Validation and psychometric properties of the Somatic and Psychological HEalth REport (SPHERE) in a young Australian-based population sample using non-parametric item response theory.

Science.gov (United States)

Couvy-Duchesne, Baptiste; Davenport, Tracey A; Martin, Nicholas G; Wright, Margaret J; Hickie, Ian B

2017-08-01

The Somatic and Psychological HEalth REport (SPHERE) is a 34-item self-report questionnaire that assesses symptoms of mental distress and persistent fatigue. As it was developed as a screening instrument for use mainly in primary care-based clinical settings, its validity and psychometric properties have not been studied extensively in population-based samples. We used non-parametric Item Response Theory to assess scale validity and item properties of the SPHERE-34 scales, collected through four waves of the Brisbane Longitudinal Twin Study (N = 1707, mean age = 12, 51% females; N = 1273, mean age = 14, 50% females; N = 1513, mean age = 16, 54% females, N = 1263, mean age = 18, 56% females). We estimated the heritability of the new scores, their genetic correlation, and their predictive ability in a sub-sample (N = 1993) who completed the Composite International Diagnostic Interview. After excluding items most responsible for noise, sex or wave bias, the SPHERE-34 questionnaire was reduced to 21 items (SPHERE-21), comprising a 14-item scale for anxiety-depression and a 10-item scale for chronic fatigue (3 items overlapping). These new scores showed high internal consistency (alpha > 0.78), moderate three months reliability (ICC = 0.47-0.58) and item scalability (Hi > 0.23), and were positively correlated (phenotypic correlations r = 0.57-0.70; rG = 0.77-1.00). Heritability estimates ranged from 0.27 to 0.51. In addition, both scores were associated with later DSM-IV diagnoses of MDD, social anxiety and alcohol dependence (OR in 1.23-1.47). Finally, a post-hoc comparison showed that several psychometric properties of the SPHERE-21 were similar to those of the Beck Depression Inventory. The scales of SPHERE-21 measure valid and comparable constructs across sex and age groups (from 9 to 28 years). SPHERE-21 scores are heritable, genetically correlated and show good predictive ability of mental health in an Australian-based population

Development of a short form Social Interaction Anxiety (SIAS) and Social Phobia Scale (SPS) using nonparametric item response theory: the SIAS-6 and the SPS-6.

Science.gov (United States)

Peters, Lorna; Sunderland, Matthew; Andrews, Gavin; Rapee, Ronald M; Mattick, Richard P

2012-03-01

Shortened forms of the Social Interaction Anxiety Scale (SIAS) and the Social Phobia Scale (SPS) were developed using nonparametric item response theory methods. Using data from socially phobic participants enrolled in 5 treatment trials (N = 456), 2 six-item scales (the SIAS-6 and the SPS-6) were developed. The validity of the scores on the SIAS-6 and the SPS-6 was then tested using traditional methods for their convergent validity in an independent clinical sample and a student sample, as well as for their sensitivity to change and diagnostic sensitivity in the clinical sample. The scores on the SIAS-6 and the SPS-6 correlated as well as the scores on the original SIAS and SPS, with scores on measures of related constructs, discriminated well between those with and without a diagnosis of social phobia, providing cutoffs for diagnosis and were as sensitive to measuring change associated with treatment as were the SIAS and SPS. Together, the SIAS-6 and the SPS-6 appear to be an efficient method of measuring symptoms of social phobia and provide a brief screening tool.

The linear transformation model with frailties for the analysis of item response times.

Science.gov (United States)

Wang, Chun; Chang, Hua-Hua; Douglas, Jeffrey A

2013-02-01

The item response times (RTs) collected from computerized testing represent an underutilized source of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. In this paper, we propose a semi-parametric model for RTs, the linear transformation model with a latent speed covariate, which combines the flexibility of non-parametric modelling and the brevity as well as interpretability of parametric modelling. In this new model, the RTs, after some non-parametric monotone transformation, become a linear model with latent speed as covariate plus an error term. The distribution of the error term implicitly defines the relationship between the RT and examinees' latent speeds; whereas the non-parametric transformation is able to describe various shapes of RT distributions. The linear transformation model represents a rich family of models that includes the Cox proportional hazards model, the Box-Cox normal model, and many other models as special cases. This new model is embedded in a hierarchical framework so that both RTs and responses are modelled simultaneously. A two-stage estimation method is proposed. In the first stage, the Markov chain Monte Carlo method is employed to estimate the parametric part of the model. In the second stage, an estimating equation method with a recursive algorithm is adopted to estimate the non-parametric transformation. Applicability of the new model is demonstrated with a simulation study and a real data application. Finally, methods to evaluate the model fit are suggested. © 2012 The British Psychological Society.

Comparison of Exposure Controls, Item Pool Characteristics, and Population Distributions for CAT Using the Partial Credit Model

Science.gov (United States)

Lee, HwaYoung; Dodd, Barbara G.

2012-01-01

This study investigated item exposure control procedures under various combinations of item pool characteristics and ability distributions in computerized adaptive testing based on the partial credit model. Three variables were manipulated: item pool characteristics (120 items for each of easy, medium, and hard item pools), two ability…

INVESTIGATION OF MIS ITEM 011589A AND 3013 CONTAINERS HAVING SIMILAR CHARACTERISTICS

Energy Technology Data Exchange (ETDEWEB)

Friday, G

2006-08-23

Recent testing has identified the presence of hydrogen and oxygen in MIS Item 011589A. This isolated observation has effectuated concern regarding the potential for flammable gas mixtures in containers in the storage inventory. This study examines the known physicochemical characteristics of MIS Item 011589A and queries the ISP Database for items that are most similar or potentially similar. Items identified as most similar are believed to have the highest probability of being chemically and structurally identical to MIS Item 011589A. Items identified as potentially like MIS Item 011589A have some attributes in common, have the potential to generate gases, but have a lower probability of having similar gas generating characteristics. MIS Item 011589A is an oxide that was generated prior to 1990 at Rocky Flats in Building 707. It was associated with foundry processing and had an actinide assay of approximately 77%. Prompt gamma analysis of MIS Item 011589A indicated the presence of chloride, fluorine, magnesium, sodium, and aluminum. Queries based on MIS representation classification and process of origin were applied to the ISP Database. Evaluation criteria included binning classification (i.e., innocuous, pressure, or pressure and corrosion), availability of prompt gamma analyses, presence of chlorine and magnesium, percentage of chlorine by weight, peak ratios (i.e., Na:Cl and Mg:Na), moisture, and percent assay. These queries identified 15 items that were most similar and 106 items that were potentially like MIS Item 011589A. Although these queries identified containers that could potentially generate flammable gases, verification and confirmation can only be accomplished by destructive evaluation and testing of containers from the storage inventory.

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Directory of Open Access Journals (Sweden)

Alejandro Jara

2011-04-01

Full Text Available Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.

Content Validity and Psychometric Characteristics of the "Knowledge about Older Patients Quiz" for Nurses Using Item Response Theory.

Science.gov (United States)

Dikken, Jeroen; Hoogerduijn, Jita G; Kruitwagen, Cas; Schuurmans, Marieke J

2016-11-01

To assess the content validity and psychometric characteristics of the Knowledge about Older Patients Quiz (KOP-Q), which measures nurses' knowledge regarding older hospitalized adults and their certainty regarding this knowledge. Cross-sectional. Content validity: general hospitals. Psychometric characteristics: nursing school and general hospitals in the Netherlands. Content validity: 12 nurse specialists in geriatrics. Psychometric characteristics: 107 first-year and 78 final-year bachelor of nursing students, 148 registered nurses, and 20 nurse specialists in geriatrics. Content validity: The nurse specialists rated each item of the initial KOP-Q (52 items) on relevance. Ratings were used to calculate Item-Content Validity Index and average Scale-Content Validity Index (S-CVI/ave) scores. Items with insufficient content validity were removed. Psychometric characteristics: Ratings of students, nurses, and nurse specialists were used to test for different item functioning (DIF) and unidimensionality before item characteristics (discrimination and difficulty) were examined using Item Response Theory. Finally, norm references were calculated and nomological validity was assessed. Content validity: Forty-three items remained after assessing content validity (S-CVI/ave = 0.90). Psychometric characteristics: Of the 43 items, two demonstrating ceiling effects and 11 distorting ability estimates (DIF) were subsequently excluded. Item characteristics were assessed for the remaining 30 items, all of which demonstrated good discrimination and difficulty parameters. Knowledge was positively correlated with certainty about this knowledge. The final 30-item KOP-Q is a valid, psychometrically sound, comprehensive instrument that can be used to assess the knowledge of nursing students, hospital nurses, and nurse specialists in geriatrics regarding older hospitalized adults. It can identify knowledge and certainty deficits for research purposes or serve as a tool in educational

Plutonium Finishing Plant (PFP) Criticality Alarm System Commercial Grade Item (CGI) Critical Characteristics

International Nuclear Information System (INIS)

WHITE, W.F.

1999-01-01

This document specifies the critical characteristics for Commercial Grade Items (CGI) procured for PFP's criticality alarm system as required by HNF-PRO-268 and HNF-PRO-1819. These are the minimum specifications that the equipment must meet in order to properly perform its safety function. There may be several manufacturers or models that meet the critical characteristics for any one item. PFP's Criticality Alarm System includes the nine criticality alarm system panels and their associated hardware. This includes all parts up to the first breaker in the electrical distribution system. Specific system boundaries and justifications are contained in HNF-SD-CP-SDD-003, ''Definition and Means of Maintaining the Criticality Detectors and Alarms Portion of the PFP Safety Envelope.'' The procurement requirements associated with the system necessitates procurement of some system equipment as Commercial Grade Items in accordance with HNF-PRO-268, ''Control of Purchased Items and Services.''

Nonparametric correlation models for portfolio allocation

DEFF Research Database (Denmark)

Aslanidis, Nektarios; Casas, Isabel

2013-01-01

This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural ...... currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis....

Plutonium Finishing Plant (PFP) Safety Class and Safety Significant Commercial Grade Items (CGI) Critical Characteristic

International Nuclear Information System (INIS)

THOMAS, R.J.

2000-01-01

This document specifies the critical characteristics for Commercial Grade Items (CGI) procured for use in the Plutonium Finishing Plant as required by HNF-PRO-268 and HNF-PRO-1819. These are the minimum specifications that the equipment must meet in order to properly perform its safety function. There may be several manufacturers or models that meet the critical characteristics of any one item

Nonparametric statistics for social and behavioral sciences

CERN Document Server

Kraska-MIller, M

2013-01-01

Introduction to Research in Social and Behavioral SciencesBasic Principles of ResearchPlanning for ResearchTypes of Research Designs Sampling ProceduresValidity and Reliability of Measurement InstrumentsSteps of the Research Process Introduction to Nonparametric StatisticsData AnalysisOverview of Nonparametric Statistics and Parametric Statistics Overview of Parametric Statistics Overview of Nonparametric StatisticsImportance of Nonparametric MethodsMeasurement InstrumentsAnalysis of Data to Determine Association and Agreement Pearson Chi-Square Test of Association and IndependenceContingency

A comparative study of non-parametric models for identification of ...

African Journals Online (AJOL)

However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...

A contingency table approach to nonparametric testing

CERN Document Server

Rayner, JCW

2000-01-01

Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more comp

Nonparametric factor analysis of time series

OpenAIRE

Rodríguez-Poo, Juan M.; Linton, Oliver Bruce

1998-01-01

We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.

Nonparametric tests for censored data

CERN Document Server

Bagdonavicus, Vilijandas; Nikulin, Mikhail

2013-01-01

This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.

Using Cochran's Z Statistic to Test the Kernel-Smoothed Item Response Function Differences between Focal and Reference Groups

Science.gov (United States)

Zheng, Yinggan; Gierl, Mark J.; Cui, Ying

2010-01-01

This study combined the kernel smoothing procedure and a nonparametric differential item functioning statistic--Cochran's Z--to statistically test the difference between the kernel-smoothed item response functions for reference and focal groups. Simulation studies were conducted to investigate the Type I error and power of the proposed…

Nonparametric Bayesian Modeling of Complex Networks

DEFF Research Database (Denmark)

Schmidt, Mikkel Nørgaard; Mørup, Morten

2013-01-01

an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...

Application of Group-Level Item Response Models in the Evaluation of Consumer Reports about Health Plan Quality

Science.gov (United States)

Reise, Steven P.; Meijer, Rob R.; Ainsworth, Andrew T.; Morales, Leo S.; Hays, Ron D.

2006-01-01

Group-level parametric and non-parametric item response theory models were applied to the Consumer Assessment of Healthcare Providers and Systems (CAHPS[R]) 2.0 core items in a sample of 35,572 Medicaid recipients nested within 131 health plans. Results indicated that CAHPS responses are dominated by within health plan variation, and only weakly…

Assessing item fit for unidimensional item response theory models using residuals from estimated item response functions.

Science.gov (United States)

Haberman, Shelby J; Sinharay, Sandip; Chon, Kyong Hee

2013-07-01

Residual analysis (e.g. Hambleton & Swaminathan, Item response theory: principles and applications, Kluwer Academic, Boston, 1985; Hambleton, Swaminathan, & Rogers, Fundamentals of item response theory, Sage, Newbury Park, 1991) is a popular method to assess fit of item response theory (IRT) models. We suggest a form of residual analysis that may be applied to assess item fit for unidimensional IRT models. The residual analysis consists of a comparison of the maximum-likelihood estimate of the item characteristic curve with an alternative ratio estimate of the item characteristic curve. The large sample distribution of the residual is proved to be standardized normal when the IRT model fits the data. We compare the performance of our suggested residual to the standardized residual of Hambleton et al. (Fundamentals of item response theory, Sage, Newbury Park, 1991) in a detailed simulation study. We then calculate our suggested residuals using data from an operational test. The residuals appear to be useful in assessing the item fit for unidimensional IRT models.

Nonparametric Bounds in the Presence of Item Nonresponse, Unfolding Brackets and Anchoring

NARCIS (Netherlands)

Vazquez-Alvarez, R.; Melenberg, B.; van Soest, A.H.O.

2001-01-01

Household surveys often suffer from nonresponse on variables such as income, savings or wealth.Recent work by Manski shows how bounds on conditional quantiles of the variable of interest can be derived, allowing for any type of nonrandom item nonresponse.The width between these bounds can be reduced

Decision support using nonparametric statistics

CERN Document Server

Beatty, Warren

2018-01-01

This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.

Theory of nonparametric tests

CERN Document Server

Dickhaus, Thorsten

2018-01-01

This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.

Nonparametric statistics with applications to science and engineering

CERN Document Server

Kvam, Paul H

2007-01-01

A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provide...

Item-focussed Trees for the Identification of Items in Differential Item Functioning.

Science.gov (United States)

Tutz, Gerhard; Berger, Moritz

2016-09-01

A novel method for the identification of differential item functioning (DIF) by means of recursive partitioning techniques is proposed. We assume an extension of the Rasch model that allows for DIF being induced by an arbitrary number of covariates for each item. Recursive partitioning on the item level results in one tree for each item and leads to simultaneous selection of items and variables that induce DIF. For each item, it is possible to detect groups of subjects with different item difficulties, defined by combinations of characteristics that are not pre-specified. The way a DIF item is determined by covariates is visualized in a small tree and therefore easily accessible. An algorithm is proposed that is based on permutation tests. Various simulation studies, including the comparison with traditional approaches to identify items with DIF, show the applicability and the competitive performance of the method. Two applications illustrate the usefulness and the advantages of the new method.

Nonparametric Transfer Function Models

Science.gov (United States)

Liu, Jun M.; Chen, Rong; Yao, Qiwei

2009-01-01

In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example. PMID:20628584

Bayesian nonparametric data analysis

CERN Document Server

Müller, Peter; Jara, Alejandro; Hanson, Tim

2015-01-01

This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.

Evaluating the validity of the Work Role Functioning Questionnaire (Canadian French version) using classical test theory and item response theory.

Science.gov (United States)

Hong, Quan Nha; Coutu, Marie-France; Berbiche, Djamal

2017-01-01

The Work Role Functioning Questionnaire (WRFQ) was developed to assess workers' perceived ability to perform job demands and is used to monitor presenteeism. Still few studies on its validity can be found in the literature. The purpose of this study was to assess the items and factorial composition of the Canadian French version of the WRFQ (WRFQ-CF). Two measurement approaches were used to test the WRFQ-CF: Classical Test Theory (CTT) and non-parametric Item Response Theory (IRT). A total of 352 completed questionnaires were analyzed. A four-factor and three-factor model models were tested and shown respectively good fit with 14 items (Root Mean Square Error of Approximation (RMSEA) = 0.06, Standardized Root Mean Square Residual (SRMR) = 0.04, Bentler Comparative Fit Index (CFI) = 0.98) and with 17 items (RMSEA = 0.059, SRMR = 0.048, CFI = 0.98). Using IRT, 13 problematic items were identified, of which 9 were common with CTT. This study tested different models with fewer problematic items found in a three-factor model. Using a non-parametric IRT and CTT for item purification gave complementary results. IRT is still scarcely used and can be an interesting alternative method to enhance the quality of a measurement instrument. More studies are needed on the WRFQ-CF to refine its items and factorial composition.

Nonparametric functional mapping of quantitative trait loci.

Science.gov (United States)

Yang, Jie; Wu, Rongling; Casella, George

2009-03-01

Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.

Introduction to nonparametric statistics for the biological sciences using R

CERN Document Server

MacFarland, Thomas W

2016-01-01

This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses a...

On Cooper's Nonparametric Test.

Science.gov (United States)

Schmeidler, James

1978-01-01

The basic assumption of Cooper's nonparametric test for trend (EJ 125 069) is questioned. It is contended that the proper assumption alters the distribution of the statistic and reduces its usefulness. (JKS)

A nonparametric mixture model for cure rate estimation.

Science.gov (United States)

Peng, Y; Dear, K B

2000-03-01

Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.

A Structural Labor Supply Model with Nonparametric Preferences

NARCIS (Netherlands)

van Soest, A.H.O.; Das, J.W.M.; Gong, X.

2000-01-01

Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis.This paper presents an example where nonparametric flexibility can be attained

Nonparametric Bayesian inference for multidimensional compound Poisson processes

NARCIS (Netherlands)

Gugushvili, S.; van der Meulen, F.; Spreij, P.

2015-01-01

Given a sample from a discretely observed multidimensional compound Poisson process, we study the problem of nonparametric estimation of its jump size density r0 and intensity λ0. We take a nonparametric Bayesian approach to the problem and determine posterior contraction rates in this context,

On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests

Directory of Open Access Journals (Sweden)

Aaditya Ramdas

2017-01-01

Full Text Available Nonparametric two-sample or homogeneity testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. The literature is old and rich, with a wide variety of statistics having being designed and analyzed, both for the unidimensional and the multivariate setting. Inthisshortsurvey,wefocusonteststatisticsthatinvolvetheWassersteindistance. Usingan entropic smoothing of the Wasserstein distance, we connect these to very different tests including multivariate methods involving energy statistics and kernel based maximum mean discrepancy and univariate methods like the Kolmogorov–Smirnov test, probability or quantile (PP/QQ plots and receiver operating characteristic or ordinal dominance (ROC/ODC curves. Some observations are implicit in the literature, while others seem to have not been noticed thus far. Given nonparametric two-sample testing’s classical and continued importance, we aim to provide useful connections for theorists and practitioners familiar with one subset of methods but not others.

TWO-PARAMETER IRT MODEL APPLICATION TO ASSESS PROBABILISTIC CHARACTERISTICS OF PROHIBITED ITEMS DETECTION BY AVIATION SECURITY SCREENERS

Directory of Open Access Journals (Sweden)

Alexander K. Volkov

2017-01-01

Full Text Available The modern approaches to the aviation security screeners’ efficiency have been analyzedand, certain drawbacks have been considered. The main drawback is the complexity of ICAO recommendations implementation concerning taking into account of shadow x-ray image complexity factors during preparation and evaluation of prohibited items detection efficiency by aviation security screeners. Х-ray image based factors are the specific properties of the x-ray image that in- fluence the ability to detect prohibited items by aviation security screeners. The most important complexity factors are: geometric characteristics of a prohibited item; view difficulty of prohibited items; superposition of prohibited items byother objects in the bag; bag content complexity; the color similarity of prohibited and usual items in the luggage.The one-dimensional two-parameter IRT model and the related criterion of aviation security screeners’ qualification have been suggested. Within the suggested model the probabilistic detection characteristics of aviation security screeners are considered as functions of such parameters as the difference between level of qualification and level of x-ray images com- plexity, and also between the aviation security screeners’ responsibility and structure of their professional knowledge. On the basis of the given model it is possible to consider two characteristic functions: first of all, characteristic function of qualifica- tion level which describes multi-complexity level of x-ray image interpretation competency of the aviation security screener; secondly, characteristic function of the x-ray image complexity which describes the range of x-ray image interpretation com- petency of the aviation security screeners having various training levels to interpret the x-ray image of a certain level of com- plexity. The suggested complex criterion to assess the level of the aviation security screener qualification allows to evaluate his or

Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.

Science.gov (United States)

Du, Pang; Tang, Liansheng

2009-01-30

When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example. Copyright (c) 2008 John Wiley & Sons, Ltd.

2nd Conference of the International Society for Nonparametric Statistics

CERN Document Server

Manteiga, Wenceslao; Romo, Juan

2016-01-01

This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cádiz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers...

Bayesian nonparametric hierarchical modeling.

Science.gov (United States)

Dunson, David B

2009-04-01

In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.

Tremor Detection Using Parametric and Non-Parametric Spectral Estimation Methods: A Comparison with Clinical Assessment

Science.gov (United States)

Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H.; Maurits, Natasha M.

2016-01-01

In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

[Age- and sex-specific reference intervals for 10 health examination items: mega-data from a Japanese Health Service Association].

Science.gov (United States)

Suka, Machi; Yoshida, Katsumi; Kawai, Tadashi; Aoki, Yoshikazu; Yamane, Noriyuki; Yamauchi, Kuniaki

2005-07-01

To determine age- and sex-specific reference intervals for 10 health examination items in Japanese adults. Health examination data were accumulated from 24 different prefectural health service associations affiliated with the Japan Association of Health Service. Those who were non-smokers, drank less than 7 days/week, and had a body mass index of 18.5-24.9kg/m2 were sampled as a reference population (n = 737,538; 224,947 men and 512,591 women). After classified by age and sex, reference intervals for 10 health examination items (systolic blood pressure, diastolic blood pressure, total cholesterol, triglyceride, glucose, uric acid, AST, ALT, gamma-GT, and hemoglobin) were estimated using the parametric and nonparametric methods. In every item except for hemoglobin, men had higher reference intervals than women. Systolic blood pressure, total cholesterol, and glucose showed an upward trend in values with increasing age. Hemoglobin showed a downward trend in values with increasing age. Triglyceride, ALT, and gamma-GT reached a peak in middle age. Overall, parametric estimates showed narrower reference intervals than non-parametric estimates. Reference intervals vary with age and sex. Age- and sex-specific reference intervals may contribute to better assessment of health examination data.

A NONPARAMETRIC HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING

OpenAIRE

Temel, Tugrul T.

2001-01-01

This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test. The paper also develops a Mathematica Code for the test algorithm.

Nonparametric identification of copula structures

KAUST Repository

Li, Bo; Genton, Marc G.

2013-01-01

We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric

Evaluating the Psychometric Characteristics of Generated Multiple-Choice Test Items

Science.gov (United States)

Gierl, Mark J.; Lai, Hollis; Pugh, Debra; Touchie, Claire; Boulais, André-Philippe; De Champlain, André

2016-01-01

Item development is a time- and resource-intensive process. Automatic item generation integrates cognitive modeling with computer technology to systematically generate test items. To date, however, items generated using cognitive modeling procedures have received limited use in operational testing situations. As a result, the psychometric…

Selecting Items for Criterion-Referenced Tests.

Science.gov (United States)

Mellenbergh, Gideon J.; van der Linden, Wim J.

1982-01-01

Three item selection methods for criterion-referenced tests are examined: the classical theory of item difficulty and item-test correlation; the latent trait theory of item characteristic curves; and a decision-theoretic approach for optimal item selection. Item contribution to the standardized expected utility of mastery testing is discussed. (CM)

Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.

Science.gov (United States)

Storlie, Curtis B; Bondell, Howard D; Reich, Brian J; Zhang, Hao Helen

2011-04-01

Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.

Recent Advances and Trends in Nonparametric Statistics

CERN Document Server

Akritas, MG

2003-01-01

The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection o

A nonparametric spatial scan statistic for continuous data.

Science.gov (United States)

Jung, Inkyung; Cho, Ho Jin

2015-10-20

Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

An item response theory analysis of Harter's Self-Perception Profile for Children or why strong clinical scales should be distrusted

NARCIS (Netherlands)

Egberink, Iris J. L.; Meijer, Rob R.

The authors investigated the psychometric properties of the subscales of the Self-Perception Profile for Children with item response theory (IRT) models using a sample of 611 children. Results from a nonparametric Mokken analysis and a parametric IRT approach for boys (n = 268) and girls (n = 343)

A ¤nonparametric dynamic additive regression model for longitudinal data

DEFF Research Database (Denmark)

Martinussen, T.; Scheike, T. H.

2000-01-01

dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models......dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models...

A Bayesian nonparametric approach to causal inference on quantiles.

Science.gov (United States)

Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

2018-02-25

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

Nonparametric analysis of blocked ordered categories data: some examples revisited

Directory of Open Access Journals (Sweden)

O. Thas

2006-08-01

Full Text Available Nonparametric analysis for general block designs can be given by using the Cochran-Mantel-Haenszel (CMH statistics. We demonstrate this with four examples and note that several well-known nonparametric statistics are special cases of CMH statistics.

Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality

Directory of Open Access Journals (Sweden)

Zhanchao Li

2013-01-01

Full Text Available The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model and change of sequence distribution law of nonparametric statistical model. On this basis, through the reduction of change point problem, the establishment of basic nonparametric change point model, and asymptotic analysis on test method of basic change point problem, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is created in consideration of the situation that in practice concrete dam crack behavior may have more abnormality points. And the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is used in the actual project, demonstrating the effectiveness and scientific reasonableness of the method established. Meanwhile, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality has a complete theoretical basis and strong practicality with a broad application prospect in actual project.

Item analysis of ADAS-Cog: effect of baseline cognitive impairment in a clinical AD trial.

Science.gov (United States)

Sevigny, Jeffrey J; Peng, Yahong; Liu, Lian; Lines, Christopher R

2010-03-01

We explored the association of Alzheimer's disease (AD) Assessment Scale (ADAS-Cog) item scores with AD severity using cross-sectional and longitudinal data from the same study. Post hoc analyses were performed using placebo data from a 12-month trial of patients with mild-to-moderate AD (N =281 randomized, N =209 completed). Baseline distributions of ADAS-Cog item scores by Mini-Mental State Examination (MMSE) score and Clinical Dementia Rating (CDR) sum of boxes score (measures of dementia severity) were estimated using local and nonparametric regressions. Mixed-effect models were used to characterize ADAS-Cog item score changes over time by dementia severity (MMSE: mild =21-26, moderate =14-20; global CDR: mild =0.5-1, moderate =2). In the cross-sectional analysis of baseline ADAS-Cog item scores, orientation was the most sensitive item to differentiate patients across levels of cognitive impairment. Several items showed a ceiling effect, particularly in milder AD. In the longitudinal analysis of change scores over 12 months, orientation was the only item with noticeable decline (8%-10%) in mild AD. Most items showed modest declines (5%-20%) in moderate AD.

Nonparametric Mixture Models for Supervised Image Parcellation.

Science.gov (United States)

Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

2009-09-01

We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.

Nonparametric Inference for Periodic Sequences

KAUST Repository

Sun, Ying

2012-02-01

This article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.

An item response theory analysis of Harter’s self-perception profile for children or why strong clinical scales should be distrusted

NARCIS (Netherlands)

Egberink, I.J.L.; Meijer, R.R.

2011-01-01

The authors investigated the psychometric properties of the subscales of the Self-Perception Profile for Children with item response theory (IRT) models using a sample of 611 children. Results from a nonparametric Mokken analysis and a parametric IRT approach for boys (n = 268) and girls (n = 343)

Development and psychometric characteristics of the SCI-QOL Ability to Participate and Satisfaction with Social Roles and Activities item banks and short forms.

Science.gov (United States)

Heinemann, Allen W; Kisala, Pamela A; Hahn, Elizabeth A; Tulsky, David S

2015-05-01

To develop a spinal cord injury (SCI)-focused version of PROMIS and Neuro-QOL social domain item banks; evaluate the psychometric properties of items developed for adults with SCI; and report information to facilitate clinical and research use. We used a mixed-methods design to develop and evaluate Ability to Participate in Social Roles and Activities and Satisfaction with Social Roles and Activities items. Focus groups helped define the constructs; cognitive interviews helped revise items; and confirmatory factor analysis and item response theory methods helped calibrate item banks and evaluate differential item functioning related to demographic and injury characteristics. Five SCI Model System sites and one Veterans Administration medical center. The calibration sample consisted of 641 individuals; a reliability sample consisted of 245 individuals residing in the community. A subset of 27 Ability to Participate and 35 Satisfaction items demonstrated good measurement properties and negligible differential item functioning related to demographic and injury characteristics. The SCI-specific measures correlate strongly with the PROMIS and Neuro-QOL versions. Ten item short forms correlate >0.96 with the full banks. Variable-length CATs with a minimum of 4 items, variable-length CATs with a minimum of 8 items, fixed-length CATs of 10 items, and the 10-item short forms demonstrate construct coverage and measurement error that is comparable to the full item bank. The Ability to Participate and Satisfaction with Social Roles and Activities CATs and short forms demonstrate excellent psychometric properties and are suitable for clinical and research applications.

Non-parametric tests of productive efficiency with errors-in-variables

NARCIS (Netherlands)

Kuosmanen, T.K.; Post, T.; Scholtes, S.

2007-01-01

We develop a non-parametric test of productive efficiency that accounts for errors-in-variables, following the approach of Varian. [1985. Nonparametric analysis of optimizing behavior with measurement error. Journal of Econometrics 30(1/2), 445-458]. The test is based on the general Pareto-Koopmans

Reliability assessment of complex mechatronic systems using a modified nonparametric belief propagation algorithm

International Nuclear Information System (INIS)

Zhong, X.; Ichchou, M.; Saidi, A.

2010-01-01

Various parametric skewed distributions are widely used to model the time-to-failure (TTF) in the reliability analysis of mechatronic systems, where many items are unobservable due to the high cost of testing. Estimating the parameters of those distributions becomes a challenge. Previous research has failed to consider this problem due to the difficulty of dependency modeling. Recently the methodology of Bayesian networks (BNs) has greatly contributed to the reliability analysis of complex systems. In this paper, the problem of system reliability assessment (SRA) is formulated as a BN considering the parameter uncertainty. As the quantitative specification of BN, a normal distribution representing the stochastic nature of TTF distribution is learned to capture the interactions between the basic items and their output items. The approximation inference of our continuous BN model is performed by a modified version of nonparametric belief propagation (NBP) which can avoid using a junction tree that is inefficient for the mechatronic case because of the large treewidth. After reasoning, we obtain the marginal posterior density of each TTF model parameter. Other information from diverse sources and expert priors can be easily incorporated in this SRA model to achieve more accurate results. Simulation in simple and complex cases of mechatronic systems demonstrates that the posterior of the parameter network fits the data well and the uncertainty passes effectively through our BN based SRA model by using the modified NBP.

Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change

Directory of Open Access Journals (Sweden)

Hae-Bum Yun

2011-01-01

Full Text Available A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented.

portfolio optimization based on nonparametric estimation methods

Directory of Open Access Journals (Sweden)

mahsa ghandehari

2017-03-01

Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.

Robustifying Bayesian nonparametric mixtures for count data.

Science.gov (United States)

Canale, Antonio; Prünster, Igor

2017-03-01

Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure. © 2016, The International Biometric Society.

Comparing parametric and nonparametric regression methods for panel data

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...

Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.

Science.gov (United States)

Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A

2017-06-30

Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

A Nonparametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets

Science.gov (United States)

Carrig, Madeline M.; Manrique-Vallier, Daniel; Ranby, Krista W.; Reiter, Jerome P.; Hoyle, Rick H.

2015-01-01

Complex research questions often cannot be addressed adequately with a single data set. One sensible alternative to the high cost and effort associated with the creation of large new data sets is to combine existing data sets containing variables related to the constructs of interest. The goal of the present research was to develop a flexible, broadly applicable approach to the integration of disparate data sets that is based on nonparametric multiple imputation and the collection of data from a convenient, de novo calibration sample. We demonstrate proof of concept for the approach by integrating three existing data sets containing items related to the extent of problematic alcohol use and associations with deviant peers. We discuss both necessary conditions for the approach to work well and potential strengths and weaknesses of the method compared to other data set integration approaches. PMID:26257437

Parametric and Non-Parametric System Modelling

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg

1999-01-01

the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...... considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...

Network structure exploration via Bayesian nonparametric models

International Nuclear Information System (INIS)

Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

2015-01-01

Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

Science.gov (United States)

Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

2006-01-01

This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin

2017-01-19

In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun

2017-01-01

In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

Nonparametric methods for volatility density estimation

NARCIS (Netherlands)

Es, van Bert; Spreij, P.J.C.; Zanten, van J.H.

2009-01-01

Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on

Quantal Response: Nonparametric Modeling

Science.gov (United States)

2017-01-01

capture the behavior of observed phenomena. Higher-order polynomial and finite-dimensional spline basis models allow for more complicated responses as the...flexibility as these are nonparametric (not constrained to any particular functional form). These should be useful in identifying nonstandard behavior via... deviance ∆ = −2 log(Lreduced/Lfull) is defined in terms of the likelihood function L. For normal error, Lfull = 1, and based on Eq. A-2, we have log

Speaker Linking and Applications using Non-Parametric Hashing Methods

Science.gov (United States)

2016-09-08

nonparametric estimate of a multivariate density function,” The Annals of Math- ematical Statistics , vol. 36, no. 3, pp. 1049–1051, 1965. [9] E. A. Patrick...Speaker Linking and Applications using Non-Parametric Hashing Methods† Douglas Sturim and William M. Campbell MIT Lincoln Laboratory, Lexington, MA...with many approaches [1, 2]. For this paper, we focus on using i-vectors [2], but the methods apply to any embedding. For the task of speaker QBE and

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Science.gov (United States)

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

Conditioning factors of test-taking engagement in PIAAC: an exploratory IRT modelling approach considering person and item characteristics

Directory of Open Access Journals (Sweden)

Frank Goldhammer

2017-11-01

Full Text Available Abstract Background A potential problem of low-stakes large-scale assessments such as the Programme for the International Assessment of Adult Competencies (PIAAC is low test-taking engagement. The present study pursued two goals in order to better understand conditioning factors of test-taking disengagement: First, a model-based approach was used to investigate whether item indicators of disengagement constitute a continuous latent person variable by domain. Second, the effects of person and item characteristics were jointly tested using explanatory item response models. Methods Analyses were based on the Canadian sample of Round 1 of the PIAAC, with N = 26,683 participants completing test items in the domains of literacy, numeracy, and problem solving. Binary item disengagement indicators were created by means of item response time thresholds. Results The results showed that disengagement indicators define a latent dimension by domain. Disengagement increased with lower educational attainment, lower cognitive skills, and when the test language was not the participant’s native language. Gender did not exert any effect on disengagement, while age had a positive effect for problem solving only. An item’s location in the second of two assessment modules was positively related to disengagement, as was item difficulty. The latter effect was negatively moderated by cognitive skill, suggesting that poor test-takers are especially likely to disengage with more difficult items. Conclusions The negative effect of cognitive skill, the positive effect of item difficulty, and their negative interaction effect support the assumption that disengagement is the outcome of individual expectations about success (informed disengagement.

Testing for constant nonparametric effects in general semiparametric regression models with interactions

KAUST Repository

Wei, Jiawei; Carroll, Raymond J.; Maity, Arnab

2011-01-01

We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work

UN ANÁLISIS NO PARAMÉTRICO DE ÍTEMS DE LA PRUEBA DEL BENDER/A NONPARAMETRIC ITEM ANALYSIS OF THE BENDER GESTALT TEST MODIFIED

Directory of Open Access Journals (Sweden)

César Merino Soto

2009-05-01

Full Text Available Resumen:La presente investigación hace un estudio psicométrico de un nuevo sistema de calificación de la Prueba Gestáltica del Bendermodificada para niños, que es el Sistema de Calificación Cualitativa (Brannigan y Brunner, 2002, en un muestra de 244 niñosingresantes a primer grado de primaria en cuatro colegios públicos, ubicados en Lima. El enfoque usado es un análisis noparamétrico de ítems mediante el programa Testgraf (Ramsay, 1991. Los resultados indican niveles apropiados deconsistencia interna, identificándose la unidimensionalidad, y el buen nivel discriminativo de las categorías de calificación deeste Sistema Cualitativo. No se hallaron diferencias demográficas respecto al género ni la edad. Se discuten los presenteshallazgos en el contexto del potencial uso del Sistema de Calificación Cualitativa y del análisis no paramétrico de ítems en lainvestigación psicométrica.AbstracThis research designs a psychometric study of a new scoring system of the Bender Gestalt test modified to children: it is theQualitative Scoring System (Brannigan & Brunner, 2002, in a sample of 244 first grade children of primary level, in four public school of Lima. The approach aplied is the nonparametric item analysis using The test graft computer program (Ramsay, 1991. Our findings point to good levels of internal consistency, unidimensionality and good discriminative level ofthe categories of scoring from the Qualitative Scoring System. There are not demographic differences between gender or age.We discuss our findings within the context of the potential use of the Qualitative Scoring System and of the nonparametricitem analysis approach in the psychometric research.

Nonparametric statistical inference

CERN Document Server

Gibbons, Jean Dickinson

2014-01-01

Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.

Non-Parametric Estimation of Correlation Functions

DEFF Research Database (Denmark)

Brincker, Rune; Rytter, Anders; Krenk, Steen

In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are point...

Application of nonparametric statistic method for DNBR limit calculation

International Nuclear Information System (INIS)

Dong Bo; Kuang Bo; Zhu Xuenong

2013-01-01

Background: Nonparametric statistical method is a kind of statistical inference method not depending on a certain distribution; it calculates the tolerance limits under certain probability level and confidence through sampling methods. The DNBR margin is one important parameter of NPP design, which presents the safety level of NPP. Purpose and Methods: This paper uses nonparametric statistical method basing on Wilks formula and VIPER-01 subchannel analysis code to calculate the DNBR design limits (DL) of 300 MW NPP (Nuclear Power Plant) during the complete loss of flow accident, simultaneously compared with the DL of DNBR through means of ITDP to get certain DNBR margin. Results: The results indicate that this method can gain 2.96% DNBR margin more than that obtained by ITDP methodology. Conclusions: Because of the reduction of the conservation during analysis process, the nonparametric statistical method can provide greater DNBR margin and the increase of DNBR margin is benefited for the upgrading of core refuel scheme. (authors)

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Directory of Open Access Journals (Sweden)

Saerom Park

Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

Multi-sample nonparametric treatments comparison in medical ...

African Journals Online (AJOL)

Multi-sample nonparametric treatments comparison in medical follow-up study with unequal observation processes through simulation and bladder tumour case study. P. L. Tan, N.A. Ibrahim, M.B. Adam, J. Arasan ...

Nonparametric regression using the concept of minimum energy

International Nuclear Information System (INIS)

Williams, Mike

2011-01-01

It has recently been shown that an unbinned distance-based statistic, the energy, can be used to construct an extremely powerful nonparametric multivariate two sample goodness-of-fit test. An extension to this method that makes it possible to perform nonparametric regression using multiple multivariate data sets is presented in this paper. The technique, which is based on the concept of minimizing the energy of the system, permits determination of parameters of interest without the need for parametric expressions of the parent distributions of the data sets. The application and performance of this new method is discussed in the context of some simple example analyses.

Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

Science.gov (United States)

Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

2013-04-01

In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

Bayesian Nonparametric Longitudinal Data Analysis.

Science.gov (United States)

Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen

2016-01-01

Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.

Essays on nonparametric econometrics of stochastic volatility

NARCIS (Netherlands)

Zu, Y.

2012-01-01

Volatility is a concept that describes the variation of financial returns. Measuring and modelling volatility dynamics is an important aspect of financial econometrics. This thesis is concerned with nonparametric approaches to volatility measurement and volatility model validation.

Non-parametric Tuning of PID Controllers A Modified Relay-Feedback-Test Approach

CERN Document Server

Boiko, Igor

2013-01-01

The relay feedback test (RFT) has become a popular and efficient  tool used in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text.   Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) meth...

Nonparametric Bayes Modeling of Multivariate Categorical Data.

Science.gov (United States)

Dunson, David B; Xing, Chuanhua

2012-01-01

Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.

A Case Study on an Item Writing Process: Use of Test Specifications, Nature of Group Dynamics, and Individual Item Writers' Characteristics

Science.gov (United States)

Kim, Jiyoung; Chi, Youngshin; Huensch, Amanda; Jun, Heesung; Li, Hongli; Roullion, Vanessa

2010-01-01

This article discusses a case study on an item writing process that reflects on our practical experience in an item development project. The purpose of the article is to share our lessons from the experience aiming to demystify item writing process. The study investigated three issues that naturally emerged during the project: how item writers use…

A Review of Classical Methods of Item Analysis.

Science.gov (United States)

French, Christine L.

Item analysis is a very important consideration in the test development process. It is a statistical procedure to analyze test items that combines methods used to evaluate the important characteristics of test items, such as difficulty, discrimination, and distractibility of the items in a test. This paper reviews some of the classical methods for…

Nonparametric predictive inference in statistical process control

NARCIS (Netherlands)

Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

2000-01-01

New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on

Vegetable parenting practices scale: Item response modeling analyses

Science.gov (United States)

Our objective was to evaluate the psychometric properties of a vegetable parenting practices scale using multidimensional polytomous item response modeling which enables assessing item fit to latent variables and the distributional characteristics of the items in comparison to the respondents. We al...

Nonparametric instrumental regression with non-convex constraints

International Nuclear Information System (INIS)

Grasmair, M; Scherzer, O; Vanhems, A

2013-01-01

This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition. (paper)

Nonparametric instrumental regression with non-convex constraints

Science.gov (United States)

Grasmair, M.; Scherzer, O.; Vanhems, A.

2013-03-01

This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.

Triangles in ROC space: History and theory of "nonparametric" measures of sensitivity and response bias.

Science.gov (United States)

Macmillan, N A; Creelman, C D

1996-06-01

Can accuracy and response bias in two-stimulus, two-response recognition or detection experiments be measured nonparametrically? Pollack and Norman (1964) answered this question affirmatively for sensitivity, Hodos (1970) for bias: Both proposed measures based on triangular areas in receiver-operating characteristic space. Their papers, and especially a paper by Grier (1971) that provided computing formulas for the measures, continue to be heavily cited in a wide range of content areas. In our sample of articles, most authors described triangle-based measures as making fewer assumptions than measures associated with detection theory. However, we show that statistics based on products or ratios of right triangle areas, including a recently proposed bias index and a not-yetproposed but apparently plausible sensitivity index, are consistent with a decision process based on logistic distributions. Even the Pollack and Norman measure, which is based on non-right triangles, is approximately logistic for low values of sensitivity. Simple geometric models for sensitivity and bias are not nonparametric, even if their implications are not acknowledged in the defining publications.

Assessing errors related to characteristics of the items measured

International Nuclear Information System (INIS)

Liggett, W.

1980-01-01

Errors that are related to some intrinsic property of the items measured are often encountered in nuclear material accounting. An example is the error in nondestructive assay measurements caused by uncorrected matrix effects. Nuclear material accounting requires for each materials type one measurement method for which bounds on these errors can be determined. If such a method is available, a second method might be used to reduce costs or to improve precision. If the measurement error for the first method is longer-tailed than Gaussian, then precision might be improved by measuring all items by both methods. 8 refs

Nonparametric conditional predictive regions for time series

NARCIS (Netherlands)

de Gooijer, J.G.; Zerom Godefay, D.

2000-01-01

Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the

Nonparametric e-Mixture Estimation.

Science.gov (United States)

Takano, Ken; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru

2016-12-01

This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.

Screen Wars, Star Wars, and Sequels: Nonparametric Reanalysis of Movie Profitability

OpenAIRE

W. D. Walls

2012-01-01

In this paper we use nonparametric statistical tools to quantify motion-picture profit. We quantify the unconditional distribution of profit, the distribution of profit conditional on stars and sequels, and we also model the conditional expectation of movie profits using a non- parametric data-driven regression model. The flexibility of the non-parametric approach accommodates the full range of possible relationships among the variables without prior specification of a functional form, thereb...

Nonparametric predictive inference in reliability

International Nuclear Information System (INIS)

Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.

2002-01-01

We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere

Nonparametric estimation in models for unobservable heterogeneity

OpenAIRE

Hohmann, Daniel

2014-01-01

Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.

Nonparametric estimation of location and scale parameters

KAUST Repository

Potgieter, C.J.; Lombard, F.

2012-01-01

Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal

A Bayesian Nonparametric Approach to Factor Analysis

DEFF Research Database (Denmark)

Piatek, Rémi; Papaspiliopoulos, Omiros

2018-01-01

This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...

An item response theory analysis of Harter's Self-Perception Profile for children or why strong clinical scales should be distrusted.

Science.gov (United States)

Egberink, Iris J L; Meijer, Rob R

2011-06-01

The authors investigated the psychometric properties of the subscales of the Self-Perception Profile for Children with item response theory (IRT) models using a sample of 611 children. Results from a nonparametric Mokken analysis and a parametric IRT approach for boys (n = 268) and girls (n = 343) were compared. The authors found that most scales formed weak scales and that measurement precision was relatively low and only present for latent trait values indicating low self-perception. The subscales Physical Appearance and Global Self-Worth formed one strong scale. Children seem to interpret Global Self-Worth items as if they measure Physical Appearance. Furthermore, the authors found that strong Mokken scales (such as Global Self-Worth) consisted mostly of items that repeat the same item content. They conclude that researchers should be very careful in interpreting the total scores on the different Self-Perception Profile for Children scales. Finally, implications for further research are discussed.

Examples of the Application of Nonparametric Information Geometry to Statistical Physics

Directory of Open Access Journals (Sweden)

Giovanni Pistone

2013-09-01

Full Text Available We review a nonparametric version of Amari’s information geometry in which the set of positive probability densities on a given sample space is endowed with an atlas of charts to form a differentiable manifold modeled on Orlicz Banach spaces. This nonparametric setting is used to discuss the setting of typical problems in machine learning and statistical physics, such as black-box optimization, Kullback-Leibler divergence, Boltzmann-Gibbs entropy and the Boltzmann equation.

Symptoms of anxiety in depression: assessment of item performance of the Hamilton Anxiety Rating Scale in patients with depression.

Science.gov (United States)

Vaccarino, Anthony L; Evans, Kenneth R; Sills, Terrence L; Kalali, Amir H

2008-01-01

Although diagnostically dissociable, anxiety is strongly co-morbid with depression. To examine further the clinical symptoms of anxiety in major depressive disorder (MDD), a non-parametric item response analysis on "blinded" data from four pharmaceutical company clinical trials was performed on the Hamilton Anxiety Rating Scale (HAMA) across levels of depressive severity. The severity of depressive symptoms was assessed using the 17-item Hamilton Depression Rating Scale (HAMD). HAMA and HAMD measures were supplied for each patient on each of two post-screen visits (n=1,668 observations). Option characteristic curves were generated for all 14 HAMA items to determine the probability of scoring a particular option on the HAMA in relation to the total HAMD score. Additional analyses were conducted using Pearson's product-moment correlations. Results showed that anxiety-related symptomatology generally increased as a function of overall depressive severity, though there were clear differences between individual anxiety symptoms in their relationship with depressive severity. In particular, anxious mood, tension, insomnia, difficulties in concentration and memory, and depressed mood were found to discriminate over the full range of HAMD scores, increasing continuously with increases in depressive severity. By contrast, many somatic-related symptoms, including muscular, sensory, cardiovascular, respiratory, gastro-intestinal, and genito-urinary were manifested primarily at higher levels of depression and did not discriminate well at lower HAMD scores. These results demonstrate anxiety as a core feature of depression, and the relationship between anxiety-related symptoms and depression should be considered in the assessment of depression and evaluation of treatment strategies and outcome.

Characteristics of highly rated leadership in nursing homes using item response theory.

Science.gov (United States)

Backman, Annica; Sjögren, Karin; Lindkvist, Marie; Lövheim, Hugo; Edvardsson, David

2017-12-01

To identify characteristics of highly rated leadership in nursing homes. An ageing population entails fundamental social, economic and organizational challenges for future aged care. Knowledge is limited of both specific leadership behaviours and organizational and managerial characteristics which have an impact on the leadership of contemporary nursing home care. Cross-sectional. From 290 municipalities, 60 were randomly selected and 35 agreed to participate, providing a sample of 3605 direct-care staff employed in 169 Swedish nursing homes. The staff assessed their managers' (n = 191) leadership behaviours using the Leadership Behaviour Questionnaire. Data were collected from November 2013 - September 2014, and the study was completed in November 2016. A two-parameter item response theory approach and regression analyses were used to identify specific characteristics of highly rated leadership. Five specific behaviours of highly rated nursing home leadership were identified; that the manager: experiments with new ideas; controls work closely; relies on subordinates; coaches and gives direct feedback; and handles conflicts constructively. The regression analyses revealed that managers with social work backgrounds and privately run homes were significantly associated with higher leadership ratings. This study highlights the five most important leadership behaviours that characterize those nursing home managers rated highest in terms of leadership. Managers in privately run nursing homes and managers with social work backgrounds were associated with higher leadership ratings. Further work is needed to explore these behaviours and factors predictive of higher leadership ratings. © 2017 John Wiley & Sons Ltd.

Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices

OpenAIRE

Hiroyuki Kasahara; Katsumi Shimotsu

2006-01-01

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...

Development of a brief version of the Social Phobia Inventory using item response theory: the Mini-SPIN-R.

Science.gov (United States)

Aderka, Idan M; Pollack, Mark H; Simon, Naomi M; Smits, Jasper A J; Van Ameringen, Michael; Stein, Murray B; Hofmann, Stefan G

2013-12-01

The Social Phobia Inventory (SPIN) is a widely used measure in mental health settings and a 3-item version (mini-SPIN) has been developed as a screening instrument for social anxiety disorder. In the present study, we examined the psychometric properties of the SPIN and developed a brief version (mini-SPIN-R) designed to assess social anxiety severity using item response theory. Our sample included 569 individuals with social anxiety disorder who participated in 2 clinical trials and filled out a battery of self-report measures. Using a nonparametric kernel smoothing method we identified the most sensitive items of the SPIN. These 3 items comprised the mini-SPIN-R, which was found to have greater internal consistency, and to capture a greater range of symptoms compared to the mini-SPIN. The mini-SPIN-R evidenced superior convergent validity compared to the mini-SPIN and both measures had similar divergent validity. Thus, the mini-SPIN-R is a promising brief measure of social anxiety severity. Copyright © 2013. Published by Elsevier Ltd.

Non-parametric smoothing of experimental data

International Nuclear Information System (INIS)

Kuketayev, A.T.; Pen'kov, F.M.

2007-01-01

Full text: Rapid processing of experimental data samples in nuclear physics often requires differentiation in order to find extrema. Therefore, even at the preliminary stage of data analysis, a range of noise reduction methods are used to smooth experimental data. There are many non-parametric smoothing techniques: interval averages, moving averages, exponential smoothing, etc. Nevertheless, it is more common to use a priori information about the behavior of the experimental curve in order to construct smoothing schemes based on the least squares techniques. The latter methodology's advantage is that the area under the curve can be preserved, which is equivalent to conservation of total speed of counting. The disadvantages of this approach include the lack of a priori information. For example, very often the sums of undifferentiated (by a detector) peaks are replaced with one peak during the processing of data, introducing uncontrolled errors in the determination of the physical quantities. The problem is solvable only by having experienced personnel, whose skills are much greater than the challenge. We propose a set of non-parametric techniques, which allows the use of any additional information on the nature of experimental dependence. The method is based on a construction of a functional, which includes both experimental data and a priori information. Minimum of this functional is reached on a non-parametric smoothed curve. Euler (Lagrange) differential equations are constructed for these curves; then their solutions are obtained analytically or numerically. The proposed approach allows for automated processing of nuclear physics data, eliminating the need for highly skilled laboratory personnel. Pursuant to the proposed approach is the possibility to obtain smoothing curves in a given confidence interval, e.g. according to the χ 2 distribution. This approach is applicable when constructing smooth solutions of ill-posed problems, in particular when solving

Multivariate nonparametric regression and visualization with R and applications to finance

CERN Document Server

Klemelä, Jussi

2014-01-01

A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio

Using non-parametric methods in econometric production analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

2012-01-01

by investigating the relationship between the elasticity of scale and the farm size. We use a balanced panel data set of 371~specialised crop farms for the years 2004-2007. A non-parametric specification test shows that neither the Cobb-Douglas function nor the Translog function are consistent with the "true......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...

Nonparametric Bayesian density estimation on manifolds with applications to planar shapes.

Science.gov (United States)

Bhattacharya, Abhishek; Dunson, David B

2010-12-01

Statistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article proposes a broad class of mixture models constructed using suitable kernels on a general compact metric space and then on the planar shape space in particular. Following a Bayesian approach with a nonparametric prior on the mixing distribution, conditions are obtained under which the Kullback-Leibler property holds, implying large support and weak posterior consistency. Gibbs sampling methods are developed for posterior computation, and the methods are applied to problems in density estimation and classification with shape-based predictors. Simulation studies show improved estimation performance relative to existing approaches.

Bayesian nonparametric system reliability using sets of priors

NARCIS (Netherlands)

Walter, G.M.; Aslett, L.J.M.; Coolen, F.P.A.

2016-01-01

An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test

Using personality item characteristics to predict single-item reliability, retest reliability, and self-other agreement

NARCIS (Netherlands)

de Vries, Reinout Everhard; Realo, Anu; Allik, Jüri

2016-01-01

The use of reliability estimates is increasingly scrutinized as scholars become more aware that test–retest stability and self–other agreement provide a better approximation of the theoretical and practical usefulness of an instrument than its internal reliability. In this study, we investigate item

Teaching Nonparametric Statistics Using Student Instrumental Values.

Science.gov (United States)

Anderson, Jonathan W.; Diddams, Margaret

Nonparametric statistics are often difficult to teach in introduction to statistics courses because of the lack of real-world examples. This study demonstrated how teachers can use differences in the rankings and ratings of undergraduate and graduate values to discuss: (1) ipsative and normative scaling; (2) uses of the Mann-Whitney U-test; and…

Testing for constant nonparametric effects in general semiparametric regression models with interactions

KAUST Repository

Wei, Jiawei

2011-07-01

We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.

Building an Evaluation Scale using Item Response Theory.

Science.gov (United States)

Lalor, John P; Wu, Hao; Yu, Hong

2016-11-01

Evaluation of NLP methods requires testing against a previously vetted gold-standard test set and reporting standard metrics (accuracy/precision/recall/F1). The current assumption is that all items in a given test set are equal with regards to difficulty and discriminating power. We propose Item Response Theory (IRT) from psychometrics as an alternative means for gold-standard test-set generation and NLP system evaluation. IRT is able to describe characteristics of individual items - their difficulty and discriminating power - and can account for these characteristics in its estimation of human intelligence or ability for an NLP task. In this paper, we demonstrate IRT by generating a gold-standard test set for Recognizing Textual Entailment. By collecting a large number of human responses and fitting our IRT model, we show that our IRT model compares NLP systems with the performance in a human population and is able to provide more insight into system performance than standard evaluation metrics. We show that a high accuracy score does not always imply a high IRT score, which depends on the item characteristics and the response pattern.

Smooth semi-nonparametric (SNP) estimation of the cumulative incidence function.

Science.gov (United States)

Duc, Anh Nguyen; Wolbers, Marcel

2017-08-15

This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event. The time to event distributions conditional on the event type are modeled using smooth semi-nonparametric densities. One strength of this approach is that it can handle arbitrary censoring and truncation while relying on mild parametric assumptions. A stepwise forward algorithm for model estimation and adaptive selection of smooth semi-nonparametric polynomial degrees is presented, implemented in the statistical software R, evaluated in a sequence of simulation studies, and applied to data from a clinical trial in cryptococcal meningitis. The simulations demonstrate that the proposed method frequently outperforms both parametric and nonparametric alternatives. They also support the use of 'ad hoc' asymptotic inference to derive confidence intervals. An extension to regression modeling is also presented, and its potential and challenges are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Investigation of MLE in nonparametric estimation methods of reliability function

International Nuclear Information System (INIS)

Ahn, Kwang Won; Kim, Yoon Ik; Chung, Chang Hyun; Kim, Kil Yoo

2001-01-01

There have been lots of trials to estimate a reliability function. In the ESReDA 20 th seminar, a new method in nonparametric way was proposed. The major point of that paper is how to use censored data efficiently. Generally there are three kinds of approach to estimate a reliability function in nonparametric way, i.e., Reduced Sample Method, Actuarial Method and Product-Limit (PL) Method. The above three methods have some limits. So we suggest an advanced method that reflects censored information more efficiently. In many instances there will be a unique maximum likelihood estimator (MLE) of an unknown parameter, and often it may be obtained by the process of differentiation. It is well known that the three methods generally used to estimate a reliability function in nonparametric way have maximum likelihood estimators that are uniquely exist. So, MLE of the new method is derived in this study. The procedure to calculate a MLE is similar just like that of PL-estimator. The difference of the two is that in the new method, the mass (or weight) of each has an influence of the others but the mass in PL-estimator not

Nonparametric identification of copula structures

KAUST Repository

Li, Bo

2013-06-01

We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process.We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets. © 2013 American Statistical Association.

The nonparametric bootstrap for the current status model

NARCIS (Netherlands)

Groeneboom, P.; Hendrickx, K.

2017-01-01

It has been proved that direct bootstrapping of the nonparametric maximum likelihood estimator (MLE) of the distribution function in the current status model leads to inconsistent confidence intervals. We show that bootstrapping of functionals of the MLE can however be used to produce valid

Psychometric analysis of the Generalized Anxiety Disorder scale (GAD-7) in primary care using modern item response theory.

Science.gov (United States)

Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd

2017-01-01

The Generalized Anxiety Disorder scale (GAD-7) is one of the most frequently used diagnostic self-report scales for screening, diagnosis and severity assessment of anxiety disorder. Its psychometric properties from the view of the Item Response Theory paradigm have rarely been investigated. We aimed to close this gap by analyzing the GAD-7 within a large sample of primary care patients with respect to its psychometric properties and its implications for scoring using Item Response Theory. Robust, nonparametric statistics were used to check unidimensionality of the GAD-7. A graded response model was fitted using a Bayesian approach. The model fit was evaluated using posterior predictive p-values, item information functions were derived and optimal predictions of anxiety were calculated. The sample included N = 3404 primary care patients (60% female; mean age, 52,2; standard deviation 19.2) The analysis indicated no deviations of the GAD-7 scale from unidimensionality and a decent fit of a graded response model. The commonly suggested ultra-brief measure consisting of the first two items, the GAD-2, was supported by item information analysis. The first four items discriminated better than the last three items with respect to latent anxiety. The information provided by the first four items should be weighted more heavily. Moreover, estimates corresponding to low to moderate levels of anxiety show greater variability. The psychometric validity of the GAD-2 was supported by our analysis.

Nonparametric Regression Estimation for Multivariate Null Recurrent Processes

Directory of Open Access Journals (Sweden)

Biqing Cai

2015-04-01

Full Text Available This paper discusses nonparametric kernel regression with the regressor being a \\(d\\-dimensional \\(\\beta\\-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \\(\\sqrt{n(Th^{d}}\\, where \\(n(T\\ is the number of regenerations for a \\(\\beta\\-null recurrent process and the limiting distribution (with proper normalization is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.

A Bayesian nonparametric estimation of distributions and quantiles

International Nuclear Information System (INIS)

Poern, K.

1988-11-01

The report describes a Bayesian, nonparametric method for the estimation of a distribution function and its quantiles. The method, presupposing random sampling, is nonparametric, so the user has to specify a prior distribution on a space of distributions (and not on a parameter space). In the current application, where the method is used to estimate the uncertainty of a parametric calculational model, the Dirichlet prior distribution is to a large extent determined by the first batch of Monte Carlo-realizations. In this case the results of the estimation technique is very similar to the conventional empirical distribution function. The resulting posterior distribution is also Dirichlet, and thus facilitates the determination of probability (confidence) intervals at any given point in the space of interest. Another advantage is that also the posterior distribution of a specified quantitle can be derived and utilized to determine a probability interval for that quantile. The method was devised for use in the PROPER code package for uncertainty and sensitivity analysis. (orig.)

A nonparametric empirical Bayes framework for large-scale multiple testing.

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Martin, Ryan; Tokdar, Surya T

2012-07-01

We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive recursion (PR) marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates. Simulations and real data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the nonnull density can give a much better fit in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions.

Factoring handedness data: I. Item analysis.

Science.gov (United States)

Messinger, H B; Messinger, M I

1995-12-01

Recently in this journal Peters and Murphy challenged the validity of factor analyses done on bimodal handedness data, suggesting instead that right- and left-handers be studied separately. But bimodality may be avoidable if attention is paid to Oldfield's questionnaire format and instructions for the subjects. Two characteristics appear crucial: a two-column LEFT-RIGHT format for the body of the instrument and what we call Oldfield's Admonition: not to indicate strong preference for handedness item, such as write, unless "... the preference is so strong that you would never try to use the other hand unless absolutely forced to...". Attaining unimodality of an item distribution would seem to overcome the objections of Peters and Murphy. In a 1984 survey in Boston we used Oldfield's ten-item questionnaire exactly as published. This produced unimodal item distributions. With reflection of the five-point item scale and a logarithmic transformation, we achieved a degree of normalization for the items. Two surveys elsewhere based on Oldfield's 20-item list but with changes in the questionnaire format and the instructions, yielded markedly different item distributions with peaks at each extreme and sometimes in the middle as well.

Nonparametric Estimation of Cumulative Incidence Functions for Competing Risks Data with Missing Cause of Failure

DEFF Research Database (Denmark)

Effraimidis, Georgios; Dahl, Christian Møller

In this paper, we develop a fully nonparametric approach for the estimation of the cumulative incidence function with Missing At Random right-censored competing risks data. We obtain results on the pointwise asymptotic normality as well as the uniform convergence rate of the proposed nonparametric...

Mediate gamma radiation effects on some packaged food items

International Nuclear Information System (INIS)

Inamura, Patricia Y.; Uehara, Vanessa B.; Teixeira, Christian A.H.M.; Mastro, Nelida L. del

2012-01-01

For most of prepackaged foods a 10 kGy radiation dose is considered the maximum dose needed; however, the commercially available and practically accepted packaging materials must be suitable for such application. This work describes the application of ionizing radiation on several packaged food items, using 5 dehydrated food items, 5 ready-to-eat meals and 5 ready-to-eat food items irradiated in a 60 Co gamma source with a 3 kGy dose. The quality evaluation of the irradiated samples was performed 2 and 8 months after irradiation. Microbiological analysis (bacteria, fungus and yeast load) was performed. The sensory characteristics were established for appearance, aroma, texture and flavor attributes were also established. From these data, the acceptability of all irradiated items was obtained. All ready-to-eat food items assayed like manioc flour, some pâtés and blocks of raw brown sugar and most of ready-to-eat meals like sausages and chicken with legumes were considered acceptable for microbial and sensory characteristics. On the other hand, the dehydrated food items chosen for this study, such as dehydrated bacon potatoes or pea soups were not accepted by the sensory analysis. A careful dose choice and special irradiation conditions must be used in order to achieve sensory acceptability needed for the commercialization of specific irradiated food items. - Highlights: ► We applied gamma radiation on several kinds of packaged food items. ► Microbiological and sensory analyses were performed 2 and 8 months after irradiation. ► All ready-to-eat food items assayed were approved for microbial and sensory characteristics. ► Most ready-to-eat meals like sausages and chicken with legumes were also acceptable. ► Dehydrated bacon potatoes or pea soups were considered not acceptable.

Bayesian Non-Parametric Mixtures of GARCH(1,1 Models

Directory of Open Access Journals (Sweden)

John W. Lau

2012-01-01

Full Text Available Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. Further, the number and times of regime changes are not always obvious. This article outlines a nonparametric mixture of GARCH models that is able to estimate the number and time of volatility regime changes by mixing over the Poisson-Kingman process. The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel. Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. The methodology is illustrated on the Standard and Poor's 500 financial index.

Single versus mixture Weibull distributions for nonparametric satellite reliability

International Nuclear Information System (INIS)

Castet, Jean-Francois; Saleh, Joseph H.

2010-01-01

Long recognized as a critical design attribute for space systems, satellite reliability has not yet received the proper attention as limited on-orbit failure data and statistical analyses can be found in the technical literature. To fill this gap, we recently conducted a nonparametric analysis of satellite reliability for 1584 Earth-orbiting satellites launched between January 1990 and October 2008. In this paper, we provide an advanced parametric fit, based on mixture of Weibull distributions, and compare it with the single Weibull distribution model obtained with the Maximum Likelihood Estimation (MLE) method. We demonstrate that both parametric fits are good approximations of the nonparametric satellite reliability, but that the mixture Weibull distribution provides significant accuracy in capturing all the failure trends in the failure data, as evidenced by the analysis of the residuals and their quasi-normal dispersion.

Modern nonparametric, robust and multivariate methods festschrift in honour of Hannu Oja

CERN Document Server

Taskinen, Sara

2015-01-01

Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.

Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs

NARCIS (Netherlands)

Kuosmanen, T.K.

2005-01-01

Environmental Economics and Natural Resources Group at Wageningen University in The Netherlands Weak disposability of outputs means that firms can abate harmful emissions by decreasing the activity level. Modeling weak disposability in nonparametric production analysis has caused some confusion.

On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression.

Science.gov (United States)

Pan, Wei

2003-07-22

Recently a class of nonparametric statistical methods, including the empirical Bayes (EB) method, the significance analysis of microarray (SAM) method and the mixture model method (MMM), have been proposed to detect differential gene expression for replicated microarray experiments conducted under two conditions. All the methods depend on constructing a test statistic Z and a so-called null statistic z. The null statistic z is used to provide some reference distribution for Z such that statistical inference can be accomplished. A common way of constructing z is to apply Z to randomly permuted data. Here we point our that the distribution of z may not approximate the null distribution of Z well, leading to possibly too conservative inference. This observation may apply to other permutation-based nonparametric methods. We propose a new method of constructing a null statistic that aims to estimate the null distribution of a test statistic directly. Using simulated data and real data, we assess and compare the performance of the existing method and our new method when applied in EB, SAM and MMM. Some interesting findings on operating characteristics of EB, SAM and MMM are also reported. Finally, by combining the idea of SAM and MMM, we outline a simple nonparametric method based on the direct use of a test statistic and a null statistic.

Performance of non-parametric algorithms for spatial mapping of tropical forest structure

Directory of Open Access Journals (Sweden)

Liang Xu

2016-08-01

Full Text Available Abstract Background Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation. Results By using airborne lidar data as the “truth” and focusing on the mean canopy height (MCH as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME and random forest (RF for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size. Conclusions A significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution.

Non-Parametric Analysis of Rating Transition and Default Data

DEFF Research Database (Denmark)

Fledelius, Peter; Lando, David; Perch Nielsen, Jens

2004-01-01

We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...

Adaptive nonparametric Bayesian inference using location-scale mixture priors

NARCIS (Netherlands)

Jonge, de R.; Zanten, van J.H.

2010-01-01

We study location-scale mixture priors for nonparametric statistical problems, including multivariate regression, density estimation and classification. We show that a rate-adaptive procedure can be obtained if the prior is properly constructed. In particular, we show that adaptation is achieved if

Non-parametric analysis of production efficiency of poultry egg ...

African Journals Online (AJOL)

Non-parametric analysis of production efficiency of poultry egg farmers in Delta ... analysis of factors affecting the output of poultry farmers showed that stock ... should be put in place for farmers to learn the best farm practices carried out on the ...

A Nonparametric Bayesian Approach For Emission Tomography Reconstruction

International Nuclear Information System (INIS)

Barat, Eric; Dautremer, Thomas

2007-01-01

We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM

International Conference on Robust Rank-Based and Nonparametric Methods

CERN Document Server

McKean, Joseph

2016-01-01

The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...

Bayesian nonparametric dictionary learning for compressed sensing MRI.

Science.gov (United States)

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.

Science.gov (United States)

Deshwar, Amit G; Vembu, Shankar; Morris, Quaid

2015-01-01

Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

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Xin Tian

2017-06-01

Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

Associations Between Physician Empathy, Physician Characteristics, and Standardized Measures of Patient Experience.

Science.gov (United States)

Chaitoff, Alexander; Sun, Bob; Windover, Amy; Bokar, Daniel; Featherall, Joseph; Rothberg, Michael B; Misra-Hebert, Anita D

2017-10-01

To identify correlates of physician empathy and determine whether physician empathy is related to standardized measures of patient experience. Demographic, professional, and empathy data were collected during 2013-2015 from Cleveland Clinic Health System physicians prior to participation in mandatory communication skills training. Empathy was assessed using the Jefferson Scale of Empathy. Data were also collected for seven measures (six provider communication items and overall provider rating) from the visit-specific and 12-month Consumer Assessment of Healthcare Providers and Systems Clinician and Group (CG-CAHPS) surveys. Associations between empathy and provider characteristics were assessed by linear regression, ANOVA, or a nonparametric equivalent. Significant predictors were included in a multivariable linear regression model. Correlations between empathy and CG-CAHPS scores were assessed using Spearman rank correlation coefficients. In bivariable analysis (n = 847 physicians), female sex (P empathy scores. In multivariable analysis, female sex (P empathy scores. Of the seven CG-CAHPS measures, scores on five for the 583 physicians with visit-specific data and on three for the 277 physicians with 12-month data were positively correlated with empathy. Specialty and sex were independently associated with physician empathy. Empathy was correlated with higher scores on multiple CG-CAHPS items, suggesting improving physician empathy might play a role in improving patient experience.

Decompounding random sums: A nonparametric approach

DEFF Research Database (Denmark)

Hansen, Martin Bøgsted; Pitts, Susan M.

Observations from sums of random variables with a random number of summands, known as random, compound or stopped sums arise within many areas of engineering and science. Quite often it is desirable to infer properties of the distribution of the terms in the random sum. In the present paper we...... review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...

The Dif Identification in Constructed Response Items Using Partial Credit Model

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Heri Retnawati

2017-10-01

Full Text Available The study was to identify the load, the type and the significance of differential item functioning (DIF in constructed response item using the partial credit model (PCM. The data in the study were the students’ instruments and the students’ responses toward the PISA-like test items that had been completed by 386 ninth grade students and 460 tenth grade students who had been about 15 years old in the Province of Yogyakarta Special Region in Indonesia. The analysis toward the item characteristics through the student categorization based on their class was conducted toward the PCM using CONQUEST software. Furthermore, by applying these items characteristics, the researcher draw the category response function (CRF graphic in order to identify whether the type of DIF content had been in uniform or non-uniform. The significance of DIF was identified by comparing the discrepancy between the difficulty level parameter and the error in the CONQUEST output results. The results of the analysis showed that from 18 items that had been analyzed there were 4 items which had not been identified load DIF, there were 5 items that had been identified containing DIF but not statistically significant and there were 9 items that had been identified containing DIF significantly. The causes of items containing DIF were discussed.

Nonparametric modeling of dynamic functional connectivity in fmri data

DEFF Research Database (Denmark)

Nielsen, Søren Føns Vind; Madsen, Kristoffer H.; Røge, Rasmus

2015-01-01

dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a nonparametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted...

Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support

Czech Academy of Sciences Publication Activity Database

Kalina, Jan; Zvárová, Jana

2017-01-01

Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability

Nonparametric model validations for hidden Markov models with applications in financial econometrics.

Science.gov (United States)

Zhao, Zhibiao

2011-06-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

Do people with and without medical conditions respond similarly to the short health anxiety inventory? An assessment of differential item functioning using item response theory.

Science.gov (United States)

LeBouthillier, Daniel M; Thibodeau, Michel A; Alberts, Nicole M; Hadjistavropoulos, Heather D; Asmundson, Gordon J G

2015-04-01

Individuals with medical conditions are likely to have elevated health anxiety; however, research has not demonstrated how medical status impacts response patterns on health anxiety measures. Measurement bias can undermine the validity of a questionnaire by overestimating or underestimating scores in groups of individuals. We investigated whether the Short Health Anxiety Inventory (SHAI), a widely-used measure of health anxiety, exhibits medical condition-based bias on item and subscale levels, and whether the SHAI subscales adequately assess the health anxiety continuum. Data were from 963 individuals with diabetes, breast cancer, or multiple sclerosis, and 372 healthy individuals. Mantel-Haenszel tests and item characteristic curves were used to classify the severity of item-level differential item functioning in all three medical groups compared to the healthy group. Test characteristic curves were used to assess scale-level differential item functioning and whether the SHAI subscales adequately assess the health anxiety continuum. Nine out of 14 items exhibited differential item functioning. Two items exhibited differential item functioning in all medical groups compared to the healthy group. In both Thought Intrusion and Fear of Illness subscales, differential item functioning was associated with mildly deflated scores in medical groups with very high levels of the latent traits. Fear of Illness items poorly discriminated between individuals with low and very low levels of the latent trait. While individuals with medical conditions may respond differentially to some items, clinicians and researchers can confidently use the SHAI with a variety of medical populations without concern of significant bias. Copyright © 2015 Elsevier Inc. All rights reserved.

Development of a lack of appetite item bank for computer-adaptive testing (CAT)

DEFF Research Database (Denmark)

Thamsborg, Lise Laurberg Holst; Petersen, Morten Aa; Aaronson, Neil K

2015-01-01

to 12 lack of appetite items. CONCLUSIONS: Phases 1-3 resulted in 12 lack of appetite candidate items. Based on a field testing (phase 4), the psychometric characteristics of the items will be assessed and the final item bank will be generated. This CAT item bank is expected to provide precise...

Nonparametric method for failures diagnosis in the actuating subsystem of aircraft control system

Science.gov (United States)

Terentev, M. N.; Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.

2018-02-01

In this paper we design a nonparametric method for failures diagnosis in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on analytical nonparametric one-step-ahead state prediction approach. This makes it possible to predict the behavior of unidentified and failure dynamic systems, to weaken the requirements to control signals, and to reduce the diagnostic time and problem complexity.

Nonparametric identification of nonlinear dynamic systems using a synchronisation-based method

Science.gov (United States)

Kenderi, Gábor; Fidlin, Alexander

2014-12-01

The present study proposes an identification method for highly nonlinear mechanical systems that does not require a priori knowledge of the underlying nonlinearities to reconstruct arbitrary restoring force surfaces between degrees of freedom. This approach is based on the master-slave synchronisation between a dynamic model of the system as the slave and the real system as the master using measurements of the latter. As the model synchronises to the measurements, it becomes an observer of the real system. The optimal observer algorithm in a least-squares sense is given by the Kalman filter. Using the well-known state augmentation technique, the Kalman filter can be turned into a dual state and parameter estimator to identify parameters of a priori characterised nonlinearities. The paper proposes an extension of this technique towards nonparametric identification. A general system model is introduced by describing the restoring forces as bilateral spring-dampers with time-variant coefficients, which are estimated as augmented states. The estimation procedure is followed by an a posteriori statistical analysis to reconstruct noise-free restoring force characteristics using the estimated states and their estimated variances. Observability is provided using only one measured mechanical quantity per degree of freedom, which makes this approach less demanding in the number of necessary measurement signals compared with truly nonparametric solutions, which typically require displacement, velocity and acceleration signals. Additionally, due to the statistical rigour of the procedure, it successfully addresses signals corrupted by significant measurement noise. In the present paper, the method is described in detail, which is followed by numerical examples of one degree of freedom (1DoF) and 2DoF mechanical systems with strong nonlinearities of vibro-impact type to demonstrate the effectiveness of the proposed technique.

Nonparametric Mixture of Regression Models.

Science.gov (United States)

Huang, Mian; Li, Runze; Wang, Shaoli

2013-07-01

Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.

Nonparametric NAR-ARCH Modelling of Stock Prices by the Kernel Methodology

Directory of Open Access Journals (Sweden)

Mohamed Chikhi

2018-02-01

Full Text Available This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over 01/03/2000 to 02/02/2017 by testing the nonlinearities through a class of conditional heteroscedastic nonparametric models. The linearity and Gaussianity assumptions are rejected for Orange Stock returns and informational shocks have transitory effects on returns and volatility. The forecasting results show that Orange stock prices are short-term predictable and nonparametric NAR-ARCH model has better performance over parametric MA-APARCH model for short horizons. Plus, the estimates of this model are also better comparing to the predictions of the random walk model. This finding provides evidence for weak form of inefficiency in Paris stock market with limited rationality, thus it emerges arbitrage opportunities.

On the robust nonparametric regression estimation for a functional regressor

OpenAIRE

Azzedine , Nadjia; Laksaci , Ali; Ould-Saïd , Elias

2009-01-01

On the robust nonparametric regression estimation for a functional regressor correspondance: Corresponding author. (Ould-Said, Elias) (Azzedine, Nadjia) (Laksaci, Ali) (Ould-Said, Elias) Departement de Mathematiques--> , Univ. Djillali Liabes--> , BP 89--> , 22000 Sidi Bel Abbes--> - ALGERIA (Azzedine, Nadjia) Departement de Mathema...

Bayesian Nonparametric Clustering for Positive Definite Matrices.

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Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos

2016-05-01

Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.

Nonparametric decision tree: The impact of ISO 9000 on certified and non certified companies Nonparametric decision tree: The impact of ISO 9000 on certified and non certified companies Nonparametric decision tree: The impact of ISO 9000 on certified and non certified companies

Directory of Open Access Journals (Sweden)

Joaquín Texeira Quirós

2013-09-01

Full Text Available Purpose: This empirical study analyzes a questionnaire answered by a sample of ISO 9000 certified companies and a control sample of companies which have not been certified, using a multivariate predictive model. With this approach, we assess which quality practices are associated to the likelihood of the firm being certified. Design/methodology/approach: We implemented nonparametric decision trees, in order to see which variables influence more the fact that the company be certified or not, i.e., the motivations that lead companies to make sure. Findings: The results show that only four questionnaire items are sufficient to predict if a firm is certified or not. It is shown that companies in which the respondent manifests greater concern with respect to customers relations; motivations of the employees and strategic planning have higher likelihood of being certified. Research implications: the reader should note that this study is based on data from a single country and, of course, these results capture many idiosyncrasies if its economic and corporate environment. It would be of interest to understand if this type of analysis reveals some regularities across different countries. Practical implications: companies should look for a set of practices congruent with total quality management and ISO 9000 certified. Originality/value: This study contributes to the literature on the internal motivation of companies to achieve certification under the ISO 9000 standard, by performing a comparative analysis of questionnaires answered by a sample of certified companies and a control sample of companies which have not been certified. In particular, we assess how the manager’s perception on the intensity in which quality practices are deployed in their firms is associated to the likelihood of the firm being certified.Purpose: This empirical study analyzes a questionnaire answered by a sample of ISO 9000 certified companies and a control sample of companies

Nonparametric combinatorial sequence models.

Science.gov (United States)

Wauthier, Fabian L; Jordan, Michael I; Jojic, Nebojsa

2011-11-01

This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This article presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three biological sequence families which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution over sequence representations induced by the prior. By integrating out the posterior, our method compares favorably to leading binding predictors.

Non-parametric production analysis of pesticides use in the Netherlands

NARCIS (Netherlands)

Oude Lansink, A.G.J.M.; Silva, E.

2004-01-01

Many previous empirical studies on the productivity of pesticides suggest that pesticides are under-utilized in agriculture despite the general held believe that these inputs are substantially over-utilized. This paper uses data envelopment analysis (DEA) to calculate non-parametric measures of the

Item selection via Bayesian IRT models.

Science.gov (United States)

Arima, Serena

2015-02-10

With reference to a questionnaire that aimed to assess the quality of life for dysarthric speakers, we investigate the usefulness of a model-based procedure for reducing the number of items. We propose a mixed cumulative logit model, which is known in the psychometrics literature as the graded response model: responses to different items are modelled as a function of individual latent traits and as a function of item characteristics, such as their difficulty and their discrimination power. We jointly model the discrimination and the difficulty parameters by using a k-component mixture of normal distributions. Mixture components correspond to disjoint groups of items. Items that belong to the same groups can be considered equivalent in terms of both difficulty and discrimination power. According to decision criteria, we select a subset of items such that the reduced questionnaire is able to provide the same information that the complete questionnaire provides. The model is estimated by using a Bayesian approach, and the choice of the number of mixture components is justified according to information criteria. We illustrate the proposed approach on the basis of data that are collected for 104 dysarthric patients by local health authorities in Lecce and in Milan. Copyright © 2014 John Wiley & Sons, Ltd.

Genomic breeding value estimation using nonparametric additive regression models

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Solberg Trygve

2009-01-01

Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.

Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods

DEFF Research Database (Denmark)

Høg, Esben

In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...

Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods

DEFF Research Database (Denmark)

Høg, Esben

2003-01-01

In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...

Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination.

Science.gov (United States)

Yau, Christopher; Holmes, Chris

2011-07-01

We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. This allows us to perform full Gibbs sampling to obtain posterior distributions over parameters of interest including an explicit measure of each covariate's relevance and a distribution over the number of potential clusters present in the data. This also allows for individual cluster specific variable selection. We demonstrate improved inference on a number of canonical problems.

Item validity vs. item discrimination index: a redundancy?

Science.gov (United States)

Panjaitan, R. L.; Irawati, R.; Sujana, A.; Hanifah, N.; Djuanda, D.

2018-03-01

In several literatures about evaluation and test analysis, it is common to find that there are calculations of item validity as well as item discrimination index (D) with different formula for each. Meanwhile, other resources said that item discrimination index could be obtained by calculating the correlation between the testee’s score in a particular item and the testee’s score on the overall test, which is actually the same concept as item validity. Some research reports, especially undergraduate theses tend to include both item validity and item discrimination index in the instrument analysis. It seems that these concepts might overlap for both reflect the test quality on measuring the examinees’ ability. In this paper, examples of some results of data processing on item validity and item discrimination index were compared. It would be discussed whether item validity and item discrimination index can be represented by one of them only or it should be better to present both calculations for simple test analysis, especially in undergraduate theses where test analyses were included.

A Nonparametric Test for Seasonal Unit Roots

OpenAIRE

Kunst, Robert M.

2009-01-01

Abstract: We consider a nonparametric test for the null of seasonal unit roots in quarterly time series that builds on the RUR (records unit root) test by Aparicio, Escribano, and Sipols. We find that the test concept is more promising than a formalization of visual aids such as plots by quarter. In order to cope with the sensitivity of the original RUR test to autocorrelation under its null of a unit root, we suggest an augmentation step by autoregression. We present some evidence on the siz...

NONPARAMETRIC FIXED EFFECT PANEL DATA MODELS: RELATIONSHIP BETWEEN AIR POLLUTION AND INCOME FOR TURKEY

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Rabia Ece OMAY

2013-06-01

Full Text Available In this study, relationship between gross domestic product (GDP per capita and sulfur dioxide (SO2 and particulate matter (PM10 per capita is modeled for Turkey. Nonparametric fixed effect panel data analysis is used for the modeling. The panel data covers 12 territories, in first level of Nomenclature of Territorial Units for Statistics (NUTS, for period of 1990-2001. Modeling of the relationship between GDP and SO2 and PM10 for Turkey, the non-parametric models have given good results.

Developing an immigration policy for Germany on the basis of a nonparametric labor market classification

OpenAIRE

Froelich, Markus; Puhani, Patrick

2004-01-01

Based on a nonparametrically estimated model of labor market classifications, this paper makes suggestions for immigration policy using data from western Germany in the 1990s. It is demonstrated that nonparametric regression is feasible in higher dimensions with only a few thousand observations. In sum, labor markets able to absorb immigrants are characterized by above average age and by professional occupations. On the other hand, labor markets for young workers in service occupations are id...

Post-fire debris flow prediction in Western United States: Advancements based on a nonparametric statistical technique

Science.gov (United States)

Nikolopoulos, E. I.; Destro, E.; Bhuiyan, M. A. E.; Borga, M., Sr.; Anagnostou, E. N.

2017-12-01

Fire disasters affect modern societies at global scale inducing significant economic losses and human casualties. In addition to their direct impacts they have various adverse effects on hydrologic and geomorphologic processes of a region due to the tremendous alteration of the landscape characteristics (vegetation, soil properties etc). As a consequence, wildfires often initiate a cascade of hazards such as flash floods and debris flows that usually follow the occurrence of a wildfire thus magnifying the overall impact in a region. Post-fire debris flows (PFDF) is one such type of hazards frequently occurring in Western United States where wildfires are a common natural disaster. Prediction of PDFD is therefore of high importance in this region and over the last years a number of efforts from United States Geological Survey (USGS) and National Weather Service (NWS) have been focused on the development of early warning systems that will help mitigate PFDF risk. This work proposes a prediction framework that is based on a nonparametric statistical technique (random forests) that allows predicting the occurrence of PFDF at regional scale with a higher degree of accuracy than the commonly used approaches that are based on power-law thresholds and logistic regression procedures. The work presented is based on a recently released database from USGS that reports a total of 1500 storms that triggered and did not trigger PFDF in a number of fire affected catchments in Western United States. The database includes information on storm characteristics (duration, accumulation, max intensity etc) and other auxiliary information of land surface properties (soil erodibility index, local slope etc). Results show that the proposed model is able to achieve a satisfactory prediction accuracy (threat score > 0.6) superior of previously published prediction frameworks highlighting the potential of nonparametric statistical techniques for development of PFDF prediction systems.

A semi-nonparametric mixture model for selecting functionally consistent proteins.

Science.gov (United States)

Yu, Lianbo; Doerge, Rw

2010-09-28

High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein.

1st Conference of the International Society for Nonparametric Statistics

CERN Document Server

Lahiri, S; Politis, Dimitris

2014-01-01

This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for NonParametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI, and other organizations. M.G. Akritas, S.N. Lahiri, and D.N. Politis are the first executive committee members of ISNPS, and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao, and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world.   The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the wo...

On Parametric (and Non-Parametric Variation

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Neil Smith

2009-11-01

Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.

Non-parametric estimation of the individual's utility map

OpenAIRE

Noguchi, Takao; Sanborn, Adam N.; Stewart, Neil

2013-01-01

Models of risky choice have attracted much attention in behavioural economics. Previous research has repeatedly demonstrated that individuals' choices are not well explained by expected utility theory, and a number of alternative models have been examined using carefully selected sets of choice alternatives. The model performance however, can depend on which choice alternatives are being tested. Here we develop a non-parametric method for estimating the utility map over the wide range of choi...

Nonparametric Bayesian models through probit stick-breaking processes.

Science.gov (United States)

Rodríguez, Abel; Dunson, David B

2011-03-01

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.

Glaucoma Monitoring in a Clinical Setting Glaucoma Progression Analysis vs Nonparametric Progression Analysis in the Groningen Longitudinal Glaucoma Study

NARCIS (Netherlands)

Wesselink, Christiaan; Heeg, Govert P.; Jansonius, Nomdo M.

Objective: To compare prospectively 2 perimetric progression detection algorithms for glaucoma, the Early Manifest Glaucoma Trial algorithm (glaucoma progression analysis [GPA]) and a nonparametric algorithm applied to the mean deviation (MD) (nonparametric progression analysis [NPA]). Methods:

A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.

Science.gov (United States)

Fronczyk, Kassandra; Kottas, Athanasios

2014-03-01

We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature. © 2013, The International Biometric Society.

Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland

Science.gov (United States)

Schiemann, R.; Erdin, R.; Willi, M.; Frei, C.; Berenguer, M.; Sempere-Torres, D.

2011-05-01

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications. Various formulations of geostatistical combination (Kriging) methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK) are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages. Furthermore, two variants of Kriging with external drift (KED) are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain). The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases as well as an extended evaluation

Kernel bandwidth estimation for non-parametric density estimation: a comparative study

CSIR Research Space (South Africa)

Van der Walt, CM

2013-12-01

Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...

A general approach to posterior contraction in nonparametric inverse problems

NARCIS (Netherlands)

Knapik, Bartek; Salomond, Jean Bernard

In this paper, we propose a general method to derive an upper bound for the contraction rate of the posterior distribution for nonparametric inverse problems. We present a general theorem that allows us to derive contraction rates for the parameter of interest from contraction rates of the related

Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures.

Science.gov (United States)

Filippi, Sarah; Holmes, Chris C; Nieto-Barajas, Luis E

2016-11-16

In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.

A non-parametric method for correction of global radiation observations

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Perers, Bengt

2013-01-01

in the observations are corrected. These are errors such as: tilt in the leveling of the sensor, shadowing from surrounding objects, clipping and saturation in the signal processing, and errors from dirt and wear. The method is based on a statistical non-parametric clear-sky model which is applied to both...

Criteria for eliminating items of a Test of Figural Analogies

Directory of Open Access Journals (Sweden)

Diego Blum

2013-12-01

Full Text Available This paper describes the steps taken to eliminate two of the items in a Test of Figural Analogies (TFA. The main guidelines of psychometric analysis concerning Classical Test Theory (CTT and Item Response Theory (IRT are explained. The item elimination process was based on both the study of the CTT difficulty and discrimination index, and the unidimensionality analysis. The a, b, and c parameters of the Three Parameter Logistic Model of IRT were also considered for this purpose, as well as the assessment of each item fitting this model. The unfavourable characteristics of a group of TFA items are detailed, and decisions leading to their possible elimination are discussed.

Three controversies over item disclosure in medical licensure examinations

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Yoon Soo Park

2015-09-01

Full Text Available In response to views on public's right to know, there is growing attention to item disclosure – release of items, answer keys, and performance data to the public – in medical licensure examinations and their potential impact on the test's ability to measure competence and select qualified candidates. Recent debates on this issue have sparked legislative action internationally, including South Korea, with prior discussions among North American countries dating over three decades. The purpose of this study is to identify and analyze three issues associated with item disclosure in medical licensure examinations – 1 fairness and validity, 2 impact on passing levels, and 3 utility of item disclosure – by synthesizing existing literature in relation to standards in testing. Historically, the controversy over item disclosure has centered on fairness and validity. Proponents of item disclosure stress test takers’ right to know, while opponents argue from a validity perspective. Item disclosure may bias item characteristics, such as difficulty and discrimination, and has consequences on setting passing levels. To date, there has been limited research on the utility of item disclosure for large scale testing. These issues requires ongoing and careful consideration.

Application of nonparametric statistics to material strength/reliability assessment

International Nuclear Information System (INIS)

Arai, Taketoshi

1992-01-01

An advanced material technology requires data base on a wide variety of material behavior which need to be established experimentally. It may often happen that experiments are practically limited in terms of reproducibility or a range of test parameters. Statistical methods can be applied to understanding uncertainties in such a quantitative manner as required from the reliability point of view. Statistical assessment involves determinations of a most probable value and the maximum and/or minimum value as one-sided or two-sided confidence limit. A scatter of test data can be approximated by a theoretical distribution only if the goodness of fit satisfies a test criterion. Alternatively, nonparametric statistics (NPS) or distribution-free statistics can be applied. Mathematical procedures by NPS are well established for dealing with most reliability problems. They handle only order statistics of a sample. Mathematical formulas and some applications to engineering assessments are described. They include confidence limits of median, population coverage of sample, required minimum number of a sample, and confidence limits of fracture probability. These applications demonstrate that a nonparametric statistical estimation is useful in logical decision making in the case a large uncertainty exists. (author)

CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions

Science.gov (United States)

Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.

Exact nonparametric inference for detection of nonlinear determinism

OpenAIRE

Luo, Xiaodong; Zhang, Jie; Small, Michael; Moroz, Irene

2005-01-01

We propose an exact nonparametric inference scheme for the detection of nonlinear determinism. The essential fact utilized in our scheme is that, for a linear stochastic process with jointly symmetric innovations, its ordinary least square (OLS) linear prediction error is symmetric about zero. Based on this viewpoint, a class of linear signed rank statistics, e.g. the Wilcoxon signed rank statistic, can be derived with the known null distributions from the prediction error. Thus one of the ad...

Difference in method of administration did not significantly impact item response

DEFF Research Database (Denmark)

Bjorner, Jakob B; Rose, Matthias; Gandek, Barbara

2014-01-01

assistant (PDA), or personal computer (PC) on the Internet, and a second form by PC, in the same administration. Structural invariance, equivalence of item responses, and measurement precision were evaluated using confirmatory factor analysis and item response theory methods. RESULTS: Multigroup...... levels in IVR, PQ, or PDA administration as compared to PC. Availability of large item response theory-calibrated PROMIS item banks allowed for innovations in study design and analysis.......PURPOSE: To test the impact of method of administration (MOA) on the measurement characteristics of items developed in the Patient-Reported Outcomes Measurement Information System (PROMIS). METHODS: Two non-overlapping parallel 8-item forms from each of three PROMIS domains (physical function...

Analysis test of understanding of vectors with the three-parameter logistic model of item response theory and item response curves technique

Directory of Open Access Journals (Sweden)

Suttida Rakkapao

2016-10-01

Full Text Available This study investigated the multiple-choice test of understanding of vectors (TUV, by applying item response theory (IRT. The difficulty, discriminatory, and guessing parameters of the TUV items were fit with the three-parameter logistic model of IRT, using the parscale program. The TUV ability is an ability parameter, here estimated assuming unidimensionality and local independence. Moreover, all distractors of the TUV were analyzed from item response curves (IRC that represent simplified IRT. Data were gathered on 2392 science and engineering freshmen, from three universities in Thailand. The results revealed IRT analysis to be useful in assessing the test since its item parameters are independent of the ability parameters. The IRT framework reveals item-level information, and indicates appropriate ability ranges for the test. Moreover, the IRC analysis can be used to assess the effectiveness of the test’s distractors. Both IRT and IRC approaches reveal test characteristics beyond those revealed by the classical analysis methods of tests. Test developers can apply these methods to diagnose and evaluate the features of items at various ability levels of test takers.

Analysis test of understanding of vectors with the three-parameter logistic model of item response theory and item response curves technique

Science.gov (United States)

Rakkapao, Suttida; Prasitpong, Singha; Arayathanitkul, Kwan

2016-12-01

This study investigated the multiple-choice test of understanding of vectors (TUV), by applying item response theory (IRT). The difficulty, discriminatory, and guessing parameters of the TUV items were fit with the three-parameter logistic model of IRT, using the parscale program. The TUV ability is an ability parameter, here estimated assuming unidimensionality and local independence. Moreover, all distractors of the TUV were analyzed from item response curves (IRC) that represent simplified IRT. Data were gathered on 2392 science and engineering freshmen, from three universities in Thailand. The results revealed IRT analysis to be useful in assessing the test since its item parameters are independent of the ability parameters. The IRT framework reveals item-level information, and indicates appropriate ability ranges for the test. Moreover, the IRC analysis can be used to assess the effectiveness of the test's distractors. Both IRT and IRC approaches reveal test characteristics beyond those revealed by the classical analysis methods of tests. Test developers can apply these methods to diagnose and evaluate the features of items at various ability levels of test takers.

Using nonparametrics to specify a model to measure the value of travel time

DEFF Research Database (Denmark)

Fosgerau, Mogens

2007-01-01

Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source...... of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing...... unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory....

Promotion time cure rate model with nonparametric form of covariate effects.

Science.gov (United States)

Chen, Tianlei; Du, Pang

2018-05-10

Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model. The covariate effect function is estimated by smoothing splines via the optimization of a penalized profile likelihood. Point-wise interval estimates are also derived from the Bayesian interpretation of the penalized profile likelihood. Asymptotic convergence rates are established for the proposed estimates. Simulations show excellent performance of the proposed nonparametric method, which is then applied to a melanoma study. Copyright © 2018 John Wiley & Sons, Ltd.

Using automatic item generation to create multiple-choice test items.

Science.gov (United States)

Gierl, Mark J; Lai, Hollis; Turner, Simon R

2012-08-01

Many tests of medical knowledge, from the undergraduate level to the level of certification and licensure, contain multiple-choice items. Although these are efficient in measuring examinees' knowledge and skills across diverse content areas, multiple-choice items are time-consuming and expensive to create. Changes in student assessment brought about by new forms of computer-based testing have created the demand for large numbers of multiple-choice items. Our current approaches to item development cannot meet this demand. We present a methodology for developing multiple-choice items based on automatic item generation (AIG) concepts and procedures. We describe a three-stage approach to AIG and we illustrate this approach by generating multiple-choice items for a medical licensure test in the content area of surgery. To generate multiple-choice items, our method requires a three-stage process. Firstly, a cognitive model is created by content specialists. Secondly, item models are developed using the content from the cognitive model. Thirdly, items are generated from the item models using computer software. Using this methodology, we generated 1248 multiple-choice items from one item model. Automatic item generation is a process that involves using models to generate items using computer technology. With our method, content specialists identify and structure the content for the test items, and computer technology systematically combines the content to generate new test items. By combining these outcomes, items can be generated automatically. © Blackwell Publishing Ltd 2012.

The Prediction of Item Parameters Based on Classical Test Theory and Latent Trait Theory

Science.gov (United States)

Anil, Duygu

2008-01-01

In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

Science.gov (United States)

Kumar, Sricharan; Srivistava, Ashok N.

2012-01-01

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems

Science.gov (United States)

Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J.

2017-06-01

We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.

A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems.

Science.gov (United States)

Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J

2017-06-01

We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.

Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks

Science.gov (United States)

Gray-Davies, Tristan; Holmes, Chris C.; Caron, François

2018-01-01

We present a novel Bayesian nonparametric regression model for covariates X and continuous response variable Y ∈ ℝ. The model is parametrized in terms of marginal distributions for Y and X and a regression function which tunes the stochastic ordering of the conditional distributions F (y|x). By adopting an approximate composite likelihood approach, we show that the resulting posterior inference can be decoupled for the separate components of the model. This procedure can scale to very large datasets and allows for the use of standard, existing, software from Bayesian nonparametric density estimation and Plackett-Luce ranking estimation to be applied. As an illustration, we show an application of our approach to a US Census dataset, with over 1,300,000 data points and more than 100 covariates. PMID:29623150

Nonparametric statistics a step-by-step approach

CERN Document Server

Corder, Gregory W

2014-01-01

"…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory.  It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca

A structural nonparametric reappraisal of the CO2 emissions-income relationship

NARCIS (Netherlands)

Azomahou, T.T.; Goedhuys - Degelin, Micheline; Nguyen-Van, P.

Relying on a structural nonparametric estimation, we show that co2 emissions clearly increase with income at low income levels. For higher income levels, we observe a decreasing relationship, though not significant. We also find thatco2 emissions monotonically increases with energy use at a

Nonparametric estimation of the stationary M/G/1 workload distribution function

DEFF Research Database (Denmark)

Hansen, Martin Bøgsted

2005-01-01

In this paper it is demonstrated how a nonparametric estimator of the stationary workload distribution function of the M/G/1-queue can be obtained by systematic sampling the workload process. Weak convergence results and bootstrap methods for empirical distribution functions for stationary associ...

Impulse response identification with deterministic inputs using non-parametric methods

International Nuclear Information System (INIS)

Bhargava, U.K.; Kashyap, R.L.; Goodman, D.M.

1985-01-01

This paper addresses the problem of impulse response identification using non-parametric methods. Although the techniques developed herein apply to the truncated, untruncated, and the circulant models, we focus on the truncated model which is useful in certain applications. Two methods of impulse response identification will be presented. The first is based on the minimization of the C/sub L/ Statistic, which is an estimate of the mean-square prediction error; the second is a Bayesian approach. For both of these methods, we consider the effects of using both the identity matrix and the Laplacian matrix as weights on the energy in the impulse response. In addition, we present a method for estimating the effective length of the impulse response. Estimating the length is particularly important in the truncated case. Finally, we develop a method for estimating the noise variance at the output. Often, prior information on the noise variance is not available, and a good estimate is crucial to the success of estimating the impulse response with a nonparametric technique

Exploring differential item functioning (DIF) with the Rasch model: a comparison of gender differences on eighth grade science items in the United States and Spain.

Science.gov (United States)

Babiar, Tasha Calvert

2011-01-01

Traditionally, women and minorities have not been fully represented in science and engineering. Numerous studies have attributed these differences to gaps in science achievement as measured by various standardized tests. Rather than describe mean group differences in science achievement across multiple cultures, this study focused on an in-depth item-level analysis across two countries: Spain and the United States. This study investigated eighth-grade gender differences on science items across the two countries. A secondary purpose of the study was to explore the nature of gender differences using the many-faceted Rasch Model as a way to estimate gender DIF. A secondary analysis of data from the Third International Mathematics and Science Study (TIMSS) was used to address three questions: 1) Does gender DIF in science achievement exist? 2) Is there a relationship between gender DIF and characteristics of the science items? 3) Do the relationships between item characteristics and gender DIF in science items replicate across countries. Participants included 7,087 eight grade students from the United States and 3,855 students from Spain who participated in TIMSS. The Facets program (Linacre and Wright, 1992) was used to estimate gender DIF. The results of the analysis indicate that the content of the item seemed to be related to gender DIF. The analysis also suggests that there is a relationship between gender DIF and item format. No pattern of gender DIF related to cognitive demand was found. The general pattern of gender DIF was similar across the two countries used in the analysis. The strength of item-level analysis as opposed to group mean difference analysis is that gender differences can be detected at the item level, even when no mean differences can be detected at the group level.

Item Response Theory for Peer Assessment

Science.gov (United States)

Uto, Masaki; Ueno, Maomi

2016-01-01

As an assessment method based on a constructivist approach, peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. Those models are expected to improve…

A nonparametric approach to medical survival data: Uncertainty in the context of risk in mortality analysis

International Nuclear Information System (INIS)

Janurová, Kateřina; Briš, Radim

2014-01-01

Medical survival right-censored data of about 850 patients are evaluated to analyze the uncertainty related to the risk of mortality on one hand and compare two basic surgery techniques in the context of risk of mortality on the other hand. Colorectal data come from patients who underwent colectomy in the University Hospital of Ostrava. Two basic surgery operating techniques are used for the colectomy: either traditional (open) or minimally invasive (laparoscopic). Basic question arising at the colectomy operation is, which type of operation to choose to guarantee longer overall survival time. Two non-parametric approaches have been used to quantify probability of mortality with uncertainties. In fact, complement of the probability to one, i.e. survival function with corresponding confidence levels is calculated and evaluated. First approach considers standard nonparametric estimators resulting from both the Kaplan–Meier estimator of survival function in connection with Greenwood's formula and the Nelson–Aalen estimator of cumulative hazard function including confidence interval for survival function as well. The second innovative approach, represented by Nonparametric Predictive Inference (NPI), uses lower and upper probabilities for quantifying uncertainty and provides a model of predictive survival function instead of the population survival function. The traditional log-rank test on one hand and the nonparametric predictive comparison of two groups of lifetime data on the other hand have been compared to evaluate risk of mortality in the context of mentioned surgery techniques. The size of the difference between two groups of lifetime data has been considered and analyzed as well. Both nonparametric approaches led to the same conclusion, that the minimally invasive operating technique guarantees the patient significantly longer survival time in comparison with the traditional operating technique

Nonparametric Estimation of Interval Reliability for Discrete-Time Semi-Markov Systems

DEFF Research Database (Denmark)

Georgiadis, Stylianos; Limnios, Nikolaos

2016-01-01

In this article, we consider a repairable discrete-time semi-Markov system with finite state space. The measure of the interval reliability is given as the probability of the system being operational over a given finite-length time interval. A nonparametric estimator is proposed for the interval...

Assessing pupil and school performance by non-parametric and parametric techniques

NARCIS (Netherlands)

de Witte, K.; Thanassoulis, E.; Simpson, G.; Battisti, G.; Charlesworth-May, A.

2010-01-01

This paper discusses the use of the non-parametric free disposal hull (FDH) and the parametric multi-level model (MLM) as alternative methods for measuring pupil and school attainment where hierarchical structured data are available. Using robust FDH estimates, we show how to decompose the overall

Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression

NARCIS (Netherlands)

Yoo, W.W.; Ghosal, S

2016-01-01

In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and its mixed partial derivatives. We use a random series of tensor product of B-splines with normal basis coefficients as a

A non-parametric hierarchical model to discover behavior dynamics from tracks

NARCIS (Netherlands)

Kooij, J.F.P.; Englebienne, G.; Gavrila, D.M.

2012-01-01

We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by

Using non-parametric methods in econometric production analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...

Estimation of Stochastic Volatility Models by Nonparametric Filtering

DEFF Research Database (Denmark)

Kanaya, Shin; Kristensen, Dennis

2016-01-01

/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...

Nonparametric Bayesian inference in biostatistics

CERN Document Server

Müller, Peter

2015-01-01

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...

Hadron Energy Reconstruction for ATLAS Barrel Combined Calorimeter Using Non-Parametrical Method

CERN Document Server

Kulchitskii, Yu A

2000-01-01

Hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter in the framework of the non-parametrical method is discussed. The non-parametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to fast energy reconstruction in a first level trigger. The reconstructed mean values of the hadron energies are within \\pm1% of the true values and the fractional energy resolution is [(58\\pm 3)%{\\sqrt{GeV}}/\\sqrt{E}+(2.5\\pm0.3)%]\\bigoplus(1.7\\pm0.2) GeV/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74\\pm0.04. Results of a study of the longitudinal hadronic shower development are also presented.

Bayesian nonparametric estimation of continuous monotone functions with applications to dose-response analysis.

Science.gov (United States)

Bornkamp, Björn; Ickstadt, Katja

2009-03-01

In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose-response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose-response analysis.

Low default credit scoring using two-class non-parametric kernel density estimation

CSIR Research Space (South Africa)

Rademeyer, E

2016-12-01

Full Text Available This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes’ rule, to include either...

The Role of Item Models in Automatic Item Generation

Science.gov (United States)

Gierl, Mark J.; Lai, Hollis

2012-01-01

Automatic item generation represents a relatively new but rapidly evolving research area where cognitive and psychometric theories are used to produce tests that include items generated using computer technology. Automatic item generation requires two steps. First, test development specialists create item models, which are comparable to templates…

Problems with the factor analysis of items: Solutions based on item response theory and item parcelling

Directory of Open Access Journals (Sweden)

Gideon P. De Bruin

2004-10-01

Full Text Available The factor analysis of items often produces spurious results in the sense that unidimensional scales appear multidimensional. This may be ascribed to failure in meeting the assumptions of linearity and normality on which factor analysis is based. Item response theory is explicitly designed for the modelling of the non-linear relations between ordinal variables and provides a strong alternative to the factor analysis of items. Items may also be combined in parcels that are more likely to satisfy the assumptions of factor analysis than do the items. The use of the Rasch rating scale model and the factor analysis of parcels is illustrated with data obtained with the Locus of Control Inventory. The results of these analyses are compared with the results obtained through the factor analysis of items. It is shown that the Rasch rating scale model and the factoring of parcels produce superior results to the factor analysis of items. Recommendations for the analysis of scales are made. Opsomming Die faktorontleding van items lewer dikwels misleidende resultate op, veral in die opsig dat eendimensionele skale as meerdimensioneel voorkom. Hierdie resultate kan dikwels daaraan toegeskryf word dat daar nie aan die aannames van lineariteit en normaliteit waarop faktorontleding berus, voldoen word nie. Itemresponsteorie, wat eksplisiet vir die modellering van die nie-liniêre verbande tussen ordinale items ontwerp is, bied ’n aantreklike alternatief vir die faktorontleding van items. Items kan ook in pakkies gegroepeer word wat meer waarskynlik aan die aannames van faktorontleding voldoen as individuele items. Die gebruik van die Rasch beoordelingskaalmodel en die faktorontleding van pakkies word aan die hand van data wat met die Lokus van Beheervraelys verkry is, gedemonstreer. Die resultate van hierdie ontledings word vergelyk met die resultate wat deur ‘n faktorontleding van die individuele items verkry is. Die resultate dui daarop dat die Rasch

Evaluation of Nonparametric Probabilistic Forecasts of Wind Power

DEFF Research Database (Denmark)

Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008

Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...

The Dysexecutive Questionnaire advanced: item and test score characteristics, 4-factor solution, and severity classification.

Science.gov (United States)

Bodenburg, Sebastian; Dopslaff, Nina

2008-01-01

The Dysexecutive Questionnaire (DEX, , Behavioral assessment of the dysexecutive syndrome, 1996) is a standardized instrument to measure possible behavioral changes as a result of the dysexecutive syndrome. Although initially intended only as a qualitative instrument, the DEX has also been used increasingly to address quantitative problems. Until now there have not been more fundamental statistical analyses of the questionnaire's testing quality. The present study is based on an unselected sample of 191 patients with acquired brain injury and reports on the data relating to the quality of the items, the reliability and the factorial structure of the DEX. Item 3 displayed too great an item difficulty, whereas item 11 was not sufficiently discriminating. The DEX's reliability in self-rating is r = 0.85. In addition to presenting the statistical values of the tests, a clinical severity classification of the overall scores of the 4 found factors and of the questionnaire as a whole is carried out on the basis of quartile standards.

Item information and discrimination functions for trinary PCM items

NARCIS (Netherlands)

Akkermans, Wies; Muraki, Eiji

1997-01-01

For trinary partial credit items the shape of the item information and the item discrimination function is examined in relation to the item parameters. In particular, it is shown that these functions are unimodal if δ2 – δ1 < 4 ln 2 and bimodal otherwise. The locations and values of the maxima are

Using Likert-type and ipsative/forced choice items in sequence to generate a preference.

Science.gov (United States)

Ried, L Douglas

2014-01-01

Collaboration and implementation of a minimum, standardized set of core global educational and professional competencies seems appropriate given the expanding international evolution of pharmacy practice. However, winnowing down hundreds of competencies from a plethora of local, national and international competency frameworks to select the most highly preferred to be included in the core set is a daunting task. The objective of this paper is to describe a combination of strategies used to ascertain the most highly preferred items among a large number of disparate items. In this case, the items were >100 educational and professional competencies that might be incorporated as the core components of new and existing competency frameworks. Panelists (n = 30) from the European Union (EU) and United States (USA) were chosen to reflect a variety of practice settings. Each panelist completed two electronic surveys. The first survey presented competencies in a Likert-type format and the second survey presented many of the same competencies in an ipsative/forced choice format. Item mean scores were calculated for each competency, the competencies were ranked, and non-parametric statistical tests were used to ascertain the consistency in the rankings achieved by the two strategies. This exploratory study presented over 100 competencies to the panelists in the beginning. The two methods provided similar results, as indicated by the significant correlation between the rankings (Spearman's rho = 0.30, P < 0.09). A two-step strategy using Likert-type and ipsative/forced choice formats in sequence, appears to be useful in a situation where a clear preference is required from among a large number of choices. The ipsative/forced choice format resulted in some differences in the competency preferences because the panelists could not rate them equally by design. While this strategy was used for the selection of professional educational competencies in this exploratory study, it is

A multi-instrument non-parametric reconstruction of the electron pressure profile in the galaxy cluster CLJ1226.9+3332

Science.gov (United States)

Romero, C.; McWilliam, M.; Macías-Pérez, J.-F.; Adam, R.; Ade, P.; André, P.; Aussel, H.; Beelen, A.; Benoît, A.; Bideaud, A.; Billot, N.; Bourrion, O.; Calvo, M.; Catalano, A.; Coiffard, G.; Comis, B.; de Petris, M.; Désert, F.-X.; Doyle, S.; Goupy, J.; Kramer, C.; Lagache, G.; Leclercq, S.; Lestrade, J.-F.; Mauskopf, P.; Mayet, F.; Monfardini, A.; Pascale, E.; Perotto, L.; Pisano, G.; Ponthieu, N.; Revéret, V.; Ritacco, A.; Roussel, H.; Ruppin, F.; Schuster, K.; Sievers, A.; Triqueneaux, S.; Tucker, C.; Zylka, R.

2018-04-01

Context. In the past decade, sensitive, resolved Sunyaev-Zel'dovich (SZ) studies of galaxy clusters have become common. Whereas many previous SZ studies have parameterized the pressure profiles of galaxy clusters, non-parametric reconstructions will provide insights into the thermodynamic state of the intracluster medium. Aim. We seek to recover the non-parametric pressure profiles of the high redshift (z = 0.89) galaxy cluster CLJ 1226.9+3332 as inferred from SZ data from the MUSTANG, NIKA, Bolocam, and Planck instruments, which all probe different angular scales. Methods: Our non-parametric algorithm makes use of logarithmic interpolation, which under the assumption of ellipsoidal symmetry is analytically integrable. For MUSTANG, NIKA, and Bolocam we derive a non-parametric pressure profile independently and find good agreement among the instruments. In particular, we find that the non-parametric profiles are consistent with a fitted generalized Navaro-Frenk-White (gNFW) profile. Given the ability of Planck to constrain the total signal, we include a prior on the integrated Compton Y parameter as determined by Planck. Results: For a given instrument, constraints on the pressure profile diminish rapidly beyond the field of view. The overlap in spatial scales probed by these four datasets is therefore critical in checking for consistency between instruments. By using multiple instruments, our analysis of CLJ 1226.9+3332 covers a large radial range, from the central regions to the cluster outskirts: 0.05 R500 generation of SZ instruments such as NIKA2 and MUSTANG2.

Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems.

Science.gov (United States)

Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

2015-01-01

In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes.

Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems

Science.gov (United States)

Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

2015-01-01

In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes. PMID:26267477

PENGEMBANGAN TES BERPIKIR KRITIS DENGAN PENDEKATAN ITEM RESPONSE THEORY

Directory of Open Access Journals (Sweden)

Fajrianthi Fajrianthi

2016-06-01

Full Text Available Penelitian ini bertujuan untuk menghasilkan sebuah alat ukur (tes berpikir kritis yang valid dan reliabel untuk digunakan, baik dalam lingkup pendidikan maupun kerja di Indonesia. Tahapan penelitian dilakukan berdasarkan tahap pengembangan tes menurut Hambleton dan Jones (1993. Kisi-kisi dan pembuatan butir didasarkan pada konsep dalam tes Watson-Glaser Critical Thinking Appraisal (WGCTA. Pada WGCTA, berpikir kritis terdiri dari lima dimensi yaitu Inference, Recognition Assumption, Deduction, Interpretation dan Evaluation of arguments. Uji coba tes dilakukan pada 1.453 peserta tes seleksi karyawan di Surabaya, Gresik, Tuban, Bojonegoro, Rembang. Data dikotomi dianalisis dengan menggunakan model IRT dengan dua parameter yaitu daya beda dan tingkat kesulitan butir. Analisis dilakukan dengan menggunakan program statistik Mplus versi 6.11 Sebelum melakukan analisis dengan IRT, dilakukan pengujian asumsi yaitu uji unidimensionalitas, independensi lokal dan Item Characteristic Curve (ICC. Hasil analisis terhadap 68 butir menghasilkan 15 butir dengan daya beda yang cukup baik dan tingkat kesulitan butir yang berkisar antara –4 sampai dengan 2.448. Sedikitnya jumlah butir yang berkualitas baik disebabkan oleh kelemahan dalam menentukan subject matter experts di bidang berpikir kritis dan pemilihan metode skoring. Kata kunci: Pengembangan tes, berpikir kritis, item response theory   DEVELOPING CRITICAL THINKING TEST UTILISING ITEM RESPONSE THEORY Abstract The present study was aimed to develop a valid and reliable instrument in assesing critical thinking which can be implemented both in educational and work settings in Indonesia. Following the Hambleton and Jones’s (1993 procedures on test development, the study developed the instrument by employing the concept of critical thinking from Watson-Glaser Critical Thinking Appraisal (WGCTA. The study included five dimensions of critical thinking as adopted from the WGCTA: Inference, Recognition

Analyzing cost efficient production behavior under economies of scope : A nonparametric methodology

NARCIS (Netherlands)

Cherchye, L.J.H.; de Rock, B.; Vermeulen, F.M.P.

2008-01-01

In designing a production model for firms that generate multiple outputs, we take as a starting point that such multioutput production refers to economies of scope, which in turn originate from joint input use and input externalities. We provide a nonparametric characterization of cost-efficient

A non-parametric Bayesian approach to decompounding from high frequency data

NARCIS (Netherlands)

Gugushvili, Shota; van der Meulen, F.H.; Spreij, Peter

2016-01-01

Given a sample from a discretely observed compound Poisson process, we consider non-parametric estimation of the density f0 of its jump sizes, as well as of its intensity λ0. We take a Bayesian approach to the problem and specify the prior on f0 as the Dirichlet location mixture of normal densities.

Item level diagnostics and model - data fit in item response theory ...

African Journals Online (AJOL)

Item response theory (IRT) is a framework for modeling and analyzing item response data. Item-level modeling gives IRT advantages over classical test theory. The fit of an item score pattern to an item response theory (IRT) models is a necessary condition that must be assessed for further use of item and models that best fit ...

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

Science.gov (United States)

Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

kruX: matrix-based non-parametric eQTL discovery.

Science.gov (United States)

Qi, Jianlong; Asl, Hassan Foroughi; Björkegren, Johan; Michoel, Tom

2014-01-14

The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure and is freely available from http://krux.googlecode.com.

Dissociation between source and item memory in Parkinson's disease

Institute of Scientific and Technical Information of China (English)

Hu Panpan; Li Youhai; Ma Huijuan; Xi Chunhua; Chen Xianwen; Wang Kai

2014-01-01

Background Episodic memory includes information about item memory and source memory.Many researches support the hypothesis that these two memory systems are implemented by different brain structures.The aim of this study was to investigate the characteristics of item memory and source memory processing in patients with Parkinson's disease (PD),and to further verify the hypothesis of dual-process model of source and item memory.Methods We established a neuropsychological battery to measure the performance of item memory and source memory.Totally 35 PD individuals and 35 matched healthy controls (HC) were administrated with the battery.Item memory task consists of the learning and recognition of high-frequency national Chinese characters; source memory task consists of the learning and recognition of three modes (character,picture,and image) of objects.Results Compared with the controls,the idiopathic PD patients have been impaired source memory (PD vs.HC:0.65±0.06 vs.0.72±0.09,P=0.001),but not impaired in item memory (PD vs.HC:0.65±0.07 vs.0.67±0.08,P=0.240).Conclusions The present experiment provides evidence for dissociation between item and source memory in PD patients,thereby strengthening the claim that the item or source memory rely on different brain structures.PD patients show poor source memory,in which dopamine plays a critical role.

Exact nonparametric confidence bands for the survivor function.

Science.gov (United States)

Matthews, David

2013-10-12

A method to produce exact simultaneous confidence bands for the empirical cumulative distribution function that was first described by Owen, and subsequently corrected by Jager and Wellner, is the starting point for deriving exact nonparametric confidence bands for the survivor function of any positive random variable. We invert a nonparametric likelihood test of uniformity, constructed from the Kaplan-Meier estimator of the survivor function, to obtain simultaneous lower and upper bands for the function of interest with specified global confidence level. The method involves calculating a null distribution and associated critical value for each observed sample configuration. However, Noe recursions and the Van Wijngaarden-Decker-Brent root-finding algorithm provide the necessary tools for efficient computation of these exact bounds. Various aspects of the effect of right censoring on these exact bands are investigated, using as illustrations two observational studies of survival experience among non-Hodgkin's lymphoma patients and a much larger group of subjects with advanced lung cancer enrolled in trials within the North Central Cancer Treatment Group. Monte Carlo simulations confirm the merits of the proposed method of deriving simultaneous interval estimates of the survivor function across the entire range of the observed sample. This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. It was begun while the author was visiting the Department of Statistics, University of Auckland, and completed during a subsequent sojourn at the Medical Research Council Biostatistics Unit in Cambridge. The support of both institutions, in addition to that of NSERC and the University of Waterloo, is greatly appreciated.

Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

Science.gov (United States)

Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.

2018-03-01

We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.

Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

Directory of Open Access Journals (Sweden)

Jinchao Feng

2018-03-01

Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.

Analyzing Cost Efficient Production Behavior Under Economies of Scope : A Nonparametric Methodology

NARCIS (Netherlands)

Cherchye, L.J.H.; de Rock, B.; Vermeulen, F.M.P.

2006-01-01

In designing a production model for firms that generate multiple outputs, we take as a starting point that such multi-output production refers to economies of scope, which in turn originate from joint input use and input externalities. We provide a nonparametric characterization of cost efficient

Digital spectral analysis parametric, non-parametric and advanced methods

CERN Document Server

Castanié, Francis

2013-01-01

Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a

On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in Nonparametric Regression

KAUST Repository

Dai, Wenlin; Tong, Tiejun; Zhu, Lixing

2017-01-01

Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.

On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in Nonparametric Regression

KAUST Repository

Dai, Wenlin

2017-09-01

Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

Science.gov (United States)

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data

OpenAIRE

CORNELIS A. LOS

2004-01-01

The efficiency of speculative markets, as represented by Fama's 1970 fair game model, is tested on weekly price index data of six Asian stock markets - Hong Kong, Indonesia, Malaysia, Singapore, Taiwan and Thailand - using Sherry's (1992) non-parametric methods. These scientific testing methods were originally developed to analyze the information processing efficiency of nervous systems. In particular, the stationarity and independence of the price innovations are tested over ten years, from ...

Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors

Directory of Open Access Journals (Sweden)

Xibin Zhang

2016-04-01

Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.

Parametric and Non-Parametric Vibration-Based Structural Identification Under Earthquake Excitation

Science.gov (United States)

Pentaris, Fragkiskos P.; Fouskitakis, George N.

2014-05-01

The problem of modal identification in civil structures is of crucial importance, and thus has been receiving increasing attention in recent years. Vibration-based methods are quite promising as they are capable of identifying the structure's global characteristics, they are relatively easy to implement and they tend to be time effective and less expensive than most alternatives [1]. This paper focuses on the off-line structural/modal identification of civil (concrete) structures subjected to low-level earthquake excitations, under which, they remain within their linear operating regime. Earthquakes and their details are recorded and provided by the seismological network of Crete [2], which 'monitors' the broad region of south Hellenic arc, an active seismic region which functions as a natural laboratory for earthquake engineering of this kind. A sufficient number of seismic events are analyzed in order to reveal the modal characteristics of the structures under study, that consist of the two concrete buildings of the School of Applied Sciences, Technological Education Institute of Crete, located in Chania, Crete, Hellas. Both buildings are equipped with high-sensitivity and accuracy seismographs - providing acceleration measurements - established at the basement (structure's foundation) presently considered as the ground's acceleration (excitation) and at all levels (ground floor, 1st floor, 2nd floor and terrace). Further details regarding the instrumentation setup and data acquisition may be found in [3]. The present study invokes stochastic, both non-parametric (frequency-based) and parametric methods for structural/modal identification (natural frequencies and/or damping ratios). Non-parametric methods include Welch-based spectrum and Frequency response Function (FrF) estimation, while parametric methods, include AutoRegressive (AR), AutoRegressive with eXogeneous input (ARX) and Autoregressive Moving-Average with eXogeneous input (ARMAX) models[4, 5

Estimation of the limit of detection with a bootstrap-derived standard error by a partly non-parametric approach. Application to HPLC drug assays

DEFF Research Database (Denmark)

Linnet, Kristian

2005-01-01

Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors......Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors...

Item Effects in Recognition Memory for Words

Science.gov (United States)

Freeman, Emily; Heathcote, Andrew; Chalmers, Kerry; Hockley, William

2010-01-01

We investigate the effects of word characteristics on episodic recognition memory using analyses that avoid Clark's (1973) "language-as-a-fixed-effect" fallacy. Our results demonstrate the importance of modeling word variability and show that episodic memory for words is strongly affected by item noise (Criss & Shiffrin, 2004), as measured by the…

Using multinomial and imprecise probability for non-parametric modelling of rainfall in Manizales (Colombia

Directory of Open Access Journals (Sweden)

Ibsen Chivatá Cárdenas

2008-05-01

Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions

Measuring stigma after spinal cord injury: Development and psychometric characteristics of the SCI-QOL Stigma item bank and short form.

Science.gov (United States)

Kisala, Pamela A; Tulsky, David S; Pace, Natalie; Victorson, David; Choi, Seung W; Heinemann, Allen W

2015-05-01

To develop a calibrated item bank and computer adaptive test (CAT) to assess the effects of stigma on health-related quality of life in individuals with spinal cord injury (SCI). Grounded-theory based qualitative item development methods, large-scale item calibration field testing, confirmatory factor analysis, and item response theory (IRT)-based psychometric analyses. Five SCI Model System centers and one Department of Veterans Affairs medical center in the United States. Adults with traumatic SCI. SCI-QOL Stigma Item Bank A sample of 611 individuals with traumatic SCI completed 30 items assessing SCI-related stigma. After 7 items were iteratively removed, factor analyses confirmed a unidimensional pool of items. Graded Response Model IRT analyses were used to estimate slopes and thresholds for the final 23 items. The SCI-QOL Stigma item bank is unique not only in the assessment of SCI-related stigma but also in the inclusion of individuals with SCI in all phases of its development. Use of confirmatory factor analytic and IRT methods provide flexibility and precision of measurement. The item bank may be administered as a CAT or as a 10-item fixed-length short form and can be used for research and clinical applications.

Procurement Engineering Process for Commercial Grade Item Dedication

International Nuclear Information System (INIS)

Park, Jong-Hyuck; Park, Jong-Eun; Kwak, Tack-Hun; Yoo, Keun-Bae; Lee, Sang-Guk; Hong, Sung-Yull

2006-01-01

Procurement Engineering Process for commercial grade item dedication plays an increasingly important role in operation management of Korea Nuclear Power Plants. The purpose of the Procurement Engineering Process is the provision and assurance of a high quality and quantity of spare, replacement, retrofit and new parts and equipment while maximizing plant availability, minimizing downtime due to parts unavailability and providing reasonable overall program and inventory cost. In this paper, we will review the overview requirements, responsibilities and the process for demonstrating with reasonable assurance that a procured item for potential nuclear safety related services or other essential plant service is adequate with reasonable assurance for its application. This paper does not cover the details of technical evaluation, selecting critical characteristics, selecting acceptance methods, performing failure modes and effects analysis, performing source surveillance, performing quality surveys, performing special tests and inspections, and the other aspects of effective Procurement Engineering and Commercial Grade Item Dedication. The main contribution of this paper is to provide the provision of an overview of Procurement Engineering Process for commercial grade item

Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network.

Science.gov (United States)

He, Jichao; Wanik, David W; Hartman, Brian M; Anagnostou, Emmanouil N; Astitha, Marina; Frediani, Maria E B

2017-03-01

This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources. © 2016 Society for Risk Analysis.

Nonparametric estimation of location and scale parameters

KAUST Repository

Potgieter, C.J.

2012-12-01

Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.

A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses.

Science.gov (United States)

Zhang, Linlin; Guindani, Michele; Versace, Francesco; Vannucci, Marina

2014-07-15

In this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis-Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data. Copyright © 2014 Elsevier Inc. All rights reserved.

Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes

Science.gov (United States)

Zhou, Wei-Xing; Sornette, Didier

We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(tc-t) variable, where tc is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.

Hyperspectral image segmentation using a cooperative nonparametric approach

Science.gov (United States)

Taher, Akar; Chehdi, Kacem; Cariou, Claude

2013-10-01

In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

Science.gov (United States)

Fukuhara, Hirotaka; Kamata, Akihito

2011-01-01

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into…

Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

Science.gov (United States)

Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.

2011-01-01

Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433

Gender-Based Differential Item Performance in Mathematics Achievement Items.

Science.gov (United States)

Doolittle, Allen E.; Cleary, T. Anne

1987-01-01

Eight randomly equivalent samples of high school seniors were each given a unique form of the ACT Assessment Mathematics Usage Test (ACTM). Signed measures of differential item performance (DIP) were obtained for each item in the eight ACTM forms. DIP estimates were analyzed and a significant item category effect was found. (Author/LMO)

Evaluation of psychometric properties and differential item functioning of 8-item Child Perceptions Questionnaires using item response theory.

Science.gov (United States)

Yau, David T W; Wong, May C M; Lam, K F; McGrath, Colman

2015-08-19

Four-factor structure of the two 8-item short forms of Child Perceptions Questionnaire CPQ11-14 (RSF:8 and ISF:8) has been confirmed. However, the sum scores are typically reported in practice as a proxy of Oral health-related Quality of Life (OHRQoL), which implied a unidimensional structure. This study first assessed the unidimensionality of 8-item short forms of CPQ11-14. Item response theory (IRT) was employed to offer an alternative and complementary approach of validation and to overcome the limitations of classical test theory assumptions. A random sample of 649 12-year-old school children in Hong Kong was analyzed. Unidimensionality of the scale was tested by confirmatory factor analysis (CFA), principle component analysis (PCA) and local dependency (LD) statistic. Graded response model was fitted to the data. Contribution of each item to the scale was assessed by item information function (IIF). Reliability of the scale was assessed by test information function (TIF). Differential item functioning (DIF) across gender was identified by Wald test and expected score functions. Both CPQ11-14 RSF:8 and ISF:8 did not deviate much from the unidimensionality assumption. Results from CFA indicated acceptable fit of the one-factor model. PCA indicated that the first principle component explained >30 % of the total variation with high factor loadings for both RSF:8 and ISF:8. Almost all LD statistic items suggesting little contribution of information to the scale and item removal caused little practical impact. Comparing the TIFs, RSF:8 showed slightly better information than ISF:8. In addition to oral symptoms items, the item "Concerned with what other people think" demonstrated a uniform DIF (p Items related to oral symptoms were not informative to OHRQoL and deletion of these items is suggested. The impact of DIF across gender on the overall score was minimal. CPQ11-14 RSF:8 performed slightly better than ISF:8 in measurement precision. The 6-item short forms

Short-term forecasting of meteorological time series using Nonparametric Functional Data Analysis (NPFDA)

Science.gov (United States)

Curceac, S.; Ternynck, C.; Ouarda, T.

2015-12-01

Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed

Entry-Item-Quantity-ABC Analysis-Based Multitype Cigarette Fast Sorting System

Directory of Open Access Journals (Sweden)

Ying Zhao

2012-01-01

Full Text Available Numerous items, small order, and frequent delivery are the characteristics of many distribution centers. Such characteristics generally increase the operating costs of the distribution center. To remedy this problem, this study employs the Entry-Item-Quantity (EIQ method to identify the characteristic of the cigarette distribution center and further analyzes the importance degree of customers and the frequently ordered products by means of EQ/EN/IQ-B/IK statistic charts. Based on these analyses as well as the total replenishment cost optimization model, multipicking strategies and combined multitype picking equipment allocation is then formulated accordingly. With such design scheme, the cigarette picking costs of the distribution center are expected to reduce. Finally, the specific number of equipment is figured out in order to meet the capability demand of the case cigarette distribution center.

STATCAT, Statistical Analysis of Parametric and Non-Parametric Data

International Nuclear Information System (INIS)

David, Hugh

1990-01-01

1 - Description of program or function: A suite of 26 programs designed to facilitate the appropriate statistical analysis and data handling of parametric and non-parametric data, using classical and modern univariate and multivariate methods. 2 - Method of solution: Data is read entry by entry, using a choice of input formats, and the resultant data bank is checked for out-of- range, rare, extreme or missing data. The completed STATCAT data bank can be treated by a variety of descriptive and inferential statistical methods, and modified, using other standard programs as required

Non-parametric system identification from non-linear stochastic response

DEFF Research Database (Denmark)

Rüdinger, Finn; Krenk, Steen

2001-01-01

An estimation method is proposed for identification of non-linear stiffness and damping of single-degree-of-freedom systems under stationary white noise excitation. Non-parametric estimates of the stiffness and damping along with an estimate of the white noise intensity are obtained by suitable...... of the energy at mean-level crossings, which yields the damping relative to white noise intensity. Finally, an estimate of the noise intensity is extracted by estimating the absolute damping from the autocovariance functions of a set of modified phase plane variables at different energy levels. The method...

Testing a parametric function against a nonparametric alternative in IV and GMM settings

DEFF Research Database (Denmark)

Gørgens, Tue; Wurtz, Allan

This paper develops a specification test for functional form for models identified by moment restrictions, including IV and GMM settings. The general framework is one where the moment restrictions are specified as functions of data, a finite-dimensional parameter vector, and a nonparametric real ...

Nonparametric predictive inference for combining diagnostic tests with parametric copula

Science.gov (United States)

Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.

2017-09-01

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.

A simple non-parametric goodness-of-fit test for elliptical copulas

Directory of Open Access Journals (Sweden)

Jaser Miriam

2017-12-01

Full Text Available In this paper, we propose a simple non-parametric goodness-of-fit test for elliptical copulas of any dimension. It is based on the equality of Kendall’s tau and Blomqvist’s beta for all bivariate margins. Nominal level and power of the proposed test are investigated in a Monte Carlo study. An empirical application illustrates our goodness-of-fit test at work.

Nonparametric inference of network structure and dynamics

Science.gov (United States)

Peixoto, Tiago P.

The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model.

Science.gov (United States)

Lee, Soojeong; Rajan, Sreeraman; Jeon, Gwanggil; Chang, Joon-Hyuk; Dajani, Hilmi R; Groza, Voicu Z

2017-06-01

Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients. This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities. The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE. The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models

OpenAIRE

GROENEBOOM, PIET; JONGBLOED, GEURT; WELLNER, JON A.

2008-01-01

In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M-estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the ‘Aspect problem’ in quantum physics.

Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

OpenAIRE

Wang, Wenshuo; Xi, Junqiang; Zhao, Ding

2017-01-01

Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number...

Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality

OpenAIRE

Li, Zhanchao; Gu, Chongshi; Wu, Zhongru

2013-01-01

The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model ...

A non-parametric framework for estimating threshold limit values

Directory of Open Access Journals (Sweden)

Ulm Kurt

2005-11-01

Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.

A note on monotonicity of item response functions for ordered polytomous item response theory models.

Science.gov (United States)

Kang, Hyeon-Ah; Su, Ya-Hui; Chang, Hua-Hua

2018-03-08

A monotone relationship between a true score (τ) and a latent trait level (θ) has been a key assumption for many psychometric applications. The monotonicity property in dichotomous response models is evident as a result of a transformation via a test characteristic curve. Monotonicity in polytomous models, in contrast, is not immediately obvious because item response functions are determined by a set of response category curves, which are conceivably non-monotonic in θ. The purpose of the present note is to demonstrate strict monotonicity in ordered polytomous item response models. Five models that are widely used in operational assessments are considered for proof: the generalized partial credit model (Muraki, 1992, Applied Psychological Measurement, 16, 159), the nominal model (Bock, 1972, Psychometrika, 37, 29), the partial credit model (Masters, 1982, Psychometrika, 47, 147), the rating scale model (Andrich, 1978, Psychometrika, 43, 561), and the graded response model (Samejima, 1972, A general model for free-response data (Psychometric Monograph no. 18). Psychometric Society, Richmond). The study asserts that the item response functions in these models strictly increase in θ and thus there exists strict monotonicity between τ and θ under certain specified conditions. This conclusion validates the practice of customarily using τ in place of θ in applied settings and provides theoretical grounds for one-to-one transformations between the two scales. © 2018 The British Psychological Society.

Does Private Tutoring Work? The Effectiveness of Private Tutoring: A Nonparametric Bounds Analysis

Science.gov (United States)

Hof, Stefanie

2014-01-01

Private tutoring has become popular throughout the world. However, evidence for the effect of private tutoring on students' academic outcome is inconclusive; therefore, this paper presents an alternative framework: a nonparametric bounds method. The present examination uses, for the first time, a large representative data-set in a European setting…

Data analysis with small samples and non-normal data nonparametrics and other strategies

CERN Document Server

Siebert, Carl F

2017-01-01

Written in everyday language for non-statisticians, this book provides all the information needed to successfully conduct nonparametric analyses. This ideal reference book provides step-by-step instructions to lead the reader through each analysis, screenshots of the software and output, and case scenarios to illustrate of all the analytic techniques.

Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach

International Nuclear Information System (INIS)

Wang, H.; Ang, B.W.; Wang, Q.W.; Zhou, P.

2017-01-01

Evaluating economy-wide energy performance is an integral part of assessing the effectiveness of a country's energy efficiency policy. Non-parametric frontier approach has been widely used by researchers for such a purpose. This paper proposes an extended non-parametric frontier approach to studying economy-wide energy efficiency and productivity performances by accounting for sectoral heterogeneity. Relevant techniques in index number theory are incorporated to quantify the driving forces behind changes in the economy-wide energy productivity index. The proposed approach facilitates flexible modelling of different sectors' production processes, and helps to examine sectors' impact on the aggregate energy performance. A case study of China's economy-wide energy efficiency and productivity performances in its 11th five-year plan period (2006–2010) is presented. It is found that sectoral heterogeneities in terms of energy performance are significant in China. Meanwhile, China's economy-wide energy productivity increased slightly during the study period, mainly driven by the technical efficiency improvement. A number of other findings have also been reported. - Highlights: • We model economy-wide energy performance by considering sectoral heterogeneity. • The proposed approach can identify sectors' impact on the aggregate energy performance. • Obvious sectoral heterogeneities are identified in evaluating China's energy performance.

Estimating technical efficiency in the hospital sector with panel data: a comparison of parametric and non-parametric techniques.

Science.gov (United States)

Siciliani, Luigi

2006-01-01

Policy makers are increasingly interested in developing performance indicators that measure hospital efficiency. These indicators may give the purchasers of health services an additional regulatory tool to contain health expenditure. Using panel data, this study compares different parametric (econometric) and non-parametric (linear programming) techniques for the measurement of a hospital's technical efficiency. This comparison was made using a sample of 17 Italian hospitals in the years 1996-9. Highest correlations are found in the efficiency scores between the non-parametric data envelopment analysis under the constant returns to scale assumption (DEA-CRS) and several parametric models. Correlation reduces markedly when using more flexible non-parametric specifications such as data envelopment analysis under the variable returns to scale assumption (DEA-VRS) and the free disposal hull (FDH) model. Correlation also generally reduces when moving from one output to two-output specifications. This analysis suggests that there is scope for developing performance indicators at hospital level using panel data, but it is important that extensive sensitivity analysis is carried out if purchasers wish to make use of these indicators in practice.

Categorical and nonparametric data analysis choosing the best statistical technique

CERN Document Server

Nussbaum, E Michael

2014-01-01

Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain

Adaptive nonparametric estimation for L\\'evy processes observed at low frequency

OpenAIRE

Kappus, Johanna

2013-01-01

This article deals with adaptive nonparametric estimation for L\\'evy processes observed at low frequency. For general linear functionals of the L\\'evy measure, we construct kernel estimators, provide upper risk bounds and derive rates of convergence under regularity assumptions. Our focus lies on the adaptive choice of the bandwidth, using model selection techniques. We face here a non-standard problem of model selection with unknown variance. A new approach towards this problem is proposed, ...

Comparative analysis of automotive paints by laser induced breakdown spectroscopy and nonparametric permutation tests

International Nuclear Information System (INIS)

McIntee, Erin; Viglino, Emilie; Rinke, Caitlin; Kumor, Stephanie; Ni Liqiang; Sigman, Michael E.

2010-01-01

Laser-induced breakdown spectroscopy (LIBS) has been investigated for the discrimination of automobile paint samples. Paint samples from automobiles of different makes, models, and years were collected and separated into sets based on the color, presence or absence of effect pigments and the number of paint layers. Twelve LIBS spectra were obtained for each paint sample, each an average of a five single shot 'drill down' spectra from consecutive laser ablations in the same spot on the sample. Analyses by a nonparametric permutation test and a parametric Wald test were performed to determine the extent of discrimination within each set of paint samples. The discrimination power and Type I error were assessed for each data analysis method. Conversion of the spectral intensity to a log-scale (base 10) resulted in a higher overall discrimination power while observing the same significance level. Working on the log-scale, the nonparametric permutation tests gave an overall 89.83% discrimination power with a size of Type I error being 4.44% at the nominal significance level of 5%. White paint samples, as a group, were the most difficult to differentiate with the power being only 86.56% followed by 95.83% for black paint samples. Parametric analysis of the data set produced lower discrimination (85.17%) with 3.33% Type I errors, which is not recommended for both theoretical and practical considerations. The nonparametric testing method is applicable across many analytical comparisons, with the specific application described here being the pairwise comparison of automotive paint samples.

A nonparametric statistical method for determination of a confidence interval for the mean of a set of results obtained in a laboratory intercomparison

International Nuclear Information System (INIS)

Veglia, A.

1981-08-01

In cases where sets of data are obviously not normally distributed, the application of a nonparametric method for the estimation of a confidence interval for the mean seems to be more suitable than some other methods because such a method requires few assumptions about the population of data. A two-step statistical method is proposed which can be applied to any set of analytical results: elimination of outliers by a nonparametric method based on Tchebycheff's inequality, and determination of a confidence interval for the mean by a non-parametric method based on binominal distribution. The method is appropriate only for samples of size n>=10

Scale-Free Nonparametric Factor Analysis: A User-Friendly Introduction with Concrete Heuristic Examples.

Science.gov (United States)

Mittag, Kathleen Cage

Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…

Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering

NARCIS (Netherlands)

M. Kagie (Martijn); M.J.H.M. van der Loos (Matthijs); M.C. van Wezel (Michiel)

2008-01-01

textabstractWe propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its

Poisson and negative binomial item count techniques for surveys with sensitive question.

Science.gov (United States)

Tian, Guo-Liang; Tang, Man-Lai; Wu, Qin; Liu, Yin

2017-04-01

Although the item count technique is useful in surveys with sensitive questions, privacy of those respondents who possess the sensitive characteristic of interest may not be well protected due to a defect in its original design. In this article, we propose two new survey designs (namely the Poisson item count technique and negative binomial item count technique) which replace several independent Bernoulli random variables required by the original item count technique with a single Poisson or negative binomial random variable, respectively. The proposed models not only provide closed form variance estimate and confidence interval within [0, 1] for the sensitive proportion, but also simplify the survey design of the original item count technique. Most importantly, the new designs do not leak respondents' privacy. Empirical results show that the proposed techniques perform satisfactorily in the sense that it yields accurate parameter estimate and confidence interval.

Measuring resilience after spinal cord injury: Development, validation and psychometric characteristics of the SCI-QOL Resilience item bank and short form.

Science.gov (United States)

Victorson, David; Tulsky, David S; Kisala, Pamela A; Kalpakjian, Claire Z; Weiland, Brian; Choi, Seung W

2015-05-01

To describe the development and psychometric properties of the Spinal Cord Injury--Quality of Life (SCI-QOL) Resilience item bank and short form. Using a mixed-methods design, we developed and tested a resilience item bank through the use of focus groups with individuals with SCI and clinicians with expertise in SCI, cognitive interviews, and item-response theory based analytic approaches, including tests of model fit and differential item functioning (DIF). We tested a 32-item pool at several medical institutions across the United States, including the University of Michigan, Kessler Foundation, the Rehabilitation Institute of Chicago, the University of Washington, Craig Hospital and the James J. Peters/Bronx Department of Veterans Affairs medical center. A total of 717 individuals with SCI completed the Resilience items. A unidimensional model was observed (CFI=0.968; RMSEA=0.074) and measurement precision was good (theta range between -3.1 and 0.9). Ten items were flagged for DIF, however, after examination of effect sizes we found this to be negligible with little practical impact on score estimates. The final calibrated item bank resulted in 21 retained items. This study indicates that the SCI-QOL Resilience item bank represents a psychometrically robust measurement tool. Short form items are also suggested and computer adaptive tests are available.

Two non-parametric methods for derivation of constraints from radiotherapy dose–histogram data

International Nuclear Information System (INIS)

Ebert, M A; Kennedy, A; Joseph, D J; Gulliford, S L; Buettner, F; Foo, K; Haworth, A; Denham, J W

2014-01-01

Dose constraints based on histograms provide a convenient and widely-used method for informing and guiding radiotherapy treatment planning. Methods of derivation of such constraints are often poorly described. Two non-parametric methods for derivation of constraints are described and investigated in the context of determination of dose-specific cut-points—values of the free parameter (e.g., percentage volume of the irradiated organ) which best reflect resulting changes in complication incidence. A method based on receiver operating characteristic (ROC) analysis and one based on a maximally-selected standardized rank sum are described and compared using rectal toxicity data from a prostate radiotherapy trial. Multiple test corrections are applied using a free step-down resampling algorithm, which accounts for the large number of tests undertaken to search for optimal cut-points and the inherent correlation between dose–histogram points. Both methods provide consistent significant cut-point values, with the rank sum method displaying some sensitivity to the underlying data. The ROC method is simple to implement and can utilize a complication atlas, though an advantage of the rank sum method is the ability to incorporate all complication grades without the need for grade dichotomization. (note)

Differential item functioning analysis of the Vanderbilt Expertise Test for cars.

Science.gov (United States)

Lee, Woo-Yeol; Cho, Sun-Joo; McGugin, Rankin W; Van Gulick, Ana Beth; Gauthier, Isabel

2015-01-01

The Vanderbilt Expertise Test for cars (VETcar) is a test of visual learning for contemporary car models. We used item response theory to assess the VETcar and in particular used differential item functioning (DIF) analysis to ask if the test functions the same way in laboratory versus online settings and for different groups based on age and gender. An exploratory factor analysis found evidence of multidimensionality in the VETcar, although a single dimension was deemed sufficient to capture the recognition ability measured by the test. We selected a unidimensional three-parameter logistic item response model to examine item characteristics and subject abilities. The VETcar had satisfactory internal consistency. A substantial number of items showed DIF at a medium effect size for test setting and for age group, whereas gender DIF was negligible. Because online subjects were on average older than those tested in the lab, we focused on the age groups to conduct a multigroup item response theory analysis. This revealed that most items on the test favored the younger group. DIF could be more the rule than the exception when measuring performance with familiar object categories, therefore posing a challenge for the measurement of either domain-general visual abilities or category-specific knowledge.

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

DEFF Research Database (Denmark)

Tan, Qihua; Thomassen, Mads; Burton, Mark

2017-01-01

the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....

MEASURING DARK MATTER PROFILES NON-PARAMETRICALLY IN DWARF SPHEROIDALS: AN APPLICATION TO DRACO

International Nuclear Information System (INIS)

Jardel, John R.; Gebhardt, Karl; Fabricius, Maximilian H.; Williams, Michael J.; Drory, Niv

2013-01-01

We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 ≤ r ≤ 700 pc. The profile for r ≥ 20 pc is well fit by a power law with slope α = –1.0 ± 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.

Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

KAUST Repository

Ryu, Duchwan

2010-09-28

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.

Panel data nonparametric estimation of production risk and risk preferences

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

approaches for obtaining firm-specific measures of risk attitudes. We found that Polish dairy farmers are risk averse regarding production risk and price uncertainty. According to our results, Polish dairy farmers perceive the production risk as being more significant than the risk related to output price......We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...

Evaluation of item candidates for a diabetic retinopathy quality of life item bank.

Science.gov (United States)

Fenwick, Eva K; Pesudovs, Konrad; Khadka, Jyoti; Rees, Gwyn; Wong, Tien Y; Lamoureux, Ecosse L

2013-09-01

We are developing an item bank assessing the impact of diabetic retinopathy (DR) on quality of life (QoL) using a rigorous multi-staged process combining qualitative and quantitative methods. We describe here the first two qualitative phases: content development and item evaluation. After a comprehensive literature review, items were generated from four sources: (1) 34 previously validated patient-reported outcome measures; (2) five published qualitative articles; (3) eight focus groups and 18 semi-structured interviews with 57 DR patients; and (4) seven semi-structured interviews with diabetes or ophthalmic experts. Items were then evaluated during 3 stages, namely binning (grouping) and winnowing (reduction) based on key criteria and panel consensus; development of item stems and response options; and pre-testing of items via cognitive interviews with patients. The content development phase yielded 1,165 unique items across 7 QoL domains. After 3 sessions of binning and winnowing, items were reduced to a minimally representative set (n = 312) across 9 domains of QoL: visual symptoms; ocular surface symptoms; activity limitation; mobility; emotional; health concerns; social; convenience; and economic. After 8 cognitive interviews, 42 items were amended resulting in a final set of 314 items. We have employed a systematic approach to develop items for a DR-specific QoL item bank. The psychometric properties of the nine QoL subscales will be assessed using Rasch analysis. The resulting validated item bank will allow clinicians and researchers to better understand the QoL impact of DR and DR therapies from the patient's perspective.

Nonparametric Estimation of Distributions in Random Effects Models

KAUST Repository

Hart, Jeffrey D.

2011-01-01

We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.

Multi-Directional Non-Parametric Analysis of Agricultural Efficiency

DEFF Research Database (Denmark)

Balezentis, Tomas

This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... to the Multi-Directional Efficiency Analysis approach when the proposed models were employed to analyse empirical data of Lithuanian family farm performance, we saw substantial differences in efficiencies associated with different inputs. In particular, assets appeared to be the least efficiently used input...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...

The role of attention in item-item binding in visual working memory.

Science.gov (United States)

Peterson, Dwight J; Naveh-Benjamin, Moshe

2017-09-01

An important yet unresolved question regarding visual working memory (VWM) relates to whether or not binding processes within VWM require additional attentional resources compared with processing solely the individual components comprising these bindings. Previous findings indicate that binding of surface features (e.g., colored shapes) within VWM is not demanding of resources beyond what is required for single features. However, it is possible that other types of binding, such as the binding of complex, distinct items (e.g., faces and scenes), in VWM may require additional resources. In 3 experiments, we examined VWM item-item binding performance under no load, articulatory suppression, and backward counting using a modified change detection task. Binding performance declined to a greater extent than single-item performance under higher compared with lower levels of concurrent load. The findings from each of these experiments indicate that processing item-item bindings within VWM requires a greater amount of attentional resources compared with single items. These findings also highlight an important distinction between the role of attention in item-item binding within VWM and previous studies of long-term memory (LTM) where declines in single-item and binding test performance are similar under divided attention. The current findings provide novel evidence that the specific type of binding is an important determining factor regarding whether or not VWM binding processes require attention. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A multitemporal and non-parametric approach for assessing the impacts of drought on vegetation greenness

DEFF Research Database (Denmark)

Carrao, Hugo; Sepulcre, Guadalupe; Horion, Stéphanie Marie Anne F

2013-01-01

This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric...... and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC...... for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009...

Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis.

Science.gov (United States)

Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A

2015-05-01

Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.

17 CFR 229.102 - (Item 102) Description of property.

Science.gov (United States)

2010-04-01

... segment(s), as reported in the financial statements, that use the properties described. If any such... by the registrant. Detailed descriptions of the physical characteristics of individual properties or... qualitative factors. See Instruction 1 to Item 101 of Regulation S-K (§ 229.101). 3. In the case of an...

Applying Item Response Theory methods to design a learning progression-based science assessment

Science.gov (United States)

Chen, Jing

Learning progressions are used to describe how students' understanding of a topic progresses over time and to classify the progress of students into steps or levels. This study applies Item Response Theory (IRT) based methods to investigate how to design learning progression-based science assessments. The research questions of this study are: (1) how to use items in different formats to classify students into levels on the learning progression, (2) how to design a test to give good information about students' progress through the learning progression of a particular construct and (3) what characteristics of test items support their use for assessing students' levels. Data used for this study were collected from 1500 elementary and secondary school students during 2009--2010. The written assessment was developed in several formats such as the Constructed Response (CR) items, Ordered Multiple Choice (OMC) and Multiple True or False (MTF) items. The followings are the main findings from this study. The OMC, MTF and CR items might measure different components of the construct. A single construct explained most of the variance in students' performances. However, additional dimensions in terms of item format can explain certain amount of the variance in student performance. So additional dimensions need to be considered when we want to capture the differences in students' performances on different types of items targeting the understanding of the same underlying progression. Items in each item format need to be improved in certain ways to classify students more accurately into the learning progression levels. This study establishes some general steps that can be followed to design other learning progression-based tests as well. For example, first, the boundaries between levels on the IRT scale can be defined by using the means of the item thresholds across a set of good items. Second, items in multiple formats can be selected to achieve the information criterion at all

Work ability as prognostic risk marker of disability pension: single-item work ability score versus multi-item work ability index.

Science.gov (United States)

Roelen, Corné A M; van Rhenen, Willem; Groothoff, Johan W; van der Klink, Jac J L; Twisk, Jos W R; Heymans, Martijn W

2014-07-01

Work ability predicts future disability pension (DP). A single-item work ability score (WAS) is emerging as a measure for work ability. This study compared single-item WAS with the multi-item work ability index (WAI) in its ability to identify workers at risk of DP. This prospective cohort study comprised 11 537 male construction workers, who completed the WAI at baseline and reported DP after a mean 2.3 years of follow-up. WAS and WAI were calibrated for DP risk predictions with the Hosmer-Lemeshow (H-L) test and their ability to discriminate between high- and low-risk construction workers was investigated with the area under the receiver operating characteristic curve (AUC). At follow-up, 336 (3%) construction workers reported DP. Both WAS [odds ratio (OR) 0.72, 95% confidence interval (95% CI) 0.66-0.78] and WAI (OR 0.57, 95% CI 0.52-0.63) scores were associated with DP at follow-up. The WAS showed miscalibration (H-L model χ (�)=10.60; df=3; P=0.01) and poorly discriminated between high- and low-risk construction workers (AUC 0.67, 95% CI 0.64-0.70). In contrast, calibration (H-L model χ �=8.20; df=8; P=0.41) and discrimination (AUC 0.78, 95% CI 0.75-0.80) were both adequate for the WAI. Although associated with the risk of future DP, the single-item WAS poorly identified male construction workers at risk of DP. We recommend using the multi-item WAI to screen for risk of DP in occupational health practice.

Effects of Misbehaving Common Items on Aggregate Scores and an Application of the Mantel-Haenszel Statistic in Test Equating. CSE Report 688

Science.gov (United States)

Michaelides, Michalis P.

2006-01-01

Consistent behavior is a desirable characteristic that common items are expected to have when administered to different groups. Findings from the literature have established that items do not always behave in consistent ways; item indices and IRT item parameter estimates of the same items differ when obtained from different administrations.…

Measuring psychological trauma after spinal cord injury: Development and psychometric characteristics of the SCI-QOL Psychological Trauma item bank and short form.

Science.gov (United States)

Kisala, Pamela A; Victorson, David; Pace, Natalie; Heinemann, Allen W; Choi, Seung W; Tulsky, David S

2015-05-01

To describe the development and psychometric properties of the SCI-QOL Psychological Trauma item bank and short form. Using a mixed-methods design, we developed and tested a Psychological Trauma item bank with patient and provider focus groups, cognitive interviews, and item response theory based analytic approaches, including tests of model fit, differential item functioning (DIF) and precision. We tested a 31-item pool at several medical institutions across the United States, including the University of Michigan, Kessler Foundation, Rehabilitation Institute of Chicago, the University of Washington, Craig Hospital and the James J. Peters/Bronx Veterans Administration hospital. A total of 716 individuals with SCI completed the trauma items The 31 items fit a unidimensional model (CFI=0.952; RMSEA=0.061) and demonstrated good precision (theta range between 0.6 and 2.5). Nine items demonstrated negligible DIF with little impact on score estimates. The final calibrated item bank contains 19 items The SCI-QOL Psychological Trauma item bank is a psychometrically robust measurement tool from which a short form and a computer adaptive test (CAT) version are available.

Debt and growth: A non-parametric approach

Science.gov (United States)

Brida, Juan Gabriel; Gómez, David Matesanz; Seijas, Maria Nela

2017-11-01

In this study, we explore the dynamic relationship between public debt and economic growth by using a non-parametric approach based on data symbolization and clustering methods. The study uses annual data of general government consolidated gross debt-to-GDP ratio and gross domestic product for sixteen countries between 1977 and 2015. Using symbolic sequences, we introduce a notion of distance between the dynamical paths of different countries. Then, a Minimal Spanning Tree and a Hierarchical Tree are constructed from time series to help detecting the existence of groups of countries sharing similar economic performance. The main finding of the study appears for the period 2008-2016 when several countries surpassed the 90% debt-to-GDP threshold. During this period, three groups (clubs) of countries are obtained: high, mid and low indebted countries, suggesting that the employed debt-to-GDP threshold drives economic dynamics for the selected countries.

The Spanish version of the Self-Determination Inventory Student Report: application of item response theory to self-determination measurement.

Science.gov (United States)

Mumbardó-Adam, C; Guàrdia-Olmos, J; Giné, C; Raley, S K; Shogren, K A

2018-04-01

A new measure of self-determination, the Self-Determination Inventory: Student Report (Spanish version), has recently been adapted and empirically validated in Spanish language. As it is the first instrument intended to measure self-determination in youth with and without disabilities, there is a need to further explore and strengthen its psychometric analysis based on item response patterns. Through item response theory approach, this study examined item observed distributions across the essential characteristics of self-determination. The results demonstrated satisfactory to excellent item functioning patterns across characteristics, particularly within agentic action domains. Increased variability across items was also found within action-control beliefs dimensions, specifically within the self-realisation subdomain. These findings further support the instrument's psychometric properties and outline future research directions. © 2017 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

A Study on the Systematization of Classification Process for NSG Trigger List Items

Energy Technology Data Exchange (ETDEWEB)

Yang, Seunghyo; Tae, Jaewoong; Shin, Donghoon [Korea Institute of Nuclear Nonproliferation and Control/Nuclear Export Control Div., Daejeon (Korea, Republic of)

2013-05-15

In 1978, Nuclear Suppliers Group (NSG) was established to prevent nuclear items from being used for nuclear weapons. NSG drew up the NSG Guidelines (INFCIRC/254) that regulates export control items(so-called NSG trigger list items) and procedures. NSG recommends its member countries to reflect these guidelines on their export control systems and fulfill their obligations. Korea has carried out export controls on nuclear items by reflecting NSG Guidelines on Notice on Trade of Strategic Item of Foreign Trade Act since joining NSG in 1995. Nuclear export control starts with Classification that determines whether export items can be used for strategic items (goods and technologies that can be exclusively used for the manufacture, development and use of WMD). The standard of Classification is based on the NSG Guidelines. However, due to the qualitative characteristics of the Guidelines, there take place lots of difficulties in the Classification. Thus this study aims to suggest the systematic Classification process. Recently, the number of Classification requests is rapidly increasing due to the UAE commercial nuclear power plants and the Jordan reactors export. It is required to provide a more systematic Classification standard and process in order to provide an efficient and consistent Classification. Thus, this study analyzed limitations of EDP which causes difficulties in the process of classification due to its qualitative characteristics. Besides, it established systematic Classification process by quantitatively analyzing EDP. Consequently, it is expected that the results of this study will be used for as actual Classification. It still remains to establish a criterion of detailed information, which is one of the most important in the Classification for technology. Therefore, a further study will be conducted to establish a criterion of detailed information by analyzing Classification cases through the text mining techniques.

A Study on the Systematization of Classification Process for NSG Trigger List Items

International Nuclear Information System (INIS)

Yang, Seunghyo; Tae, Jaewoong; Shin, Donghoon

2013-01-01

In 1978, Nuclear Suppliers Group (NSG) was established to prevent nuclear items from being used for nuclear weapons. NSG drew up the NSG Guidelines (INFCIRC/254) that regulates export control items(so-called NSG trigger list items) and procedures. NSG recommends its member countries to reflect these guidelines on their export control systems and fulfill their obligations. Korea has carried out export controls on nuclear items by reflecting NSG Guidelines on Notice on Trade of Strategic Item of Foreign Trade Act since joining NSG in 1995. Nuclear export control starts with Classification that determines whether export items can be used for strategic items (goods and technologies that can be exclusively used for the manufacture, development and use of WMD). The standard of Classification is based on the NSG Guidelines. However, due to the qualitative characteristics of the Guidelines, there take place lots of difficulties in the Classification. Thus this study aims to suggest the systematic Classification process. Recently, the number of Classification requests is rapidly increasing due to the UAE commercial nuclear power plants and the Jordan reactors export. It is required to provide a more systematic Classification standard and process in order to provide an efficient and consistent Classification. Thus, this study analyzed limitations of EDP which causes difficulties in the process of classification due to its qualitative characteristics. Besides, it established systematic Classification process by quantitatively analyzing EDP. Consequently, it is expected that the results of this study will be used for as actual Classification. It still remains to establish a criterion of detailed information, which is one of the most important in the Classification for technology. Therefore, a further study will be conducted to establish a criterion of detailed information by analyzing Classification cases through the text mining techniques

Robust non-parametric one-sample tests for the analysis of recurrent events.

Science.gov (United States)

Rebora, Paola; Galimberti, Stefania; Valsecchi, Maria Grazia

2010-12-30

One-sample non-parametric tests are proposed here for inference on recurring events. The focus is on the marginal mean function of events and the basis for inference is the standardized distance between the observed and the expected number of events under a specified reference rate. Different weights are considered in order to account for various types of alternative hypotheses on the mean function of the recurrent events process. A robust version and a stratified version of the test are also proposed. The performance of these tests was investigated through simulation studies under various underlying event generation processes, such as homogeneous and nonhomogeneous Poisson processes, autoregressive and renewal processes, with and without frailty effects. The robust versions of the test have been shown to be suitable in a wide variety of event generating processes. The motivating context is a study on gene therapy in a very rare immunodeficiency in children, where a major end-point is the recurrence of severe infections. Robust non-parametric one-sample tests for recurrent events can be useful to assess efficacy and especially safety in non-randomized studies or in epidemiological studies for comparison with a standard population. Copyright © 2010 John Wiley & Sons, Ltd.

Effects of Initial Values and Convergence Criterion in the Two-Parameter Logistic Model When Estimating the Latent Distribution in BILOG-MG 3.

Directory of Open Access Journals (Sweden)

Ingo W Nader

Full Text Available Parameters of the two-parameter logistic model are generally estimated via the expectation-maximization algorithm, which improves initial values for all parameters iteratively until convergence is reached. Effects of initial values are rarely discussed in item response theory (IRT, but initial values were recently found to affect item parameters when estimating the latent distribution with full non-parametric maximum likelihood. However, this method is rarely used in practice. Hence, the present study investigated effects of initial values on item parameter bias and on recovery of item characteristic curves in BILOG-MG 3, a widely used IRT software package. Results showed notable effects of initial values on item parameters. For tighter convergence criteria, effects of initial values decreased, but item parameter bias increased, and the recovery of the latent distribution worsened. For practical application, it is advised to use the BILOG default convergence criterion with appropriate initial values when estimating the latent distribution from data.

Exploring differential item functioning (DIF) with the Rasch model: A comparison of gender differences on eighth-grade science items in the United States and Spain

Science.gov (United States)

Calvert, Tasha

Despite the attention that has been given to gender and science, boys continue to outperform girls in science achievement, particularly by the end of secondary school. Because it is unclear whether gender differences have narrowed over time (Leder, 1992; Willingham & Cole, 1997), it is important to continue a line of inquiry into the nature of gender differences, specifically at the international level. The purpose of this study was to investigate gender differences in science achievement across two countries: United States and Spain. A secondary purpose was to demonstrate an alternative method for exploring gender differences based on the many-faceted Rasch model (1980). A secondary analysis of the data from the Third International Mathematics and Science Study (TIMSS) was used to examine the relationship between gender DIF (differential item functioning) and item characteristics (item type, content, and performance expectation) across both countries. Nationally representative samples of eighth grade students in the United States and Spain who participated in TIMSS were analyzed to answer the research questions in this study. In both countries, girls showed an advantage over boys on life science items and most extended response items, whereas boys, by and large, had an advantage on earth science, physics, and chemistry items. However, even within areas that favored boys, such as physics, there were items that were differentially easier for girls. In general, patterns in gender differences were similar across both countries although there were a few differences between the countries on individual items. It was concluded that simply looking at mean differences does not provide an adequate understanding of the nature of gender differences in science achievement.

Nonparametric estimation in an "illness-death" model when all transition times are interval censored

DEFF Research Database (Denmark)

Frydman, Halina; Gerds, Thomas; Grøn, Randi

2013-01-01

We develop nonparametric maximum likelihood estimation for the parameters of an irreversible Markov chain on states {0,1,2} from the observations with interval censored times of 0 → 1, 0 → 2 and 1 → 2 transitions. The distinguishing aspect of the data is that, in addition to all transition times ...

Non-Parametric Bayesian Updating within the Assessment of Reliability for Offshore Wind Turbine Support Structures

DEFF Research Database (Denmark)

Ramirez, José Rangel; Sørensen, John Dalsgaard

2011-01-01

This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian u...

Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison

NARCIS (Netherlands)

Verrelst, Jochem; Rivera, Juan Pablo; Veroustraete, Frank; Muñoz-Marí, Jordi; Clevers, J.G.P.W.; Camps-Valls, Gustau; Moreno, José

2015-01-01

Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC),

Differential item functioning magnitude and impact measures from item response theory models.

Science.gov (United States)

Kleinman, Marjorie; Teresi, Jeanne A

2016-01-01

Measures of magnitude and impact of differential item functioning (DIF) at the item and scale level, respectively are presented and reviewed in this paper. Most measures are based on item response theory models. Magnitude refers to item level effect sizes, whereas impact refers to differences between groups at the scale score level. Reviewed are magnitude measures based on group differences in the expected item scores and impact measures based on differences in the expected scale scores. The similarities among these indices are demonstrated. Various software packages are described that provide magnitude and impact measures, and new software presented that computes all of the available statistics conveniently in one program with explanations of their relationships to one another.

ITEM LEVEL DIAGNOSTICS AND MODEL - DATA FIT IN ITEM ...

African Journals Online (AJOL)

Global Journal

Item response theory (IRT) is a framework for modeling and analyzing item response ... data. Though, there is an argument that the evaluation of fit in IRT modeling has been ... National Council on Measurement in Education ... model data fit should be based on three types of ... prediction should be assessed through the.

Measuring anxiety after spinal cord injury: Development and psychometric characteristics of the SCI-QOL Anxiety item bank and linkage with GAD-7.

Science.gov (United States)

Kisala, Pamela A; Tulsky, David S; Kalpakjian, Claire Z; Heinemann, Allen W; Pohlig, Ryan T; Carle, Adam; Choi, Seung W

2015-05-01

To develop a calibrated item bank and computer adaptive test to assess anxiety symptoms in individuals with spinal cord injury (SCI), transform scores to the Patient Reported Outcomes Measurement Information System (PROMIS) metric, and create a statistical linkage with the Generalized Anxiety Disorder (GAD)-7, a widely used anxiety measure. Grounded-theory based qualitative item development methods; large-scale item calibration field testing; confirmatory factor analysis; graded response model item response theory analyses; statistical linking techniques to transform scores to a PROMIS metric; and linkage with the GAD-7. Setting Five SCI Model System centers and one Department of Veterans Affairs medical center in the United States. Participants Adults with traumatic SCI. Spinal Cord Injury-Quality of Life (SCI-QOL) Anxiety Item Bank Seven hundred sixteen individuals with traumatic SCI completed 38 items assessing anxiety, 17 of which were PROMIS items. After 13 items (including 2 PROMIS items) were removed, factor analyses confirmed unidimensionality. Item response theory analyses were used to estimate slopes and thresholds for the final 25 items (15 from PROMIS). The observed Pearson correlation between the SCI-QOL Anxiety and GAD-7 scores was 0.67. The SCI-QOL Anxiety item bank demonstrates excellent psychometric properties and is available as a computer adaptive test or short form for research and clinical applications. SCI-QOL Anxiety scores have been transformed to the PROMIS metric and we provide a method to link SCI-QOL Anxiety scores with those of the GAD-7.

Nonparametric predictive pairwise comparison with competing risks

International Nuclear Information System (INIS)

Coolen-Maturi, Tahani

2014-01-01

In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed

Nonparametric bootstrap analysis with applications to demographic effects in demand functions.

Science.gov (United States)

Gozalo, P L

1997-12-01

"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt

Measuring depression after spinal cord injury: Development and psychometric characteristics of the SCI-QOL Depression item bank and linkage with PHQ-9.

Science.gov (United States)

Tulsky, David S; Kisala, Pamela A; Kalpakjian, Claire Z; Bombardier, Charles H; Pohlig, Ryan T; Heinemann, Allen W; Carle, Adam; Choi, Seung W

2015-05-01

To develop a calibrated spinal cord injury-quality of life (SCI-QOL) item bank, computer adaptive test (CAT), and short form to assess depressive symptoms experienced by individuals with SCI, transform scores to the Patient Reported Outcomes Measurement Information System (PROMIS) metric, and create a crosswalk to the Patient Health Questionnaire (PHQ)-9. We used grounded-theory based qualitative item development methods, large-scale item calibration field testing, confirmatory factor analysis, item response theory (IRT) analyses, and statistical linking techniques to transform scores to a PROMIS metric and to provide a crosswalk with the PHQ-9. Five SCI Model System centers and one Department of Veterans Affairs medical center in the United States. Adults with traumatic SCI. Spinal Cord Injury--Quality of Life (SCI-QOL) Depression Item Bank Individuals with SCI were involved in all phases of SCI-QOL development. A sample of 716 individuals with traumatic SCI completed 35 items assessing depression, 18 of which were PROMIS items. After removing 7 non-PROMIS items, factor analyses confirmed a unidimensional pool of items. We used a graded response IRT model to estimate slopes and thresholds for the 28 retained items. The SCI-QOL Depression measure correlated 0.76 with the PHQ-9. The SCI-QOL Depression item bank provides a reliable and sensitive measure of depressive symptoms with scores reported in terms of general population norms. We provide a crosswalk to the PHQ-9 to facilitate comparisons between measures. The item bank may be administered as a CAT or as a short form and is suitable for research and clinical applications.

NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance

Directory of Open Access Journals (Sweden)

Richard C. Zink

2012-07-01

Full Text Available Analysis of covariance serves two important purposes in a randomized clinical trial. First, there is a reduction of variance for the treatment effect which provides more powerful statistical tests and more precise confidence intervals. Second, it provides estimates of the treatment effect which are adjusted for random imbalances of covariates between the treatment groups. The nonparametric analysis of covariance method of Koch, Tangen, Jung, and Amara (1998 defines a very general methodology using weighted least-squares to generate covariate-adjusted treatment effects with minimal assumptions. This methodology is general in its applicability to a variety of outcomes, whether continuous, binary, ordinal, incidence density or time-to-event. Further, its use has been illustrated in many clinical trial settings, such as multi-center, dose-response and non-inferiority trials.NParCov3 is a SAS/IML macro written to conduct the nonparametric randomization-based covariance analyses of Koch et al. (1998. The software can analyze a variety of outcomes and can account for stratification. Data from multiple clinical trials will be used for illustration.

Hadron energy reconstruction for the ATLAS calorimetry in the framework of the nonparametrical method

CERN Document Server

Akhmadaliev, S Z; Ambrosini, G; Amorim, A; Anderson, K; Andrieux, M L; Aubert, Bernard; Augé, E; Badaud, F; Baisin, L; Barreiro, F; Battistoni, G; Bazan, A; Bazizi, K; Belymam, A; Benchekroun, D; Berglund, S R; Berset, J C; Blanchot, G; Bogush, A A; Bohm, C; Boldea, V; Bonivento, W; Bosman, M; Bouhemaid, N; Breton, D; Brette, P; Bromberg, C; Budagov, Yu A; Burdin, S V; Calôba, L P; Camarena, F; Camin, D V; Canton, B; Caprini, M; Carvalho, J; Casado, M P; Castillo, M V; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Chadelas, R; Chalifour, M; Chekhtman, A; Chevalley, J L; Chirikov-Zorin, I E; Chlachidze, G; Citterio, M; Cleland, W E; Clément, C; Cobal, M; Cogswell, F; Colas, Jacques; Collot, J; Cologna, S; Constantinescu, S; Costa, G; Costanzo, D; Crouau, M; Daudon, F; David, J; David, M; Davidek, T; Dawson, J; De, K; de La Taille, C; Del Peso, J; Del Prete, T; de Saintignon, P; Di Girolamo, B; Dinkespiler, B; Dita, S; Dodd, J; Dolejsi, J; Dolezal, Z; Downing, R; Dugne, J J; Dzahini, D; Efthymiopoulos, I; Errede, D; Errede, S; Evans, H; Eynard, G; Fassi, F; Fassnacht, P; Ferrari, A; Ferrer, A; Flaminio, Vincenzo; Fournier, D; Fumagalli, G; Gallas, E; Gaspar, M; Giakoumopoulou, V; Gianotti, F; Gildemeister, O; Giokaris, N; Glagolev, V; Glebov, V Yu; Gomes, A; González, V; González de la Hoz, S; Grabskii, V; Graugès-Pous, E; Grenier, P; Hakopian, H H; Haney, M; Hébrard, C; Henriques, A; Hervás, L; Higón, E; Holmgren, Sven Olof; Hostachy, J Y; Hoummada, A; Huston, J; Imbault, D; Ivanyushenkov, Yu M; Jézéquel, S; Johansson, E K; Jon-And, K; Jones, R; Juste, A; Kakurin, S; Karyukhin, A N; Khokhlov, Yu A; Khubua, J I; Klioukhine, V I; Kolachev, G M; Kopikov, S V; Kostrikov, M E; Kozlov, V; Krivkova, P; Kukhtin, V V; Kulagin, M; Kulchitskii, Yu A; Kuzmin, M V; Labarga, L; Laborie, G; Lacour, D; Laforge, B; Lami, S; Lapin, V; Le Dortz, O; Lefebvre, M; Le Flour, T; Leitner, R; Leltchouk, M; Li, J; Liablin, M V; Linossier, O; Lissauer, D; Lobkowicz, F; Lokajícek, M; Lomakin, Yu F; López-Amengual, J M; Lund-Jensen, B; Maio, A; Makowiecki, D S; Malyukov, S N; Mandelli, L; Mansoulié, B; Mapelli, Livio P; Marin, C P; Marrocchesi, P S; Marroquim, F; Martin, P; Maslennikov, A L; Massol, N; Mataix, L; Mazzanti, M; Mazzoni, E; Merritt, F S; Michel, B; Miller, R; Minashvili, I A; Miralles, L; Mnatzakanian, E A; Monnier, E; Montarou, G; Mornacchi, Giuseppe; Moynot, M; Muanza, G S; Nayman, P; Némécek, S; Nessi, Marzio; Nicoleau, S; Niculescu, M; Noppe, J M; Onofre, A; Pallin, D; Pantea, D; Paoletti, R; Park, I C; Parrour, G; Parsons, J; Pereira, A; Perini, L; Perlas, J A; Perrodo, P; Pilcher, J E; Pinhão, J; Plothow-Besch, Hartmute; Poggioli, Luc; Poirot, S; Price, L; Protopopov, Yu; Proudfoot, J; Puzo, P; Radeka, V; Rahm, David Charles; Reinmuth, G; Renzoni, G; Rescia, S; Resconi, S; Richards, R; Richer, J P; Roda, C; Rodier, S; Roldán, J; Romance, J B; Romanov, V; Romero, P; Rossel, F; Rusakovitch, N A; Sala, P; Sanchis, E; Sanders, H; Santoni, C; Santos, J; Sauvage, D; Sauvage, G; Sawyer, L; Says, L P; Schaffer, A C; Schwemling, P; Schwindling, J; Seguin-Moreau, N; Seidl, W; Seixas, J M; Selldén, B; Seman, M; Semenov, A; Serin, L; Shaldaev, E; Shochet, M J; Sidorov, V; Silva, J; Simaitis, V J; Simion, S; Sissakian, A N; Snopkov, R; Söderqvist, J; Solodkov, A A; Soloviev, A; Soloviev, I V; Sonderegger, P; Soustruznik, K; Spanó, F; Spiwoks, R; Stanek, R; Starchenko, E A; Stavina, P; Stephens, R; Suk, M; Surkov, A; Sykora, I; Takai, H; Tang, F; Tardell, S; Tartarelli, F; Tas, P; Teiger, J; Thaler, J; Thion, J; Tikhonov, Yu A; Tisserant, S; Tokar, S; Topilin, N D; Trka, Z; Turcotte, M; Valkár, S; Varanda, M J; Vartapetian, A H; Vazeille, F; Vichou, I; Vinogradov, V; Vorozhtsov, S B; Vuillemin, V; White, A; Wielers, M; Wingerter-Seez, I; Wolters, H; Yamdagni, N; Yosef, C; Zaitsev, A; Zitoun, R; Zolnierowski, Y

2002-01-01

This paper discusses hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter (consisting of a lead-liquid argon electromagnetic part and an iron-scintillator hadronic part) in the framework of the nonparametrical method. The nonparametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to an easy use in a first level trigger. The reconstructed mean values of the hadron energies are within +or-1% of the true values and the fractional energy resolution is [(58+or-3)%/ square root E+(2.5+or-0.3)%](+)(1.7+or-0.2)/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74+or-0.04 and agrees with the prediction that e/h >1.66 for this electromagnetic calorimeter. Results of a study of the longitudinal hadronic shower development are also presented. The data have been taken in the H8 beam...

Development of six PROMIS pediatrics proxy-report item banks.

Science.gov (United States)

Irwin, Debra E; Gross, Heather E; Stucky, Brian D; Thissen, David; DeWitt, Esi Morgan; Lai, Jin Shei; Amtmann, Dagmar; Khastou, Leyla; Varni, James W; DeWalt, Darren A

2012-02-22

Pediatric self-report should be considered the standard for measuring patient reported outcomes (PRO) among children. However, circumstances exist when the child is too young, cognitively impaired, or too ill to complete a PRO instrument and a proxy-report is needed. This paper describes the development process including the proxy cognitive interviews and large-field-test survey methods and sample characteristics employed to produce item parameters for the Patient Reported Outcomes Measurement Information System (PROMIS) pediatric proxy-report item banks. The PROMIS pediatric self-report items were converted into proxy-report items before undergoing cognitive interviews. These items covered six domains (physical function, emotional distress, social peer relationships, fatigue, pain interference, and asthma impact). Caregivers (n = 25) of children ages of 5 and 17 years provided qualitative feedback on proxy-report items to assess any major issues with these items. From May 2008 to March 2009, the large-scale survey enrolled children ages 8-17 years to complete the self-report version and caregivers to complete the proxy-report version of the survey (n = 1548 dyads). Caregivers of children ages 5 to 7 years completed the proxy report survey (n = 432). In addition, caregivers completed other proxy instruments, PedsQL™ 4.0 Generic Core Scales Parent Proxy-Report version, PedsQL™ Asthma Module Parent Proxy-Report version, and KIDSCREEN Parent-Proxy-52. Item content was well understood by proxies and did not require item revisions but some proxies clearly noted that determining an answer on behalf of their child was difficult for some items. Dyads and caregivers of children ages 5-17 years old were enrolled in the large-scale testing. The majority were female (85%), married (70%), Caucasian (64%) and had at least a high school education (94%). Approximately 50% had children with a chronic health condition, primarily asthma, which was diagnosed or treated within 6

Parametric and nonparametric Granger causality testing: Linkages between international stock markets

Science.gov (United States)

De Gooijer, Jan G.; Sivarajasingham, Selliah

2008-04-01

This study investigates long-term linear and nonlinear causal linkages among eleven stock markets, six industrialized markets and five emerging markets of South-East Asia. We cover the period 1987-2006, taking into account the on-set of the Asian financial crisis of 1997. We first apply a test for the presence of general nonlinearity in vector time series. Substantial differences exist between the pre- and post-crisis period in terms of the total number of significant nonlinear relationships. We then examine both periods, using a new nonparametric test for Granger noncausality and the conventional parametric Granger noncausality test. One major finding is that the Asian stock markets have become more internationally integrated after the Asian financial crisis. An exception is the Sri Lankan market with almost no significant long-term linear and nonlinear causal linkages with other markets. To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VAR filtered residuals and VAR filtered squared residuals for the post-crisis sample. We find quite a few remaining significant bi- and uni-directional causal nonlinear relationships in these series. Finally, after filtering the VAR-residuals with GARCH-BEKK models, we show that the nonparametric test statistics are substantially smaller in both magnitude and statistical significance than those before filtering. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects.

Application of the Commercial Grade Item (CGI) Dedication Process for Procurement of Nuclear Safety Related Items at Nuclear Power Plant Krsko (NEK)

International Nuclear Information System (INIS)

Heruc, Z.; Pozar, J.

1998-01-01

CGI procurement is a process whereby parts are brought without imposing Appendix B Quality Assurance requirements on the supplier, and than dedicated for use in safety-related applications. The dedication process involves 1) based upon required safety function, an engineering evaluation to identify critical characteristic of the item and specification of acceptance criteria; and 2) quality control activities to ensure the item(s) supplied meets the acceptance criteria specified. CGI Dedication supports the supply of certified components/parts for the plant operation in an environment where the number of nuclear qualified suppliers diminishes. It requires a more active role of the plant personnel, therefore presenting an additional burden on human resources, but at the same time increases the technical KNOW-HOW and improves the confidence of test and inspection data presented in the certificates. Very often it is also cost beneficial. This paper is a continuation to last year presentation of the introduction of this method into NEK's procurement process and presents the current approach and some practical examples. (author)

Evaluating HIV Knowledge Questionnaires Among Men Who Have Sex with Men: A Multi-Study Item Response Theory Analysis.

Science.gov (United States)

Janulis, Patrick; Newcomb, Michael E; Sullivan, Patrick; Mustanski, Brian

2018-01-01

Knowledge about the transmission, prevention, and treatment of HIV remains a critical element in psychosocial models of HIV risk behavior and is commonly used as an outcome in HIV prevention interventions. However, most HIV knowledge questions have not undergone rigorous psychometric testing such as using item response theory. The current study used data from six studies of men who have sex with men (MSM; n = 3565) to (1) examine the item properties of HIV knowledge questions, (2) test for differential item functioning on commonly studied characteristics (i.e., age, race/ethnicity, and HIV risk behavior), (3) select items with the optimal item characteristics, and (4) leverage this combined dataset to examine the potential moderating effect of age on the relationship between condomless anal sex (CAS) and HIV knowledge. Findings indicated that existing questions tend to poorly differentiate those with higher levels of HIV knowledge, but items were relatively robust across diverse individuals. Furthermore, age moderated the relationship between CAS and HIV knowledge with older MSM having the strongest association. These findings suggest that additional items are required in order to capture a more nuanced understanding of HIV knowledge and that the association between CAS and HIV knowledge may vary by age.

Evolution of a Test Item

Science.gov (United States)

Spaan, Mary

2007-01-01

This article follows the development of test items (see "Language Assessment Quarterly", Volume 3 Issue 1, pp. 71-79 for the article "Test and Item Specifications Development"), beginning with a review of test and item specifications, then proceeding to writing and editing of items, pretesting and analysis, and finally selection of an item for a…

Tag-Driven Online Novel Recommendation with Collaborative Item Modeling

Directory of Open Access Journals (Sweden)

Fenghuan Li

2018-04-01

Full Text Available Online novel recommendation recommends attractive novels according to the preferences and characteristics of users or novels and is increasingly touted as an indispensable service of many online stores and websites. The interests of the majority of users remain stable over a certain period. However, there are broad categories in the initial recommendation list achieved by collaborative filtering (CF. That is to say, it is very possible that there are many inappropriately recommended novels. Meanwhile, most algorithms assume that users can provide an explicit preference. However, this assumption does not always hold, especially in online novel reading. To solve these issues, a tag-driven algorithm with collaborative item modeling (TDCIM is proposed for online novel recommendation. Online novel reading is different from traditional book marketing and lacks preference rating. In addition, collaborative filtering frequently suffers from the Matthew effect, leading to ignored personalized recommendations and serious long tail problems. Therefore, item-based CF is improved by latent preference rating with a punishment mechanism based on novel popularity. Consequently, a tag-driven algorithm is constructed by means of collaborative item modeling and tag extension. Experimental results show that online novel recommendation is improved greatly by a tag-driven algorithm with collaborative item modeling.

Efficiency Analysis of German Electricity Distribution Utilities : Non-Parametric and Parametric Tests

OpenAIRE

von Hirschhausen, Christian R.; Cullmann, Astrid

2005-01-01

Abstract This paper applies parametric and non-parametric and parametric tests to assess the efficiency of electricity distribution companies in Germany. We address traditional issues in electricity sector benchmarking, such as the role of scale effects and optimal utility size, as well as new evidence specific to the situation in Germany. We use labour, capital, and peak load capacity as inputs, and units sold and the number of customers as output. The data cover 307 (out of 553) ...

Bootstrapping the economy -- a non-parametric method of generating consistent future scenarios

OpenAIRE

Müller, Ulrich A; Bürgi, Roland; Dacorogna, Michel M

2004-01-01

The fortune and the risk of a business venture depends on the future course of the economy. There is a strong demand for economic forecasts and scenarios that can be applied to planning and modeling. While there is an ongoing debate on modeling economic scenarios, the bootstrapping (or resampling) approach presented here has several advantages. As a non-parametric method, it directly relies on past market behaviors rather than debatable assumptions on models and parameters. Simultaneous dep...

Recommended core items to assess e-cigarette use in population-based surveys.

Science.gov (United States)

Pearson, Jennifer L; Hitchman, Sara C; Brose, Leonie S; Bauld, Linda; Glasser, Allison M; Villanti, Andrea C; McNeill, Ann; Abrams, David B; Cohen, Joanna E

2018-05-01

A consistent approach using standardised items to assess e-cigarette use in both youth and adult populations will aid cross-survey and cross-national comparisons of the effect of e-cigarette (and tobacco) policies and improve our understanding of the population health impact of e-cigarette use. Focusing on adult behaviour, we propose a set of e-cigarette use items, discuss their utility and potential adaptation, and highlight e-cigarette constructs that researchers should avoid without further item development. Reliable and valid items will strengthen the emerging science and inform knowledge synthesis for policy-making. Building on informal discussions at a series of international meetings of 65 experts from 15 countries, the authors provide recommendations for assessing e-cigarette use behaviour, relative perceived harm, device type, presence of nicotine, flavours and reasons for use. We recommend items assessing eight core constructs: e-cigarette ever use, frequency of use and former daily use; relative perceived harm; device type; primary flavour preference; presence of nicotine; and primary reason for use. These items should be standardised or minimally adapted for the policy context and target population. Researchers should be prepared to update items as e-cigarette device characteristics change. A minimum set of e-cigarette items is proposed to encourage consensus around items to allow for cross-survey and cross-jurisdictional comparisons of e-cigarette use behaviour. These proposed items are a starting point. We recognise room for continued improvement, and welcome input from e-cigarette users and scientific colleagues. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Non-parametric transformation for data correlation and integration: From theory to practice

Energy Technology Data Exchange (ETDEWEB)

Datta-Gupta, A.; Xue, Guoping; Lee, Sang Heon [Texas A& M Univ., College Station, TX (United States)

1997-08-01

The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require a priori functional relationship between response and predictor variables which is the case with traditional multiple regression. The transformations are very general, computationally efficient and can easily handle mixed data types for example, continuous variables such as porosity, permeability and categorical variables such as rock type, lithofacies. The power of the non-parametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we utilize these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its fullest potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus, greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes.

Instructional Topics in Educational Measurement (ITEMS) Module: Using Automated Processes to Generate Test Items

Science.gov (United States)

Gierl, Mark J.; Lai, Hollis

2013-01-01

Changes to the design and development of our educational assessments are resulting in the unprecedented demand for a large and continuous supply of content-specific test items. One way to address this growing demand is with automatic item generation (AIG). AIG is the process of using item models to generate test items with the aid of computer…

A 67-Item Stress Resilience item bank showing high content validity was developed in a psychosomatic sample.

Science.gov (United States)

Obbarius, Nina; Fischer, Felix; Obbarius, Alexander; Nolte, Sandra; Liegl, Gregor; Rose, Matthias

2018-04-10

To develop the first item bank to measure Stress Resilience (SR) in clinical populations. Qualitative item development resulted in an initial pool of 131 items covering a broad theoretical SR concept. These items were tested in n=521 patients at a psychosomatic outpatient clinic. Exploratory and Confirmatory Factor Analysis (CFA), as well as other state-of-the-art item analyses and IRT were used for item evaluation and calibration of the final item bank. Out of the initial item pool of 131 items, we excluded 64 items (54 factor loading .3, 2 non-discriminative Item Response Curves, 4 Differential Item Functioning). The final set of 67 items indicated sufficient model fit in CFA and IRT analyses. Additionally, a 10-item short form with high measurement precision (SE≤.32 in a theta range between -1.8 and +1.5) was derived. Both the SR item bank and the SR short form were highly correlated with an existing static legacy tool (Connor-Davidson Resilience Scale). The final SR item bank and 10-item short form showed good psychometric properties. When further validated, they will be ready to be used within a framework of Computer-Adaptive Tests for a comprehensive assessment of the Stress-Construct. Copyright © 2018. Published by Elsevier Inc.

Teoria da Resposta ao Item Teoria de la respuesta al item Item response theory

Directory of Open Access Journals (Sweden)

Eutalia Aparecida Candido de Araujo

2009-12-01

Full Text Available A preocupação com medidas de traços psicológicos é antiga, sendo que muitos estudos e propostas de métodos foram desenvolvidos no sentido de alcançar este objetivo. Entre os trabalhos propostos, destaca-se a Teoria da Resposta ao Item (TRI que, a princípio, veio completar limitações da Teoria Clássica de Medidas, empregada em larga escala até hoje na medida de traços psicológicos. O ponto principal da TRI é que ela leva em consideração o item particularmente, sem relevar os escores totais; portanto, as conclusões não dependem apenas do teste ou questionário, mas de cada item que o compõe. Este artigo propõe-se a apresentar esta Teoria que revolucionou a teoria de medidas.La preocupación con las medidas de los rasgos psicológicos es antigua y muchos estudios y propuestas de métodos fueron desarrollados para lograr este objetivo. Entre estas propuestas de trabajo se incluye la Teoría de la Respuesta al Ítem (TRI que, en principio, vino a completar las limitaciones de la Teoría Clásica de los Tests, ampliamente utilizada hasta hoy en la medida de los rasgos psicológicos. El punto principal de la TRI es que se tiene en cuenta el punto concreto, sin relevar las puntuaciones totales; por lo tanto, los resultados no sólo dependen de la prueba o cuestionario, sino que de cada ítem que lo compone. En este artículo se propone presentar la Teoría que revolucionó la teoría de medidas.The concern with measures of psychological traits is old and many studies and proposals of methods were developed to achieve this goal. Among these proposed methods highlights the Item Response Theory (IRT that, in principle, came to complete limitations of the Classical Test Theory, which is widely used until nowadays in the measurement of psychological traits. The main point of IRT is that it takes into account the item in particular, not relieving the total scores; therefore, the findings do not only depend on the test or questionnaire

Measuring self-esteem after spinal cord injury: Development, validation and psychometric characteristics of the SCI-QOL Self-esteem item bank and short form.

Science.gov (United States)

Kalpakjian, Claire Z; Tate, Denise G; Kisala, Pamela A; Tulsky, David S

2015-05-01

To describe the development and psychometric properties of the Spinal Cord Injury-Quality of Life (SCI-QOL) Self-esteem item bank. Using a mixed-methods design, we developed and tested a self-esteem item bank through the use of focus groups with individuals with SCI and clinicians with expertise in SCI, cognitive interviews, and item-response theory-(IRT) based analytic approaches, including tests of model fit, differential item functioning (DIF) and precision. We tested a pool of 30 items at several medical institutions across the United States, including the University of Michigan, Kessler Foundation, the Rehabilitation Institute of Chicago, the University of Washington, Craig Hospital, and the James J. Peters/Bronx Department of Veterans Affairs hospital. A total of 717 individuals with SCI completed the self-esteem items. A unidimensional model was observed (CFI=0.946; RMSEA=0.087) and measurement precision was good (theta range between -2.7 and 0.7). Eleven items were flagged for DIF; however, effect sizes were negligible with little practical impact on score estimates. The final calibrated item bank resulted in 23 retained items. This study indicates that the SCI-QOL Self-esteem item bank represents a psychometrically robust measurement tool. Short form items are also suggested and computer adaptive tests are available.

Clusters of cultures: diversity in meaning of family value and gender role items across Europe.

Science.gov (United States)

van Vlimmeren, Eva; Moors, Guy B D; Gelissen, John P T M

2017-01-01

Survey data are often used to map cultural diversity by aggregating scores of attitude and value items across countries. However, this procedure only makes sense if the same concept is measured in all countries. In this study we argue that when (co)variances among sets of items are similar across countries, these countries share a common way of assigning meaning to the items. Clusters of cultures can then be observed by doing a cluster analysis on the (co)variance matrices of sets of related items. This study focuses on family values and gender role attitudes. We find four clusters of cultures that assign a distinct meaning to these items, especially in the case of gender roles. Some of these differences reflect response style behavior in the form of acquiescence. Adjusting for this style effect impacts on country comparisons hence demonstrating the usefulness of investigating the patterns of meaning given to sets of items prior to aggregating scores into cultural characteristics.

Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

Science.gov (United States)

Aybek, Eren Can; Demirtasli, R. Nukhet

2017-01-01

This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response…

Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance

Science.gov (United States)

Bhattacharjee, A.; Sutradhar, A.

2012-12-01

Modern close loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a frequency domain nonparametric identification of the nonlinear glucose-insulin process in an insulin dependent diabetes mellitus patient that captures the process dynamics in presence of uncertainties and parameter variations. An online frequency domain kernel estimation method has been proposed that uses the input-output data from the 19th order first principle model of the patient in intravenous route. Volterra equations up to second order kernels with extended input vector for a Hammerstein model are solved online by adaptive recursive least square (ARLS) algorithm. The frequency domain kernels are estimated using the harmonic excitation input data sequence from the virtual patient model. A short filter memory length of M = 2 was found sufficient to yield acceptable accuracy with lesser computation time. The nonparametric models are useful for closed loop control, where the frequency domain kernels can be directly used as the transfer function. The validation results show good fit both in frequency and time domain responses with nominal patient as well as with parameter variations.

Bayesian nonparametric inference on quantile residual life function: Application to breast cancer data.

Science.gov (United States)

Park, Taeyoung; Jeong, Jong-Hyeon; Lee, Jae Won

2012-08-15

There is often an interest in estimating a residual life function as a summary measure of survival data. For ease in presentation of the potential therapeutic effect of a new drug, investigators may summarize survival data in terms of the remaining life years of patients. Under heavy right censoring, however, some reasonably high quantiles (e.g., median) of a residual lifetime distribution cannot be always estimated via a popular nonparametric approach on the basis of the Kaplan-Meier estimator. To overcome the difficulties in dealing with heavily censored survival data, this paper develops a Bayesian nonparametric approach that takes advantage of a fully model-based but highly flexible probabilistic framework. We use a Dirichlet process mixture of Weibull distributions to avoid strong parametric assumptions on the unknown failure time distribution, making it possible to estimate any quantile residual life function under heavy censoring. Posterior computation through Markov chain Monte Carlo is straightforward and efficient because of conjugacy properties and partial collapse. We illustrate the proposed methods by using both simulated data and heavily censored survival data from a recent breast cancer clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project. Copyright © 2012 John Wiley & Sons, Ltd.

Language-related differential item functioning between English and German PROMIS Depression items is negligible.

Science.gov (United States)

Fischer, H Felix; Wahl, Inka; Nolte, Sandra; Liegl, Gregor; Brähler, Elmar; Löwe, Bernd; Rose, Matthias

2017-12-01

To investigate differential item functioning (DIF) of PROMIS Depression items between US and German samples we compared data from the US PROMIS calibration sample (n = 780), a German general population survey (n = 2,500) and a German clinical sample (n = 621). DIF was assessed in an ordinal logistic regression framework, with 0.02 as criterion for R 2 -change and 0.096 for Raju's non-compensatory DIF. Item parameters were initially fixed to the PROMIS Depression metric; we used plausible values to account for uncertainty in depression estimates. Only four items showed DIF. Accounting for DIF led to negligible effects for the full item bank as well as a post hoc simulated computer-adaptive test (German general population sample was considerably lower compared to the US reference value of 50. Overall, we found little evidence for language DIF between US and German samples, which could be addressed by either replacing the DIF items by items not showing DIF or by scoring the short form in German samples with the corrected item parameters reported. Copyright © 2016 John Wiley & Sons, Ltd.

Development of six PROMIS pediatrics proxy-report item banks

Directory of Open Access Journals (Sweden)

Irwin Debra E

2012-02-01

Full Text Available Abstract Background Pediatric self-report should be considered the standard for measuring patient reported outcomes (PRO among children. However, circumstances exist when the child is too young, cognitively impaired, or too ill to complete a PRO instrument and a proxy-report is needed. This paper describes the development process including the proxy cognitive interviews and large-field-test survey methods and sample characteristics employed to produce item parameters for the Patient Reported Outcomes Measurement Information System (PROMIS pediatric proxy-report item banks. Methods The PROMIS pediatric self-report items were converted into proxy-report items before undergoing cognitive interviews. These items covered six domains (physical function, emotional distress, social peer relationships, fatigue, pain interference, and asthma impact. Caregivers (n = 25 of children ages of 5 and 17 years provided qualitative feedback on proxy-report items to assess any major issues with these items. From May 2008 to March 2009, the large-scale survey enrolled children ages 8-17 years to complete the self-report version and caregivers to complete the proxy-report version of the survey (n = 1548 dyads. Caregivers of children ages 5 to 7 years completed the proxy report survey (n = 432. In addition, caregivers completed other proxy instruments, PedsQL™ 4.0 Generic Core Scales Parent Proxy-Report version, PedsQL™ Asthma Module Parent Proxy-Report version, and KIDSCREEN Parent-Proxy-52. Results Item content was well understood by proxies and did not require item revisions but some proxies clearly noted that determining an answer on behalf of their child was difficult for some items. Dyads and caregivers of children ages 5-17 years old were enrolled in the large-scale testing. The majority were female (85%, married (70%, Caucasian (64% and had at least a high school education (94%. Approximately 50% had children with a chronic health condition, primarily

Prior processes and their applications nonparametric Bayesian estimation

CERN Document Server

Phadia, Eswar G

2016-01-01

This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and P...

Consensus on items and quantities of clinical equipment required to deal with a mass casualties big bang incident: a national Delphi study.

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Duncan, Edward A S; Colver, Keith; Dougall, Nadine; Swingler, Kevin; Stephenson, John; Abhyankar, Purva

2014-02-22

Major short-notice or sudden impact incidents, which result in a large number of casualties, are rare events. However health services must be prepared to respond to such events appropriately. In the United Kingdom (UK), a mass casualties incident is when the normal response of several National Health Service organizations to a major incident, has to be supported with extraordinary measures. Having the right type and quantity of clinical equipment is essential, but planning for such emergencies is challenging. To date, the equipment stored for such events has been selected on the basis of local clinical judgment and has evolved without an explicit evidence-base. This has resulted in considerable variations in the types and quantities of clinical equipment being stored in different locations. This study aimed to develop an expert consensus opinion of the essential items and minimum quantities of clinical equipment that is required to treat 100 people at the scene of a big bang mass casualties event. A three round modified Delphi study was conducted with 32 experts using a specifically developed web-based platform. Individuals were invited to participate if they had personal clinical experience of providing a pre-hospital emergency medical response to a mass casualties incident, or had responsibility in health emergency planning for mass casualties incidents and were in a position of authority within the sphere of emergency health planning. Each item's importance was measured on a 5-point Likert scale. The quantity of items required was measured numerically. Data were analyzed using nonparametric statistics. Experts achieved consensus on a total of 134 items (54%) on completion of the study. Experts did not reach consensus on 114 (46%) items. Median quantities and interquartile ranges of the items, and their recommended quantities were identified and are presented. This study is the first to produce an expert consensus on the items and quantities of clinical equipment

Geriatric Anxiety Scale: item response theory analysis, differential item functioning, and creation of a ten-item short form (GAS-10).

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Mueller, Anne E; Segal, Daniel L; Gavett, Brandon; Marty, Meghan A; Yochim, Brian; June, Andrea; Coolidge, Frederick L

2015-07-01

The Geriatric Anxiety Scale (GAS; Segal et al. (Segal, D. L., June, A., Payne, M., Coolidge, F. L. and Yochim, B. (2010). Journal of Anxiety Disorders, 24, 709-714. doi:10.1016/j.janxdis.2010.05.002) is a self-report measure of anxiety that was designed to address unique issues associated with anxiety assessment in older adults. This study is the first to use item response theory (IRT) to examine the psychometric properties of a measure of anxiety in older adults. A large sample of older adults (n = 581; mean age = 72.32 years, SD = 7.64 years, range = 60 to 96 years; 64% women; 88% European American) completed the GAS. IRT properties were examined. The presence of differential item functioning (DIF) or measurement bias by age and sex was assessed, and a ten-item short form of the GAS (called the GAS-10) was created. All GAS items had discrimination parameters of 1.07 or greater. Items from the somatic subscale tended to have lower discrimination parameters than items on the cognitive or affective subscales. Two items were flagged for DIF, but the impact of the DIF was negligible. Women scored significantly higher than men on the GAS and its subscales. Participants in the young-old group (60 to 79 years old) scored significantly higher on the cognitive subscale than participants in the old-old group (80 years old and older). Results from the IRT analyses indicated that the GAS and GAS-10 have strong psychometric properties among older adults. We conclude by discussing implications and future research directions.

Asymptotic Standard Errors for Item Response Theory True Score Equating of Polytomous Items

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Cher Wong, Cheow

2015-01-01

Building on previous works by Lord and Ogasawara for dichotomous items, this article proposes an approach to derive the asymptotic standard errors of item response theory true score equating involving polytomous items, for equivalent and nonequivalent groups of examinees. This analytical approach could be used in place of empirical methods like…

Psychometric properties of the Chinese version of resilience scale specific to cancer: an item response theory analysis.

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Ye, Zeng Jie; Liang, Mu Zi; Zhang, Hao Wei; Li, Peng Fei; Ouyang, Xue Ren; Yu, Yuan Liang; Liu, Mei Ling; Qiu, Hong Zhong

2018-06-01

Classic theory test has been used to develop and validate the 25-item Resilience Scale Specific to Cancer (RS-SC) in Chinese patients with cancer. This study was designed to provide additional information about the discriminative value of the individual items tested with an item response theory analysis. A two-parameter graded response model was performed to examine whether any of the items of the RS-SC exhibited problems with the ordering and steps of thresholds, as well as the ability of items to discriminate patients with different resilience levels using item characteristic curves. A sample of 214 Chinese patients with cancer diagnosis was analyzed. The established three-dimension structure of the RS-SC was confirmed. Several items showed problematic thresholds or discrimination ability and require further revision. Some problematic items should be refined and a short-form of RS-SC maybe feasible in clinical settings in order to reduce burden on patients. However, the generalizability of these findings warrants further investigations.

Bayesian nonparametric areal wombling for small-scale maps with an application to urinary bladder cancer data from Connecticut.

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Guhaniyogi, Rajarshi

2017-11-10

With increasingly abundant spatial data in the form of case counts or rates combined over areal regions (eg, ZIP codes, census tracts, or counties), interest turns to formal identification of difference "boundaries," or barriers on the map, in addition to the estimated statistical map itself. "Boundary" refers to a border that describes vastly disparate outcomes in the adjacent areal units, perhaps caused by latent risk factors. This article focuses on developing a model-based statistical tool, equipped to identify difference boundaries in maps with a small number of areal units, also referred to as small-scale maps. This article proposes a novel and robust nonparametric boundary detection rule based on nonparametric Dirichlet processes, later referred to as Dirichlet process wombling (DPW) rule, by employing Dirichlet process-based mixture models for small-scale maps. Unlike the recently proposed nonparametric boundary detection rules based on false discovery rates, the DPW rule is free of ad hoc parameters, computationally simple, and readily implementable in freely available software for public health practitioners such as JAGS and OpenBUGS and yet provides statistically interpretable boundary detection in small-scale wombling. We offer a detailed simulation study and an application of our proposed approach to a urinary bladder cancer incidence rates dataset between 1990 and 2012 in the 8 counties in Connecticut. Copyright © 2017 John Wiley & Sons, Ltd.

MIMIC Methods for Assessing Differential Item Functioning in Polytomous Items

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Wang, Wen-Chung; Shih, Ching-Lin

2010-01-01

Three multiple indicators-multiple causes (MIMIC) methods, namely, the standard MIMIC method (M-ST), the MIMIC method with scale purification (M-SP), and the MIMIC method with a pure anchor (M-PA), were developed to assess differential item functioning (DIF) in polytomous items. In a series of simulations, it appeared that all three methods…

Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model

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Isaac Mugume

2016-01-01

Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.

Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model

International Nuclear Information System (INIS)

Kuosmanen, Timo

2012-01-01

Electricity distribution network is a prime example of a natural local monopoly. In many countries, electricity distribution is regulated by the government. Many regulators apply frontier estimation techniques such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) as an integral part of their regulatory framework. While more advanced methods that combine nonparametric frontier with stochastic error term are known in the literature, in practice, regulators continue to apply simplistic methods. This paper reports the main results of the project commissioned by the Finnish regulator for further development of the cost frontier estimation in their regulatory framework. The key objectives of the project were to integrate a stochastic SFA-style noise term to the nonparametric, axiomatic DEA-style cost frontier, and to take the heterogeneity of firms and their operating environments better into account. To achieve these objectives, a new method called stochastic nonparametric envelopment of data (StoNED) was examined. Based on the insights and experiences gained in the empirical analysis using the real data of the regulated networks, the Finnish regulator adopted the StoNED method in use from 2012 onwards.

Nonparametric estimation for censored mixture data with application to the Cooperative Huntington's Observational Research Trial.

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Wang, Yuanjia; Garcia, Tanya P; Ma, Yanyuan

2012-01-01

This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington's Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk

Evaluation of Northwest University, Kano Post-UTME Test Items Using Item Response Theory

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Bichi, Ado Abdu; Hafiz, Hadiza; Bello, Samira Abdullahi

2016-01-01

High-stakes testing is used for the purposes of providing results that have important consequences. Validity is the cornerstone upon which all measurement systems are built. This study applied the Item Response Theory principles to analyse Northwest University Kano Post-UTME Economics test items. The developed fifty (50) economics test items was…

Further Empirical Results on Parametric Versus Non-Parametric IRT Modeling of Likert-Type Personality Data

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Maydeu-Olivares, Albert

2005-01-01

Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…

Analysing the length of care episode after hip fracture: a nonparametric and a parametric Bayesian approach.

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Riihimäki, Jaakko; Sund, Reijo; Vehtari, Aki

2010-06-01

Effective utilisation of limited resources is a challenge for health care providers. Accurate and relevant information extracted from the length of stay distributions is useful for management purposes. Patient care episodes can be reconstructed from the comprehensive health registers, and in this paper we develop a Bayesian approach to analyse the length of care episode after a fractured hip. We model the large scale data with a flexible nonparametric multilayer perceptron network and with a parametric Weibull mixture model. To assess the performances of the models, we estimate expected utilities using predictive density as a utility measure. Since the model parameters cannot be directly compared, we focus on observables, and estimate the relevances of patient explanatory variables in predicting the length of stay. To demonstrate how the use of the nonparametric flexible model is advantageous for this complex health care data, we also study joint effects of variables in predictions, and visualise nonlinearities and interactions found in the data.

Item Response Data Analysis Using Stata Item Response Theory Package

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Yang, Ji Seung; Zheng, Xiaying

2018-01-01

The purpose of this article is to introduce and review the capability and performance of the Stata item response theory (IRT) package that is available from Stata v.14, 2015. Using a simulated data set and a publicly available item response data set extracted from Programme of International Student Assessment, we review the IRT package from…

Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing

DEFF Research Database (Denmark)

Hald, Ditte Høvenhoff

The goal of this thesis is to explore two improvements for functional magnetic resonance imaging (fMRI) analysis; namely our proposed decomposition method and an extension to the non-parametric testing framework. Analysis of fMRI allows researchers to investigate the functional processes...... of the brain, and provides insight into neuronal coupling during mental processes or tasks. The decomposition method is a Gaussian process-based independent components analysis (GPICA), which incorporates a temporal dependency in the sources. A hierarchical model specification is used, featuring both...... instantaneous and convolutive mixing, and the inferred temporal patterns. Spatial maps are seen to capture smooth and localized stimuli-related components, and often identifiable noise components. The implementation is freely available as a GUI/SPM plugin, and we recommend using GPICA as an additional tool when...

Item Banking with Embedded Standards

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MacCann, Robert G.; Stanley, Gordon

2009-01-01

An item banking method that does not use Item Response Theory (IRT) is described. This method provides a comparable grading system across schools that would be suitable for low-stakes testing. It uses the Angoff standard-setting method to obtain item ratings that are stored with each item. An example of such a grading system is given, showing how…

Use of multilevel logistic regression to identify the causes of differential item functioning.

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Balluerka, Nekane; Gorostiaga, Arantxa; Gómez-Benito, Juana; Hidalgo, María Dolores

2010-11-01

Given that a key function of tests is to serve as evaluation instruments and for decision making in the fields of psychology and education, the possibility that some of their items may show differential behaviour is a major concern for psychometricians. In recent decades, important progress has been made as regards the efficacy of techniques designed to detect this differential item functioning (DIF). However, the findings are scant when it comes to explaining its causes. The present study addresses this problem from the perspective of multilevel analysis. Starting from a case study in the area of transcultural comparisons, multilevel logistic regression is used: 1) to identify the item characteristics associated with the presence of DIF; 2) to estimate the proportion of variation in the DIF coefficients that is explained by these characteristics; and 3) to evaluate alternative explanations of the DIF by comparing the explanatory power or fit of different sequential models. The comparison of these models confirmed one of the two alternatives (familiarity with the stimulus) and rejected the other (the topic area) as being a cause of differential functioning with respect to the compared groups.

P2-19: The Effect of item Repetition on Item-Context Association Depends on the Prior Exposure of Items

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Hongmi Lee

2012-10-01

Full Text Available Previous studies have reported conflicting findings on whether item repetition has beneficial or detrimental effects on source memory. To reconcile such contradictions, we investigated whether the degree of pre-exposure of items can be a potential modulating factor. The experimental procedures spanned two consecutive days. On Day 1, participants were exposed to a set of unfamiliar faces. On Day 2, the same faces presented on the previous day were used again in half of the participants, whereas novel faces were used for the other half. Day 2 procedures consisted of three successive phases: item repetition, source association, and source memory test. In the item repetition phase, half of the face stimuli were repeatedly presented while participants were making male/female judgments. During the source association phase, both the repeated and the unrepeated faces appeared in one of the four locations on the screen. Finally, participants were tested on the location in which a given face was presented during the previous phase and reported the confidence of their memory. Source memory accuracy was measured as the percentage of correct non-guess trials. As results, we found a significant interaction between prior exposure and repetition. Repetition impaired source memory when the items had been pre-exposed on Day 1, while it led to greater accuracy in novel ones. These results show that pre-experimental exposure can modulate the effects of repetition on associative binding between an item and its contextual information, suggesting that pre-existing representation and novelty signal interact to form new episodic memory.

Evaluation of parametric and nonparametric models to predict water flow; Avaliacao entre modelos parametricos e nao parametricos para previsao de vazoes afluentes

Energy Technology Data Exchange (ETDEWEB)

Marques, T.C.; Cruz Junior, G.; Vinhal, C. [Universidade Federal de Goias (UFG), Goiania, GO (Brazil). Escola de Engenharia Eletrica e de Computacao], Emails: thyago@eeec.ufg.br, gcruz@eeec.ufg.br, vinhal@eeec.ufg.br

2009-07-01

The goal of this paper is to present a methodology to carry out the seasonal stream flow forecasting using database of average monthly inflows of one Brazilian hydroelectric plant located at Grande, Tocantins, Paranaiba, Sao Francisco and Iguacu river's. The model is based on the Adaptive Network Based Fuzzy Inference System (ANFIS), the non-parametric model. The performance of this model was compared with a periodic autoregressive model, the parametric model. The results show that the forecasting errors of the non-parametric model considered are significantly lower than the parametric model. (author)

Probit vs. semi-nonparametric estimation: examining the role of disability on institutional entry for older adults.

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Sharma, Andy

2017-06-01

The purpose of this study was to showcase an advanced methodological approach to model disability and institutional entry. Both of these are important areas to investigate given the on-going aging of the United States population. By 2020, approximately 15% of the population will be 65 years and older. Many of these older adults will experience disability and require formal care. A probit analysis was employed to determine which disabilities were associated with admission into an institution (i.e. long-term care). Since this framework imposes strong distributional assumptions, misspecification leads to inconsistent estimators. To overcome such a short-coming, this analysis extended the probit framework by employing an advanced semi-nonparamertic maximum likelihood estimation utilizing Hermite polynomial expansions. Specification tests show semi-nonparametric estimation is preferred over probit. In terms of the estimates, semi-nonparametric ratios equal 42 for cognitive difficulty, 64 for independent living, and 111 for self-care disability while probit yields much smaller estimates of 19, 30, and 44, respectively. Public health professionals can use these results to better understand why certain interventions have not shown promise. Equally important, healthcare workers can use this research to evaluate which type of treatment plans may delay institutionalization and improve the quality of life for older adults. Implications for rehabilitation With on-going global aging, understanding the association between disability and institutional entry is important in devising successful rehabilitation interventions. Semi-nonparametric is preferred to probit and shows ambulatory and cognitive impairments present high risk for institutional entry (long-term care). Informal caregiving and home-based care require further examination as forms of rehabilitation/therapy for certain types of disabilities.

Investigating the Impact of Item Parameter Drift for Item Response Theory Models with Mixture Distributions

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Yoon Soo ePark

2016-02-01

Full Text Available This study investigates the impact of item parameter drift (IPD on parameter and ability estimation when the underlying measurement model fits a mixture distribution, thereby violating the item invariance property of unidimensional item response theory (IRT models. An empirical study was conducted to demonstrate the occurrence of both IPD and an underlying mixture distribution using real-world data. Twenty-one trended anchor items from the 1999, 2003, and 2007 administrations of Trends in International Mathematics and Science Study (TIMSS were analyzed using unidimensional and mixture IRT models. TIMSS treats trended anchor items as invariant over testing administrations and uses pre-calibrated item parameters based on unidimensional IRT. However, empirical results showed evidence of two latent subgroups with IPD. Results showed changes in the distribution of examinee ability between latent classes over the three administrations. A simulation study was conducted to examine the impact of IPD on the estimation of ability and item parameters, when data have underlying mixture distributions. Simulations used data generated from a mixture IRT model and estimated using unidimensional IRT. Results showed that data reflecting IPD using mixture IRT model led to IPD in the unidimensional IRT model. Changes in the distribution of examinee ability also affected item parameters. Moreover, drift with respect to item discrimination and distribution of examinee ability affected estimates of examinee ability. These findings demonstrate the need to caution and evaluate IPD using a mixture IRT framework to understand its effect on item parameters and examinee ability.

Investigating the Impact of Item Parameter Drift for Item Response Theory Models with Mixture Distributions.

Science.gov (United States)

Park, Yoon Soo; Lee, Young-Sun; Xing, Kuan

2016-01-01

This study investigates the impact of item parameter drift (IPD) on parameter and ability estimation when the underlying measurement model fits a mixture distribution, thereby violating the item invariance property of unidimensional item response theory (IRT) models. An empirical study was conducted to demonstrate the occurrence of both IPD and an underlying mixture distribution using real-world data. Twenty-one trended anchor items from the 1999, 2003, and 2007 administrations of Trends in International Mathematics and Science Study (TIMSS) were analyzed using unidimensional and mixture IRT models. TIMSS treats trended anchor items as invariant over testing administrations and uses pre-calibrated item parameters based on unidimensional IRT. However, empirical results showed evidence of two latent subgroups with IPD. Results also showed changes in the distribution of examinee ability between latent classes over the three administrations. A simulation study was conducted to examine the impact of IPD on the estimation of ability and item parameters, when data have underlying mixture distributions. Simulations used data generated from a mixture IRT model and estimated using unidimensional IRT. Results showed that data reflecting IPD using mixture IRT model led to IPD in the unidimensional IRT model. Changes in the distribution of examinee ability also affected item parameters. Moreover, drift with respect to item discrimination and distribution of examinee ability affected estimates of examinee ability. These findings demonstrate the need to caution and evaluate IPD using a mixture IRT framework to understand its effects on item parameters and examinee ability.

Spurious Seasonality Detection: A Non-Parametric Test Proposal

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Aurelio F. Bariviera

2018-01-01

Full Text Available This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies.

Development and Application of Methods for Estimating Operating Characteristics of Discrete Test Item Responses without Assuming any Mathematical Form.

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Samejima, Fumiko

In latent trait theory the latent space, or space of the hypothetical construct, is usually represented by some unidimensional or multi-dimensional continuum of real numbers. Like the latent space, the item response can either be treated as a discrete variable or as a continuous variable. Latent trait theory relates the item response to the latent…

A parametric interpretation of Bayesian Nonparametric Inference from Gene Genealogies: Linking ecological, population genetics and evolutionary processes.

Science.gov (United States)

Ponciano, José Miguel

2017-11-22

Using a nonparametric Bayesian approach Palacios and Minin (2013) dramatically improved the accuracy, precision of Bayesian inference of population size trajectories from gene genealogies. These authors proposed an extension of a Gaussian Process (GP) nonparametric inferential method for the intensity function of non-homogeneous Poisson processes. They found that not only the statistical properties of the estimators were improved with their method, but also, that key aspects of the demographic histories were recovered. The authors' work represents the first Bayesian nonparametric solution to this inferential problem because they specify a convenient prior belief without a particular functional form on the population trajectory. Their approach works so well and provides such a profound understanding of the biological process, that the question arises as to how truly "biology-free" their approach really is. Using well-known concepts of stochastic population dynamics, here I demonstrate that in fact, Palacios and Minin's GP model can be cast as a parametric population growth model with density dependence and environmental stochasticity. Making this link between population genetics and stochastic population dynamics modeling provides novel insights into eliciting biologically meaningful priors for the trajectory of the effective population size. The results presented here also bring novel understanding of GP as models for the evolution of a trait. Thus, the ecological principles foundation of Palacios and Minin (2013)'s prior adds to the conceptual and scientific value of these authors' inferential approach. I conclude this note by listing a series of insights brought about by this connection with Ecology. Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.

A proposal for a new Brazilian six-item version of the Edinburgh Postnatal Depression Scale

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Maicon Rodrigues Albuquerque

Full Text Available Abstract Introduction: Factor analysis of the Edinburgh Postnatal Depression Scale (EPDS could result in a shorter and easier to handle screening tool. Therefore, the aim of this study was to check and compare the metrics of two different 6-item EPDS subscales. Methods: We administered the EPDS to a total of 3,891 women who had given birth between 1 and 3 months previously. We conducted confirmatory and exploratory factor analyses and plotted receiver-operating characteristics (ROC curves to, respectively, determine construct validity, scale items' fit to the data, and ideal cutoff scores for the short versions. Results: A previously defined 6-item scale did not exhibit construct validity for our sample. Nevertheless, we used exploratory factor analysis to derive a new 6-item scale with very good construct validity. The area under the ROC curve of the new 6-item scale was 0.986 and the ideal cutoff score was ≥ 6. Conclusions: The new 6-item scale has adequate psychometric properties and similar ROC curve values to the10-item version and offers a means of reducing the cost and time taken to administer the instrument.

Rasch analysis of the 23-item version of the Roland Morris Disability Questionnaire

DEFF Research Database (Denmark)

Kent, Peter; Grotle, Margreth; Dunn, Kate M

2015-01-01

OBJECTIVE: To determine the psychometric properties of the 23-item version of the Roland Morris Disability Questionnaire (RMDQ-23) and to quantify their stability across 2 cultures/languages and 2 types of care-settings. METHODS: Rasch analysis of data from 1,000 patients with low back pain from...... clinical characteristics (such as age, gender, pain intensity, pain duration and care setting), depending on the country. CONCLUSION: As similar results have been found for the RMDQ-24, we believe it is timely to reconsider whether: (i) the RMDQ should be reconstructed using an item-response theory...

Speeding Up Non-Parametric Bootstrap Computations for Statistics Based on Sample Moments in Small/Moderate Sample Size Applications.

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Elias Chaibub Neto

Full Text Available In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson's sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling.

An Instructional Module on Mokken Scale Analysis

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Wind, Stefanie A.

2017-01-01

Mokken scale analysis (MSA) is a probabilistic-nonparametric approach to item response theory (IRT) that can be used to evaluate fundamental measurement properties with less strict assumptions than parametric IRT models. This instructional module provides an introduction to MSA as a probabilistic-nonparametric framework in which to explore…

Nonparametric estimation of benchmark doses in environmental risk assessment

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Piegorsch, Walter W.; Xiong, Hui; Bhattacharya, Rabi N.; Lin, Lizhen

2013-01-01

Summary An important statistical objective in environmental risk analysis is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre-specified benchmark response in a dose-response experiment. In such settings, representations of the risk are traditionally based on a parametric dose-response model. It is a well-known concern, however, that if the chosen parametric form is misspecified, inaccurate and possibly unsafe low-dose inferences can result. We apply a nonparametric approach for calculating benchmark doses, based on an isotonic regression method for dose-response estimation with quantal-response data (Bhattacharya and Kong, 2007). We determine the large-sample properties of the estimator, develop bootstrap-based confidence limits on the BMDs, and explore the confidence limits’ small-sample properties via a short simulation study. An example from cancer risk assessment illustrates the calculations. PMID:23914133

Power of non-parametric linkage analysis in mapping genes contributing to human longevity in long-lived sib-pairs

DEFF Research Database (Denmark)

Tan, Qihua; Zhao, J H; Iachine, I

2004-01-01

This report investigates the power issue in applying the non-parametric linkage analysis of affected sib-pairs (ASP) [Kruglyak and Lander, 1995: Am J Hum Genet 57:439-454] to localize genes that contribute to human longevity using long-lived sib-pairs. Data were simulated by introducing a recently...... developed statistical model for measuring marker-longevity associations [Yashin et al., 1999: Am J Hum Genet 65:1178-1193], enabling direct power comparison between linkage and association approaches. The non-parametric linkage (NPL) scores estimated in the region harboring the causal allele are evaluated...... in case of a dominant effect. Although the power issue may depend heavily on the true genetic nature in maintaining survival, our study suggests that results from small-scale sib-pair investigations should be referred with caution, given the complexity of human longevity....

Exploring Alternative Characteristic Curve Approaches to Linking Parameter Estimates from the Generalized Partial Credit Model.

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Roberts, James S.; Bao, Han; Huang, Chun-Wei; Gagne, Phill

Characteristic curve approaches for linking parameters from the generalized partial credit model were examined for cases in which common (anchor) items are calibrated separately in two groups. Three of these approaches are simple extensions of the test characteristic curve (TCC), item characteristic curve (ICC), and operating characteristic curve…

"Detecting Differential Item Functioning and Differential Step Functioning due to Differences that ""Should"" Matter"

Directory of Open Access Journals (Sweden)

Tess Miller

2010-07-01

Full Text Available This study illustrates the use of differential item functioning (DIF and differential step functioning (DSF analyses to detect differences in item difficulty that are related to experiences of examinees, such as their teachers' instructional practices, that are relevant to the knowledge, skill, or ability the test is intended to measure. This analysis is in contrast to the typical use of DIF or DSF to detect differences related to characteristics of examinees, such as gender, language, or cultural knowledge, that should be irrelevant. Using data from two forms of Ontario's Grade 9 Assessment of Mathematics, analyses were performed comparing groups of students defined by their teachers' instructional practices. All constructed-response items were tested for DIF using the Mantel Chi-Square, standardized Liu Agresti cumulative common log-odds ratio, and standardized Cox's noncentrality parameter. Items exhibiting moderate to large DIF were subsequently tested for DSF. In contrast to typical DIF or DSF analyses, which inform item development, these analyses have the potential to inform instructional practice.

A Point-of-Purchase Intervention Using Grocery Store Tour Podcasts About Omega-3s Increases Long-Term Purchases of Omega-3-Rich Food Items.

Science.gov (United States)

Bangia, Deepika; Shaffner, Donald W; Palmer-Keenan, Debra M

2017-06-01

To assess the impacts associated with a grocery store tour point-of-purchase intervention using podcasts about omega-3 fatty acid (n-3)-rich food items. A repeated-measures secondary data analysis of food purchase records obtained from a convenience sample of shoppers' loyalty cards. Shoppers (n = 251) who had listened to podcasts regarding n-3-rich foods while shopping. The number of omega-3-rich food purchases made according to food or food category by participants determined via spreadsheets obtained from grocery store chain. Descriptive statistics were performed on demographic characteristics. Wilcoxon signed-rank tests were used to assess whether food purchases increased from 6 months before to 6 months after intervention. Correlations assessed the relationship between intentions to purchase n-3-rich foods expressed on the intervention day with actual long-term n-3-rich food purchases. Nonparametric Kruskal-Wallis ANOVAs and Wilcoxon signed-rank tests were used to analyze differences between changes made and demographic variables (ie, participants' gender, race, and education levels). Most shoppers (59%) increased n-3-rich food purchases, with significant mean purchase changes (t[172] = -6.9; P < .001; pre = 0.2 ± 0.7; post = 3.6 ± 5.1). Podcasts are promising nutrition education tools. Longer studies could assess whether lasting change results from podcast use. Copyright © 2017 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Bayesian nonparametric modeling for comparison of single-neuron firing intensities.

Science.gov (United States)

Kottas, Athanasios; Behseta, Sam

2010-03-01

We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.

Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three

Science.gov (United States)

Steinhardt, Charles L.; Jermyn, Adam S.

2018-02-01

Telescopes are much more expensive than astronomers, so it is essential to minimize required sample sizes by using the most data-efficient statistical methods possible. However, the most commonly used model-independent techniques for finding the relationship between two variables in astronomy are flawed. In the worst case they can lead without warning to subtly yet catastrophically wrong results, and even in the best case they require more data than necessary. Unfortunately, there is no single best technique for nonparametric regression. Instead, we provide a guide for how astronomers can choose the best method for their specific problem and provide a python library with both wrappers for the most useful existing algorithms and implementations of two new algorithms developed here.

The evolution of illness phases in schizophrenia: A non-parametric item response analysis of the Positive and Negative Syndrome Scale

Directory of Open Access Journals (Sweden)

Anzalee Khan

2014-06-01

Conclusion: Findings confirm differences in symptom presentation and predominance of particular domains in subpopulations of schizophrenia. Identifying symptom domains characteristic of subpopulations may be more useful in assessing efficacy endpoints than total or subscale scores.

The Technical Quality of Test Items Generated Using a Systematic Approach to Item Writing.

Science.gov (United States)

Siskind, Theresa G.; Anderson, Lorin W.

The study was designed to examine the similarity of response options generated by different item writers using a systematic approach to item writing. The similarity of response options to student responses for the same item stems presented in an open-ended format was also examined. A non-systematic (subject matter expertise) approach and a…

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.

Science.gov (United States)

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns

Directory of Open Access Journals (Sweden)

Urbi Garay

2016-03-01

Full Text Available We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return and betas (to a choice set of explanatory factors in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers.

A new powerful non-parametric two-stage approach for testing multiple phenotypes in family-based association studies

NARCIS (Netherlands)

Lange, C; Lyon, H; DeMeo, D; Raby, B; Silverman, EK; Weiss, ST

2003-01-01

We introduce a new powerful nonparametric testing strategy for family-based association studies in which multiple quantitative traits are recorded and the phenotype with the strongest genetic component is not known prior to the analysis. In the first stage, using a population-based test based on the

Bayesian nonparametric adaptive control using Gaussian processes.

Science.gov (United States)

Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A

2015-03-01

Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.

Parametric and non-parametric approach for sensory RATA (Rate-All-That-Apply) method of ledre profile attributes

Science.gov (United States)

Hastuti, S.; Harijono; Murtini, E. S.; Fibrianto, K.

2018-03-01

This current study is aimed to investigate the use of parametric and non-parametric approach for sensory RATA (Rate-All-That-Apply) method. Ledre as Bojonegoro unique local food product was used as point of interest, in which 319 panelists were involved in the study. The result showed that ledre is characterized as easy-crushed texture, sticky in mouth, stingy sensation and easy to swallow. It has also strong banana flavour with brown in colour. Compared to eggroll and semprong, ledre has more variances in terms of taste as well the roll length. As RATA questionnaire is designed to collect categorical data, non-parametric approach is the common statistical procedure. However, similar results were also obtained as parametric approach, regardless the fact of non-normal distributed data. Thus, it suggests that parametric approach can be applicable for consumer study with large number of respondents, even though it may not satisfy the assumption of ANOVA (Analysis of Variances).

Nonparametric estimation for censored mixture data with application to the Cooperative Huntington’s Observational Research Trial

Science.gov (United States)

Wang, Yuanjia; Garcia, Tanya P.; Ma, Yanyuan

2012-01-01

This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington’s Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk

The Number of Response Categories and the Reverse Directional Item Problem in Likert-Type Scales: A Study with the Rasch Model

Directory of Open Access Journals (Sweden)

Mustafa İLHAN

2017-09-01

Full Text Available This study addressed reverse directional item and the number of response categories problems in Likert-type scales. The Fear of Negative Evaluation Scale (FNES and the Oxford Happiness Questionnaire (OHQ were used as data collection tools. The data of the study were analyzed according to the Rasch model. The analysis found that the observed and expected test characteristic curves were largely overlapped, each of the three rating scales worked effectively, and the differences between response categories could be distinguished successfully by the participants in straightforward directional items. On the other hand, it was determined that there were significant differences between the observed and expected test characteristic curves in reverse directional items. It was also found that no matter which one of these three, five and seven-point rating scales was used, the participants could not distinguish the response categories of the reverse directional items on the FNES and the OHQ. Afterwards, the reverse directional items were removed from the data file, and the analysis was repeated. The analysis results revealed that item discrimination, reliability coefficients for person facet, separation ratios and Chi square values calculated for the facets of person and items were higher in five-pointed rating compared to three and seven pointed rating.

Nonparametric methods in actigraphy: An update

Directory of Open Access Journals (Sweden)

Bruno S.B. Gonçalves

2014-09-01

Full Text Available Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm results for each time interval. Simulated data showed that (1 synchronization analysis depends on sample size, and (2 fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization.

The association between socioeconomic characteristics and consumption of food items among Brazilian industry workers.

Science.gov (United States)

Vinholes, Daniele B; Melo, Ione M F; Machado, Carlos Alberto; de Castro Chaves, Hilton; Fuchs, Flavio D; Fuchs, Sandra C

2012-01-01

Dietary pattern plays a causative role in the rising of noncommunicable diseases. The SESI (Serviço Social da Indústria) study was designed to evaluate risk factors for noncommunicable diseases. We aimed to describe food items consumed by Brazilian workers and to assess their association with socioeconomic status. Cross-sectional study was carried out among Brazilian industrial workers, selected by multistage sampling, from 157 companies. Interviews were conducted at the work place using standardized forms. 4818 workers were interviewed, aged 35.4 ± 10.7 years, 76.5% were men. The workers had an average of 8.7 ± 4.1 years of schooling and 25.4 ± 4.1 kg/m² of BMI. Men and individuals with less than high school education were less likely to consume dairy products, fruits, and vegetables daily, even after control for confounding factors. Men consumed rice and beans daily more often than women. In comparison to workers aged 50-76 years, those under 30 years old consumed less fruits and green leafy vegetables daily. The food items consumed by Brazilian workers show that there are insufficient consumption according to the guidelines of healthy foods, particularly of dairy products, vegetables, and fruits.

Acceptance issues for large items and difficult waste

International Nuclear Information System (INIS)

Palmer, J.; Lock, Peter

2002-01-01

Peter Lock described some particular cases which had given rise to difficult acceptance issues at NIREX, ranging from large size items to the impacts of chemicals used during decontamination on the mobility of radionuclides in a disposal facility: The UK strategy for intermediate level and certain low level radioactive waste disposal is based on production of cementitious waste-forms packaged in a standard range of containers as follows: 500 litre Drum - the normal container for most operational ILW (0.8 m diameter x 1.2 m high); 3 m"3 Box - a larger container for solid wastes (1.72 m x 1.72 m plan x 1.2 m high); 3 m"3 Drum - a larger container for in-drum mixing and immobilisation of sludge waste-forms (1.72 m diameter x 1.2 m high); 4 m Box - for large items of waste, especially from decommissioning (4.0 m x 2.4 m plan x 2.2 m high); 2 m LLW Box - for higher-density wastes (2.0 m x 2.4 m plan x 2.2 m high). In addition the majority of LLW is packaged by supercompaction followed by grouting in modified ISO freight containers (6 m x 2.5 m x 2.5 m). Some wastes do not fit easily into this strategy. These wastes include: very large items, (too big for the 4 m box) which, if dealt with whole, pose transport and disposal problems. These items are discussed further in Section 2; waste whose characteristics make packaging difficult. Such wastes are described in more detail in Section 3

Commercial grade item (CGI) dedication of generators for nuclear safety related applications

International Nuclear Information System (INIS)

Das, R.K.; Hajos, L.G.

1993-01-01

The number of nuclear safety related equipment suppliers and the availability of spare and replacement parts designed specifically for nuclear safety related application are shrinking rapidly. These have made it necessary for utilities to apply commercial grade spare and replacement parts in nuclear safety related applications after implementing proper acceptance and dedication process to verify that such items conform with the requirements of their use in nuclear safety related application. The general guidelines for the commercial grade item (CGI) acceptance and dedication are provided in US Nuclear Regulatory Commission (NRC) Generic Letters and Electric Power Research Institute (EPRI) Report NP-5652, Guideline for the Utilization of Commercial Grade Items in Nuclear Safety Related Applications. This paper presents an application of these generic guidelines for procurement, acceptance, and dedication of a commercial grade generator for use as a standby generator at Salem Generating Station Units 1 and 2. The paper identifies the critical characteristics of the generator which once verified, will provide reasonable assurance that the generator will perform its intended safety function. The paper also delineates the method of verification of the critical characteristics through tests and provide acceptance criteria for the test results. The methodology presented in this paper may be used as specific guidelines for reliable and cost effective procurement and dedication of commercial grade generators for use as standby generators at nuclear power plants

Analysis of Critical Characteristics for Safety Graded Personnel Computers in the KNICS Architecture

International Nuclear Information System (INIS)

Lee, Hyun Chul; Lee, Dong Young

2009-01-01

Critical characteristics analysis of a safety related item is to identify characteristics to be verified to replace an original item with the dedicated item. It is sure that the dedicated item meeting critical characteristics would perform its intended safety function instead of the specified item. KNICS project developed two safety systems: IDiPS RPS (Reactor Protection System) and IDiPS ESF-CCS (Engineered Safety Features-Component Control System). Two safety systems of IDiPS are equipped with personnel computers, so-called COMs (Cabinet Operator Modules), in their cabinets. The personnel computers, COMs, are responsible for safety system monitoring, testing, and maintaining. Even though two safety systems are safety critical system, the personnel computers of two systems, i.e. COMs, are not graded as safety-graded items. Regulation requirements are expected to be strengthened, and the functions of the personnel computer may be enhanced to include safety-related functions and safety functions, it would be necessary that the grade of the personnel computers is adjusted to a higher level, the safety grade. To try to upgrade a non safety system, i.e. COMs, to a safety system, its safety functions and requirements, i.e. critical characteristics, must be identified and verified. This paper describes the process of the identification of critical characteristics and the results of analysis

Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes.

Science.gov (United States)

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng

2018-05-01

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.

Generalizability theory and item response theory

NARCIS (Netherlands)

Glas, Cornelis A.W.; Eggen, T.J.H.M.; Veldkamp, B.P.

2012-01-01

Item response theory is usually applied to items with a selected-response format, such as multiple choice items, whereas generalizability theory is usually applied to constructed-response tasks assessed by raters. However, in many situations, raters may use rating scales consisting of items with a

TIIREC: A Tensor Approach for Tag-Driven Item Recommendation with Sparse User Generated Content

KAUST Repository

Yu, Lu

2017-05-17

In recent years, tagging system has become a building block o summarize the content of items for further functions like retrieval or personalized recommendation in various web applications. One nontrivial requirement is to precisely deliver a list of suitable items when users interact with the systems via inputing a specific tag (i.e. a query term). Different from traditional recommender systems, we need deal with a collaborative retrieval (CR) problem, where both characteristics of retrieval and recommendation should be considered to model a ternary relationship involved with query× user× item. Recently, several works are proposed to study CR task from users’ perspective. However, they miss a significant challenge raising from the sparse content of items. In this work, we argue that items will suffer from the sparsity problem more severely than users, since items are usually observed with fewer features to support a feature-based or content-based algorithm. To tackle this problem, we aim to sufficiently explore the sophisticated relationship of each query× user× item triple from items’ perspective. By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given query-user pair. In addition, we suggest to employ a recently proposed bayesian personalized ranking (BPR) algorithm to optimize latent collaborative retrieval problem from pairwise learning perspective. The experimental results on two real-world datasets, (i.e. Last.fm, Yelp), verified the efficiency and effectiveness of our proposed approach at top-k ranking metric.

TIIREC: A Tensor Approach for Tag-Driven Item Recommendation with Sparse User Generated Content

KAUST Repository

Yu, Lu; Huang, Junming; Zhou, Ge; Liu, Chuang; Zhang, Zi-Ke

2017-01-01

In recent years, tagging system has become a building block o summarize the content of items for further functions like retrieval or personalized recommendation in various web applications. One nontrivial requirement is to precisely deliver a list of suitable items when users interact with the systems via inputing a specific tag (i.e. a query term). Different from traditional recommender systems, we need deal with a collaborative retrieval (CR) problem, where both characteristics of retrieval and recommendation should be considered to model a ternary relationship involved with query× user× item. Recently, several works are proposed to study CR task from users’ perspective. However, they miss a significant challenge raising from the sparse content of items. In this work, we argue that items will suffer from the sparsity problem more severely than users, since items are usually observed with fewer features to support a feature-based or content-based algorithm. To tackle this problem, we aim to sufficiently explore the sophisticated relationship of each query× user× item triple from items’ perspective. By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given query-user pair. In addition, we suggest to employ a recently proposed bayesian personalized ranking (BPR) algorithm to optimize latent collaborative retrieval problem from pairwise learning perspective. The experimental results on two real-world datasets, (i.e. Last.fm, Yelp), verified the efficiency and effectiveness of our proposed approach at top-k ranking metric.

Sharing the cost of redundant items

DEFF Research Database (Denmark)

Hougaard, Jens Leth; Moulin, Hervé

2014-01-01

We ask how to share the cost of finitely many public goods (items) among users with different needs: some smaller subsets of items are enough to serve the needs of each user, yet the cost of all items must be covered, even if this entails inefficiently paying for redundant items. Typical examples...... are network connectivity problems when an existing (possibly inefficient) network must be maintained. We axiomatize a family cost ratios based on simple liability indices, one for each agent and for each item, measuring the relative worth of this item across agents, and generating cost allocation rules...... additive in costs....

Nonparametric modeling of US interest rate term structure dynamics and implications on the prices of derivative securities

NARCIS (Netherlands)

Jiang, GJ

1998-01-01

This paper develops a nonparametric model of interest rate term structure dynamics based an a spot rate process that permits only positive interest rates and a market price of interest rate risk that precludes arbitrage opportunities. Both the spot rate process and the market price of interest rate

An Alternative to the 3PL: Using Asymmetric Item Characteristic Curves to Address Guessing Effects

Science.gov (United States)

Lee, Sora; Bolt, Daniel M.

2018-01-01

Both the statistical and interpretational shortcomings of the three-parameter logistic (3PL) model in accommodating guessing effects on multiple-choice items are well documented. We consider the use of a residual heteroscedasticity (RH) model as an alternative, and compare its performance to the 3PL with real test data sets and through simulation…

Dissociating the neural correlates of intra-item and inter-item working-memory binding.

Directory of Open Access Journals (Sweden)

Carinne Piekema

Full Text Available BACKGROUND: Integration of information streams into a unitary representation is an important task of our cognitive system. Within working memory, the medial temporal lobe (MTL has been conceptually linked to the maintenance of bound representations. In a previous fMRI study, we have shown that the MTL is indeed more active during working-memory maintenance of spatial associations as compared to non-spatial associations or single items. There are two explanations for this result, the mere presence of the spatial component activates the MTL, or the MTL is recruited to bind associations between neurally non-overlapping representations. METHODOLOGY/PRINCIPAL FINDINGS: The current fMRI study investigates this issue further by directly comparing intrinsic intra-item binding (object/colour, extrinsic intra-item binding (object/location, and inter-item binding (object/object. The three binding conditions resulted in differential activation of brain regions. Specifically, we show that the MTL is important for establishing extrinsic intra-item associations and inter-item associations, in line with the notion that binding of information processed in different brain regions depends on the MTL. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that different forms of working-memory binding rely on specific neural structures. In addition, these results extend previous reports indicating that the MTL is implicated in working-memory maintenance, challenging the classic distinction between short-term and long-term memory systems.

Generalizability theory and item response theory

OpenAIRE

Glas, Cornelis A.W.; Eggen, T.J.H.M.; Veldkamp, B.P.

2012-01-01

Item response theory is usually applied to items with a selected-response format, such as multiple choice items, whereas generalizability theory is usually applied to constructed-response tasks assessed by raters. However, in many situations, raters may use rating scales consisting of items with a selected-response format. This chapter presents a short overview of how item response theory and generalizability theory were integrated to model such assessments. Further, the precision of the esti...

Item Response Theory with Covariates (IRT-C): Assessing Item Recovery and Differential Item Functioning for the Three-Parameter Logistic Model

Science.gov (United States)

Tay, Louis; Huang, Qiming; Vermunt, Jeroen K.

2016-01-01

In large-scale testing, the use of multigroup approaches is limited for assessing differential item functioning (DIF) across multiple variables as DIF is examined for each variable separately. In contrast, the item response theory with covariate (IRT-C) procedure can be used to examine DIF across multiple variables (covariates) simultaneously. To…

Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.

Science.gov (United States)

Lu, Tao

2016-01-01

The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.

Estimation from PET data of transient changes in dopamine concentration induced by alcohol: support for a non-parametric signal estimation method

Energy Technology Data Exchange (ETDEWEB)

Constantinescu, C C; Yoder, K K; Normandin, M D; Morris, E D [Department of Radiology, Indiana University School of Medicine, Indianapolis, IN (United States); Kareken, D A [Department of Neurology, Indiana University School of Medicine, Indianapolis, IN (United States); Bouman, C A [Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN (United States); O' Connor, S J [Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN (United States)], E-mail: emorris@iupui.edu

2008-03-07

We previously developed a model-independent technique (non-parametric ntPET) for extracting the transient changes in neurotransmitter concentration from paired (rest and activation) PET studies with a receptor ligand. To provide support for our method, we introduced three hypotheses of validation based on work by Endres and Carson (1998 J. Cereb. Blood Flow Metab. 18 1196-210) and Yoder et al (2004 J. Nucl. Med. 45 903-11), and tested them on experimental data. All three hypotheses describe relationships between the estimated free (synaptic) dopamine curves (F{sup DA}(t)) and the change in binding potential ({delta}BP). The veracity of the F{sup DA}(t) curves recovered by nonparametric ntPET is supported when the data adhere to the following hypothesized behaviors: (1) {delta}BP should decline with increasing DA peak time, (2) {delta}BP should increase as the strength of the temporal correlation between F{sup DA}(t) and the free raclopride (F{sup RAC}(t)) curve increases, (3) {delta}BP should decline linearly with the effective weighted availability of the receptor sites. We analyzed regional brain data from 8 healthy subjects who received two [{sup 11}C]raclopride scans: one at rest, and one during which unanticipated IV alcohol was administered to stimulate dopamine release. For several striatal regions, nonparametric ntPET was applied to recover F{sup DA}(t), and binding potential values were determined. Kendall rank-correlation analysis confirmed that the F{sup DA}(t) data followed the expected trends for all three validation hypotheses. Our findings lend credence to our model-independent estimates of F{sup DA}(t). Application of nonparametric ntPET may yield important insights into how alterations in timing of dopaminergic neurotransmission are involved in the pathologies of addiction and other psychiatric disorders.

Guidelines for the technical evaluation of replacement items in nuclear power plants (NCIG-11)

International Nuclear Information System (INIS)

Craig, W.E.; Fakhar, A.A.; Shulman, M.N.

1989-12-01

This document presents guidelines and supporting information for the technical evaluation of replacement items in nuclear power plants. These guidelines contain six major sections which provide the practical knowledge and a programmatic approach to determine the technical and quality requirements necessary to generate purchase documents to procure the proper replacement items. The technical evaluation methodology includes the following steps. (1) Identification of the need for a technical evaluation. (2) Component/part functional classification procedures. (3) Performance of a Failure Modes and Effects Analysis. (4) Selection of Critical Characteristics for Design Determination. (5) Performance of a ''Like-For-Like'' or ''Alternate'' item Evaluation. (6) Preparation of the Technical and Quality Requirements Specification. Work on this document was initiated in response to the increased emphasis by the utilities owning nuclear power plants and nuclear industry on procurement of replacement items for use in safety related applications at nuclear power plants. 20 refs., 9 figs., 14 tabs

The randomly renewed general item and the randomly inspected item with exponential life distribution

International Nuclear Information System (INIS)

Schneeweiss, W.G.

1979-01-01

For a randomly renewed item the probability distributions of the time to failure and of the duration of down time and the expectations of these random variables are determined. Moreover, it is shown that the same theory applies to randomly checked items with exponential probability distribution of life such as electronic items. The case of periodic renewals is treated as an example. (orig.) [de

Nonparametric test of consistency between cosmological models and multiband CMB measurements

Energy Technology Data Exchange (ETDEWEB)

Aghamousa, Amir [Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 790-784 (Korea, Republic of); Shafieloo, Arman, E-mail: amir@apctp.org, E-mail: shafieloo@kasi.re.kr [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of)

2015-06-01

We present a novel approach to test the consistency of the cosmological models with multiband CMB data using a nonparametric approach. In our analysis we calibrate the REACT (Risk Estimation and Adaptation after Coordinate Transformation) confidence levels associated with distances in function space (confidence distances) based on the Monte Carlo simulations in order to test the consistency of an assumed cosmological model with observation. To show the applicability of our algorithm, we confront Planck 2013 temperature data with concordance model of cosmology considering two different Planck spectra combination. In order to have an accurate quantitative statistical measure to compare between the data and the theoretical expectations, we calibrate REACT confidence distances and perform a bias control using many realizations of the data. Our results in this work using Planck 2013 temperature data put the best fit ΛCDM model at 95% (∼ 2σ) confidence distance from the center of the nonparametric confidence set while repeating the analysis excluding the Planck 217 × 217 GHz spectrum data, the best fit ΛCDM model shifts to 70% (∼ 1σ) confidence distance. The most prominent features in the data deviating from the best fit ΛCDM model seems to be at low multipoles  18 < ℓ < 26 at greater than 2σ, ℓ ∼ 750 at ∼1 to 2σ and ℓ ∼ 1800 at greater than 2σ level. Excluding the 217×217 GHz spectrum the feature at ℓ ∼ 1800 becomes substantially less significance at ∼1 to 2σ confidence level. Results of our analysis based on the new approach we propose in this work are in agreement with other analysis done using alternative methods.

Penicillium salamii strain ITEM 15302: A new promising fungal starter for salami production.

Science.gov (United States)

Magistà, D; Ferrara, M; Del Nobile, M A; Gammariello, D; Conte, A; Perrone, G

2016-08-16

Traditional sausages are often considered of superior quality to sausages inoculated with commercial starter cultures and this is partially due to the action of the typical house microflora. Penicillium nalgiovense is the species commonly used as starter culture for dry-cured meat production. Recently a new species, Penicillium salamii, was described as typical colonizer during salami seasoning. In order to understand its contribution to the seasoning process, two different experiments on curing of fresh pork sausages were conducted using P. salamii ITEM 15302 in comparison with P. nalgiovense ITEM 15292 at small and industrial scale, and the dry-cured sausages were subjected to sensory analyses. Additionally, proteolytic and lipolytic in vitro assays were performed on both strains. P. salamii ITEM 15302 proved to be a fast growing mould on dry-cured sausage casings, well adapted to the seasoning process, with high lipolytic and proteolytic enzymatic activity that confers typical sensory characteristics to meat products. Therefore, P. salamii ITEM 15302 was shown to be a good candidate as new starter for meat industry. Copyright © 2015 Elsevier B.V. All rights reserved.

Bayesian Nonparametric Estimation of Targeted Agent Effects on Biomarker Change to Predict Clinical Outcome

Science.gov (United States)

Graziani, Rebecca; Guindani, Michele; Thall, Peter F.

2015-01-01

Summary The effect of a targeted agent on a cancer patient's clinical outcome putatively is mediated through the agent's effect on one or more early biological events. This is motivated by pre-clinical experiments with cells or animals that identify such events, represented by binary or quantitative biomarkers. When evaluating targeted agents in humans, central questions are whether the distribution of a targeted biomarker changes following treatment, the nature and magnitude of this change, and whether it is associated with clinical outcome. Major difficulties in estimating these effects are that a biomarker's distribution may be complex, vary substantially between patients, and have complicated relationships with clinical outcomes. We present a probabilistically coherent framework for modeling and estimation in this setting, including a hierarchical Bayesian nonparametric mixture model for biomarkers that we use to define a functional profile of pre-versus-post treatment biomarker distribution change. The functional is similar to the receiver operating characteristic used in diagnostic testing. The hierarchical model yields clusters of individual patient biomarker profile functionals, and we use the profile as a covariate in a regression model for clinical outcome. The methodology is illustrated by analysis of a dataset from a clinical trial in prostate cancer using imatinib to target platelet-derived growth factor, with the clinical aim to improve progression-free survival time. PMID:25319212

[Do we always correctly interpret the results of statistical nonparametric tests].

Science.gov (United States)

Moczko, Jerzy A

2014-01-01

Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman tests create a group of commonly used tests to analyze the results of clinical and laboratory data. These tests are considered to be extremely flexible and their asymptotic relative efficiency exceeds 95 percent. Compared with the corresponding parametric tests they do not require checking the fulfillment of the conditions such as the normality of data distribution, homogeneity of variance, the lack of correlation means and standard deviations, etc. They can be used both in the interval and or-dinal scales. The article presents an example Mann-Whitney test, that does not in any case the choice of these four nonparametric tests treated as a kind of gold standard leads to correct inference.

Nonparametric predictive inference for combined competing risks data

International Nuclear Information System (INIS)

Coolen-Maturi, Tahani; Coolen, Frank P.A.

2014-01-01

The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies

17 CFR 260.7a-16 - Inclusion of items, differentiation between items and answers, omission of instructions.

Science.gov (United States)

2010-04-01

... 17 Commodity and Securities Exchanges 3 2010-04-01 2010-04-01 false Inclusion of items, differentiation between items and answers, omission of instructions. 260.7a-16 Section 260.7a-16 Commodity and... INDENTURE ACT OF 1939 Formal Requirements § 260.7a-16 Inclusion of items, differentiation between items and...

Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation

DEFF Research Database (Denmark)

Katebi, S.D.; Blanke, M.; Katebi, M.R.

This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second...... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...

The Effects of Test Length and Sample Size on Item Parameters in Item Response Theory

Science.gov (United States)

Sahin, Alper; Anil, Duygu

2017-01-01

This study investigates the effects of sample size and test length on item-parameter estimation in test development utilizing three unidimensional dichotomous models of item response theory (IRT). For this purpose, a real language test comprised of 50 items was administered to 6,288 students. Data from this test was used to obtain data sets of…

Approximation Preserving Reductions among Item Pricing Problems

Science.gov (United States)

Hamane, Ryoso; Itoh, Toshiya; Tomita, Kouhei

When a store sells items to customers, the store wishes to determine the prices of the items to maximize its profit. Intuitively, if the store sells the items with low (resp. high) prices, the customers buy more (resp. less) items, which provides less profit to the store. So it would be hard for the store to decide the prices of items. Assume that the store has a set V of n items and there is a set E of m customers who wish to buy those items, and also assume that each item i ∈ V has the production cost di and each customer ej ∈ E has the valuation vj on the bundle ej ⊆ V of items. When the store sells an item i ∈ V at the price ri, the profit for the item i is pi = ri - di. The goal of the store is to decide the price of each item to maximize its total profit. We refer to this maximization problem as the item pricing problem. In most of the previous works, the item pricing problem was considered under the assumption that pi ≥ 0 for each i ∈ V, however, Balcan, et al. [In Proc. of WINE, LNCS 4858, 2007] introduced the notion of “loss-leader, ” and showed that the seller can get more total profit in the case that pi < 0 is allowed than in the case that pi < 0 is not allowed. In this paper, we derive approximation preserving reductions among several item pricing problems and show that all of them have algorithms with good approximation ratio.

Which Statistic Should Be Used to Detect Item Preknowledge When the Set of Compromised Items Is Known?

Science.gov (United States)

Sinharay, Sandip

2017-09-01

Benefiting from item preknowledge is a major type of fraudulent behavior during educational assessments. Belov suggested the posterior shift statistic for detection of item preknowledge and showed its performance to be better on average than that of seven other statistics for detection of item preknowledge for a known set of compromised items. Sinharay suggested a statistic based on the likelihood ratio test for detection of item preknowledge; the advantage of the statistic is that its null distribution is known. Results from simulated and real data and adaptive and nonadaptive tests are used to demonstrate that the Type I error rate and power of the statistic based on the likelihood ratio test are very similar to those of the posterior shift statistic. Thus, the statistic based on the likelihood ratio test appears promising in detecting item preknowledge when the set of compromised items is known.

USING A DEA MANAGEMENT TOOLTHROUGH A NONPARAMETRIC APPROACH: AN EXAMINATION OF URBAN-RURAL EFFECTS ON THAI SCHOOL EFFICIENCY

Directory of Open Access Journals (Sweden)

SANGCHAN KANTABUTRA

2009-04-01

Full Text Available This paper examines urban-rural effects on public upper-secondary school efficiency in northern Thailand. In the study, efficiency was measured by a nonparametric technique, data envelopment analysis (DEA. Urban-rural effects were examined through a Mann-Whitney nonparametric statistical test. Results indicate that urban schools appear to have access to and practice different production technologies than rural schools, and rural institutions appear to operate less efficiently than their urban counterparts. In addition, a sensitivity analysis, conducted to ascertain the robustness of the analytical framework, revealed the stability of urban-rural effects on school efficiency. Policy to improve school eff iciency should thus take varying geographical area differences into account, viewing rural and urban schools as different from one another. Moreover, policymakers might consider shifting existing resources from urban schools to rural schools, provided that the increase in overall rural efficiency would be greater than the decrease, if any, in the city. Future research directions are discussed.

An emotional functioning item bank of 24 items for computerized adaptive testing (CAT) was established

DEFF Research Database (Denmark)

Petersen, Morten Aa.; Gamper, Eva-Maria; Costantini, Anna

2016-01-01

of the widely used EORTC Quality of Life questionnaire (QLQ-C30). STUDY DESIGN AND SETTING: On the basis of literature search and evaluations by international samples of experts and cancer patients, 38 candidate items were developed. The psychometric properties of the items were evaluated in a large...... international sample of cancer patients. This included evaluations of dimensionality, item response theory (IRT) model fit, differential item functioning (DIF), and of measurement precision/statistical power. RESULTS: Responses were obtained from 1,023 cancer patients from four countries. The evaluations showed...... that 24 items could be included in a unidimensional IRT model. DIF did not seem to have any significant impact on the estimation of EF. Evaluations indicated that the CAT measure may reduce sample size requirements by up to 50% compared to the QLQ-C30 EF scale without reducing power. CONCLUSION...

Item Modeling Concept Based on Multimedia Authoring

Directory of Open Access Journals (Sweden)

Janez Stergar

2008-09-01

Full Text Available In this paper a modern item design framework for computer based assessment based on Flash authoring environment will be introduced. Question design will be discussed as well as the multimedia authoring environment used for item modeling emphasized. Item type templates are a structured means of collecting and storing item information that can be used to improve the efficiency and security of the innovative item design process. Templates can modernize the item design, enhance and speed up the development process. Along with content creation, multimedia has vast potential for use in innovative testing. The introduced item design template is based on taxonomy of innovative items which have great potential for expanding the content areas and construct coverage of an assessment. The presented item design approach is based on GUI's – one for question design based on implemented item design templates and one for user interaction tracking/retrieval. The concept of user interfaces based on Flash technology will be discussed as well as implementation of the innovative approach of the item design forms with multimedia authoring. Also an innovative method for user interaction storage/retrieval based on PHP extending Flash capabilities in the proposed framework will be introduced.

Item analysis of single-peaked response data : the psychometric evaluation of bipolar measurement scales

NARCIS (Netherlands)

Polak, Maaike Geertruida

2011-01-01

The thesis explains the fundamental difference between unipolar and bipolar measurement scales for psychological characteristics. We explore the use of correspondence analysis (CA), a technique that is similar to principal component analysis and is available in SAS and SPSS, to select items that

The medial temporal lobes distinguish between within-item and item-context relations during autobiographical memory retrieval.

Science.gov (United States)

Sheldon, Signy; Levine, Brian

2015-12-01

During autobiographical memory retrieval, the medial temporal lobes (MTL) relate together multiple event elements, including object (within-item relations) and context (item-context relations) information, to create a cohesive memory. There is consistent support for a functional specialization within the MTL according to these relational processes, much of which comes from recognition memory experiments. In this study, we compared brain activation patterns associated with retrieving within-item relations (i.e., associating conceptual and sensory-perceptual object features) and item-context relations (i.e., spatial relations among objects) with respect to naturalistic autobiographical retrieval. We developed a novel paradigm that cued participants to retrieve information about past autobiographical events, non-episodic within-item relations, and non-episodic item-context relations with the perceptuomotor aspects of retrieval equated across these conditions. We used multivariate analysis techniques to extract common and distinct patterns of activity among these conditions within the MTL and across the whole brain, both in terms of spatial and temporal patterns of activity. The anterior MTL (perirhinal cortex and anterior hippocampus) was preferentially recruited for generating within-item relations later in retrieval whereas the posterior MTL (posterior parahippocampal cortex and posterior hippocampus) was preferentially recruited for generating item-context relations across the retrieval phase. These findings provide novel evidence for functional specialization within the MTL with respect to naturalistic memory retrieval. © 2015 Wiley Periodicals, Inc.

Differential item functioning of the patient-reported outcomes information system (PROMIS®) pain interference item bank by language (Spanish versus English).

Science.gov (United States)

Paz, Sylvia H; Spritzer, Karen L; Reise, Steven P; Hays, Ron D

2017-06-01

About 70% of Latinos, 5 years old or older, in the United States speak Spanish at home. Measurement equivalence of the PROMIS ® pain interference (PI) item bank by language of administration (English versus Spanish) has not been evaluated. A sample of 527 adult Spanish-speaking Latinos completed the Spanish version of the 41-item PROMIS ® pain interference item bank. We evaluate dimensionality, monotonicity and local independence of the Spanish-language items. Then we evaluate differential item functioning (DIF) using ordinal logistic regression with item response theory scores estimated from DIF-free "anchor" items. One of the 41 items in the Spanish version of the PROMIS ® PI item bank was identified as having significant uniform DIF. English- and Spanish-speaking subjects with the same level of pain interference responded differently to 1 of the 41 items in the PROMIS ® PI item bank. This item was not retained due to proprietary issues. The original English language item parameters can be used when estimating PROMIS ® PI scores.

Nonparametric predictive inference for reliability of a k-out-of-m:G system with multiple component types

International Nuclear Information System (INIS)

Aboalkhair, Ahmad M.; Coolen, Frank P.A.; MacPhee, Iain M.

2014-01-01

Nonparametric predictive inference for system reliability has recently been presented, with specific focus on k-out-of-m:G systems. The reliability of systems is quantified by lower and upper probabilities of system functioning, given binary test results on components, taking uncertainty about component functioning and indeterminacy due to limited test information explicitly into account. Thus far, systems considered were series configurations of subsystems, with each subsystem i a k i -out-of-m i :G system which consisted of only one type of components. Key results are briefly summarized in this paper, and as an important generalization new results are presented for a single k-out-of-m:G system consisting of components of multiple types. The important aspects of redundancy and diversity for such systems are discussed. - Highlights: • New results on nonparametric predictive inference for system reliability. • Prediction of system reliability based on test data for components. • New insights on system redundancy optimization and diversity. • Components that appear inferior in tests may be included to enhance redundancy

A strategy for optimizing item-pool management

NARCIS (Netherlands)

Ariel, A.; van der Linden, Willem J.; Veldkamp, Bernard P.

2006-01-01

Item-pool management requires a balancing act between the input of new items into the pool and the output of tests assembled from it. A strategy for optimizing item-pool management is presented that is based on the idea of a periodic update of an optimal blueprint for the item pool to tune item

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

Science.gov (United States)

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Evaluation of the Fecal Incontinence Quality of Life Scale (FIQL) using item response theory reveals limitations and suggests revisions.

Science.gov (United States)

Peterson, Alexander C; Sutherland, Jason M; Liu, Guiping; Crump, R Trafford; Karimuddin, Ahmer A

2018-06-01

The Fecal Incontinence Quality of Life Scale (FIQL) is a commonly used patient-reported outcome measure for fecal incontinence, often used in clinical trials, yet has not been validated in English since its initial development. This study uses modern methods to thoroughly evaluate the psychometric characteristics of the FIQL and its potential for differential functioning by gender. This study analyzed prospectively collected patient-reported outcome data from a sample of patients prior to colorectal surgery. Patients were recruited from 14 general and colorectal surgeons in Vancouver Coastal Health hospitals in Vancouver, Canada. Confirmatory factor analysis was used to assess construct validity. Item response theory was used to evaluate test reliability, describe item-level characteristics, identify local item dependence, and test for differential functioning by gender. 236 patients were included for analysis, with mean age 58 and approximately half female. Factor analysis failed to identify the lifestyle, coping, depression, and embarrassment domains, suggesting lack of construct validity. Items demonstrated low difficulty, indicating that the test has the highest reliability among individuals who have low quality of life. Five items are suggested for removal or replacement. Differential test functioning was minimal. This study has identified specific improvements that can be made to each domain of the Fecal Incontinence Quality of Life Scale and to the instrument overall. Formatting, scoring, and instructions may be simplified, and items with higher difficulty developed. The lifestyle domain can be used as is. The embarrassment domain should be significantly revised before use.

Use of seismic experience data for technical evaluation of commercial grade replacement items

International Nuclear Information System (INIS)

Cushing, R.W.; Campbell, R.D.

1991-01-01

This paper reports that the purchase of commercial grade replacement items which are dedicated for use in nuclear safety related systems requires a technical evaluation for those items which cannot be purchased as like-for-like replacements. One of the requirements for performing a technical evaluation is verification of seismic adequacy. Studying the performance and failure modes of equipment which has been subjected to strong motion earthquakes has provided the industry with the ability to predict the behavior characteristics of many types of commonly replaced components and parts. The use of Seismic Experience Data has developed into an efficient means of satisfying the seismic adequacy requirement

The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

OpenAIRE

Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric

2017-01-01

This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...

Item response theory - A first approach

Science.gov (United States)

Nunes, Sandra; Oliveira, Teresa; Oliveira, Amílcar

2017-07-01

The Item Response Theory (IRT) has become one of the most popular scoring frameworks for measurement data, frequently used in computerized adaptive testing, cognitively diagnostic assessment and test equating. According to Andrade et al. (2000), IRT can be defined as a set of mathematical models (Item Response Models - IRM) constructed to represent the probability of an individual giving the right answer to an item of a particular test. The number of Item Responsible Models available to measurement analysis has increased considerably in the last fifteen years due to increasing computer power and due to a demand for accuracy and more meaningful inferences grounded in complex data. The developments in modeling with Item Response Theory were related with developments in estimation theory, most remarkably Bayesian estimation with Markov chain Monte Carlo algorithms (Patz & Junker, 1999). The popularity of Item Response Theory has also implied numerous overviews in books and journals, and many connections between IRT and other statistical estimation procedures, such as factor analysis and structural equation modeling, have been made repeatedly (Van der Lindem & Hambleton, 1997). As stated before the Item Response Theory covers a variety of measurement models, ranging from basic one-dimensional models for dichotomously and polytomously scored items and their multidimensional analogues to models that incorporate information about cognitive sub-processes which influence the overall item response process. The aim of this work is to introduce the main concepts associated with one-dimensional models of Item Response Theory, to specify the logistic models with one, two and three parameters, to discuss some properties of these models and to present the main estimation procedures.

Semi-nonparametric estimates of interfuel substitution in US energy demand

Energy Technology Data Exchange (ETDEWEB)

Serletis, A.; Shahmoradi, A. [University of Calgary, Calgary, AB (Canada). Dept. of Economics

2008-09-15

This paper focuses on the demand for crude oil, natural gas, and coal in the United States in the context of two globally flexible functional forms - the Fourier and the Asymptotically Ideal Model (AIM) - estimated subject to full regularity, using methods suggested over 20 years ago by Gallant and Golub (Gallant, A. Ronald and Golub, Gene H. Imposing Curvature Restrictions on Flexible Functional Forms. Journal of Econometrics 26 (1984), 295-321) and recently used by Serletis and Shahmoradi (Serletis, A., Shahmoradi, A., 2005. Semi-nonparametric estimates of the demand for money in the United States. Macroeconomic Dynamics 9, 542-559) in the monetary demand systems literature. We provide a comparison in terms of a full set of elasticities and also a policy perspective, using (for the first time) parameter estimates that are consistent with global regularity.

Adult Attachment Ratings (AAR): an item response theory analysis.

Science.gov (United States)

Pilkonis, Paul A; Kim, Yookyung; Yu, Lan; Morse, Jennifer Q

2014-01-01

The Adult Attachment Ratings (AAR) include 3 scales for anxious, ambivalent attachment (excessive dependency, interpersonal ambivalence, and compulsive care-giving), 3 for avoidant attachment (rigid self-control, defensive separation, and emotional detachment), and 1 for secure attachment. The scales include items (ranging from 6-16 in their original form) scored by raters using a 3-point format (0 = absent, 1 = present, and 2 = strongly present) and summed to produce a total score. Item response theory (IRT) analyses were conducted with data from 414 participants recruited from psychiatric outpatient, medical, and community settings to identify the most informative items from each scale. The IRT results allowed us to shorten the scales to 5-item versions that are more precise and easier to rate because of their brevity. In general, the effective range of measurement for the scales was 0 to +2 SDs for each of the attachment constructs; that is, from average to high levels of attachment problems. Evidence for convergent and discriminant validity of the scales was investigated by comparing them with the Experiences of Close Relationships-Revised (ECR-R) scale and the Kobak Attachment Q-sort. The best consensus among self-reports on the ECR-R, informant ratings on the ECR-R, and expert judgments on the Q-sort and the AAR emerged for anxious, ambivalent attachment. Given the good psychometric characteristics of the scale for secure attachment, however, this measure alone might provide a simple alternative to more elaborate procedures for some measurement purposes. Conversion tables are provided for the 7 scales to facilitate transformation from raw scores to IRT-calibrated (theta) scores.

Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies.

Science.gov (United States)

Palacios, Julia A; Minin, Vladimir N

2013-03-01

Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method. Copyright © 2013, The International Biometric Society.

Investigating Separate and Concurrent Approaches for Item Parameter Drift in 3PL Item Response Theory Equating

Science.gov (United States)

Arce-Ferrer, Alvaro J.; Bulut, Okan

2017-01-01

This study examines separate and concurrent approaches to combine the detection of item parameter drift (IPD) and the estimation of scale transformation coefficients in the context of the common item nonequivalent groups design with the three-parameter item response theory equating. The study uses real and synthetic data sets to compare the two…

Analysis of differential item functioning in the depression item bank from the Patient Reported Outcome Measurement Information System (PROMIS: An item response theory approach

Directory of Open Access Journals (Sweden)

JOSEPH P. EIMICKE

2009-06-01

Full Text Available The aims of this paper are to present findings related to differential item functioning (DIF in the Patient Reported Outcome Measurement Information System (PROMIS depression item bank, and to discuss potential threats to the validity of results from studies of DIF. The 32 depression items studied were modified from several widely used instruments. DIF analyses of gender, age and education were performed using a sample of 735 individuals recruited by a survey polling firm. DIF hypotheses were generated by asking content experts to indicate whether or not they expected DIF to be present, and the direction of the DIF with respect to the studied comparison groups. Primary analyses were conducted using the graded item response model (for polytomous, ordered response category data with likelihood ratio tests of DIF, accompanied by magnitude measures. Sensitivity analyses were performed using other item response models and approaches to DIF detection. Despite some caveats, the items that are recommended for exclusion or for separate calibration were "I felt like crying" and "I had trouble enjoying things that I used to enjoy." The item, "I felt I had no energy," was also flagged as evidencing DIF, and recommended for additional review. On the one hand, false DIF detection (Type 1 error was controlled to the extent possible by ensuring model fit and purification. On the other hand, power for DIF detection might have been compromised by several factors, including sparse data and small sample sizes. Nonetheless, practical and not just statistical significance should be considered. In this case the overall magnitude and impact of DIF was small for the groups studied, although impact was relatively large for some individuals.

76 FR 60474 - Commercial Item Handbook

Science.gov (United States)

2011-09-29

... DEPARTMENT OF DEFENSE Defense Acquisition Regulations System Commercial Item Handbook AGENCY.... SUMMARY: DoD has updated its Commercial Item Handbook. The purpose of the Handbook is to help acquisition personnel develop sound business strategies for procuring commercial items. DoD is seeking industry input on...

The effect of sociodemographic (mis)match between interviewers and respondents on unit and item nonresponse in Belgium.

Science.gov (United States)

Vercruyssen, Anina; Wuyts, Celine; Loosveldt, Geert

2017-09-01

Interviewer characteristics affect nonresponse and measurement errors in face-to-face surveys. Some studies have shown that mismatched sociodemographic characteristics - for example gender - affect people's behavior when interacting with an interviewer at the door and during the survey interview, resulting in more nonresponse. We investigate the effect of sociodemographic (mis)matching on nonresponse in two successive rounds of the European Social Survey in Belgium. As such, we replicate the analyses of the effect of (mis)matching gender and age on unit nonresponse on the one hand, and of gender, age and education level (mis)matching on item nonresponse on the other hand. Recurring effects of sociodemographic (mis)match are found for both unit and item nonresponse. Copyright © 2017 Elsevier Inc. All rights reserved.

A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy

Directory of Open Access Journals (Sweden)

Archer Kellie J

2008-02-01

Full Text Available Abstract Background With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN to those with normal functioning allograft. Results The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. Conclusion We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been

Evaluation of the Multiple Sclerosis Walking Scale-12 (MSWS-12) in a Dutch sample: Application of item response theory.

Science.gov (United States)

Mokkink, Lidwine Brigitta; Galindo-Garre, Francisca; Uitdehaag, Bernard Mj

2016-12-01

The Multiple Sclerosis Walking Scale-12 (MSWS-12) measures walking ability from the patients' perspective. We examined the quality of the MSWS-12 using an item response theory model, the graded response model (GRM). A total of 625 unique Dutch multiple sclerosis (MS) patients were included. After testing for unidimensionality, monotonicity, and absence of local dependence, a GRM was fit and item characteristics were assessed. Differential item functioning (DIF) for the variables gender, age, duration of MS, type of MS and severity of MS, reliability, total test information, and standard error of the trait level (θ) were investigated. Confirmatory factor analysis showed a unidimensional structure of the 12 items of the scale, explaining 88% of the variance. Item 2 did not fit into the GRM model. Reliability was 0.93. Items 8 and 9 (of the 11 and 12 item version respectively) showed DIF on the variable severity, based on the Expanded Disability Status Scale (EDSS). However, the EDSS is strongly related to the content of both items. Our results confirm the good quality of the MSWS-12. The trait level (θ) scores and item parameters of both the 12- and 11-item versions were highly comparable, although we do not suggest to change the content of the MSWS-12. © The Author(s), 2016.

Spare Items validation

International Nuclear Information System (INIS)

Fernandez Carratala, L.

1998-01-01

There is an increasing difficulty for purchasing safety related spare items, with certifications by manufacturers for maintaining the original qualifications of the equipment of destination. The main reasons are, on the top of the logical evolution of technology, applied to the new manufactured components, the quitting of nuclear specific production lines and the evolution of manufacturers quality systems, originally based on nuclear codes and standards, to conventional industry standards. To face this problem, for many years different Dedication processes have been implemented to verify whether a commercial grade element is acceptable to be used in safety related applications. In the same way, due to our particular position regarding the spare part supplies, mainly from markets others than the american, C.N. Trillo has developed a methodology called Spare Items Validation. This methodology, which is originally based on dedication processes, is not a single process but a group of coordinated processes involving engineering, quality and management activities. These are to be performed on the spare item itself, its design control, its fabrication and its supply for allowing its use in destinations with specific requirements. The scope of application is not only focussed on safety related items, but also to complex design, high cost or plant reliability related components. The implementation in C.N. Trillo has been mainly curried out by merging, modifying and making the most of processes and activities which were already being performed in the company. (Author)

HIV/AIDS knowledge among men who have sex with men: applying the item response theory.

Science.gov (United States)

Gomes, Raquel Regina de Freitas Magalhães; Batista, José Rodrigues; Ceccato, Maria das Graças Braga; Kerr, Lígia Regina Franco Sansigolo; Guimarães, Mark Drew Crosland

2014-04-01

To evaluate the level of HIV/AIDS knowledge among men who have sex with men in Brazil using the latent trait model estimated by Item Response Theory. Multicenter, cross-sectional study, carried out in ten Brazilian cities between 2008 and 2009. Adult men who have sex with men were recruited (n = 3,746) through Respondent Driven Sampling. HIV/AIDS knowledge was ascertained through ten statements by face-to-face interview and latent scores were obtained through two-parameter logistic modeling (difficulty and discrimination) using Item Response Theory. Differential item functioning was used to examine each item characteristic curve by age and schooling. Overall, the HIV/AIDS knowledge scores using Item Response Theory did not exceed 6.0 (scale 0-10), with mean and median values of 5.0 (SD = 0.9) and 5.3, respectively, with 40.7% of the sample with knowledge levels below the average. Some beliefs still exist in this population regarding the transmission of the virus by insect bites, by using public restrooms, and by sharing utensils during meals. With regard to the difficulty and discrimination parameters, eight items were located below the mean of the scale and were considered very easy, and four items presented very low discrimination parameter (items contributed to the inaccuracy of the measurement of knowledge among those with median level and above. Item Response Theory analysis, which focuses on the individual properties of each item, allows measures to be obtained that do not vary or depend on the questionnaire, which provides better ascertainment and accuracy of knowledge scores. Valid and reliable scales are essential for monitoring HIV/AIDS knowledge among the men who have sex with men population over time and in different geographic regions, and this psychometric model brings this advantage.

Nonparametric Bayes Classification and Hypothesis Testing on Manifolds

Science.gov (United States)

Bhattacharya, Abhishek; Dunson, David

2012-01-01

Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provide simple sufficient conditions for large support and weak and strong posterior consistency in estimating both the joint distribution of the response and predictors and the conditional distribution of the response. Focusing on a Dirichlet process prior for the mixing measure, these conditions hold using von Mises-Fisher kernels when the manifold is the unit hypersphere. In this case, Bayesian methods are developed for efficient posterior computation using slice sampling. Next we develop Bayesian nonparametric methods for testing whether there is a difference in distributions between groups of observations on the manifold having unknown densities. We prove consistency of the Bayes factor and develop efficient computational methods for its calculation. The proposed classification and testing methods are evaluated using simulation examples and applied to spherical data applications. PMID:22754028

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Directory of Open Access Journals (Sweden)

González Adriana

2016-01-01

Full Text Available Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF. Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting. The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.

Item Analysis in Introductory Economics Testing.

Science.gov (United States)

Tinari, Frank D.

1979-01-01

Computerized analysis of multiple choice test items is explained. Examples of item analysis applications in the introductory economics course are discussed with respect to three objectives: to evaluate learning; to improve test items; and to help improve classroom instruction. Problems, costs and benefits of the procedures are identified. (JMD)

A review of the effects on IRT item parameter estimates with a focus on misbehaving common items in test equating

Directory of Open Access Journals (Sweden)

Michalis P Michaelides

2010-10-01

Full Text Available Many studies have investigated the topic of change or drift in item parameter estimates in the context of Item Response Theory. Content effects, such as instructional variation and curricular emphasis, as well as context effects, such as the wording, position, or exposure of an item have been found to impact item parameter estimates. The issue becomes more critical when items with estimates exhibiting differential behavior across test administrations are used as common for deriving equating transformations. This paper reviews the types of effects on IRT item parameter estimates and focuses on the impact of misbehaving or aberrant common items on equating transformations. Implications relating to test validity and the judgmental nature of the decision to keep or discard aberrant common items are discussed, with recommendations for future research into more informed and formal ways of dealing with misbehaving common items.

A Review of the Effects on IRT Item Parameter Estimates with a Focus on Misbehaving Common Items in Test Equating.

Science.gov (United States)

Michaelides, Michalis P

2010-01-01

Many studies have investigated the topic of change or drift in item parameter estimates in the context of item response theory (IRT). Content effects, such as instructional variation and curricular emphasis, as well as context effects, such as the wording, position, or exposure of an item have been found to impact item parameter estimates. The issue becomes more critical when items with estimates exhibiting differential behavior across test administrations are used as common for deriving equating transformations. This paper reviews the types of effects on IRT item parameter estimates and focuses on the impact of misbehaving or aberrant common items on equating transformations. Implications relating to test validity and the judgmental nature of the decision to keep or discard aberrant common items are discussed, with recommendations for future research into more informed and formal ways of dealing with misbehaving common items.

A Comparison of the 27-Item and 12-Item Intolerance of Uncertainty Scales

Science.gov (United States)

Khawaja, Nigar G.; Yu, Lai Ngo Heidi

2010-01-01

The 27-item Intolerance of Uncertainty Scale (IUS) has become one of the most frequently used measures of Intolerance of Uncertainty. More recently, an abridged, 12-item version of the IUS has been developed. The current research used clinical (n = 50) and non-clinical (n = 56) samples to examine and compare the psychometric properties of both…

Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R

Directory of Open Access Journals (Sweden)

Terrance D. Savitsky

2016-08-01

Full Text Available We present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains. This data structure arises from combining periodically published government survey statistics, such as are reported in the Current Population Study (CPS. The CPS publishes monthly, by-state estimates of employment levels, where each state expresses a noisy time series. Published state-level estimates from the CPS are composed from household survey responses in a model-free manner and express high levels of volatility due to insufficient sample sizes. Existing software solutions borrow information over a modeled time-based dependence to extract a de-noised time series for each domain. These solutions, however, ignore the dependence among the domains that may be additionally leveraged to improve estimation efficiency. The growfunctions package offers two fully nonparametric mixture models that simultaneously estimate both a time and domain-indexed dependence structure for a collection of time series: (1 A Gaussian process (GP construction, which is parameterized through the covariance matrix, estimates a latent function for each domain. The covariance parameters of the latent functions are indexed by domain under a Dirichlet process prior that permits estimation of the dependence among functions across the domains: (2 An intrinsic Gaussian Markov random field prior construction provides an alternative to the GP that expresses different computation and estimation properties. In addition to performing denoised estimation of latent functions from published domain estimates, growfunctions allows estimation of collections of functions for observation units (e.g., households, rather than aggregated domains, by accounting for an informative sampling design under which the probabilities for inclusion of observation units are related to the response variable. growfunctions includes plot

More is not Always Better: The Relation between Item Response and Item Response Time in Raven’s Matrices

Directory of Open Access Journals (Sweden)

Frank Goldhammer

2015-03-01

Full Text Available The role of response time in completing an item can have very different interpretations. Responding more slowly could be positively related to success as the item is answered more carefully. However, the association may be negative if working faster indicates higher ability. The objective of this study was to clarify the validity of each assumption for reasoning items considering the mode of processing. A total of 230 persons completed a computerized version of Raven’s Advanced Progressive Matrices test. Results revealed that response time overall had a negative effect. However, this effect was moderated by items and persons. For easy items and able persons the effect was strongly negative, for difficult items and less able persons it was less negative or even positive. The number of rules involved in a matrix problem proved to explain item difficulty significantly. Most importantly, a positive interaction effect between the number of rules and item response time indicated that the response time effect became less negative with an increasing number of rules. Moreover, exploratory analyses suggested that the error type influenced the response time effect.

Response pattern of depressive symptoms among college students: What lies behind items of the Beck Depression Inventory-II?

Science.gov (United States)

de Sá Junior, Antonio Reis; de Andrade, Arthur Guerra; Andrade, Laura Helena; Gorenstein, Clarice; Wang, Yuan-Pang

2018-07-01

This study examines the response pattern of depressive symptoms in a nationwide student sample, through item analyses of a rating scale by both classical test theory (CTT) and item response theory (IRT). The 21-item Beck Depression Inventory-II (BDI-II) was administered to 12,711 college students. First, the psychometric properties of the scale were described. Thereafter, the endorsement probability of depressive symptom in each scale item was analyzed through CTT and IRT. Graphical plots depicted the endorsement probability of scale items and intensity of depression. Three items of different difficulty level were compared through CTT and IRT approach. Four in five students reported the presence of depressive symptoms. The BDI-II items presented good reliability and were distributed along the symptomatic continuum of depression. Similarly, in both CTT and IRT approaches, the item 'changes in sleep' was easily endorsed, 'loss of interest' moderately and 'suicidal thoughts' hardly. Graphical representation of BDI-II of both methods showed much equivalence in terms of item discrimination and item difficulty. The item characteristic curve of the IRT method provided informative evaluation of item performance. The inventory was applied only in college students. Depressive symptoms were frequent psychopathological manifestations among college students. The performance of the BDI-II items indicated convergent results from both methods of analysis. While the CTT was easy to understand and to apply, the IRT was more complex to understand and to implement. Comprehensive assessment of the functioning of each BDI-II item might be helpful in efficient detection of depressive conditions in college students. Copyright © 2018 Elsevier B.V. All rights reserved.

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

Science.gov (United States)

Zhang, Tingting; Kou, S C

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.

Nonparametric tests for equality of psychometric functions.

Science.gov (United States)

García-Pérez, Miguel A; Núñez-Antón, Vicente

2017-12-07

Many empirical studies measure psychometric functions (curves describing how observers' performance varies with stimulus magnitude) because these functions capture the effects of experimental conditions. To assess these effects, parametric curves are often fitted to the data and comparisons are carried out by testing for equality of mean parameter estimates across conditions. This approach is parametric and, thus, vulnerable to violations of the implied assumptions. Furthermore, testing for equality of means of parameters may be misleading: Psychometric functions may vary meaningfully across conditions on an observer-by-observer basis with no effect on the mean values of the estimated parameters. Alternative approaches to assess equality of psychometric functions per se are thus needed. This paper compares three nonparametric tests that are applicable in all situations of interest: The existing generalized Mantel-Haenszel test, a generalization of the Berry-Mielke test that was developed here, and a split variant of the generalized Mantel-Haenszel test also developed here. Their statistical properties (accuracy and power) are studied via simulation and the results show that all tests are indistinguishable as to accuracy but they differ non-uniformly as to power. Empirical use of the tests is illustrated via analyses of published data sets and practical recommendations are given. The computer code in MATLAB and R to conduct these tests is available as Electronic Supplemental Material.

Nonparametric autocovariance estimation from censored time series by Gaussian imputation.

Science.gov (United States)

Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K

2009-02-01

One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.

Assessing T cell clonal size distribution: a non-parametric approach.

Science.gov (United States)

Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V

2014-01-01

Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.

Negative effects of item repetition on source memory.

Science.gov (United States)

Kim, Kyungmi; Yi, Do-Joon; Raye, Carol L; Johnson, Marcia K

2012-08-01

In the present study, we explored how item repetition affects source memory for new item-feature associations (picture-location or picture-color). We presented line drawings varying numbers of times in Phase 1. In Phase 2, each drawing was presented once with a critical new feature. In Phase 3, we tested memory for the new source feature of each item from Phase 2. Experiments 1 and 2 demonstrated and replicated the negative effects of item repetition on incidental source memory. Prior item repetition also had a negative effect on source memory when different source dimensions were used in Phases 1 and 2 (Experiment 3) and when participants were explicitly instructed to learn source information in Phase 2 (Experiments 4 and 5). Importantly, when the order between Phases 1 and 2 was reversed, such that item repetition occurred after the encoding of critical item-source combinations, item repetition no longer affected source memory (Experiment 6). Overall, our findings did not support predictions based on item predifferentiation, within-dimension source interference, or general interference from multiple traces of an item. Rather, the findings were consistent with the idea that prior item repetition reduces attention to subsequent presentations of the item, decreasing the likelihood that critical item-source associations will be encoded.

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

Science.gov (United States)

Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

2000-09-01

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

Examination of the PROMIS upper extremity item bank.

Science.gov (United States)

Hung, Man; Voss, Maren W; Bounsanga, Jerry; Crum, Anthony B; Tyser, Andrew R

Clinical measurement. The psychometric properties of the PROMIS v1.2 UE item bank were tested on various samples prior to its release, but have not been fully evaluated among the orthopaedic population. This study assesses the performance of the UE item bank within the UE orthopaedic patient population. The UE item bank was administered to 1197 adult patients presenting to a tertiary orthopaedic clinic specializing in hand and UE conditions and was examined using traditional statistics and Rasch analysis. The UE item bank fits a unidimensional model (outfit MNSQ range from 0.64 to 1.70) and has adequate reliabilities (person = 0.84; item = 0.82) and local independence (item residual correlations range from -0.37 to 0.34). Only one item exhibits gender differential item functioning. Most items target low levels of function. The UE item bank is a useful clinical assessment tool. Additional items covering higher functions are needed to enhance validity. Supplemental testing is recommended for patients at higher levels of function until more high function UE items are developed. 2c. Copyright © 2016 Hanley & Belfus. Published by Elsevier Inc. All rights reserved.

Development and psychometric characteristics of the SCI-QOL Bladder Management Difficulties and Bowel Management Difficulties item banks and short forms and the SCI-QOL Bladder Complications scale.

Science.gov (United States)

Tulsky, David S; Kisala, Pamela A; Tate, Denise G; Spungen, Ann M; Kirshblum, Steven C

2015-05-01

To describe the development and psychometric properties of the Spinal Cord Injury--Quality of Life (SCI-QOL) Bladder Management Difficulties and Bowel Management Difficulties item banks and Bladder Complications scale. Using a mixed-methods design, a pool of items assessing bladder and bowel-related concerns were developed using focus groups with individuals with spinal cord injury (SCI) and SCI clinicians, cognitive interviews, and item response theory (IRT) analytic approaches, including tests of model fit and differential item functioning. Thirty-eight bladder items and 52 bowel items were tested at the University of Michigan, Kessler Foundation Research Center, the Rehabilitation Institute of Chicago, the University of Washington, Craig Hospital, and the James J. Peters VA Medical Center, Bronx, NY. Seven hundred fifty-seven adults with traumatic SCI. The final item banks demonstrated unidimensionality (Bladder Management Difficulties CFI=0.965; RMSEA=0.093; Bowel Management Difficulties CFI=0.955; RMSEA=0.078) and acceptable fit to a graded response IRT model. The final calibrated Bladder Management Difficulties bank includes 15 items, and the final Bowel Management Difficulties item bank consists of 26 items. Additionally, 5 items related to urinary tract infections (UTI) did not fit with the larger Bladder Management Difficulties item bank but performed relatively well independently (CFI=0.992, RMSEA=0.050) and were thus retained as a separate scale. The SCI-QOL Bladder Management Difficulties and Bowel Management Difficulties item banks are psychometrically robust and are available as computer adaptive tests or short forms. The SCI-QOL Bladder Complications scale is a brief, fixed-length outcomes instrument for individuals with a UTI.

Common data items in seven European oesophagogastric cancer surgery registries: towards a European upper GI cancer audit (EURECCA Upper GI).

Science.gov (United States)

de Steur, W O; Henneman, D; Allum, W H; Dikken, J L; van Sandick, J W; Reynolds, J; Mariette, C; Jensen, L; Johansson, J; Kolodziejczyk, P; Hardwick, R H; van de Velde, C J H

2014-03-01

Seven countries (Denmark, France, Ireland, the Netherlands, Poland, Sweden, United Kingdom) collaborated to initiate a EURECCA (European Registration of Cancer Care) Upper GI project. The aim of this study was to identify a core dataset of shared items in the different data registries which can be used for future collaboration between countries. Item lists from all participating Upper GI cancer registries were collected. Items were scored 'present' when included in the registry, or when the items could be deducted from other items in the registry. The definition of a common item was that it was present in at least six of the seven participating countries. The number of registered items varied between 40 (Poland) and 650 (Ireland). Among the 46 shared items were data on patient characteristics, staging and diagnostics, neoadjuvant treatment, surgery, postoperative course, pathology, and adjuvant treatment. Information on non-surgical treatment was available in only 4 registries. A list of 46 shared items from seven participating Upper GI cancer registries was created, providing a basis for future quality assurance and research in Upper GI cancer treatment on a European level. Copyright © 2013 Elsevier Ltd. All rights reserved.

Psychometric Consequences of Subpopulation Item Parameter Drift

Science.gov (United States)

Huggins-Manley, Anne Corinne

2017-01-01

This study defines subpopulation item parameter drift (SIPD) as a change in item parameters over time that is dependent on subpopulations of examinees, and hypothesizes that the presence of SIPD in anchor items is associated with bias and/or lack of invariance in three psychometric outcomes. Results show that SIPD in anchor items is associated…

Location Indices for Ordinal Polytomous Items Based on Item Response Theory. Research Report. ETS RR-15-20

Science.gov (United States)

Ali, Usama S.; Chang, Hua-Hua; Anderson, Carolyn J.

2015-01-01

Polytomous items are typically described by multiple category-related parameters; situations, however, arise in which a single index is needed to describe an item's location along a latent trait continuum. Situations in which a single index would be needed include item selection in computerized adaptive testing or test assembly. Therefore single…

Few items in the thyroid-related quality of life instrument ThyPRO exhibited differential item functioning.

Science.gov (United States)

Watt, Torquil; Groenvold, Mogens; Hegedüs, Laszlo; Bonnema, Steen Joop; Rasmussen, Åse Krogh; Feldt-Rasmussen, Ulla; Bjorner, Jakob Bue

2014-02-01

To evaluate the extent of differential item functioning (DIF) within the thyroid-specific quality of life patient-reported outcome measure, ThyPRO, according to sex, age, education and thyroid diagnosis. A total of 838 patients with benign thyroid diseases completed the ThyPRO questionnaire (84 five-point items, 13 scales). Uniform and nonuniform DIF were investigated using ordinal logistic regression, testing for both statistical significance and magnitude (∆R(2) > 0.02). Scale level was estimated by the sum score, after purification. Twenty instances of DIF in 17 of the 84 items were found. Eight according to diagnosis, where the goiter scale was the one most affected, possibly due to differing perceptions in patients with auto-immune thyroid diseases compared to patients with simple goiter. Eight DIFs according to age were found, of which 5 were in positively worded items, which younger patients were more likely to endorse; one according to gender: women were more likely to report crying, and three according to educational level. The vast majority of DIF had only minor influence on the scale scores (0.1-2.3 points on the 0-100 scales), but two DIF corresponded to a difference of 4.6 and 9.8, respectively. Ordinal logistic regression identified DIF in 17 of 84 items. The potential impact of this on the present scales was low, but items displaying DIF could be avoided when developing abbreviated scales, where the potential impact of DIF (due to fewer items) will be larger.

Research on the re-establishment of the classification criteria of strategic items

Energy Technology Data Exchange (ETDEWEB)

Han, Seong Mi; Yang, Seunghyo; Shin, Dong Hoon [Korea Institute of Nuclear Nonproliferation and Control, Daejeon (Korea, Republic of)

2014-05-15

According to these export control laws and regulations, the exporters have to apply the review for classification and export licensing to their own government. In this process, a technical review institute such as Korea Institute of Nuclear Nonproliferation and Control (institute under the NSSC) are referring to Minister's Regulation for the Export and Import of Strategic Goods. In this regulation, there are many criteria to classify the strategic items to be exported. But there are some problems in these criteria. At Typical problem is that classification criteria of Trigger List Items generally is very qualitative and very obscure in contrast with Dual Use Items. So, in most cases, this characteristics of classification criteria of trigger list items have caused much trouble for stakeholders such as government and nuclear related companies. So, there were needs that the classification criteria had to be more correct, obvious and objective. To solve these problems, the past classification cases for technology were re-analyzed and the general criteria were deducted in this study. Previously mentioned, the classification process and criteria were very qualitative and very obscure for the Trigger List Items. So, the re-establishment of the classification criteria was done to solve these problems in this study. Each extracted results were shown in Tables I and II. This re-established criteria are expected to contribute to quantification, disambiguation and objectification of the classification review process. As the future works, we will establish the probability or numerical factor for the extracted criteria through statistical surveys, to make better use of these criteria. And we will push ahead with the NSSC approval to use as the classification guidelines of the trigger list items in review processes.

Loglinear multidimensional IRT models for polytomously scired Items

NARCIS (Netherlands)

Kelderman, Henk

1988-01-01

A loglinear item response theory (IRT) model is proposed that relates polytomously scored item responses to a multidimensional latent space. Each item may have a different response function where each item response may be explained by one or more latent traits. Item response functions may follow a

Wastewater Characteristics, Treatment and Disposal

OpenAIRE

Von Sperling, Marcos

2007-01-01

"Wastewater Characteristics, Treatment and Disposal is the first volume in the series Biological Wastewater Treatment, presenting an integrated view of water quality and wastewater treatment. The book covers the following topics: wastewater characteristics (flow and major constituents) impact of wastewater discharges to rivers and lakes overview of wastewater treatment systems complementary items in planning studies. This book, with its clear and practical approach, lays the foundations f...

48 CFR 852.214-72 - Alternate item(s).

Science.gov (United States)

2010-10-01

... AND FORMS SOLICITATION PROVISIONS AND CONTRACT CLAUSES Texts of Provisions and Clauses 852.214-72... 2008) Bids on []* will be given equal consideration along with bids on []** and any such bids received... [].** * Contracting officer will insert an alternate item that is considered acceptable. ** Contracting officer will...

A local non-parametric model for trade sign inference

Science.gov (United States)

Blazejewski, Adam; Coggins, Richard

2005-03-01

We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.

Rank-based permutation approaches for non-parametric factorial designs.

Science.gov (United States)

Umlauft, Maria; Konietschke, Frank; Pauly, Markus

2017-11-01

Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.

VGC analyzer: a software for statistical analysis of fully crossed multiple-reader multiple-case visual grading characteristics studies

International Nuclear Information System (INIS)

Baath, Magnus; Hansson, Jonny

2016-01-01

Visual grading characteristics (VGC) analysis is a non-parametric rank-invariant method for analysis of visual grading data. In VGC analysis, image quality ratings for two different conditions are compared by producing a VGC curve, similar to how the ratings for normal and abnormal cases in receiver operating characteristic (ROC) analysis are used to create an ROC curve. The use of established ROC software for the analysis of VGC data has therefore previously been proposed. However, the ROC analysis is based on the assumption of independence between normal and abnormal cases. In VGC analysis, this independence cannot always be assumed, e.g. if the ratings are based on the same patients imaged under both conditions. A dedicated software intended for analysis of VGC studies, which takes possible dependencies between ratings into account in the statistical analysis of a VGC study, has therefore been developed. The software-VGC Analyzer-determines the area under the VGC curve and its uncertainty using non-parametric re-sampling techniques. This article gives an introduction to VGC Analyzer, describes the types of analyses that can be performed and instructs the user about the input and output data. (authors)

A more general model for testing measurement invariance and differential item functioning.

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Bauer, Daniel J

2017-09-01

The evaluation of measurement invariance is an important step in establishing the validity and comparability of measurements across individuals. Most commonly, measurement invariance has been examined using 1 of 2 primary latent variable modeling approaches: the multiple groups model or the multiple-indicator multiple-cause (MIMIC) model. Both approaches offer opportunities to detect differential item functioning within multi-item scales, and thereby to test measurement invariance, but both approaches also have significant limitations. The multiple groups model allows 1 to examine the invariance of all model parameters but only across levels of a single categorical individual difference variable (e.g., ethnicity). In contrast, the MIMIC model permits both categorical and continuous individual difference variables (e.g., sex and age) but permits only a subset of the model parameters to vary as a function of these characteristics. The current article argues that moderated nonlinear factor analysis (MNLFA) constitutes an alternative, more flexible model for evaluating measurement invariance and differential item functioning. We show that the MNLFA subsumes and combines the strengths of the multiple group and MIMIC models, allowing for a full and simultaneous assessment of measurement invariance and differential item functioning across multiple categorical and/or continuous individual difference variables. The relationships between the MNLFA model and the multiple groups and MIMIC models are shown mathematically and via an empirical demonstration. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Does the high–tech industry consistently reduce CO{sub 2} emissions? Results from nonparametric additive regression model

Energy Technology Data Exchange (ETDEWEB)

Xu, Bin [School of Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013 (China); Research Center of Applied Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013 (China); Lin, Boqiang, E-mail: bqlin@xmu.edu.cn [Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Xiamen, Fujian 361005 (China)

2017-03-15

China is currently the world's largest carbon dioxide (CO{sub 2}) emitter. Moreover, total energy consumption and CO{sub 2} emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO{sub 2} emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO{sub 2} emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Based on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO{sub 2} emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO{sub 2} emissions in the three regions should be given adequate attention in developing effective abatement policies. - Highlights: • The nonlinear effect of the high–tech industry on CO{sub 2} emissions was investigated. • The high–tech industry yields an inverted “U–shaped” effect in the eastern region. • The high–tech industry has a positive “U–shaped” nonlinear effect in other regions. • The linear impact

Macrostructural Treatment of Multi-word Lexical Items

Directory of Open Access Journals (Sweden)

Alenka Vrbinc

2011-05-01

Full Text Available The paper discusses the macrostructural treatment of multi-word lexical items in mono- and bilingual dictionaries. First, the classification of multi-word lexical items is presented, and special attention is paid to the discussion of compounds – a specific group of multi-word lexical items that is most commonly afforded headword status but whose inclusion in the headword list may also depend on spelling. Then the inclusion of multi-word lexical items in monolingual dictionaries is dealt with in greater detail, while the results of a short survey on the inclusion of five randomly chosen multi-word lexical items in seven English monolingual dictionaries are presented. The proposals as to how to treat these five multi-word lexical items in bilingual dictionaries are presented in the section about the inclusion of multi-word lexical items in bilingual dictionaries. The conclusion is that it is most important to take the users’ needs into consideration and to make any dictionary as user friendly as possible.

Losing Items in the Psychogeriatric Nursing Home

Directory of Open Access Journals (Sweden)

J. van Hoof PhD

2016-09-01

Full Text Available Introduction: Losing items is a time-consuming occurrence in nursing homes that is ill described. An explorative study was conducted to investigate which items got lost by nursing home residents, and how this affects the residents and family caregivers. Method: Semi-structured interviews and card sorting tasks were conducted with 12 residents with early-stage dementia and 12 family caregivers. Thematic analysis was applied to the outcomes of the sessions. Results: The participants stated that numerous personal items and assistive devices get lost in the nursing home environment, which had various emotional, practical, and financial implications. Significant amounts of time are spent on trying to find items, varying from 1 hr up to a couple of weeks. Numerous potential solutions were identified by the interviewees. Discussion: Losing items often goes together with limitations to the participation of residents. Many family caregivers are reluctant to replace lost items, as these items may get lost again.

A nonparametric approach to calculate critical micelle concentrations: the local polynomial regression method

Energy Technology Data Exchange (ETDEWEB)

Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)

2004-02-01

The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)

Examining Multiple Sources of Differential Item Functioning on the Clinician & Group CAHPS® Survey

Science.gov (United States)

Rodriguez, Hector P; Crane, Paul K

2011-01-01

Objective To evaluate psychometric properties of a widely used patient experience survey. Data Sources English-language responses to the Clinician & Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS®) survey (n = 12,244) from a 2008 quality improvement initiative involving eight southern California medical groups. Methods We used an iterative hybrid ordinal logistic regression/item response theory differential item functioning (DIF) algorithm to identify items with DIF related to patient sociodemographic characteristics, duration of the physician–patient relationship, number of physician visits, and self-rated physical and mental health. We accounted for all sources of DIF and determined its cumulative impact. Principal Findings The upper end of the CG-CAHPS® performance range is measured with low precision. With sensitive settings, some items were found to have DIF. However, overall DIF impact was negligible, as 0.14 percent of participants had salient DIF impact. Latinos who spoke predominantly English at home had the highest prevalence of salient DIF impact at 0.26 percent. Conclusions The CG-CAHPS® functions similarly across commercially insured respondents from diverse backgrounds. Consequently, previously documented racial and ethnic group differences likely reflect true differences rather than measurement bias. The impact of low precision at the upper end of the scale should be clarified. PMID:22092021

Uncertainty in decision models analyzing cost-effectiveness : The joint distribution of incremental costs and effectiveness evaluated with a nonparametric bootstrap method

NARCIS (Netherlands)

Hunink, Maria; Bult, J.R.; De Vries, J; Weinstein, MC

1998-01-01

Purpose. To illustrate the use of a nonparametric bootstrap method in the evaluation of uncertainty in decision models analyzing cost-effectiveness. Methods. The authors reevaluated a previously published cost-effectiveness analysis that used a Markov model comparing initial percutaneous

‘Forget me (not?’ – Remembering forget-items versus un-cued items in directed forgetting

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Bastian eZwissler

2015-11-01

Full Text Available Humans need to be able to selectively control their memories. Here, we investigate the underlying processes in item-method directed forgetting and compare the classic active memory cues in this paradigm with a passive instruction. Typically, individual items are presented and each is followed by either a forget- or remember-instruction. On a surprise test of all items, memory is then worse for to-be-forgotten items (TBF compared to to-be-remembered items (TBR. This is thought to result from selective rehearsal of TBR, or from active inhibition of TBF, or from both. However, evidence suggests that if a forget instruction initiates active processing, paradoxical effects may also arise. To investigate the underlying mechanisms, four experiments were conducted where un-cued items (UI were introduced and recognition performance was compared between TBR, TBF and UI stimuli. Accuracy was encouraged via a performance-dependent monetary bonus. Across all experiments, including perceptually fully matched variants, memory accuracy for TBF was reduced compared to TBR, but better than for UI. Moreover, participants used a more conservative response criterion when responding to TBF stimuli. Thus, ironically, the F cue results in active processing, but this does not have inhibitory effects that would impair recognition memory beyond a un-cued baseline condition. This casts doubts on inhibitory accounts of item-method directed forgetting and is also difficult to reconcile with pure selective rehearsal of TBR. While the F-cue does induce active processing, this does not result in particularly successful forgetting. The pattern seems most consistent with the notion of ironic processing.

Diagnostics of transparent polymer coatings of metal items

Science.gov (United States)

Varepo, L. G.; Ermakova, I. N.; Nagornova, I. V.; Kondratov, A. P.

2017-08-01

The methods of visual and instrumental express diagnostics of safety critical defects and non-uniform thickness of transparent mono- and multilayer polyolefin surface coating of metal items are analyzed in the paper. The instrumental diagnostics method relates to colorimetric measuring based on effects, which appear in the polarized light for extrusion polymer coatings. A color coordinates dependence (in the color system CIE La*b*) on both HDPE / PVC coating thickness fluctuation values (from average ones) and coating interlayer or adhesion layer delaminating is shown. A variation of color characteristics in the polarized light at a liquid penetration into delaminated polymer layers is found. Measuring parameters and critical uncertainties are defined.

A Development of Group Decision Support System for Strategic Item Classification using Analytic Hierarchy Process