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Optimal BIBD-extended designsFeb 12 2019Balanced incomplete block designs (BIBDs) are a class of designs with v treatments and b blocks of size k that are optimal with regards to a wide range of optimality criteria, but it is not clear which designs to choose for combinations of v, b and k ... More

Learning spatially-correlated temporal dictionaries for calcium imagingFeb 08 2019Calcium imaging has become a fundamental neural imaging technique, aiming to recover the individual activity of hundreds of neurons in a cortical region. Current methods (mostly matrix factorization) are aimed at detecting neurons in the field-of-view ... More

Characterization of Sine- Skewed von Mises DistributionFeb 07 2019The von Mises distribution is one of the most important distribution in statistics to deal with circular data. In this paper we will consider some basic properties and characterizations of the sine skewed von Mises distribution.

CMS Sematrix: A Tool to Aid the Development of Clinical Quality Measures (CQMs)Feb 05 2019As part of the effort to improve quality and to reduce national healthcare costs, the Centers for Medicare and Medicaid Services (CMS) are responsible for creating and maintaining an array of clinical quality measures (CQMs) for assessing healthcare structure, ... More

Uncertainty Quantification in Molecular Signals using Polynomial Chaos ExpansionJan 30 2019Molecular signals are abundant in engineering and biological contexts, and undergo stochastic propagation in fluid dynamic channels. The received signal is sensitive to a variety of input and channel parameter variations. Currently we do not understand ... More

Shannon's entropy and its Generalizations towards Statistics, Reliability and Information Science during 1948-2018Jan 28 2019Starting from the pioneering works of Shannon and Weiner in 1948, a plethora of works have been reported on entropy in different directions. Entropy-related review work in the direction of statistics, reliability and information science, to the best of ... More

Variability in the interpretation of Dutch probability phrases - a risk for miscommunicationJan 28 2019Verbal probability phrases are often used to express estimated risk. In this study, focus was on the numerical interpretation of 29 Dutch probability and frequency phrases, including several complementary phrases to test (a)symmetry in their interpretation. ... More

Organic Fiducial InferenceJan 23 2019A substantial generalization is put forward of the theory of subjective fiducial inference as it was outlined in earlier papers. In particular, this theory is extended to deal with cases where the data are discrete or categorical rather than continuous, ... More

Custodes: Auditable Hypothesis TestingJan 19 2019We present Custodes: a new approach to solving the complex issue of preventing "p-hacking" in scientific studies. The novel protocol provides a concrete and publicly auditable method for controlling false-discoveries and eliminates any potential for data ... More

Systemic Risk: Conditional Distortion Risk MeasuresJan 15 2019Jan 28 2019In this paper, we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution ($\Delta$CoD) measures as measures of systemic risk and analyze their properties and representations. The classes include the well-known ... More

Projective Decomposition and Matrix Equivalence up to ScaleJan 04 2019A data matrix may be seen simply as a means of organizing observations into rows ( e.g., by measured object) and into columns ( e.g., by measured variable) so that the observations can be analyzed with mathematical tools. As a mathematical object, a matrix ... More

Pragmatic hypotheses in the evolution of scienceDec 25 2018This paper introduces pragmatic hypotheses and relates this concept to the spiral of scientific evolution. Previous works determined a characterization of logically consistent statistical hypothesis tests and showed that the modal operators obtained from ... More

Application of Robust Estimators in Shewhart S-ChartsDec 24 2018Maintaining the quality of manufactured products at a desired level is known to increase customer satisfaction and profitability. Shewhart control chart is the most widely used in statistical process control (SPC) technique to monitor the quality of products ... More

Low-temperature marginal ferromagnetism explains anomalous scale-free correlations in natural flocksDec 18 2018We introduce a new ferromagnetic model capable of reproducing one of the most intriguing properties of collective behaviour in starling flocks, namely the fact that strong collective order of the system coexists with scale-free correlations of the modulus ... More

On a flexible construction of a negative binomial modelDec 18 2018This work presents a construction of stationary Markov models with negative binomial marginal distributions. The proposal is novel in that a simple form of the corresponding transition probabilities is available, thus revealing uninvolved simulation and ... More

Rapid Prototyping Model for Healthcare Alternative Payment Models: Replicating the Federally Qualified Health Center Advanced Primary Care Practice DemonstrationDec 10 2018Innovation in healthcare payment and service delivery utilizes high cost, high risk pilots paired with traditional program evaluations. Decision-makers are unable to reliably forecast the impacts of pilot interventions in this complex system, complicating ... More

The Holy Grail and the Bad Sampling - A test for the homogeneity of missing proportions for evaluating the agreement between peer review and bibliometrics in the Italian research assessment exercisesOct 29 2018Two experiments for evaluating the agreement between bibliometrics and informed peer review - depending on two large samples of journal articles - were performed by the Italian governmental agency for research evaluation. They were presented as successful ... More

I can see clearly now: reinterpreting statistical significanceOct 15 2018Null hypothesis significance testing remains popular despite decades of concern about misuse and misinterpretation. We believe that much of the problem is due to language: significance testing has little to do with other meanings of the word "significance". ... More

Benchmarking in cluster analysis: A white paperSep 27 2018Oct 01 2018To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods ... More

Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load ForecastingSep 10 2018We present a simple quantile regression-based forecasting method that was applied in a probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data is log transformed and split into a long-term ... More

Discussion on Using Stacking to Average Bayesian Predictive Distributions by Yao et alJun 27 2018I begin by summarizing key ideas of the paper under discussion. Then I will talk about a graphical modeling perspective, posterior contraction rates and alternative methods of aggregation. Moreover, I will also discuss possible applications of the stacking ... More

Hyperspectral Data Analysis in R: the hsdar PackageMay 14 2018Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new \hsdar package for R statistical software, which performs a variety of ... More

On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapiesMay 04 2018Sep 21 2018The analysis of adverse events (AEs) is a key component in the assessment of a drug's safety profile. Inappropriate analysis methods may result in misleading conclusions about a therapy's safety and consequently its benefit-risk ratio. The statistical ... More

Bayesian model-data synthesis with an application to global Glacio-Isostatic AdjustmentApr 17 2018Apr 30 2018We introduce a framework for updating large scale geospatial processes using a model-data synthesis method based on Bayesian hierarchical modelling. Two major challenges come from updating large-scale Gaussian process and modelling non-stationarity. To ... More

Contest models highlight inherent inefficiencies of scientific funding competitionsApr 10 2018Jan 02 2019Scientific research funding is allocated largely through a system of soliciting and ranking competitive grant proposals. In these competitions, the proposals themselves are not the deliverables that the funder seeks, but instead are used by the funder ... More

Quantifying the Contributions of Training Data and Algorithm Logic to the Performance of Automated Cause-assignment Algorithms for Verbal AutopsyMar 06 2018Nov 15 2018A verbal autopsy (VA) consists of a survey with a relative or close contact of a person who has recently died. VA surveys are commonly used to infer likely causes of death for individuals when deaths happen outside of hospitals or healthcare facilities. ... More

On Statistical Non-SignificanceMar 01 2018Significance tests are probably the most extended form of inference in empirical research, and significance is often interpreted as providing greater informational content than non-significance. In this article we show, however, that rejection of a point ... More

Elements of the Kopula (eventological copula) theoryFeb 17 2018New in the probability theory and eventology theory, the concept of Kopula (eventological copula) is introduced. The theorem on the characterization of the sets of events by Kopula is proved, which serves as the eventological pre-image of the well-known ... More

Combining Empirical Likelihood and Robust Estimation Methods for Linear Regression ModelsJan 26 2018Ordinary least square (OLS), maximum likelihood (ML) and robust methods are the widely used methods to estimate the parameters of a linear regression model. It is well known that these methods perform well under some distributional assumptions on error ... More

Curriculum Guidelines for Undergraduate Programs in Data ScienceJan 21 2018The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the ... More

Using Random Variables to Predict Experimental OutcomesJan 05 2018We shall show in this paper that there are experiments which are Bernoulli trials with success probability p > 0.5, and which have the curious feature that it is possible to correctly predict the outcome with probability > p.

Implementation of the Bin Hierarchy Method for restoring a smooth function from a sampled histogramNov 12 2017We present $\texttt{BHM}$, a tool for restoring a smooth function from a sampled histogram using the bin hierarchy method. The theoretical background of the method is presented in [arXiv:1707.07625]. The code automatically generates a smooth polynomial ... More

Restoring a smooth function from its noisy integralsJul 21 2017May 11 2018Numerical (and experimental) data analysis often requires the restoration of a smooth function from a set of sampled integrals over finite bins. We present the bin hierarchy method that efficiently computes the maximally smooth function from the sampled ... More

Coherent combination of probabilistic outputs for group decision making: an algebraic approachJul 07 2017Current decision support systems address domains that are heterogeneous in nature and becoming progressively larger. Such systems often require the input of expert judgement about a variety of different fields and an intensive computational power to produce ... More

A novel entropy recurrence quantification analysisJul 04 2017The growing study of time series, especially those related to nonlinear systems, has challenged the methodologies to characterize and classify dynamical structures of a signal. Here we conceive a new diagnostic tool for time series based on the concept ... More

Asymptotic properties of a componentwise ARH(1) plug-in predictorJun 20 2017Sep 04 2018This paper presents new results on prediction of linear processes in function spaces. The autoregressive Hilbertian process framework of order one (ARH(1) process framework) is adopted. A componentwise estimator of the autocorrelation operator is formulated, ... More

Consistency of the plug-in functional predictor of the Ornstein-Uhlenbeck process in Hilbert and Banach spacesJun 20 2017Sep 04 2018New results on functional prediction of the Ornstein-Uhlenbeck process in an autoregressive Hilbert-valued and Banach-valued frameworks are derived. Specifically, consistency of the maximum likelihood estimator of the autocorrelation operator, and of ... More

Fiducial on a stringJun 12 2017The fiducial argument of Fisher (1973) has been described as his biggest blunder, but the recent review of Hannig et al. (2016) demonstrates the current and increasing interest in this brilliant idea. This short note analyses an example introduced by ... More

Comparing the Finite-Time Performance of Simulation-Optimization AlgorithmsMay 22 2017We empirically evaluate the finite-time performance of several simulation-optimization algorithms on a testbed of problems with the goal of motivating further development of algorithms with strong finite-time performance. We investigate if the observed ... More

Classical and bayesian componentwise predictors for non-compact correlated ARH(1) processesApr 19 2017Sep 04 2018A special class of standard Gaussian Autoregressive Hilbertian processes of order one (Gaussian ARH(1) processes), with bounded linear autocorrelation operator, which does not satisfy the usual Hilbert-Schmidt assumption, is considered. To compensate ... More

Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based FeaturesApr 05 2017This contribution reports an application of MultiFractal Detrended Fluctuation Analysis, MFDFA based novel feature extraction technique for automated detection of epilepsy. In fractal geometry, Multifractal Detrended Fluctuation Analysis MFDFA is a popular ... More

Building Communication Skills in a Theoretical Statistics CourseDec 07 2016The traditional theoretical statistics course which develops the theoretical underpinnings of the discipline (usually following a probability course) is undergoing near-continuous revision in the statistics community. In particular, recent versions of ... More

High-dimensional nonparametric monotone function estimation using BARTDec 06 2016For the estimation of a regression relationship between Y and a large set of po- tential predictors x 1 , . . . , x p , the flexible nature of a nonparametric approach such as BART (Bayesian Additive Regression Trees) allows for a much richer set of possibilities ... More

The BIN_COUNTS Constraint: Filtering and ApplicationsNov 28 2016We introduce the BIN_COUNTS constraint, which deals with the problem of counting the number of decision variables in a set which are assigned values that lie in given bins. We illustrate a decomposition and a filtering algorithm that achieves generalised ... More

BayesVarSel: Bayesian Testing, Variable Selection and model averaging in Linear Models using RNov 24 2016This paper introduces the R package BayesVarSel which implements objective Bayesian methodology for hypothesis testing and variable selection in linear models. The package computes posterior probabilities of the competing hypotheses/models and provides ... More

Stop the tests: Opinion bias and statistical testsNov 20 2016When statisticians quarrel about hypothesis testing, the debate usually focus on which method is the correct one. The fundamental question of whether we should test hypothesis at all tends to be forgotten. This lack of debate has its roots on our desire ... More

On $p$-valuesNov 18 2016Models are consistently treated as approximations and all procedures are consistent with this. They do not treat the model as being true. In this context $p$-values are one measure of approximation, a small $p$-value indicating a poor approximation. Approximation ... More

On approximations via convolution-defined mixture modelsNov 12 2016An often-cited fact regarding mixing distributions is that their densities can approximate the densities of any unknown distribution to arbitrary degrees of accuracy provided that the mixing distribution is sufficiently complex. This fact is often not ... More

Apocalypse Now? Reviving the Doomsday ArgumentNov 01 2016Whether the fate of our species can be forecast from its past has been the topic of considerable controversy. One refutation of the so-called Doomsday Argument is based on the premise that we are more likely to exist in a universe containing a greater ... More

Eigenvector statistics of the product of Ginibre matricesOct 28 2016We develop a method to calculate left-right eigenvector correlations of the product of $m$ independent $N\times N$ complex Ginibre matrices. For illustration, we present explicit analytical results for the vector overlap for a couple of examples for small ... More

Causal influence in linear response modelsOct 25 2016The intuition of causation is so fundamental that almost every research study in life sciences refers to this concept. However a widely accepted formal definition of causal influence between observables is still missing. In the framework of linear Langevin ... More

A Devastating Example for the Halfer RuleOct 17 2016How should we update de dicto beliefs in the face of de se evidence? The Sleeping Beauty problem divides philosophers into two camps, halfers and thirders. But there is some disagreement among halfers about how their position should generalize to other ... More

Research and Education in Computational Science and EngineeringOct 09 2016Jan 01 2018Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the ... More

Research and Education in Computational Science and EngineeringOct 09 2016Oct 11 2016Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the ... More

Research and Education in Computational Science and EngineeringOct 09 2016Oct 17 2016Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the ... More

Scale and curvature effects in principal geodesic analysisOct 05 2016Oct 06 2016There is growing interest in using the close connection between differential geometry and statistics to model smooth manifold-valued data. In particular, much work has been done recently to generalize principal component analysis (PCA), the method of ... More

Conditional Visualization for Statistical Models: An Introduction to the condvis Package in ROct 02 2016The condvis package is for interactive visualization of sections in data space, showing fitted models on the section, and observed data near the section. The primary goal is the interpretation of complex models, and showing how the observed data support ... More

Spatial Temporal Exponential-Family Point Process Models for the Evolution of Social SystemsSep 30 2016We develop a class of exponential-family point processes based on a latent social space to model the coevolution of social structure and behavior over time. Temporal dynamics are modeled as a discrete Markov process specified through individual transition ... More

Graphical Models for Discrete and Continuous DataSep 18 2016We introduce a general framework for undirected graphical models. It generalizes Gaussian graphical models to a wide range of continuous, discrete, and combinations of different types of data. We also show that the models in the framework, called exponential ... More

Sterrett procedure for the generalized group testing problemSep 15 2016Sep 20 2016Group testing is a useful method that has broad applications in medicine, engineering, and even in airport security control. Consider a finite population of $N$ items, where item $i$ has a probability $p_i$ to be defective. The goal is to identify all ... More

Probabilistic Population Projections for Countries with Generalized HIV/AIDS EpidemicsSep 14 2016The United Nations (UN) issued official probabilistic population projections for all countries to 2100 in July 2015. This was done by simulating future levels of total fertility and life expectancy from Bayesian hierarchical models, and combining the ... More

Quantitative assessment of increasing complexitySep 08 2016We study the build up of complexity on the example of 1 kg matter in different forms. We start on the simplest example of ideal gases, and then continue with more complex chemical, biological, life and social and technical structures. We assess the complexity ... More

Generalized Spatial and Spatiotemporal Autoregressive Conditional HeteroscedasticitySep 02 2016In this paper, we introduce a new spatial model that incorporates heteroscedastic variance depending on neighboring locations. The proposed process is regarded as the spatial equivalent to the temporal autoregressive conditional heteroscedasticity (ARCH) ... More

Publication bias and the canonization of false factsSep 02 2016In the process of scientific inquiry, certain claims accumulate enough support to be established as facts. Unfortunately, not every claim accorded the status of fact turns out to be true. In this paper, we model the dynamic process by which claims are ... More

Publication bias and the canonization of false factsSep 02 2016Nov 20 2016In the process of scientific inquiry, certain claims accumulate enough support to be established as facts. Unfortunately, not every claim accorded the status of fact turns out to be true. In this paper, we model the dynamic process by which claims are ... More

Revisiting nested group testing procedures: new results, comparisons, and robustnessAug 22 2016Nov 01 2016Group testing has its origin in the identification of syphilis in the US army during World War II. Much of the theoretical framework of group testing was developed starting in the late 1950's, with continued work into the 1990's. Recently, with the advent ... More

Revisiting nested group testing procedures: new results, comparisons, and robustnessAug 22 2016Group testing has its origin in the identification of syphilis in the US army during World War II. Much of the theoretical framework of group testing was developed starting in the late 1950's, with continued work into the 1990's. Recently, with the advent ... More

Unifying Markov Properties for Graphical ModelsAug 20 2016Aug 28 2016Several types of graph with different conditional independence interpretations --- also known as Markov properties --- have been proposed and used in graphical models. In this paper we unify these Markov properties by introducing a class of graphs with ... More

Putting Down Roots: A Graphical Exploration of Community AttachmentAug 17 2016In this paper, we explore the relationships that individuals have with their communities. This work was prepared as part of the ASA Data Expo '13 sponsored by the Graphics Section and the Computing Section, using data provided by the Knight Foundation ... More

Designing Modular Software: A Case Study in Introductory StatisticsAug 08 2016Oct 20 2016Modular programming is a development paradigm that emphasizes self-contained, flexible, and independent pieces of functionality. This practice allows new features to be seamlessly added when desired, and unwanted features to be removed, thus simplifying ... More

Introductory statistics with intRoAug 08 2016intRo is a web-based application for performing basic data analysis and statistical routines. Leveraging the power of R and Shiny, intRo implements common statistical functions in an extensible modular structure, while including a point-and-click interface ... More

Statistical Methods in Topological Data Analysis for Complex, High-Dimensional DataJul 18 2016The utilization of statistical methods an their applications within the new field of study known as Topological Data Analysis has has tremendous potential for broadening our exploration and understanding of complex, high-dimensional data spaces. This ... More

Progress on a Conjecture Regarding the Triangular DistributionJul 16 2016Nov 05 2016Triangular distributions are a well-known class of distributions that are often used as an elementary example of a probability model. Maximum likelihood estimation of the mode parameter of the triangular distribution over the unit interval can be performed ... More

Progress on a Conjecture Regarding the Triangular DistributionJul 16 2016Triangular distributions are a well-known class of distributions that are often used as an elementary example of a probability model. Maximum likelihood estimation of the mode parameter of the triangular distribution over the unit interval can be performed ... More

Dynamic Question Ordering in Online SurveysJul 14 2016Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, ... More

Embracing Data ScienceJul 04 2016Statistics is running the risk of appearing irrelevant to today's undergraduate students. Today's undergraduate students are familiar with data science projects and they judge statistics against what they have seen. Statistics, especially at the introductory ... More

The Simulator: An Engine to Streamline SimulationsJun 30 2016The simulator is an R package that streamlines the process of performing simulations by creating a common infrastructure that can be easily used and reused across projects. Methodological statisticians routinely write simulations to compare their methods ... More

Consider avoiding the .05 significance levelJun 29 2016It is suggested that some shortcomings of Null Hypothesis Significance Testing (NHST), viewed from the perspective of Bayesian statistics, turn benign once the traditional threshold p value of .05 is substituted by a sufficiently smaller value. To illustrate, ... More

Identifiability and testability in GRT with Individual DifferencesJun 17 2016Jul 29 2016Silbert and Thomas (2013) showed that failures of decisional separability are not, in general, identifiable in fully parameterized $2 \times 2$ Gaussian GRT models. A recent extension of $2 \times 2$ GRT models (GRTwIND) was developed to solve this problem ... More

Bringing Order to the Chaos in the BrickyardJun 10 2016An allegory published in 1963 titled Chaos in the Brickyard spoke to the decline in the quality of research. In the intervening time greater awareness of the issues and actions to improve research endeavors have emerged. Still, problems persist. This ... More

A statistical inference course based on p-valuesJun 07 2016Introductory statistical inference texts and courses treat the point estimation, hypothesis testing, and interval estimation problems separately, with primary emphasis on large-sample approximations. Here I present an alternative approach to teaching ... More

When Does a Boltzmannian Equilibrium Exist?Jun 03 2016The received wisdom in statistical mechanics is that isolated systems, when left to themselves, approach equilibrium. But under what circumstances does an equilibrium state exist and an approach to equilibrium take place? In this paper we address these ... More

Peter Hall's work on high-dimensional data and classificationJun 03 2016In this article, I summarise Peter Hall's contributions to high-dimensional data, including their geometric representations and variable selection methods based on ranking. I also discuss his work on classification problems, concluding with some personal ... More

Different numerical estimators for main effect global sensitivity indicesJun 02 2016The variance-based method of global sensitivity indices based on Sobol sensitivity indices became very popular among practitioners due to its easiness of interpretation. For complex practical problems computation of Sobol indices generally requires a ... More

Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticityJun 02 2016In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive ... More

Fairly Random: The Impact of Winning the Toss on the Probability of WinningMay 27 2016In a competitive sport, every little thing matters. Yet, many sports leave some large levers out of the reach of the teams, and in the hands of fate. In cricket, world's second most popular sport by some measures, one such lever---the toss---has been ... More

Some Mathematical Aspects of Price OptimisationMay 19 2016Calculation of an optimal tariff is a principal challenge for pricing actuaries. In this contribution we are concerned with the renewal insurance business discussing various mathematical aspects of calculation of an optimal renewal tariff. Our motivation ... More

Sobol' indices for problems defined in non-rectangular domainsMay 17 2016A novel theoretical and numerical framework for the estimation of Sobol sensitivity indices for models in which inputs are confined to a non-rectangular domain (e.g., in presence of inequality constraints) is developed. Two numerical methods, namely the ... More

Bayesian Lower Bounds for Dense or Sparse (Outlier) Noise in the RMT FrameworkMay 14 2016Robust estimation is an important and timely research subject. In this paper, we investigate performance lower bounds on the mean-square-error (MSE) of any estimator for the Bayesian linear model, corrupted by a noise distributed according to an i.i.d. ... More

Teaching Data ScienceApr 25 2016We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the Python programming language with an emphasis ... More

Ubiquity of Benfords law and emergence of the reciprocal distributionApr 23 2016We apply the Law of Total Probability to the construction of scale-invariant probability distribution functions (pdfs), and require that probability measures be dimensionless and unitless under a continuous change of scales. If the scale-change distribution ... More

Its All on the Square- The Importance of the Sum of Squares and Making the General Linear Model SimpleApr 19 2016Statistics is one of the most valuable of disciplines. Science is based on proof and it alone produces results, other approaches are not, and do not. Statistics is the only acceptable language of proof in science. Yet statistics is difficult to understand ... More

BFDA: A Matlab Toolbox for Bayesian Functional Data AnalysisApr 18 2016We provide a Matlab toolbox, BFDA, that implements a Bayesian hierarchical model for smoothing functional data and estimating mean-covariance functions simultaneously and nonparametricaly, with the assumptions of Gaussian process for functional data and ... More

Box-Cox symmetric distributions and applications to nutritional dataApr 08 2016We introduce the Box-Cox symmetric class of distributions, which is useful for modeling positively skewed, possibly heavy-tailed, data. The new class of distributions includes the Box-Cox t, Box-Cox Cole-Gree, Box-Cox power exponential distributions, ... More

Box-Cox symmetric distributions and applications to nutritional dataApr 08 2016Oct 11 2016We introduce the Box-Cox symmetric class of distributions, which is useful for modeling positively skewed, possibly heavy-tailed, data. The new class of distributions includes the Box-Cox t, Box-Cox Cole-Gree, Box-Cox power exponential distributions, ... More

Statistical sensitiveness for scienceApr 07 2016Research often necessitates of samples, yet obtaining large enough samples is not always possible. When it is, the researcher may use one of two methods for deciding upon the required sample size: rules-of-thumb, quick yet uncertain, and estimations for ... More

Picking Winners Using Integer ProgrammingApr 06 2016Jul 06 2016We consider the problem of selecting a portfolio of entries of fixed cardinality for a winner take all contest such that the probability of at least one entry winning is maximized. This framework is very general and can be used to model a variety of problems, ... More

An overview and perspective on social network monitoringMar 31 2016In this expository paper we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the ... More

Risk contagion under regular variation and asymptotic tail independenceMar 30 2016Risk contagion concerns any entity dealing with large scale risks. Suppose (X,Y) denotes a risk vector pertaining to two components in some system. A relevant measurement of risk contagion would be to quantify the amount of influence of high values of ... More

Risk contagion under regular variation and asymptotic tail independenceMar 30 2016Apr 25 2017Risk contagion concerns any entity dealing with large scale risks. Suppose (X,Y) denotes a risk vector pertaining to two components in some system. A relevant measurement of risk contagion would be to quantify the amount of influence of high values of ... More