methods. Simulation results suggest that for large sample situations, Johnson distributions yield the most accurate VaR approximation. For small sample situations with small tail probabilities, Johnson distributions yield the worst approximation. A particularly relevant case would be in banking applications for calculating the size of operational risk to cover certain loss types. For this case, the rearranged Gram–Charlier method is recommended. [ABSTRACT FROM PUBLISHER]

Data-based choice of the bandwidth is an important problem in kernel density estimation. The pseudo-likelihood and the least-squares cross-validation bandwidth selectors are well known, but widely criticized in the literature. For heavy-tailed distributions, theL1distance between the pseudo-likelihood-based estimator and the density does not seem to converge in probability to zero with increasing sample size. Even for normal-tailed densities, the rate ofL1convergence is disappointingly slow. In this article, we report an interesting finding that with minor modifications both the cross-validation methods can be implemented effectively, even for heavy-tailed densities. For both these estimators, theL1distance (from the density) are shown to converge completely to zero irrespective of the tail of the density. The expectedL1distance also goes to zero. These results hold even in the presence of a strongly mixing-type dependence. Monte Carlo simulations and analysis of the Old Faithful geyser data suggest that if implemented appropriately, contrary to the traditional belief, the cross-validation estimators compare well with the sophisticated plug-in and bootstrap-based estimators. [ABSTRACT FROM PUBLISHER]

Recently, Rayleigh distribution has received considerable attention in the statistical literature. In this article, we consider the point and interval estimation of the functions of the unknown parameters of a two-parameter Rayleigh distribution. First, we obtain the maximum likelihood estimators (MLEs) of the unknown parameters. The MLEs cannot be obtained in explicit forms, and we propose to use the maximization of the profile log-likelihood function to compute the MLEs. We further consider the Bayesian inference of the unknown parameters. The Bayes’ estimates and the associated credible intervals cannot be obtained in closed forms. We use the importance sampling technique to approximate (compute) the Bayes’ estimates and the associated credible intervals. For comparison purposes, we have also used the exact method to compute the Bayes’ estimates and the corresponding credible intervals. Monte Carlo simulations are performed to compare the performances of the proposed method, and one dataset has been analyzed for illustrative purposes. We further consider the Bayes’ prediction problem based on the observed samples, and provide the appropriate predictive intervals. A data example has been provided for illustrative purposes. [ABSTRACT FROM PUBLISHER]

The VLT/Multi Unit Spectrograph Explorer (MUSE) integral-field spectrograph can detect Lyα emitters (LAE) in the redshift range 2.8≲z≲6.7 in a homogeneous way. Ongoing MUSE surveys will notably probe faint Lyα sources that are usually missed by current narrow-band surveys. We provide quantitative predictions for a typical wedding-cake observing strategy with MUSE based on mock catalogues generated with a semi-analytic model of galaxy formation coupled to numerical Lyα radiation transfer models in gas outflows.We expect ≈1500 bright LAEs (FLyα ≳ 10-17 erg s-1 -2) in a typical shallow field (SF) survey carried over ≈100 arcmin2 , and ≈2000 sources as faint as 10-18 erg s-1 cm-2 in a medium-deep field (MDF) survey over 10 arcmin2. In a typical deep field (DF) survey of 1 arcmin2 , we predict that ≈500 extremely faint LAEs (FLyα ≳ 4 × 10-19 erg s-1 -2) will be found. Our results suggest that faint Lyα sources contribute significantly to the cosmic Lyα luminosity and SFR budget. While the host haloes of bright LAEs at z ≈ 3 and 6 have descendants with median masses of 2 × 1012 and 5 × 1013 M≳, respectively, the faintest sources detectable by MUSE at these redshifts are predicted to reside in haloes which evolve into typical sub-L* and L* galaxy haloes at z = 0.We expect typical DF and MDF surveys to uncover the building blocks of Milky Way-like objects, even probing the bulk of the stellar mass content of LAEs located in their progenitor haloes at z ≈ 3. [ABSTRACT FROM AUTHOR]

A total of 36 street dust samples were collected from the streets of the Organised Industrial District in Kayseri, Turkey. This region includes a total of 818 work places in various industrial areas. The modified BCR (the European Community Bureau of Reference) sequential extraction procedure was applied to evaluate the mobility and bioavailability of trace elements (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) in street dusts of the study area. The BCR was classified into three steps: water/acid soluble fraction, reducible and oxidisable fraction. The remaining residue was dissolved by using aqua regia. The concentrations of the metals in street dust samples were determined by flame atomic absorption spectrometry. Also the effect of the different grain sizes (<38 µm, 38–53 µm and 53–74 µm) of the 36 street dust samples on the mobility of the metals was investigated using the modified BCR procedure. The mobility sequence based on the sum of the first three phases (for <74 µm grain size) was: Cd (71.3)>Cu (48.9)>Pb (42.8)=Cr (42.1)>Ni (41.4)>Zn (40.9)>Co (36.6)=Mn (36.3)>Fe (3.1). No significant difference was observed among metal partitioning for the three particle sizes. Correlation, principal component and cluster analysis were applied to identify probable natural and anthropogenic sources in the region. The principal component analysis results showed that this industrial district was influenced by traffic, industrial activities, air-borne emissions and natural sources. The accuracy of the results was checked by analysis of both the BCR-701 certified reference material and by recovery studies in street dust samples. [ABSTRACT FROM AUTHOR]

In this paper, the dependence of the melting temperature of metals Cu, Ag and Au under pressure in the interval from 0 kbar to 40 kbar is studied by the statistical moment method (SMM). This dependence has the form of near linearity and the calculated slopes of melting curve are 3.9 for Cu, 5.7 for Ag and 6 for Au. These results are in good agreement with the experimental data. [ABSTRACT FROM AUTHOR]

The low intensity of the X-ray pulsar signal and the strong X-ray background radiation lead to low signal-to-noise ratio (SNR) of the X-ray pulsar observed profile obtained through epoch folding, especially when the observation time is not long enough. This signifies the necessity of denoising of the observed profile. In this paper, the statistical characteristics of the X-ray pulsar signal are studied, and a signal-dependent noise model is established for the observed profile. Based on this, a profile noise reduction method by performing a local linear minimum mean square error filtering in the un-decimated wavelet domain is developed. The detail wavelet coefficients are rescaled by multiplying their amplitudes by a locally adaptive factor, which is the local variance ratio of the noiseless coefficients to the noisy ones. All the nonstationary statistics needed in the algorithm are calculated from the observed profile, without a priori information. The results of experiments, carried out on simulated data obtained by the ground-based simulation system and real data obtained by Rossi X-Ray Timing Explorer satellite, indicate that the proposed method is excellent in both noise suppression and preservation of peak sharpness, and it also clearly outperforms four widely accepted and used wavelet denoising methods, in terms of SNR, Pearson correlation coefficient and root mean square error. [ABSTRACT FROM AUTHOR]

We introduce a novel approach to sufficient dimension-reduction problems using distance covariance. Our method requires very mild conditions on the predictors. It estimates the central subspace effectively even when many predictors are categorical or discrete. Our method keeps the model-free advantage without estimating link function. Under regularity conditions, root-nconsistency and asymptotic normality are established for our estimator. We compare the performance of our method with some existing dimension-reduction methods by simulations and find that our method is very competitive and robust across a number of models. We also analyze the Auto MPG data to demonstrate the efficacy of our method. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

We consider efficient implementations of the generalized lasso dual path algorithm given by Tibshirani and Taylor in 2011. We first describe a generic approach that covers any penalty matrixDand any (full column rank) matrixXof predictor variables. We then describe fast implementations for the special cases of trend filtering problems, fused lasso problems, and sparse fused lasso problems, both withX=Iand a general matrixX. These specialized implementations offer a considerable improvement over the generic implementation, both in terms of numerical stability and efficiency of the solution path computation. These algorithms are all available for use in the genlasso R package, which can be found in the CRAN repository. [ABSTRACT FROM AUTHOR]

Any decision-making process that relies on a probabilistic forecast of future events necessarily requires a calibrated forecast. This article proposes new methods for empirically assessing forecast calibration in a multivariate setting where the probabilistic forecast is given by an ensemble of equally probable forecast scenarios. Multivariate properties are mapped to a single dimension through a prerank function and the calibration is subsequently assessed visually through a histogram of the ranks of the observation’s preranks. Average ranking assigns a prerank based on the average univariate rank while band depth ranking employs the concept of functional band depth where the centrality of the observation within the forecast ensemble is assessed. Several simulation examples and a case study of temperature forecast trajectories at Berlin Tegel Airport in Germany demonstrate that both multivariate ranking methods can successfully detect various sources of miscalibration and scale efficiently to high-dimensional settings. Supplemental material in form of computer code is available online. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is often used to visualize data when the rows and the columns are both of interest. In such a setting, there is a lack of inferential methods on the PCA output. We study the asymptotic variance of a fixed-effects model for PCA, and propose several approaches to assessing the variability of PCA estimates: a method based on a parametric bootstrap, a new cell-wise jackknife, as well as a computationally cheaper approximation to the jackknife. We visualize the confidence regions by Procrustes rotation. Using a simulation study, we compare the proposed methods and highlight the strengths and drawbacks of each method as we vary the number of rows, the number of columns, and the strength of the relationships between variables. [ABSTRACT FROM AUTHOR]

Optional Pólya tree (OPT) is a flexible nonparametric Bayesian prior for density estimation. Despite its merits, the computation for OPT inference is challenging. In this article, we present time complexity analysis for OPT inference and propose two algorithmic improvements. The first improvement, named limited-lookahead optional Pólya tree (LL-OPT), aims at accelerating the computation for OPT inference. The second improvement modifies the output of OPT or LL-OPT and produces a continuous piecewise linear density estimate. We demonstrate the performance of these two improvements using simulated and real date examples. [ABSTRACT FROM AUTHOR]

The notion of a 'commune' has become a part of evolutionistic view on social development over the course of 19th-20th cc. and heavily influenced various fields of Social Sciences and Humanities. The Russian statisticians have also accepted the category of a commune while they were investigating the Russian peasants' household budgets. This theoretical pattern was also applied to Kazakh land tenure during the carrying out the Russian colonial project on searching land 'surpluses' for Russian settlers in the Kazakh steppes. In particular, it was used in the statistical research "Materials on Kirgiz land tenure collected and developed by the expedition for research of the Steppe area" under Fedor Shcherbina's leadership (1896-1903). In fact, the statisticians could not identify the commune borders among the nomads as those borders were very conditional. As a result, the surveyors turned from investigating commune to their creation. Soon the maps on Kazakh land tenure were made and 'communes' were established on the juridical base. After finishing that statistical research those invented communes served for colonial authorities as the ground for ceasing land in favor of the Russian peasants. [ABSTRACT FROM AUTHOR]

In this study, a ‘consecutive-project-based learning’ structure applied to the antennas and propagation course is introduced. The first project, carried out with the faculty of management, aims to familiarize the teams with the challenges of ‘real life tender-like conditions’. In the following three projects, teams perform their assigned antenna projects. Each team researches the theoretical background, studies the main parameters of antennas and evaluates them. After designing, simulating, specifying the testing requirements of the antennas, each team makes one financial and one time budget. At the end of the term, each team makes a presentation to a jury. Finally, a questionnaire is conducted to evaluate the achievement of the outcomes. This paper focuses on the first two projects because they constitute the critical stages of the course as the first project enables inexperienced students to discover the issues they might face in a ‘real world’ project and draw lessons from that experience to be used in the more comprehensive following project. The first project’s aim is to equip the inexperienced students with elementary knowledge on working in a team; paying attention to time, budget, project phases and people management issues so that they can use that knowledge in the second project, thus learning takes place as a product of consecutive projects. Statistical information on the students’ academic performances is analyzed to observe if this course has improved their academic performance. The results indicate that ‘consecutive-project-based learning’ is an effective method in antennas and propagation course. [ABSTRACT FROM AUTHOR]

During the 20th century, population ecology and science in general relied on two very different statistical paradigms to solve its inferential problems: error statistics (also referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great deal of good science was done using these tools, but both schools suffer from technical and philosophical difficulties. At the turning of the 21st century (Royall in Statistical evidence: a likelihood paradigm. Chapman & Hall, London, ; Lele in The nature of scientific evidence: statistical, philosophical and empirical considerations. The University of Chicago Press, Chicago, pp 191-216, ), evidential statistics emerged as a seriously contending paradigm. Drawing on and refining elements from error statistics, likelihoodism, Bayesian statistics, information criteria, and robust methods, evidential statistics is a statistical modern synthesis that smoothly incorporates model identification, model uncertainty, model comparison, parameter estimation, parameter uncertainty, pre-data control of error, and post-data strength of evidence into a single coherent framework. We argue that evidential statistics is currently the most effective statistical paradigm to support 21st century science. Despite the power of the evidential paradigm, we think that there is no substitute for learning how to clarify scientific arguments with statistical arguments. In this paper we sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential statistics. We also discuss a number of misconceptions about the paradigms that have hindered practitioners, as well as some real problems with the error and Bayesian statistical paradigms solved by evidential statistics. [ABSTRACT FROM AUTHOR]

We population ecologists who are believed to be good at dealing with statistics often get confused about what kinds of statistical methods we should apply to our nuisance data. There are a couple of conflicting paradigms and many associated methods in statistics. Classical frequentists' approaches that have dominated in science have been severely criticized by the newcomers: Bayesian and evidential statistics. But, both newcomers also have weak points. Researchers devoted to different statistical approaches are seeking soft landing places where they can compromise each other. Key aspects of statistical inference are discriminating model selection and parameter estimation. Likelihood and Fisher information play important roles in both processes. As an overview of the compromise processes, here I will introduce three contributing papers by M. L. Taper, J. M. Ponciano, R. M. Dorazio, and K. Yamamura for the special feature entitled 'Bayesian, Fisherian, error, and evidential statistical approaches for population ecology.' This special feature is based on a symposium held in Tsukuba, Japan, on 11 October 2014 [ABSTRACT FROM AUTHOR]

Context: Not accounting for spatial heterogeneity in ecological analyses can cause modeled relationships to vary across spatial scales, specifically different levels of spatial resolution. These varying results hinder both the utility of data collected at one spatial scale for analyses at others and the determination of underlying processes. Objectives: To briefly review existing methods for analyzing data collected at multiple scales, highlight the effects of spatial heterogeneity on the utility of these methods, and to illustrate a practical statistical method to account for the sources of spatial heterogeneity when they are unknown. Methods: Using simulated examples, we show how not accounting for the drivers of spatial heterogeneity in statistical models can cause contradictory findings regarding relationship direction across spatial scales. We then show how mixed effects models can remedy this multiscaling issue. Results: Ignoring sources of spatial heterogeneity in statistical models with coarse spatial scales produced contradictory results to the true underlying relationship. Treating drivers of spatial heterogeneity as random effects in a mixed effects model, however, allowed us to uncover this true relationship. Conclusions: Mixed effects models is advantageous as it is not always necessary to know the influential explanatory variables that cause spatial heterogeneity and no additional data are required. Furthermore, this approach is well documented, can be applied to data having various distribution types, and is easily executable using multiple statistical packages. [ABSTRACT FROM AUTHOR]

In an article that recently appeared in this journal, Marshall (2015) argued that the systematic component of the SD of a stock or of a portfolio of stocks is its beta scaled by the SD of the market returns. She also contended that the beta mispredicts the actual systematic risk of a stock or of a portfolio of stocks. In this article, I dispute this conclusion, showing that it has been induced by an imperfection in the construction of the empirical application and by some misinterpretations of the results. A corrected replication of the empirical study of Marshall (2015) is provided, along with some comments. I conclude that both the beta and the systematic component in Marshall (2015) are effective measures of systematic risk. [ABSTRACT FROM PUBLISHER]

In the present study, we investigated forecasting of hydrate formation pressure of binary mixtures including at least one hydrocarbon using a feed-forward multi-layer artificial neural network. For this purpose, 895 experimental data which cover a wide range of temperatures and compositions were collected from different studies cited in the literatures. In order to find the best model, different ANN types are tested through the absolute average relative deviation percent (AARD), mean square error (MSE) and the regression coefficient ( R 2 ) and the optimal configuration is selected. It is found that the selected ANN model is based on the statistical analysis has an excellent agreement (AARD=1.02, MSE=1.27×10 −5 and R 2 =0.9938) with the collected experimental data. The obtained results reveal that the developed MLPNN model is an applicable and feasible tool to predict hydrate formation pressure with high accuracy with respect to Colorado School of Mines Hydrate (CSMHYD) Program. [ABSTRACT FROM AUTHOR]

Associations between stress-related biomarkers, like cortisol or catecholamines, and somatic or psychological symptoms have often been examined at the group level. Studies using this nomothetic approach reported equivocal findings, which may be due to high levels of intra-individual variance of stress biomarkers. More importantly, analyses at the group level provide information about the average patient, but do not necessarily have meaning for individual patients. An alternative approach is to examine data at the level of individual patients in so-called idiographic research. This method allows identifying individuals in whom symptoms are explained by preceding alterations in specific stress biomarkers, based on time series of symptoms and stress biomarkers. To create time series of sufficient length for statistical analysis, many subsequent stress biomarker measurements are needed for each participant. In the current paper, different matrices (i.e. saliva, urine, nail and hair) are discussed in light of their applicability for idiographic research. This innovative approach might lead to promising new insights in the association between stress biomarkers and psychological or somatic symptoms. New collection tools for stress biomarkers, like the use of sweat pads, automated microdialysis systems, dried blood spots, or smartphone applications, might contribute to the feasibility and implementation of idiographic research in the future. [ABSTRACT FROM AUTHOR]

The covariate-adjusted response adaptive (CARA) design has been shown to be better than traditional designs in terms of both ethics and efficiency. However, its mechanism for allocating subjects makes certain stochastic processes such as allocated response sequences very complicated. Consequently, the validation of statistical inference is usually challenging, and few theoretical results have been obtained. In this paper we systematically solve some fundamental problems for statistical inference with CARA designs. First, we obtain the conditional independence and distribution of allocated response sequences, which is the basis for further theoretical investigation. Second, we propose a new family of CARA designs, which is extensively applicable. We more importantly provide a framework for new CARA designs with unified asymptotic results for statistical inference. The numerical results demonstrate the advantages of the proposed CARA designs. Our findings are crucial in understanding the CARA design as well as its development and application. [ABSTRACT FROM AUTHOR]

In the two-sample location-shift problem, Student'sttest or Wilcoxon's rank-sum test are commonly applied. The latter test can be more powerful for non-normal data. Here, we propose to combine the two tests within a maximum test. We show that the constructed maximum test controls the type I error rate and has good power characteristics for a variety of distributions; its power is close to that of the more powerful of the two tests. Thus, irrespective of the distribution, the maximum test stabilizes the power. To carry out the maximum test is a more powerful strategy than selecting one of the single tests. The proposed test is applied to data of a clinical trial. [ABSTRACT FROM AUTHOR]

The statistics of model temporal variability ought to be the same as those of the filtered version of reality that the model is designed to represent. Here, simple diagnostics are introduced to quantify temporal variability on different time scales and are then applied to NCEP and CMC global ensemble forecasting systems. These diagnostics enable comparison of temporal variability in forecasts with temporal variability in the initial states from which the forecasts are produced. They also allow for an examination of how day-to-day variability in the forecast model changes as forecast integration time increases. Because the error in subsequent analyses will differ, it is shown that forecast temporal variability should lie between corresponding analysis variability and analysis variability minus 2 times the analysis error variance. This expectation is not always met and possible causes are discussed. The day-to-day variability in NCEP forecasts steadily decreases at a slow rate as forecast time increases. In contrast, temporal variability increases during the first few days in the CMC control forecasts, and then levels off, consistent with a spinup of the forecasts starting from overly smoothed analyses. The diagnostics successfully reflect a reduction in the temporal variability of the CMC perturbed forecasts after a system upgrade. The diagnostics also illustrate a shift in variability maxima from storm-track regions for 1-day variability to blocking regions for 10-day variability. While these patterns are consistent with previous studies examining temporal variability on different time scales, they have the advantage of being obtainable without the need for extended (e.g., multimonth) forecast integrations. [ABSTRACT FROM AUTHOR]

One of the cornerstones of the R system for statistical computing is the multitude of packages contributed by numerous package authors. This amount of packages makes an extremely broad range of statistical techniques and other quantitative methods freely available. Thus far, no empirical study has investigated psychological factors that drive authors to participate in the R project. This article presents a study of R package authors, collecting data on different types of participation (number of packages, participation in mailing lists, participation in conferences), three psychological scales (types of motivation, psychological values, and work design characteristics), and various socio-demographic factors. The data are analyzed using item response models and subsequent generalized linear models, showing that the most important determinants for participation are a hybrid form of motivation and the social characteristics of the work design. Other factors are found to have less impact or influence only specific aspects of participation. [ABSTRACT FROM AUTHOR]

Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. This study aimed to identify the optimal quantitative method for landslide susceptibility mapping in Mizunami City, Japan. Three mathematical methods, logistic regression (LR), bivariate statistical analysis (BS), and multivariate adaptive regression spline models (MARSplines), were used to create landslide-susceptibility maps by comparing the past landslide distribution and the conditioning factor thematic maps. A landslide inventory map with a total of 222 landslide locations was extracted from aerial photographs provided by NIED (National Research Institute for Earth Science and Disaster Prevention, Japan). Then, the landslide inventory was randomly divided into two datasets: 50% was used for training the models and the remaining 50% for validation purposes. The landslide inventory map provided by NIED and an area under the ROC curve were used to evaluate model performance. We found that the MARSpline method resulted in a better prediction rate (79%) when compared to LR (75%) and BS (77%). In addition, a higher percentage of landslide polygons were found in the high to very high classes using the MARSpline method. Therefore, we concluded that the MARSpline method was the most efficient method for landslide susceptibility mapping in this study area. [ABSTRACT FROM AUTHOR]

This paper discusses a new topic, DEM super resolution, to improve the resolution of an original DEM based on its partial new measurements obtained with high resolution. A nonlocal algorithm is introduced to perform this task. The original DEM was first divided into overlapping patches, which were classified either as “test” or “learning” data depending on whether or not they are related to high resolution measurements. For each test patch, the similar patches in the learning dataset were identified via template matching. Finally, the high resolution DEM of the test patch was restored by the weighted sum of similar patches under the condition that the reconstruction weights were the same in different resolution cases. A key assumption of this strategy is that there are some repeated or similar modes in the original DEM, which is quite common. Experiments were done to demonstrate that we can restore a DEM by preserving the details without introducing artifacts. Statistic analysis was also conducted to show that this method can obtain higher accuracy than traditional interpolation methods. [ABSTRACT FROM AUTHOR]

Official statistics is dipping its toe in the ocean of big data, and leaders are emphasizing the need for a major paradigm change. One aspect is the increased volume of data that are not collected on probability samples of the target population. Making full use of these data requires a fundamental change, not only in data collection and dissemination, but also in the methods of statistical inference. The classical "design-based" approach to survey inference, developed from the seminal work of Neyman [41], is simply not applicable to these data. Rather, statistical models are needed that potentially reflect selection bias from the lack of random sampling. I suggest that "Calibrated Bayes" is the appropriate statistical paradigm for addressing the analysis. Under this paradigm, inferences for a particular data set are Bayesian, but models are sought that yield inferences with robust repeated sampling properties. Probability sampling remains a powerful tool under this paradigm, since by ensuring that the selection mechanism is ignorable it enhances robust modeling, but it is not essential for the inference. I outline two applications of Calibrated Bayes to data collected by the U.S. Census Bureau. [ABSTRACT FROM AUTHOR]

This paper presents the evolution of the Greek statistical system and the journey towards the "professional independence and accountability of statistical agencies" which, according to the preamble of the resolution adopted by the United Nations General Assembly on the ten Fundamental Principles of Official Statistics, are imperative for ensuring public trust in the integrity of official statistical systems and confidence in official statistics. Also, the individual contribution of the United Nations experts for the establishment of the modernized statistical agency during the 1950s and 1960s is recorded and a brief history of the census is presented. [ABSTRACT FROM AUTHOR]

The article presents the authors' comments in response to comments made by researchers and editors on their articles on statistical research that are published in this issue. Topics discussed include the adaptive resampling test (ART) and calibration; centered percentile bootstrap (CPB); and construction of confidence intervals (CIs) for marginal regression.

The authors express their views on an article on the use of marginal screening for testing a global null hypothesis in linear models, that is published in this issue. Topics discussed include the modification of standard bootstrap methods in statistics; the use of null model instead of adaptive resampling test (ART) for getting the critical value; and the use of permutation test in statistical distribution.

The authors express their views on an article on marginal screening in high-dimensional linear regression, that is published in this issue. Topics discussed include statistical screening methods developed by modifying the adaptive resampling test (ART); parametric bootstrap version of ART; and Monte Carlo replications.

The authors express their views on an article on the use of bootstrap approximations in distributions of nonregular estimators under local asymptotics, that is published in this issue. Topics discussed include the importance of adaptive resampling test (ART) for bootstrapping nonregular estimators; the use of weighted empirical distribution function in linear regression models; and the role of truncation level in determining the performance of the ART.

The authors express their views on an article on the role of maximum statistic in testing the null hypothesis for covariates in a linear-model analysis, that is published in this issue. Topics discussed include the use of bootstrap method for estimating the distribution of maximum statistic; testing of the null hypothesis on the basis of simple regressions; and the use of bootstrap to estimate a covariance matrix.

When conducting a randomized experiment, if an allocation yields treatment groups that differ meaningfully with respect to relevant covariates, groups should be rerandomized. The process involves specifying an explicit criterion for whether an allocation is acceptable, based on a measure of covariate balance, and rerandomizing units until an acceptable allocation is obtained. Here, we illustrate how rerandomization could have improved the design of an already conducted randomized experiment on vocabulary and mathematics training programs, then provide a rerandomization procedure for covariates that vary in importance, and finally offer other extensions for rerandomization, including methods addressing computational efficiency. When covariates vary in a priori importance, better balance should be required for more important covariates. Rerandomization based on Mahalanobis distance preserves the joint distribution of covariates, but balances all covariates equally. Here, we propose rerandomizing based on Mahalanobis distance within tiers of covariate importance. Because balancing covariates in one tier will in general also partially balance covariates in other tiers, for each subsequent tier we explicitly balance only the components orthogonal to covariates in more important tiers. [ABSTRACT FROM AUTHOR]