Thirty years of nonparametric item response theory
Molenaar, W.
2001-01-01
Relationships between a mathematical measurement model and its real-world applications are discussed. A distinction is made between large data matrices commonly found in educational measurement and smaller matrices found in attitude and personality measurement. Nonparametric methods are evaluated fo
DIF Trees: Using Classification Trees to Detect Differential Item Functioning
Vaughn, Brandon K.; Wang, Qiu
2010-01-01
A nonparametric tree classification procedure is used to detect differential item functioning for items that are dichotomously scored. Classification trees are shown to be an alternative procedure to detect differential item functioning other than the use of traditional Mantel-Haenszel and logistic regression analysis. A nonparametric…
Directory of Open Access Journals (Sweden)
Fernandez Ana
2010-05-01
Full Text Available Abstract Background Previous studies have analyzed the psychometric properties of the World Health Organization Disability Assessment Schedule II (WHO-DAS II using classical omnibus measures of scale quality. These analyses are sample dependent and do not model item responses as a function of the underlying trait level. The main objective of this study was to examine the effectiveness of the WHO-DAS II items and their options in discriminating between changes in the underlying disability level by means of item response analyses. We also explored differential item functioning (DIF in men and women. Methods The participants were 3615 adult general practice patients from 17 regions of Spain, with a first diagnosed major depressive episode. The 12-item WHO-DAS II was administered by the general practitioners during the consultation. We used a non-parametric item response method (Kernel-Smoothing implemented with the TestGraf software to examine the effectiveness of each item (item characteristic curves and their options (option characteristic curves in discriminating between changes in the underliying disability level. We examined composite DIF to know whether women had a higher probability than men of endorsing each item. Results Item response analyses indicated that the twelve items forming the WHO-DAS II perform very well. All items were determined to provide good discrimination across varying standardized levels of the trait. The items also had option characteristic curves that showed good discrimination, given that each increasing option became more likely than the previous as a function of increasing trait level. No gender-related DIF was found on any of the items. Conclusions All WHO-DAS II items were very good at assessing overall disability. Our results supported the appropriateness of the weights assigned to response option categories and showed an absence of gender differences in item functioning.
Screening Test Items for Differential Item Functioning
Longford, Nicholas T.
2014-01-01
A method for medical screening is adapted to differential item functioning (DIF). Its essential elements are explicit declarations of the level of DIF that is acceptable and of the loss function that quantifies the consequences of the two kinds of inappropriate classification of an item. Instead of a single level and a single function, sets of…
Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations
Gugushvili, S.; Spreij, P.
2014-01-01
We consider nonparametric Bayesian estimation of the drift coefficient of a multidimensional stochastic differential equation from discrete-time observations on the solution of this equation. Under suitable regularity conditions, we establish posterior consistency in this context.
A Non-Parametric Item Response Theory Evaluation of the CAGE Instrument Among Older Adults.
Abdin, Edimansyah; Sagayadevan, Vathsala; Vaingankar, Janhavi Ajit; Picco, Louisa; Chong, Siow Ann; Subramaniam, Mythily
2017-08-04
The validity of the CAGE using item response theory (IRT) has not yet been examined in older adult population. This study aims to investigate the psychometric properties of the CAGE using both non-parametric and parametric IRT models, assess whether there is any differential item functioning (DIF) by age, gender and ethnicity and examine the measurement precision at the cut-off scores. We used data from the Well-being of the Singapore Elderly study to conduct Mokken scaling analysis (MSA), dichotomous Rasch and 2-parameter logistic IRT models. The measurement precision at the cut-off scores were evaluated using classification accuracy (CA) and classification consistency (CC). The MSA showed the overall scalability H index was 0.459, indicating a medium performing instrument. All items were found to be homogenous, measuring the same construct and able to discriminate well between respondents with high levels of the construct and the ones with lower levels. The item discrimination ranged from 1.07 to 6.73 while the item difficulty ranged from 0.33 to 2.80. Significant DIF was found for 2-item across ethnic group. More than 90% (CC and CA ranged from 92.5% to 94.3%) of the respondents were consistently and accurately classified by the CAGE cut-off scores of 2 and 3. The current study provides new evidence on the validity of the CAGE from the IRT perspective. This study provides valuable information of each item in the assessment of the overall severity of alcohol problem and the precision of the cut-off scores in older adult population.
A nonparametric approach to the analysis of dichotomous item responses
Mokken, R.J.; Lewis, C.
1982-01-01
An item response theory is discussed which is based on purely ordinal assumptions about the probabilities that people respond positively to items. It is considered as a natural generalization of both Guttman scaling and classical test theory. A distinction is drawn between construction and evaluatio
An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models
Liang, Tie; Wells, Craig S.; Hambleton, Ronald K.
2014-01-01
As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting…
非参数项目反应理论回顾与展望%The Retrospect and Prospect of Non-parametric Item Response Theory
Institute of Scientific and Technical Information of China (English)
陈婧; 康春花; 钟晓玲
2013-01-01
相比参数项目反应理论，非参数项目反应理论提供了更吻合实践情境的理论框架。目前非参数项目反应理论研究主要关注参数估计方法及其比较、数据-模型拟合验证等方面，其应用研究则集中于量表修订及个性数据和项目功能差异分析，而在认知诊断理论基础上发展起来的非参数认知诊断理论更是凸显其应用优势。未来研究应更多侧重于非参数项目反应理论的实践应用，对非参数认知诊断理论的研究也值得关注，以充分发挥非参数方法在实践领域的应用优势。% Compared to parametric item response theory, non-parametric item response theory provide a more appropriate theoretical framework of practice situations. Non-parametric item response theory research focuses on parameter estimation methods and its comparison, data- model fitting verify etc. currently.Its applied research concentrate on scale amendments, personalized data and differential item functioning analysis. Non-parametric cognitive diagnostic theory which based on the parametric cognitive diagnostic theory gives prominence to the advantages of its application.To give full play to the advantages of non-parametric methods in practice,future studies should emphasis on the application of non-parametric item response theory while cognitive diagnosis of the non-parametric study is also worth of attention.
Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana
2015-03-01
The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology.
Nonparametric estimation of stochastic differential equations with sparse Gaussian processes
García, Constantino A.; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G.
2017-08-01
The application of stochastic differential equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a nonparametric method for estimating the drift and diffusion terms of SDEs from a densely observed discrete time series. The use of Gaussian processes as priors permits working directly in a function-space view and thus the inference takes place directly in this space. To cope with the computational complexity that requires the use of Gaussian processes, a sparse Gaussian process approximation is provided. This approximation permits the efficient computation of predictions for the drift and diffusion terms by using a distribution over a small subset of pseudosamples. The proposed method has been validated using both simulated data and real data from economy and paleoclimatology. The application of the method to real data demonstrates its ability to capture the behavior of complex systems.
Real and Artificial Differential Item Functioning
Andrich, David; Hagquist, Curt
2012-01-01
The literature in modern test theory on procedures for identifying items with differential item functioning (DIF) among two groups of persons includes the Mantel-Haenszel (MH) procedure. Generally, it is not recognized explicitly that if there is real DIF in some items which favor one group, then as an artifact of this procedure, artificial DIF…
Sueiro, Manuel J.; Abad, Francisco J.
2011-01-01
The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…
Estimating the Importance of Differential Item Functioning.
Rudas, Tamas; Zwick, Rebecca
1997-01-01
The mixture index of fit (T. Rudas et al, 1994) is used to estimate the fraction of a population for which differential item functioning (DIF) occurs, and this approach is compared to the Mantel Haenszel test of DIF. The proposed noniterative procedure provides information about data portions contributing to DIF. (SLD)
Estimating the Importance of Differential Item Functioning.
Rudas, Tamas; Zwick, Rebecca
1997-01-01
The mixture index of fit (T. Rudas et al, 1994) is used to estimate the fraction of a population for which differential item functioning (DIF) occurs, and this approach is compared to the Mantel Haenszel test of DIF. The proposed noniterative procedure provides information about data portions contributing to DIF. (SLD)
Detection of Differential Item Functioning Using the Lasso Approach
Magis, David; Tuerlinckx, Francis; De Boeck, Paul
2015-01-01
This article proposes a novel approach to detect differential item functioning (DIF) among dichotomously scored items. Unlike standard DIF methods that perform an item-by-item analysis, we propose the "LR lasso DIF method": logistic regression (LR) model is formulated for all item responses. The model contains item-specific intercepts,…
Nonparametric Bounds in the Presence of Item Nonresponse, Unfolding Brackets and Anchoring
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
Nonparametric Bounds on the Income Distribution in the Presence of Item Nonresponse
Vazquez-Alvarez, R.; Melenberg, B.; van Soest, A.H.O.
1999-01-01
Item nonresponse in micro surveys can lead to biased estimates of the parameters of interest if such nonresponse is nonrandom. Selection models can be used to correct for this, but parametric and semiparametric selection models require additional assumptions. Manski has recently developed a new appr
Differential PIXE analysis of Mesoamerican jewelry items
Demortier, G.; Ruvalcaba-Sil, J. L.
1996-09-01
Gold jewelry items of Mesoamerican origin (from Peru, Colombia, Mexico, etc,…) are usually cast in Tumbaga: a man-made gold-copper-silver alloy containing a large proportion of copper. In order to give the objects a colour close to that of pure gold, ancient Mesoamerican goldsmiths experimented with a procedure to eliminate less noble metals (like copper and silver) from the surface. RBS may be used to identify a possible enrichment in gold in the most external layer of the items but due to the low capability of this technique to separate scattered particles on gold and silver and due to the low Rutherford cross section for α-particles on copper by comparison with those on gold, the determination of the exact depth depletion of copper cannot be easily reached. Differential PIXE is an appropriate method to achieve this goal. It takes the relative X-ray intensities of Cu and Au lines into account. By varying the incident proton energy, this ratio is modified in a completely different way if the sample is homogeneous or exhibits a layered or depth profile structure.
Curriculum, Translation, and Differential Functioning of Measurement and Geometry Items
Emenogu, Barnabas C.; Childs, Ruth A.
2005-01-01
A test item exhibits differential item functioning (DIF) if students with the same ability find it differentially difficult. When the item is administered in French and English, differences in language difficulty and meaning are the most likely explanations. However, curriculum differences may also contribute to DIF. The responses of Ontario…
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…
Effect of Multiple Testing Adjustment in Differential Item Functioning Detection
Kim, Jihye; Oshima, T. C.
2013-01-01
In a typical differential item functioning (DIF) analysis, a significance test is conducted for each item. As a test consists of multiple items, such multiple testing may increase the possibility of making a Type I error at least once. The goal of this study was to investigate how to control a Type I error rate and power using adjustment…
Analysis of Differential Item Functioning in the NAEP History Assessment.
Zwick, Rebecca; Ercikan, Kadriye
The Mantel-Haenszel approach for investigating differential item functioning (DIF) was applied to U.S. history items that were administered as part of the National Assessment of Educational Progress (NAEP). DIF analyses were based on the responses of 7,743 students in grade 11. On some items, Blacks, Hispanics, and females performed more poorly…
Differential functioning of Bender Visual-Motor Gestalt Test items.
Sisto, Fermino Fernandes; Dos Santos, Acácia Aparecida Angeli; Noronha, Ana Paula Porto
2010-02-01
Differential Item Functioning (DIF) refers to items that do not function the same way for comparable members of different groups. The present study focuses on analyzing and classifying sex-related differential item functioning in the Bender Visual-Motor Gestalt Test. Subjects were 1,052 children attending public schools (513 boys, 539 girls, ages 6-10 years). The protocols were scored using the Bender Graduated Scoring System, which evaluates only the distortion criterion using the Rasch logistic response model. The scoring system fit the Rasch model, although two items were found to be biased by sex. When analyzing differential functioning of items for boys and girls separately, the number of differentially functioning items was equal.
Ohkubo, Jun
2011-12-01
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.
Non-parametric change-point method for differential gene expression detection.
Directory of Open Access Journals (Sweden)
Yao Wang
Full Text Available BACKGROUND: We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short, by using a single equation for detecting differential gene expression (DGE in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability. METHODOLOGY: NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods. CONCLUSIONS: Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods.
Bares, Cristina; Andrade, Fernando; Delva, Jorge; Grogan-Kaylor, Andrew; Kamata, Akihito
2012-07-01
Although much is known about the higher prevalence of anxiety and depressive disorders among adolescent females, less is known about the differential item endorsement due to gender in items of scales commonly used to measure anxiety and depression. We conducted a study to examine if adolescent males and females from Chile differed on how they endorsed the items of the Youth Self Report (YSR) anxious/depressed problem scale. We used data from a cross-sectional sample consisting of 925 participants (mean age = 14, SD 1.3, 49% females) of low to lower-middle socioeconomic status. A two-parameter logistic (2PL) IRT DIF model was fit. s revealed differential item functioning (DIF) by gender for six of the 13 items, with adolescent females being more likely to endorse a depression item while males were found more likely to endorse anxiety items. Findings suggest that items found in commonly used measures of anxiety and depression symptoms may not equally capture the true levels of these behavioural problems in adolescent males and females. Given the high levels of mental disorders in Chile and the surrounding countries, further attention should be focused on increasing the number of empirical studies examining potential gender differences in the assessment of mental health problems among Latin American populations to better aid our understanding of the phenomenology and determinants of these problems in the region.
Detecting Differential Item Functioning and Differential Test Functioning on Math School Final-exam
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- Mansyur
2016-08-01
Full Text Available This study aims at finding out the characteristics of Differential Item Functioning (DIF and Differential Test Functioning (DTF on school final-exam for Math subject based on Item Response Theory (ITR. The subjects of this study were questions and all of the students’ answer sheets chosen by using convenience sampling method and obtained 286 responses consisted of 147 male and 149 female students’ responses. The data of this study collected using documentation technique by quoting the response of Math school final-exam participants. The data analysis of this study was Item Response Theory approach with model 2P of Lord’s chi-square DIF method. This study showed that from 40 question items analysed theoretically using Item Response Theory (ITR, affected Differential Item Functioning (DIF gender was ten items and affected DIF location (area was 13 items. Meanwhile, Differential Test Functioning (DTF was benefitted for female and least profitable to citizen.
Differential Item Functioning on the International Personality Item Poolâ s Neuroticism Scale
McBride, Nadine LeBarron
2008-01-01
As use of the public-domain International Personality Item Pool (IPIP) scales has grown significantly over the past decade (Goldberg, Johnson, Eber, Hogan, Ashton, Cloninger, & Gough, 2006) research on the psychometric properties of the items and scales have become increasingly important. This research study examines the IPIP scale constructed to measure the Five Factor Model (FFM) domain of Neuroticism (as measured by the NEO-PI-R) for occurrences of differential functioning a...
Weisscher, Nadine; Glas, Cees A; Vermeulen, Marinus; De Haan, Rob J
2010-05-01
There is not a single universally accepted activity of daily living (ADL) instrument available to compare disability assessments across different patient groups. We developed a generic item bank of ADL items using item response theory, the Academic Medical Center Linear Disability Scale (ALDS). When comparing outcomes of the ALDS between patients groups, item characteristics of the ALDS should be comparable across groups. The aim of the study was to assess the differential item functioning (DIF) in a group of patients with various disorders to investigate the comparability across these groups. Cross-sectional, multicenter study including 1,283 in- and outpatients with a variety of disorders and disability levels. The sample was divided in two groups: (1) mainly neurological patients (n=497; vascular medicine, Parkinson's disease and neuromuscular disorders) and (2) patients from internal medicine (n=786; pulmonary diseases, chronic pain, rheumatoid arthritis, and geriatric patients). Eighteen of 72 ALDS items showed statistically significant DIF (P<0.01). However, the DIF could effectively be modeled by the introduction of disease-specific parameters. In the subgroups studied, DIF could be modeled in such a way that the ensemble of the items comprised a scale applicable in both groups.
Gugushvili, S.; Spreij, P.
2016-01-01
We consider the problem of non-parametric estimation of the deterministic dispersion coefficient of a linear stochastic differential equation based on discrete time observations on its solution. We take a Bayesian approach to the problem and under suitable regularity assumptions derive the posteror
Akour, Mutasem; Sabah, Saed; Hammouri, Hind
2015-01-01
The purpose of this study was to apply two types of Differential Item Functioning (DIF), net and global DIF, as well as the framework of Differential Step Functioning (DSF) to real testing data to investigate measurement invariance related to test language. Data from the Program for International Student Assessment (PISA)-2006 polytomously scored…
Differential Weighting of Items to Improve University Admission Test Validity
Eduardo Backhoff Escudero; Felipe Tirado Segura; Norma Larrazolo Reyna
2001-01-01
This paper gives an evaluation of different ways to increase university admission test criterion-related validity, by differentially weighting test items. We compared four methods of weighting multiple-choice items of the Basic Skills and Knowledge Examination (EXHCOBA): (1) punishing incorrect responses by a constant factor, (2) weighting incorrect responses, considering the levels of error, (3) weighting correct responses, considering the item’s difficulty, based on the Classic Measur...
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Stochl Jan
2012-06-01
Full Text Available Abstract Background Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Methods Scalability of data from 1 a cross-sectional health survey (the Scottish Health Education Population Survey and 2 a general population birth cohort study (the National Child Development Study illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. Results and conclusions After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items we show that all items from the 12-item General Health Questionnaire (GHQ-12 – when binary scored – were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech’s “well-being” and “distress” clinical scales. An illustration of ordinal item analysis
Stochl, Jan; Jones, Peter B; Croudace, Tim J
2012-06-11
Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Scalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12)--when binary scored--were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech's "well-being" and "distress" clinical scales). An illustration of ordinal item analysis confirmed that all 14 positively worded items of the Warwick-Edinburgh Mental
2010-04-01
... 17 Commodity and Securities Exchanges 3 2010-04-01 2010-04-01 false Inclusion of items... INDENTURE ACT OF 1939 Formal Requirements § 260.7a-16 Inclusion of items, differentiation between items and... application, statement, or report shall contain all of the items of the form as well as the answers...
Kalaycioglu, Dilara Bakan; Berberoglu, Giray
2011-01-01
This study is aimed to detect differential item functioning (DIF) items across gender groups, analyze item content for the possible sources of DIF, and eventually investigate the effect of DIF items on the criterion-related validity of the test scores in the quantitative section of the university entrance examination (UEE) in Turkey. The reason…
Disentangling Sources of Differential Item Functioning in Multilanguage Assessments.
Ercikan, Kadriye
2002-01-01
Disentangled sources of differential item functioning (DIF) in a multilanguage assessment for which multiple factors were expected to be causing DIF. Data for the Third International Mathematics and Science study for four countries and two languages (3,000 to 11,000 cases in each comparison group) reveal amounts and sources of DIF. (SLD)
Using Mixed Methods to Interpret Differential Item Functioning
Benítez, Isabel; Padilla, José-Luis; Hidalgo Montesinos, María Dolores; Sireci, Stephen G.
2016-01-01
Analysis of differential item functioning (DIF) is often used to determine if cross-lingual assessments are equivalent across languages. However, evidence on the causes of cross-lingual DIF is still evasive. Expert appraisal is a qualitative method useful for obtaining detailed information about problematic elements in the different linguistic…
Exploring Crossing Differential Item Functioning by Gender in Mathematics Assessment
Ong, Yoke Mooi; Williams, Julian; Lamprianou, Iasonas
2015-01-01
The purpose of this article is to explore crossing differential item functioning (DIF) in a test drawn from a national examination of mathematics for 11-year-old pupils in England. An empirical dataset was analyzed to explore DIF by gender in a mathematics assessment. A two-step process involving the logistic regression (LR) procedure for…
Detecting Differential Item Functioning Using Logistic Regression Procedures.
Swaminathan, Hariharan; Rogers, H. Jane
1990-01-01
A logistic regression model for characterizing differential item functioning (DIF) between two groups is presented. A distinction is drawn between uniform and nonuniform DIF in terms of model parameters. A statistic for testing the hypotheses of no DIF is developed, and simulation studies compare it with the Mantel-Haenszel procedure. (Author/TJH)
Differential Item Functioning on Two Tests of EFL Proficiency.
Ryan, Katherine E.; Bachman, Lyle F.
1992-01-01
The extent to which items from the Test of English as a Foreign Language and the First Certificate in English function differently for test-takers of equal ability from different native language and curricular backgrounds was investigated. Results suggest a need for methods like logistic regression to examine nonuniform differential item…
Differential Item Functioning on the Graduate Management Admission Test.
O'Neill, Kathleen A.; And Others
The purpose of this study was to identify differentially functioning items on operational administrations of the Graduate Management Admission Test (GMAT) through the use of the Mantel-Haenszel statistic. Retrospective analyses of data collected over 3 years are reported for black/white and female/male comparisons for the Verbal and Quantitative…
Differential item functioning analysis by applying multiple comparison procedures.
Eusebi, Paolo; Kreiner, Svend
2015-01-01
Analysis within a Rasch measurement framework aims at development of valid and objective test score. One requirement of both validity and objectivity is that items do not show evidence of differential item functioning (DIF). A number of procedures exist for the assessment of DIF including those based on analysis of contingency tables by Mantel-Haenszel tests and partial gamma coefficients. The aim of this paper is to illustrate Multiple Comparison Procedures (MCP) for analysis of DIF relative to a variable defining a very large number of groups, with an unclear ordering with respect to the DIF effect. We propose a single step procedure controlling the false discovery rate for DIF detection. The procedure applies for both dichotomous and polytomous items. In addition to providing evidence against a hypothesis of no DIF, the procedure also provides information on subset of groups that are homogeneous with respect to the DIF effect. A stepwise MCP procedure for this purpose is also introduced.
Differential Weighting of Items to Improve University Admission Test Validity
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Eduardo Backhoff Escudero
2001-05-01
Full Text Available This paper gives an evaluation of different ways to increase university admission test criterion-related validity, by differentially weighting test items. We compared four methods of weighting multiple-choice items of the Basic Skills and Knowledge Examination (EXHCOBA: (1 punishing incorrect responses by a constant factor, (2 weighting incorrect responses, considering the levels of error, (3 weighting correct responses, considering the item’s difficulty, based on the Classic Measurement Theory, and (4 weighting correct responses, considering the item’s difficulty, based on the Item Response Theory. Results show that none of these methods increased the instrument’s predictive validity, although they did improve its concurrent validity. It was concluded that it is appropriate to score the test by simply adding up correct responses.
An Effect Size Measure for Raju's Differential Functioning for Items and Tests
Wright, Keith D.; Oshima, T. C.
2015-01-01
This study established an effect size measure for differential functioning for items and tests' noncompensatory differential item functioning (NCDIF). The Mantel-Haenszel parameter served as the benchmark for developing NCDIF's effect size measure for reporting moderate and large differential item functioning in test items. The effect size of…
Detecting Differential Item Functioning and Differential Test Functioning on Math School Final-exam
- Mansyur; - Muliana
2016-01-01
This study aims at finding out the characteristics of Differential Item Functioning (DIF) and Differential Test Functioning (DTF) on school final-exam for Math subject based on Item Response Theory (ITR). The subjects of this study were questions and all of the students’ answer sheets chosen by using convenience sampling method and obtained 286 responses consisted of 147 male and 149 female students’ responses. The data of this study collected using documentation technique by quoting the resp...
Zwick, Rebecca; Thayer, Dorothy T.
Several recent studies have investigated the application of statistical inference procedures to the analysis of differential item functioning (DIF) in test items that are scored on an ordinal scale. Mantel's extension of the Mantel-Haenszel test is a possible hypothesis-testing method for this purpose. The development of descriptive statistics for…
DEFF Research Database (Denmark)
Watt, Torquil; Grønvold, Mogens; Hegedüs, Laszlo;
2014-01-01
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.......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....
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…
Shi, Yang; Chinnaiyan, Arul M; Jiang, Hui
2015-07-01
High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data. The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/. jianghui@umich.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Schnipke, Deborah L.; Roussos, Louis A.; Pashley, Peter J.
Differential item functioning (DIF) analyses are conducted to investigate how items function in various subgroups. The Mantel-Haenszel (MH) DIF statistic is used at the Law School Admission Council and other testing companies. When item functioning can be well-described in terms of a one- or two-parameter logistic item response theory (IRT) model…
Zwick, Rebecca; And Others
Simulated data were used to investigate the performance of modified versions of the Mantel-Haenszel and standardization methods of differential item functioning (DIF) analysis in computer-adaptive tests (CATs). Each "examinee" received 25 items out of a 75-item pool. A three-parameter logistic item response model was assumed, and…
Raju, Nambury S.; Fortmann-Johnson, Kristen A.; Kim, Wonsuk; Morris, Scott B.; Nering, Michael L.; Oshima, T. C.
2009-01-01
The recent study of Oshima, Raju, and Nanda proposes the item parameter replication (IPR) method for assessing statistical significance of the noncompensatory differential item functioning (NCDIF) index within the differential functioning of items and tests (DFIT) framework. Previous Monte Carlo simulations have found that the appropriate cutoff…
Differential item functioning in the figure classification test
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E. van Zyl
1998-06-01
Full Text Available The elimination of unfair discrimination and cultural bias of any kind, is a contentious workplace issue in contemporary South Africa. To ensure fairness in testing, psychometric instruments are subjected to empirical investigations for the detection of possible bias that could lead to selection decisions constituting unfair discrimination. This study was conducted to explore the possible existence of differential item functioning (DIF, or potential bias, in the Figure Classification Test (A121 by means of the Mantel-Haenszel chi-square technique. The sample consisted of 498 men at a production company in the Western Cape. Although statistical analysis revealed significant differences between the mean test scores of three racial groups on the test, very few items were identified as having statistically significant DIF. The possibility is discussed that, despite the presence of some DIF, the differences between the means may not be due to the measuring instrument itself being biased/ but rather to extraneous sources of variation, such as the unequal education and socio-economic backgrounds of the racial groups. It was concluded that there is very little evidence of item bias in the test. Opsomming Die uitskakeling van onregverdige diskriminasie en kultuursydigheid van enige aard, is tans 'n omstrede kwessie in die werkpiek in Suid-Afrika. Ten einde regverdigheid in toetsing te verseker, word psigomefrriese toetse onderwerp aan empiriese ondersoeke na die moontlikheid van sydigheid wat kan lei tot keuringsbesluite wat onregverdige diskriminasie meebring. Hierdie ondersoek is ondemeem om die moontlikheid van differensiele itemfunksionering (DIF, of potensiële sydigheid, in die Figuurindelingtoets (A121, met behulp van die Mantel-Haenszel chikwadraattegniek, te ondersoek. Die steekproef het bestaan uit 498 mans by 'n produksiemaatskappy in die Wes-Kaap. Alhoewel statistiese ontleding beduidende verskille in gemiddelde toetstellings van drie
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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.
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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.
Steca, Patrizia; Monzani, Dario; Greco, Andrea; Chiesi, Francesca; Primi, Caterina
2015-06-01
This study is aimed at testing the measurement properties of the Life Orientation Test-Revised (LOT-R) for the assessment of dispositional optimism by employing item response theory (IRT) analyses. The LOT-R was administered to a large sample of 2,862 Italian adults. First, confirmatory factor analyses demonstrated the theoretical conceptualization of the construct measured by the LOT-R as a single bipolar dimension. Subsequently, IRT analyses for polytomous, ordered response category data were applied to investigate the items' properties. The equivalence of the items across gender and age was assessed by analyzing differential item functioning. Discrimination and severity parameters indicated that all items were able to distinguish people with different levels of optimism and adequately covered the spectrum of the latent trait. Additionally, the LOT-R appears to be gender invariant and, with minor exceptions, age invariant. Results provided evidence that the LOT-R is a reliable and valid measure of dispositional optimism.
Differential Item Functioning Analysis of the 2003-04 NHANES Physical Activity Questionnaire
Gao, Yong; Zhu, Weimo
2011-01-01
Using differential item functioning (DIF) analyses, this study examined whether there were any DIF items in the National Health and Nutrition Examination Survey (NHANES) physical activity (PA) questionnaire. A subset of adult data from the 2003-04 NHANES study (n = 3,083) was used. PA items related to respondents' occupational, transportation,…
Differential Item Functioning Analysis of the 2003-04 NHANES Physical Activity Questionnaire
Gao, Yong; Zhu, Weimo
2011-01-01
Using differential item functioning (DIF) analyses, this study examined whether there were any DIF items in the National Health and Nutrition Examination Survey (NHANES) physical activity (PA) questionnaire. A subset of adult data from the 2003-04 NHANES study (n = 3,083) was used. PA items related to respondents' occupational, transportation,…
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Pieter Schaap
2001-02-01
Full Text Available The objective of this article is to present the results of an investigation into the item and test characteristics of two tests of the Potential Index Batteries (PIB in terms of differential item functioning (DIP and the effect thereof on test scores of different race groups. The English Vocabulary (Index 12 and Spelling Tests (Index 22 of the PIB were analysed for white, black and coloured South Africans. Item response theory (IRT methods were used to identify items which function differentially for white, black and coloured race groups. Opsomming Die doel van hierdie artikel is om die resultate van n ondersoek na die item- en toetseienskappe van twee PIB (Potential Index Batteries toetse in terme van itemsydigheid en die invloed wat dit op die toetstellings van rassegroepe het, weer te gee. Die Potential Index Batteries (PIB se Engelse Woordeskat (Index 12 en Spellingtoetse (Index 22 is ten opsigte van blanke, swart en gekleurde Suid-Afrikaners ontleed. Itemresponsteorie (IRT is gebruik om items te identifiseer wat as sydig (DIP vir die onderskeie rassegroepe beskou kan word.
Comparative Racial Analysis of Enlisted Advancement Exams: Item Differentiation
1977-01-01
New York: Holt, Rinehart, & Winston, 1963. Henryssen, S. Gathering, analyzing, and using data on test items. In R. L. Thorndike (Ed.), Educational ...validation of test items. Journal of Educational Psychology , 1939, 30, 674-680. (See Appendix) Lawshe, C. H., Jr. A nomograph for estimating the...N. Planning the objective test. In R. L. Thorndike (Ed.), Educational measurement (2nd ed.). Washington, D.C.: American Council on Education , 1971
Item Discrimination and Type I Error in the Detection of Differential Item Functioning
Li, Yanju; Brooks, Gordon P.; Johanson, George A.
2012-01-01
In 2009, DeMars stated that when impact exists there will be Type I error inflation, especially with larger sample sizes and larger discrimination parameters for items. One purpose of this study is to present the patterns of Type I error rates using Mantel-Haenszel (MH) and logistic regression (LR) procedures when the mean ability between the…
Item Analysis and Differential Item Functioning of a Brief Conduct Problem Screen
Wu, Johnny; King, Kevin M.; Witkiewitz, Katie; Racz, Sarah Jensen; McMahon, Robert J.
2012-01-01
Research has shown that boys display higher levels of childhood conduct problems than girls, and Black children display higher levels than White children, but few studies have tested for scalar equivalence of conduct problems across gender and race. The authors conducted a 2-parameter item response theory (IRT) model to examine item…
Interpretation of differential item functioning analyses using external review
DEFF Research Database (Denmark)
Scott, Neil W; Fayers, Peter M; Aaronson, Neil K
2010-01-01
considered. Relatively few examples of blinded item reviews were identified, and these were mostly from educational studies. A case study using blinded bilingual reviewers alongside translation DIF analyses of a health-related quality of life instrument is described. Future researchers should consider...
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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.
Teresi, Jeanne A.; Ocepek-Welikson, Katja; Kleinman, Marjorie; Eimicke, Joseph P.; Crane, Paul K.; Jones, Richard N.; Lai, Jin-shei; Choi, Seung W.; Hays, Ron D.; Reeve, Bryce B.; Reise, Steven P.; Pilkonis, Paul A.; Cella, David
2009-01-01
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. PMID:20336180
Oshima, T. C.; Raju, Nambury S.; Nanda, Alice O.
2006-01-01
A new item parameter replication method is proposed for assessing the statistical significance of the noncompensatory differential item functioning (NCDIF) index associated with the differential functioning of items and tests framework. In this new method, a cutoff score for each item is determined by obtaining a (1-alpha ) percentile rank score…
Beinicke, Andrea; Pässler, Katja; Hell, Benedikt
2014-01-01
The study investigates consequences of eliminating items showing gender-specific differential item functioning (DIF) on the psychometric structure of a standard RIASEC interest inventory. Holland's hexagonal model was tested for structural invariance using a confirmatory methodological approach (confirmatory factor analysis and randomization…
Kelderman, Henk; Macready, George B.
1990-01-01
Loglinear latent class models are used to detect differential item functioning (DIF). These models are formulated in such a manner that the attribute to be assessed may be continuous, as in a Rasch model, or categorical, as in Latent Class Mastery models. Further, an item may exhibit DIF with respec
The Impact of Missing Data on the Detection of Nonuniform Differential Item Functioning
Finch, W. Holmes
2011-01-01
Missing information is a ubiquitous aspect of data analysis, including responses to items on cognitive and affective instruments. Although the broader statistical literature describes missing data methods, relatively little work has focused on this issue in the context of differential item functioning (DIF) detection. Such prior research has…
A Monte Carlo Study Investigating Missing Data, Differential Item Functioning, and Effect Size
Garrett, Phyllis
2009-01-01
The use of polytomous items in assessments has increased over the years, and as a result, the validity of these assessments has been a concern. Differential item functioning (DIF) and missing data are two factors that may adversely affect assessment validity. Both factors have been studied separately, but DIF and missing data are likely to occur…
Beinicke, Andrea; Pässler, Katja; Hell, Benedikt
2014-01-01
The study investigates consequences of eliminating items showing gender-specific differential item functioning (DIF) on the psychometric structure of a standard RIASEC interest inventory. Holland's hexagonal model was tested for structural invariance using a confirmatory methodological approach (confirmatory factor analysis and randomization…
Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul
2011-01-01
We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…
Differential Item Functioning Analysis of the Mental, Emotional, and Bodily Toughness Inventory
Gao, Yong; Mack, Mick G.; Ragan, Moira A.; Ragan, Brian
2012-01-01
In this study the authors used differential item functioning analysis to examine if there were items in the Mental, Emotional, and Bodily Toughness Inventory functioning differently across gender and athletic membership. A total of 444 male (56.3%) and female (43.7%) participants (30.9% athletes and 69.1% non-athletes) responded to the Mental,…
Raykov, Tenko; Marcoulides, George A.; Lee, Chun-Lung; Chang, Chi
2013-01-01
This note is concerned with a latent variable modeling approach for the study of differential item functioning in a multigroup setting. A multiple-testing procedure that can be used to evaluate group differences in response probabilities on individual items is discussed. The method is readily employed when the aim is also to locate possible…
Gomez, Rapson
2012-01-01
Objective: Generalized partial credit model, which is based on item response theory (IRT), was used to test differential item functioning (DIF) for the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed.), inattention (IA), and hyperactivity/impulsivity (HI) symptoms across boys and girls. Method: To accomplish this, parents completed…
DEFF Research Database (Denmark)
Bjorner, Jakob Bue; Pejtersen, Jan Hyld
2010-01-01
AIMS: To evaluate the construct validity of the Copenhagen Psychosocial Questionnaire II (COPSOQ II) by means of tests for differential item functioning (DIF) and differential item effect (DIE). METHODS: We used a Danish general population postal survey (n = 4,732 with 3,517 wage earners) with a ...... shortform measures and to improve the conceptual framework, items and scales of the COPSOQ II. CONCLUSIONS: We conclude that tests of DIF and DIE are useful for evaluating construct validity.......) with a one-year register based follow up for long-term sickness absence. DIF was evaluated against age, gender, education, social class, public/private sector employment, and job type using ordinal logistic regression. DIE was evaluated against job satisfaction and self-rated health (using ordinal logistic...
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Pollard Beth
2012-12-01
Full Text Available Abstract Background The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC is a widely used patient reported outcome in osteoarthritis. An important, but frequently overlooked, aspect of validating health outcome measures is to establish if items exhibit differential item functioning (DIF. That is, if respondents have the same underlying level of an attribute, does the item give the same score in different subgroups or is it biased towards one subgroup or another. The aim of the study was to explore DIF in the Likert format WOMAC for the first time in a UK osteoarthritis population with respect to demographic, social, clinical and psychological factors. Methods The sample comprised a community sample of 763 people with osteoarthritis who participated in the Somerset and Avon Survey of Health. The WOMAC was explored for DIF by gender, age, social deprivation, social class, employment status, distress, body mass index and clinical factors. Ordinal regression models were used to identify DIF items. Results After adjusting for age, two items were identified for the physical functioning subscale as having DIF with age identified as the DIF factor for 2 items, gender for 1 item and body mass index for 1 item. For the WOMAC pain subscale, for people with hip osteoarthritis one item was identified with age-related DIF. The impact of the DIF items rarely had a significant effect on the conclusions of group comparisons. Conclusions Overall, the WOMAC performed well with only a small number of DIF items identified. However, as DIF items were identified in for the WOMAC physical functioning subscale it would be advisable to analyse data taking into account the possible impact of the DIF items when weight, gender or especially age effects, are the focus of interest in UK-based osteoarthritis studies. Similarly for the WOMAC pain subscale in people with hip osteoarthritis it would be worthwhile to analyse data taking into account the
Thibodeau, Michel A; Leonard, Rachel C; Abramowitz, Jonathan S; Riemann, Bradley C
2015-12-01
The Dimensional Obsessive-Compulsive Scale (DOCS) is a promising measure of obsessive-compulsive disorder (OCD) symptoms but has received minimal psychometric attention. We evaluated the utility and reliability of DOCS scores. The study included 832 students and 300 patients with OCD. Confirmatory factor analysis supported the originally proposed four-factor structure. DOCS total and subscale scores exhibited good to excellent internal consistency in both samples (α = .82 to α = .96). Patient DOCS total scores reduced substantially during treatment (t = 16.01, d = 1.02). DOCS total scores discriminated between students and patients (sensitivity = 0.76, 1 - specificity = 0.23). The measure did not exhibit gender-based differential item functioning as tested by Mantel-Haenszel chi-square tests. Expected response options for each item were plotted as a function of item response theory and demonstrated that DOCS scores incrementally discriminate OCD symptoms ranging from low to extremely high severity. Incremental differences in DOCS scores appear to represent unbiased and reliable differences in true OCD symptom severity.
Roussos, Louis A.; Schnipke, Deborah L.; Pashley, Peter J.
The Mantel-Haenszel (MH) differential item functioning (DIF) parameter for uniform DIF is well defined when item responses follow the two-parameter-logistic (2PPL) item response function (IRF), but not when they follow the three-parameter-logistic (3PL) IRF, the model typically used with multiple choice items. This research report presents a…
DEFF Research Database (Denmark)
Scott, Neil W; Fayers, Peter M; Aaronson, Neil K
2010-01-01
Differential item functioning (DIF) methods can be used to determine whether different subgroups respond differently to particular items within a health-related quality of life (HRQoL) subscale, after allowing for overall subgroup differences in that scale. This article reviews issues that arise ...... when testing for DIF in HRQoL instruments. We focus on logistic regression methods, which are often used because of their efficiency, simplicity and ease of application....
DEFF Research Database (Denmark)
Scott, Neil W.; Fayers, Peter M.; Aaronson, Neil K.
2010-01-01
Differential item functioning (DIF) methods can be used to determine whether different subgroups respond differently to particular items within a health-related quality of life (HRQoL) subscale, after allowing for overall subgroup differences in that scale. This article reviews issues that arise...... when testing for DIF in HRQoL instruments. We focus on logistic regression methods, which are often used because of their efficiency, simplicity and ease of application....
[XS-DIF: program for analysis of Differential Item Functioning in Excel].
Ordóñez, Xavier G; Romero, Sonia J
2007-02-01
XS-DIF is a program for detection of Differential Item Functioning (DIF) using Item Response Theory (IRT). It calculates Lords Chi-Square, Raju's Signed Area and Unsigned Area, and Kim and Cohen's Closed-interval signed area and Closed-interval unsigned area. XS-DIF was designed to be executed in Excel 2000 and it has a capacity of analysis of up to 100 items. It is useful to support data analysis of research projects and in detection and teaching processes in DIF.
Tan, Xuan; Xiang, Bihua; Dorans, Neil J.; Qu, Yanxuan
2010-01-01
The nature of the matching criterion (usually the total score) in the study of differential item functioning (DIF) has been shown to impact the accuracy of different DIF detection procedures. One of the topics related to the nature of the matching criterion is whether the studied item should be included. Although many studies exist that suggest…
Zebehazy, Kim T.; Zigmond, Naomi; Zimmerman, George J.
2012-01-01
Introduction: This study investigated differential item functioning (DIF) of test items on Pennsylvania's Alternate System of Assessment (PASA) for students with visual impairments and severe cognitive disabilities and what the reasons for the differences may be. Methods: The Wilcoxon signed ranks test was used to analyze differences in the scores…
Murray, Aja Louise; Booth, Tom; McKenzie, Karen
2015-04-01
The Learning Disability Screening Questionnaire (LDSQ; McKenzie & Paxton, 2006) was developed as a brief screen for intellectual disability. Although several previous studies have evaluated the LDSQ with respect to its utility as a clinical and research tool, no studies have considered the fairness of the test across males and females. In the current study we, therefore, used a multi-group item response theory approach to assess differential item functioning across gender in a sample of 211 males and 132 females assessed in clinical and forensic settings. Although the test did not show evidence of differential item functioning by gender, it was necessary to exclude one item due to estimation problems and to combine two very highly related items (concerning reading and writing ability) into a single literacy item Thus, in addition to being generally supportive of the utility of the LDSQ, our results also highlight possible areas of weakness in the tool and suggest possible amendments that could be made to test content to improve the test in future revisions. Copyright © 2014 Elsevier Ltd. All rights reserved.
Institute of Scientific and Technical Information of China (English)
康春花; 任平; 曾平飞
2015-01-01
Examinations help students learn more efficiently by filling their learning gaps. To achieve this goal, we have to differentiate students who have from those who have not mastered a set of attributes as measured by the test through cognitive diagnostic assessment. K-means cluster analysis, being a nonparametric cognitive diagnosis method requires the Q-matrix only, which reflects the relationship between attributes and items. This does not require the estimation of the parameters, so is independent of sample size, simple to operate, and easy to understand. Previous research use the sum score vectors or capability scores vector as the clustering objects. These methods are only adaptive for dichotomous data. Structural response items are, however, the main type used in examinations, particularly as required in recent reforms. On the basis of previous research, this paper puts forward a method to calculate a capability matrix reflecting the mastery level on skills and is applicable to grade response items. Our study included four parts. First, we introduced the K-means cluster diagnosis method which has been adapted for dichotomous data. Second, we expanded the K-means cluster diagnosis method for grade response data (GRCDM). Third, in Part Two, we investigated the performance of the method introduced using a simulation study. Fourth, we investigated the performance of the method in an empirical study. The simulation study focused on three factors. First, the sample size was set to be 100, 500, and 1000. Second, the percentage of random errors was manipulated to be 5%, 10%, and 20%. Third, it had four hierarchies, as proposed by Leighton. All experimental conditions composed of seven attributes, different items according to hierarchies. Simulation results showed that: (1) GRCDM had a high pattern match ratio (PMR) and high marginal match ratio (MMR). This method was shown to be feasible in cognitive diagnostic assessment. (2) The classification accuracy (MMR and PMR
Overcoming the effects of differential skewness of test items in scale construction
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Johann M. Schepers
2004-10-01
Full Text Available The principal objective of the study was to develop a procedure for overcoming the effects of differential skewness of test items in scale construction. It was shown that the degree of skewness of test items places an upper limit on the correlations between the items, regardless of the contents of the items. If the items are ordered in terms of skewness the resulting inter correlation matrix forms a simplex or a pseudo simplex. Factoring such a matrix results in a multiplicity of factors, most of which are artifacts. A procedure for overcoming this problem was demonstrated with items from the Locus of Control Inventory (Schepers, 1995. The analysis was based on a sample of 1662 first year university students. Opsomming Die hoofdoel van die studie was om ’n prosedure te ontwikkel om die gevolge van differensiële skeefheid van toetsitems, in skaalkonstruksie, teen te werk. Daar is getoon dat die graad van skeefheid van toetsitems ’n boonste grens plaas op die korrelasies tussen die items ongeag die inhoud daarvan. Indien die items gerangskik word volgens graad van skeefheid, sal die interkorrelasiematriks van die items ’n simpleks of pseudosimpleks vorm. Indien so ’n matriks aan faktorontleding onderwerp word, lei dit tot ’n veelheid van faktore waarvan die meerderheid artefakte is. ’n Prosedure om hierdie probleem te bowe te kom, is gedemonstreer met behulp van die items van die Lokus van Beheer-vraelys (Schepers, 1995. Die ontledings is op ’n steekproef van 1662 eerstejaaruniversiteitstudente gebaseer.
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Huseyin Selvi
2014-04-01
Results: It was observed as a result of analyses via the method of Mantel Haenszel based on 7 exams that contained 100 multiple choice items each that only 6 items out of 700 included significant levels of Differential Item Functioning. It was also seen that item averages of foreign national students for the rest of all 694 items were lower compared to other students; however this difference was not statistically significant. Conclusion: According to the expert opinion for 6 items; long sentence structure, negative item root structure and some words that regarded as traditional play a role in the formation of Differential Item Functioning was found. It can be offered that we must not use long sentence structure, negative item root structure and some words that regarded as traditional for preparing exams to ensure validity of exams. [Cukurova Med J 2014; 39(2.000: 240-247
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Frances M. Yang
2011-12-01
Full Text Available Object naming tests are commonly included in neuropsychological test batteries. Differential item functioning (DIF in these tests due to cultural and language differences may compromise the validity of cognitive measures in diverse populations. We evaluated 26 object naming items for DIF due to Spanish and English language translations among Latinos (n=1,159, mean age of 70.5 years old (Standard Deviation (SD±7.2, using the following four item response theory-based ap-proaches: Mplus/Multiple Indicator, Multiple Causes (Mplus/MIMIC; Muthén & Muthén, 1998-2011, Item Response Theory Likelihood Ratio Differential Item Functioning (IRTLRDIF/MULTILOG; Thissen, 1991, 2001, difwithpar/Parscale (Crane, Gibbons, Jolley, & van Belle, 2006; Muraki & Bock, 2003, and Differential Functioning of Items and Tests/MULTILOG (DFIT/MULTILOG; Flowers, Oshima, & Raju, 1999; Thissen, 1991. Overall, there was moderate to near perfect agreement across methods. Fourteen items were found to exhibit DIF and 5 items observed consistently across all methods, which were more likely to be answered correctly by individuals tested in Spanish after controlling for overall ability.
Yang, Frances M; Heslin, Kevin C; Mehta, Kala M; Yang, Cheng-Wu; Ocepek-Welikson, Katja; Kleinman, Marjorie; Morales, Leo S; Hays, Ron D; Stewart, Anita L; Mungas, Dan; Jones, Richard N; Teresi, Jeanne A
2011-01-01
Object naming tests are commonly included in neuropsychological test batteries. Differential item functioning (DIF) in these tests due to cultural and language differences may compromise the validity of cognitive measures in diverse populations. We evaluated 26 object naming items for DIF due to Spanish and English language translations among Latinos (n=1,159), mean age of 70.5 years old (Standard Deviation (SD)±7.2), using the following four item response theory-based approaches: Mplus/Multiple Indicator, Multiple Causes (Mplus/MIMIC; Muthén & Muthén, 1998-2011), Item Response Theory Likelihood Ratio Differential Item Functioning (IRTLRDIF/MULTILOG; Thissen, 1991, 2001), difwithpar/Parscale (Crane, Gibbons, Jolley, & van Belle, 2006; Muraki & Bock, 2003), and Differential Functioning of Items and Tests/MULTILOG (DFIT/MULTILOG; Flowers, Oshima, & Raju, 1999; Thissen, 1991). Overall, there was moderate to near perfect agreement across methods. Fourteen items were found to exhibit DIF and 5 items observed consistently across all methods, which were more likely to be answered correctly by individuals tested in Spanish after controlling for overall ability.
Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning
Li, Zhushan
2014-01-01
Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…
Examining Type I Error and Power for Detection of Differential Item and Testlet Functioning
Lee, Young-Sun; Cohen, Allan; Toro, Maritsa
2009-01-01
In this study, the effectiveness of detection of differential item functioning (DIF) and testlet DIF using SIBTEST and Poly-SIBTEST were examined in tests composed of testlets. An example using data from a reading comprehension test showed that results from SIBTEST and Poly-SIBTEST were not completely consistent in the detection of DIF and testlet…
A Robust Outlier Approach to Prevent Type I Error Inflation in Differential Item Functioning
Magis, David; De Boeck, Paul
2012-01-01
The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the ability level by the simple raw score is a source…
Stability of Differential Item Functioning over a Single Population in Survey Data
Dodeen, Hamzeh
2004-01-01
This study investigates the stability of differential item functioning (DIF) in survey data. Surveys are conducted periodically, and their results are often reported by aggregating responses. Estimating the stability of DIF across subsets of a survey population can be an important indicator in determining the likelihood of DIF stability over…
Using Multiple-Variable Matching to Identify Cultural Sources of Differential Item Functioning
Wu, Amery D.; Ercikan, Kadriye
2006-01-01
Identifying the sources of differential item functioning (DIF) in international assessments is very challenging, because such sources are often nebulous and intertwined. Even though researchers frequently focus on test translation and content area, few actually go beyond these factors to investigate other cultural sources of DIF. This article…
A robust outlier approach to prevent type I error inflation in Differential Item Functioning
Magis, D.; de Boeck, P.
2012-01-01
The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the a
Rudas, Tamas; Zwick, Rebecca
A method is proposed to assess the importance of differential item functioning (DIF) by estimating the largest possible fraction of the population in which DIF does not occur, or equivalently, the smallest possible portion of the population in which DIF may occur. The approach is based on latent class (C. C. Clogg, 1981) or mixture concepts, and…
An Introduction to Missing Data in the Context of Differential Item Functioning
Banks, Kathleen
2015-01-01
This article introduces practitioners and researchers to the topic of missing data in the context of differential item functioning (DIF), reviews the current literature on the issue, discusses implications of the review, and offers suggestions for future research. A total of nine studies were reviewed. All of these studies determined what effect…
DEFF Research Database (Denmark)
Scott, Neil W; Fayers, Peter M; Aaronson, Neil K;
2009-01-01
Differential item functioning (DIF) analyses are increasingly used to evaluate health-related quality of life (HRQoL) instruments, which often include relatively short subscales. Computer simulations were used to explore how various factors including scale length affect analysis of DIF by ordinal...
Finch, W. Holmes
2016-01-01
Differential item functioning (DIF) assessment is a crucial component in test construction, serving as the primary way in which instrument developers ensure that measures perform in the same way for multiple groups within the population. When such is not the case, scores may not accurately reflect the trait of interest for all individuals in the…
Differential Item Functioning By Sex and Race in The Hogan Personality Inventory
Sheppard, Richard; Han, Kyunghee; Colarelli, Stephen M.; Dai, Guangdong; King, Daniel W.
2006-01-01
The authors examined measurement bias in the Hogan Personality Inventory by investigating differential item functioning (DIF) across sex and two racial groups (Caucasian and Black). The sample consisted of 1,579 Caucasians (1,023 men, 556 women) and 523 Blacks (321 men, 202 women) who were applying for entry-level, unskilled jobs in factories.…
Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning
Li, Zhushan
2014-01-01
Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…
Lee, HwaYoung; Beretvas, S. Natasha
2014-01-01
Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…
Ilich, Maria O.
Psychometricians and test developers evaluate standardized tests for potential bias against groups of test-takers by using differential item functioning (DIF). English language learners (ELLs) are a diverse group of students whose native language is not English. While they are still learning the English language, they must take their standardized tests for their school subjects, including science, in English. In this study, linguistic complexity was examined as a possible source of DIF that may result in test scores that confound science knowledge with a lack of English proficiency among ELLs. Two years of fifth-grade state science tests were analyzed for evidence of DIF using two DIF methods, Simultaneous Item Bias Test (SIBTest) and logistic regression. The tests presented a unique challenge in that the test items were grouped together into testlets---groups of items referring to a scientific scenario to measure knowledge of different science content or skills. Very large samples of 10, 256 students in 2006 and 13,571 students in 2007 were examined. Half of each sample was composed of Spanish-speaking ELLs; the balance was comprised of native English speakers. The two DIF methods were in agreement about the items that favored non-ELLs and the items that favored ELLs. Logistic regression effect sizes were all negligible, while SIBTest flagged items with low to high DIF. A decrease in socioeconomic status and Spanish-speaking ELL diversity may have led to inconsistent SIBTest effect sizes for items used in both testing years. The DIF results for the testlets suggested that ELLs lacked sufficient opportunity to learn science content. The DIF results further suggest that those constructed response test items requiring the student to draw a conclusion about a scientific investigation or to plan a new investigation tended to favor ELLs.
Liu, Yang; Magnus, Brooke E; Thissen, David
2016-06-01
Differential item functioning (DIF), referring to between-group variation in item characteristics above and beyond the group-level disparity in the latent variable of interest, has long been regarded as an important item-level diagnostic. The presence of DIF impairs the fit of the single-group item response model being used, and calls for either model modification or item deletion in practice, depending on the mode of analysis. Methods for testing DIF with continuous covariates, rather than categorical grouping variables, have been developed; however, they are restrictive in parametric forms, and thus are not sufficiently flexible to describe complex interaction among latent variables and covariates. In the current study, we formulate the probability of endorsing each test item as a general bivariate function of a unidimensional latent trait and a single covariate, which is then approximated by a two-dimensional smoothing spline. The accuracy and precision of the proposed procedure is evaluated via Monte Carlo simulations. If anchor items are available, we proposed an extended model that simultaneously estimates item characteristic functions (ICFs) for anchor items, ICFs conditional on the covariate for non-anchor items, and the latent variable density conditional on the covariate-all using regression splines. A permutation DIF test is developed, and its performance is compared to the conventional parametric approach in a simulation study. We also illustrate the proposed semiparametric DIF testing procedure with an empirical example.
Directory of Open Access Journals (Sweden)
Tülin Acar
2012-01-01
Full Text Available The aim of this research is to compare the result of the differential item functioning (DIF determining with hierarchical generalized linear model (HGLM technique and the results of the DIF determining with logistic regression (LR and item response theory–likelihood ratio (IRT-LR techniques on the test items. For this reason, first in this research, it is determined whether the students encounter DIF with HGLM, LR, and IRT-LR techniques according to socioeconomic status (SES, in the Turkish, Social Sciences, and Science subtest items of the Secondary School Institutions Examination. When inspecting the correlations among the techniques in terms of determining the items having DIF, it was discovered that there was significant correlation between the results of IRT-LR and LR techniques in all subtests; merely in Science subtest, the results of the correlation between HGLM and IRT-LR techniques were found significant. DIF applications can be made on test items with other DIF analysis techniques that were not taken to the scope of this research. The analysis results, which were determined by using the DIF techniques in different sample sizes, can be compared.
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Hamdollah Ravand
2015-05-01
Full Text Available The present study used the two-level testlet response model (MMMT-2 to assess impact, differential item functioning (DIF, and differential testlet functioning (DTLF in a reading comprehension test. The data came from 21,641 applicants into English Masters’ programs at Iranian state universities. Testlet effects were estimated, and items and testlets that were functioning differentially for test takers of different genders and majors were identified. Also parameter estimates obtained under MMMT-2 and those obtained under the two-level hierarchical generalized linear model (HGLM-2 were compared. The results indicated that ability estimates obtained under the two models were significantly different at the lower and upper ends of the ability distribution. In addition, it was found that ignoring local item dependence (LID would result in overestimation of the precision of the ability estimates. As for the difficulty of the items, the estimates obtained under the two models were almost the same, but standard errors were significantly different.
Differential item functioning (DIF) in the EORTC QLQ-C30
DEFF Research Database (Denmark)
Scott, Neil W; Fayers, Peter M; Aaronson, Neil K;
2009-01-01
Differential item functioning (DIF) analyses can be used to explore translation, cultural, gender or other differences in the performance of quality of life (QoL) instruments. These analyses are commonly performed using "baseline" or pretreatment data. We previously reported DIF analyses to examine...... the pattern of item responses for translations of the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 QoL instrument, using only data collected prior to cancer treatment. We now compare the consistency of these results with similar analyses of on-treatment and off...
Holweger, Nancy; Taylor, Grace
The fifth-grade and eighth-grade science items on a state performance assessment were compared for differential item functioning (DIF) due to gender. The grade 5 sample consisted of 8,539 females and 8,029 males and the grade 8 sample consisted of 7,477 females and 7,891 males. A total of 30 fifth grade items and 26 eighth grade items were…
[Analysis of the gender variable in the EDTC using differential item functioning techniques].
Escorial, Sergio; Navas, María J
2006-05-01
The aim of this work is to analyze the gender differences in the scales of a recently constructed test: the so-called EDTC. This test measures the following traits: sensation seeking, fearlessness, and impulsivity. Gender differences will be studied using Differential Item Functioning (DIF) techniques, in order to determine whether these differences are true differences in the assessed dimensions or if, on the contrary, they are the result of a mere artefact of the measuring instrument used. The methods used to study DIF are standardization, SIBTEST, logistic regression, Lord's chi 2 test, and indices based on the DFIT model. Despite the fact that some items with DIF exist, the gender differences observed seem to be the result of true differences in the measured personality constructs and they don't seem to be artificially produced by a bias in the test items.
Ethnic differential item functioning in the assessment of quality of life in cancer patients
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Gotay Carolyn C
2005-10-01
Full Text Available Abstract Background Past research has shown that Filipino cancer patients report lower levels of quality of life (QoL than other ethnic groups. One possible explanation for this is that Filipinos do not define QoL in the same manner as others, resulting in bias in their assessments. Hence, Filipinos would not necessarily have lower QoL. Methods Item response theory methods were used to assess differential item functioning (DIF in the quality of life (measured by the EORTC QLQ-C30 of cancer patients across four ethnic groups (Caucasian, Filipino, Hawaiian, and Japanese. The sample consisted of 359 cancer patients. Results Results showed the presence of DIF on several items, indicating ethnic differences in the assessment of quality of life. Relative to the Caucasian and Japanese groups, items related to physical functioning, cognitive functioning, social functioning, nausea and vomiting, and financial difficulties exhibited DIF for Filipinos. On these items Filipinos exhibited either higher or lower QoL scores, even though their overall QoL was the same. Conclusion This evidence may explain why Filipinos have previously been found to have lower overall QoL. Although Filipinos score lower on QoL than other groups, this may not reflect lower QoL, but rather differences in how QoL is defined. The presence of DIF did not appear, however, to alter the psychometric properties of the QLQ-C30.
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…
Berberoglu, Giray
1995-01-01
Item characteristic curves were compared across gender and socioeconomic status (SES) groups for the university entrance mathematics examination in Turkey to see if any group had an advantage in solving computation, word-problem, or geometry questions. Differential item functioning was found, and patterns are discussed. (SLD)
Yasuda, Tomoyuki; Lei, Pui-Wa; Suen, Hoi K.
2007-01-01
This study examines the differential item functioning (DIF) of the English version and the Japanese-translated version of the Multiple Affect Adjective Check List--Revised (MAACL-R) using the logistic regression (LR) procedure. The results of the LR are supplemented by multiple group confirmatory factor analysis (MGCFA). A total of five items are…
Koo, Jin; Becker, Betsy Jane; Kim, Young-Suk
2014-01-01
In this study, differential item functioning (DIF) trends were examined for English language learners (ELLs) versus non-ELL students in third and tenth grades on a large-scale reading assessment. To facilitate the analyses, a meta-analytic DIF technique was employed. The results revealed that items requiring knowledge of words and phrases in…
DEFF Research Database (Denmark)
Scott, Neil W; Fayers, Peter M; Aaronson, Neil K;
2009-01-01
Differential item functioning (DIF) analyses are commonly used to evaluate health-related quality of life (HRQoL) instruments. There is, however, a lack of consensus as to how to assess the practical impact of statistically significant DIF results.......Differential item functioning (DIF) analyses are commonly used to evaluate health-related quality of life (HRQoL) instruments. There is, however, a lack of consensus as to how to assess the practical impact of statistically significant DIF results....
Lewis, Tené T; Yang, Frances M; Jacobs, Elizabeth A; Fitchett, George
2012-03-01
The authors examined the impact of race/ethnicity on responses to the Everyday Discrimination Scale, one of the most widely used discrimination scales in epidemiologic and public health research. Participants were 3,295 middle-aged US women (African-American, Caucasian, Chinese, Hispanic, and Japanese) from the Study of Women's Health Across the Nation (SWAN) baseline examination (1996-1997). Multiple-indicator, multiple-cause models were used to examine differential item functioning (DIF) on the Everyday Discrimination Scale by race/ethnicity. After adjustment for age, education, and language of interview, meaningful DIF was observed for 3 (out of 10) items: "receiving poorer service in restaurants or stores," "being treated as if you are dishonest," and "being treated with less courtesy than other people" (all P's discrimination differed slightly for women of different racial/ethnic groups, with certain "public" experiences appearing to have more salience for African-American and Chinese women and "dishonesty" having more salience for racial/ethnic minority women overall. "Courtesy" appeared to have more salience for Hispanic women only in comparison with African-American women. Findings suggest that the Everyday Discrimination Scale could potentially be used across racial/ethnic groups as originally intended. However, researchers should use caution with items that demonstrated DIF.
Consolidation differentially modulates schema effects on memory for items and associations.
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Marlieke T R van Kesteren
Full Text Available Newly learned information that is congruent with a preexisting schema is often better remembered than information that is incongruent. This schema effect on memory has previously been associated to more efficient encoding and consolidation mechanisms. However, this effect is not always consistently supported in the literature, with differential schema effects reported for different types of memory, different retrieval cues, and the possibility of time-dependent effects related to consolidation processes. To examine these effects more directly, we tested participants on two different types of memory (item recognition and associative memory for newly encoded visuo-tactile associations at different study-test intervals, thus probing memory retrieval accuracy for schema-congruent and schema-incongruent items and associations at different time points (t = 0, t = 20, and t = 48 hours after encoding. Results show that the schema effect on visual item recognition only arises after consolidation, while the schema effect on associative memory is already apparent immediately after encoding, persisting, but getting smaller over time. These findings give further insight into different factors influencing the schema effect on memory, and can inform future schema experiments by illustrating the value of considering effects of memory type and consolidation on schema-modulated retrieval.
Differential Item Functioning of WHOQOL-BREF in nine Iberoamerican countries
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Sonia Benítez-Borrego
2016-07-01
Full Text Available WHOQOL-BREF measures the individual’s perception on their personal situation in contrast to their expectations, goals, standards and concerns. Previous results did not support the original factor structure in a sample of 9 Iberoamerican countries. However, Differential Item Functioning (DIF has yet to be thoroughly addressed in these populations. Therefore, the main purpose of this study was to analyze DIF in Iberoamerican countries. WHOQOL-BREF was administered to a sample of 1972 individuals from nine Spanish-speaking countries and ages between 17 and 34 years (mean = 21.21, SD = 3.40, 62.5% women. In order to assess the DIF, each item was modeled through a proportional odds logistic regression with nationality in the linear predictor. All models were statistically non-equivalent to the null models and the proportion of correct classification of the models ranging from 0.336 to 0.473, which leads us to conclude that the nationality of the participants plays a relevant role on the response in the items of WHOQOL-BREF. In spite of a common language, differences in cultural, historical, and social variables across these nine countries could be influencing the individual’s perception of quality of life. In order to minimize those differences, specific adaptations of the Spanish-version of WHOQOL-BREF for each country should be considered.
Differential functioning of mini-mental test items according to disease.
Prieto, G; Delgado, A R; Perea, M V; Ladera, V
2011-10-01
Comparing the height of males and females would be impossible if the measuring device did not have the same properties for both populations. In a similar way, the cognitive level of diverse groups of patients should not be compared if the test has different measurement properties for these groups. Lack of Differential Item Functioning (DIF) is a condition for measurement invariance between populations. The most internationally used screening test for dementia, the MMSE (or Mini-mental State Examination), has been analysed using an advanced psychometric technique, the Rasch Model. The objective was to determine the invariance of mini-mental measurements from diverse groups: Parkinson's disease patients, Alzheimer's type dementia and normal subjects. The hypothesis was that the scores would not show DIF against any of these groups. The total sample was composed of 400 subjects. Significant differences between groups were found. However, the quantitative comparison only makes sense if no evidence against measurement invariance was found: given the kind of items showing DIF against Parkinson's disease patients, the MMSE seems to underestimate the cognitive level of these patients. Despite the extended use of this test, 11 items out of 30 show DIF and consequently score comparisons between groups are not justified. Copyright © 2010 Sociedad Española de Neurología. Published by Elsevier Espana. All rights reserved.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Lin, Chung-Ying; Kumar, Santhosh; Pakpour, Amir H.
2016-01-01
Background: The study aimed to further evaluate the psychometric properties of one recently developed oral health related quality of life (OHRQoL) instrument (PedsQL Oral Health Scale), including student self-report and parent-proxy report. Specifically, we tested the item validity,threshold order, local dependency, and differential item functioning (DIF) across gender and rater. Methods: This is a cross-sectional study, and study population was recruited in Qazvin, Iran using one-stage sampling with the unit of school. Students and their parents (1529 dyads) separately completed the Persian version of PedsQL Oral Health Scale. The psychometric properties were analyzed using Rasch rating scale model, including item validity, threshold order for response categories, and DIF across gender (boys vs. girls in student self-report) and rater (student self report vs. parent-proxy report). Results: All items had satisfactory in fit and outfit mean square error. One disordering category (the response of often) was found in parent-proxy report, while all categories were ordered in student self-report. All items were DIF-trivial across gender and rater. Conclusion: PedsQL Oral Health Scale is a valid instrument to measure OHRQoL. However, our results indicated that the parent-proxy report was inferior to the student self-report, and healthcare providers should primarily use the student self-report. PMID:27579258
An Introduction to Missing Data in the Context of Differential Item Functioning
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Kathleen Banks
2015-04-01
Full Text Available This article introduces practitioners and researchers to the topic of missing data in the context of differential item functioning (DIF, reviews the current literature on the issue, discusses implications of the review, and offers suggestions for future research. A total of nine studies were reviewed. All of these studies determined what effect particular missing data techniques would have on the results of certain DIF detection procedures under various conditions. The most important finding of this review involved the use of zero imputation as a missing data technique. The review shows that zero imputation can lead to inflated Type I errors, especially in cases where the examinees ability level has not been taken into consideration.
Differential Item Functioning of the Psychological Domain of the Menopause Rating Scale
Portela-Buelvas, Katherin; Oviedo, Heidi C.; Herazo, Edwin; Campo-Arias, Adalberto
2016-01-01
Introduction. Quality of life could be quantified with the Menopause Rating Scale (MRS), which evaluates the severity of somatic, psychological, and urogenital symptoms in menopause. However, differential item functioning (DIF) analysis has not been applied previously. Objective. To establish the DIF of the psychological domain of the MRS in Colombian women. Methods. 4,009 women aged between 40 and 59 years, who participated in the CAVIMEC (Calidad de Vida en la Menopausia y Etnias Colombianas) project, were included. Average age was 49.0 ± 5.9 years. Women were classified in mestizo, Afro-Colombian, and indigenous. The results were presented as averages and standard deviation (X ± SD). A p value <0.001 was considered statistically significant. Results. In mestizo women, the highest X ± SD were obtained in physical and mental exhaustion (PME) (0.86 ± 0.93) and the lowest ones in anxiety (0.44 ± 0.79). In Afro-Colombian women, an average score of 0.99 ± 1.07 for PME and 0.63 ± 0.88 for anxiety was gotten. Indigenous women obtained an increased average score for PME (1.33 ± 0.93). The lowest score was evidenced in depressive mood (0.50 ± 0.81), which is different from other Colombian women (p < 0.001). Conclusions. The psychological items of the MRS show differential functioning according to the ethnic group, which may induce systematic error in the measurement of the construct. PMID:27847825
Differential Item Functioning of the Psychological Domain of the Menopause Rating Scale.
Monterrosa-Castro, Alvaro; Portela-Buelvas, Katherin; Oviedo, Heidi C; Herazo, Edwin; Campo-Arias, Adalberto
2016-01-01
Introduction. Quality of life could be quantified with the Menopause Rating Scale (MRS), which evaluates the severity of somatic, psychological, and urogenital symptoms in menopause. However, differential item functioning (DIF) analysis has not been applied previously. Objective. To establish the DIF of the psychological domain of the MRS in Colombian women. Methods. 4,009 women aged between 40 and 59 years, who participated in the CAVIMEC (Calidad de Vida en la Menopausia y Etnias Colombianas) project, were included. Average age was 49.0 ± 5.9 years. Women were classified in mestizo, Afro-Colombian, and indigenous. The results were presented as averages and standard deviation (X ± SD). A p value <0.001 was considered statistically significant. Results. In mestizo women, the highest X ± SD were obtained in physical and mental exhaustion (PME) (0.86 ± 0.93) and the lowest ones in anxiety (0.44 ± 0.79). In Afro-Colombian women, an average score of 0.99 ± 1.07 for PME and 0.63 ± 0.88 for anxiety was gotten. Indigenous women obtained an increased average score for PME (1.33 ± 0.93). The lowest score was evidenced in depressive mood (0.50 ± 0.81), which is different from other Colombian women (p < 0.001). Conclusions. The psychological items of the MRS show differential functioning according to the ethnic group, which may induce systematic error in the measurement of the construct.
Engelhard, George, Jr.; Wind, Stefanie A.; Kobrin, Jennifer L.; Chajewski, Michael
2013-01-01
The purpose of this study is to illustrate the use of explanatory models based on Rasch measurement theory to detect systematic relationships between student and item characteristics and achievement differences using differential item functioning (DIF), differential group functioning (DGF), and differential person functioning (DPF) techniques. The…
Emenogu, Barnabas; Childs, Ruth A.
This study investigated the possible impacts of language and curriculum differences on the performance of test items by subpopulations of students. Focusing on Measurement and Geometry items completed by students in French- and English-language schools in Ontario made it possible to explore the differences and to compare the item response theory…
Kettler, Ryan J.; Rodriguez, Michael C.; Bolt, Daniel M.; Elliott, Stephen N.; Beddow, Peter A.; Kurz, Alexander
2011-01-01
Federal policy on alternate assessment based on modified academic achievement standards (AA-MAS) inspired this research. Specifically, an experimental study was conducted to determine whether tests composed of modified items would have the same level of reliability as tests composed of original items, and whether these modified items helped reduce…
Owens, Sherry; Kristjansson, Alfgeir L; Hunte, Haslyn E R
2015-11-05
We investigated whether individual items on the nine item William's Perceived Everyday Discrimination Scale (EDS) functioned differently by age (racial groups in the United States: Asians (n=2,017); Hispanics (n=2,688); Black Caribbeans (n=1,377); African Americans (n=3,434); and Whites (n=854). We used data from the 2001-2003 National Survey of American Lives and the 2001-2003 National Latino and Asian Studies. Multiple-indicator, multiple-cause models (MIMIC) were used to examine differential item functioning (DIF) on the EDS by age within each racial/ethnic group. Overall, Asian and Hispanic respondents reported less discrimination than Whites; on the other hand, African Americans and Black Caribbeans reported more discrimination than Whites. Regardless of race/ethnicity, the younger respondents (aged discrimination than the older respondents (aged ≥ 45 years). In terms of age by race/ethnicity, the results were mixed for 19 out of 45 tests of DIF (40%). No differences in item function were observed among Black Caribbeans. "Being called names or insulted" and others acting as "if they are afraid" of the respondents were the only two items that did not exhibit differential item functioning by age across all racial/ethnic groups. Overall, our findings suggest that the EDS scale should be used with caution in multi-age multi-racial/ethnic samples.
Quantal Response: Nonparametric Modeling
2017-01-01
spline N−spline Fig. 3 Logistic regression 7 Approved for public release; distribution is unlimited. 5. Nonparametric QR Models Nonparametric linear ...stimulus and probability of response. The Generalized Linear Model approach does not make use of the limit distribution but allows arbitrary functional...7. Conclusions and Recommendations 18 8. References 19 Appendix A. The Linear Model 21 Appendix B. The Generalized Linear Model 33 Appendix C. B
Roth, Wolff-Michael; Oliveri, Maria Elena; Dallie Sandilands, Debra; Lyons-Thomas, Juliette; Ercikan, Kadriye
2013-03-01
Even if national and international assessments are designed to be comparable, subsequent psychometric analyses often reveal differential item functioning (DIF). Central to achieving comparability is to examine the presence of DIF, and if DIF is found, to investigate its sources to ensure differentially functioning items that do not lead to bias. In this study, sources of DIF were examined using think-aloud protocols. The think-aloud protocols of expert reviewers were conducted for comparing the English and French versions of 40 items previously identified as DIF (N = 20) and non-DIF (N = 20). Three highly trained and experienced experts in verifying and accepting/rejecting multi-lingual versions of curriculum and testing materials for government purposes participated in this study. Although there is a considerable amount of agreement in the identification of differentially functioning items, experts do not consistently identify and distinguish DIF and non-DIF items. Our analyses of the think-aloud protocols identified particular linguistic, general pedagogical, content-related, and cognitive factors related to sources of DIF. Implications are provided for the process of arriving at the identification of DIF, prior to the actual administration of tests at national and international levels.
Finch, W. Holmes; French, Brian F.
2008-01-01
A number of statistical methods exist for the detection of differential item functioning (DIF). The performance of DIF methods has been widely studied and generally found to be effective in the detection of both uniform and nonuniform DIF. Anecdotal reports suggest that these techniques may too often incorrectly detect the presence of one type of…
Hou, Likun; de la Torre, Jimmy; Nandakumar, Ratna
2014-01-01
Analyzing examinees' responses using cognitive diagnostic models (CDMs) has the advantage of providing diagnostic information. To ensure the validity of the results from these models, differential item functioning (DIF) in CDMs needs to be investigated. In this article, the Wald test is proposed to examine DIF in the context of CDMs. This study…
Sinharay, Sandip; Dorans, Neil J.; Liang, Longjuan
2009-01-01
To ensure fairness, it is important to better understand the relationship of language proficiency to standard psychometric analysis procedures. This paper examines how results of differential item functioning (DIF) analysis are affected by an increase in the proportion of examinees who report that English is not their first language in the…
Fidalgo, Angel M.; Hashimoto, Kanako; Bartram, Dave; Muniz, Jose
2007-01-01
In this study, the authors assess several strategies created on the basis of the Mantel-Haenszel (MH) procedure for conducting differential item functioning (DIF) analysis with small samples. One of the analytical strategies is a loss function (LF) that uses empirical Bayes Mantel-Haenszel estimators, whereas the other strategies use the classical…
Escorial, Sergio; Navas, Maria J.
2007-01-01
Studies in the field of personality have systematically found gender differences in two of the three dimensions of the Eysenck model: neuroticism and psychoticism. This study aims to analyze these differences in the Eysenck Personality Questionnaire--Revised (EPQ-R) scales using differential item functioning (DIF) techniques to determine whether…
Influence of test and person characteristics on nonparametric appropriateness measurement
Meijer, Rob R.; Molenaar, Ivo W.; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Influence of Test and Person Characteristics on Nonparametric Appropriateness Measurement
Meijer, Rob R; Molenaar, Ivo W; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Nonparametric statistical methods
Hollander, Myles; Chicken, Eric
2013-01-01
Praise for the Second Edition"This book should be an essential part of the personal library of every practicing statistician."-Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given sit
Bayesian nonparametric data analysis
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.
Reliability estimation for single dichotomous items based on Mokken's IRT model
Meijer, R R; Sijtsma, K; Molenaar, Ivo W
1995-01-01
Item reliability is of special interest for Mokken's nonparametric item response theory, and is useful for the evaluation of item quality in nonparametric test construction research. It is also of interest for nonparametric person-fit analysis. Three methods for the estimation of the reliability of
Reliability estimation for single dichotomous items based on Mokken's IRT model
Meijer, Rob R.; Sijtsma, Klaas; Molenaar, Ivo W.
1995-01-01
Item reliability is of special interest for Mokken’s nonparametric item response theory, and is useful for the evaluation of item quality in nonparametric test construction research. It is also of interest for nonparametric person-fit analysis. Three methods for the estimation of the reliability of
Directory of Open Access Journals (Sweden)
Zahra Sharafi
2017-01-01
Full Text Available Background. The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods. The ordinal logistic regression (OLR and hierarchical ordinal logistic regression (HOLR were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™ 4.0 collected from 576 healthy school children were analyzed. Results. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Latent Class Analysis of Differential Item Functioning on the Peabody Picture Vocabulary Test-III
Webb, Mi-young Lee; Cohen, Allan S.; Schwanenflugel, Paula J.
2008-01-01
This study investigated the use of latent class analysis for the detection of differences in item functioning on the Peabody Picture Vocabulary Test-Third Edition (PPVT-III). A two-class solution for a latent class model appeared to be defined in part by ability because Class 1 was lower in ability than Class 2 on both the PPVT-III and the…
Atalay Kabasakal, Kübra; Arsan, Nihan; Gök, Bilge; Kelecioglu, Hülya
2014-01-01
This simulation study compared the performances (Type I error and power) of Mantel-Haenszel (MH), SIBTEST, and item response theory-likelihood ratio (IRT-LR) methods under certain conditions. Manipulated factors were sample size, ability differences between groups, test length, the percentage of differential item functioning (DIF), and underlying…
Hidalgo, Mª Dolores; Gómez-Benito, Juana; Zumbo, Bruno D.
2014-01-01
The authors analyze the effectiveness of the R[superscript 2] and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing…
Hidalgo, Mª Dolores; Gómez-Benito, Juana; Zumbo, Bruno D.
2014-01-01
The authors analyze the effectiveness of the R[superscript 2] and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing…
Chen, Ying-Fang; Jiao, Hong
2014-01-01
Differential item functioning (DIF) may be caused by an interaction of multiple manifest grouping variables or unexplored manifest variables, which cannot be detected by conventional DIF detection methods that are based on a single manifest grouping variable. Such DIF may be detected by a latent approach using the mixture item response theory…
Chen, Ying-Fang; Jiao, Hong
2014-01-01
Differential item functioning (DIF) may be caused by an interaction of multiple manifest grouping variables or unexplored manifest variables, which cannot be detected by conventional DIF detection methods that are based on a single manifest grouping variable. Such DIF may be detected by a latent approach using the mixture item response theory…
Zwick, Rebecca
2012-01-01
Differential item functioning (DIF) analysis is a key component in the evaluation of the fairness and validity of educational tests. The goal of this project was to review the status of ETS DIF analysis procedures, focusing on three aspects: (a) the nature and stringency of the statistical rules used to flag items, (b) the minimum sample size…
Nonparametric statistical inference
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.
Gibbons, Laura E; Crane, Paul K; Mehta, Kala M; Pedraza, Otto; Tang, Yuxiao; Manly, Jennifer J; Narasimhalu, Kaavya; Teresi, Jeanne; Jones, Richard N; Mungas, Dan
2011-04-28
Differential item functioning (DIF) occurs when a test item has different statistical properties in subgroups, controlling for the underlying ability measured by the test. DIF assessment is necessary when evaluating measurement bias in tests used across different language groups. However, other factors such as educational attainment can differ across language groups, and DIF due to these other factors may also exist. How to conduct DIF analyses in the presence of multiple, correlated factors remains largely unexplored. This study assessed DIF related to Spanish versus English language in a 44-item object naming test. Data come from a community-based sample of 1,755 Spanish- and English-speaking older adults. We compared simultaneous accounting, a new strategy for handling differences in educational attainment across language groups, with existing methods. Compared to other methods, simultaneously accounting for language- and education-related DIF yielded salient differences in some object naming scores, particularly for Spanish speakers with at least 9 years of education. Accounting for factors that vary across language groups can be important when assessing language DIF. The use of simultaneous accounting will be relevant to other cross-cultural studies in cognition and in other fields, including health-related quality of life.
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Richard N. Jones
2016-06-01
Full Text Available We analyzed physical functioning short form items derived from the PROMIS® item bank (PF16 using data from more than 5,000 recently diagnosed, ethnically diverse cancer patients. Our goal was to determine if the short form items demonstrated evidence of differential item functioning (DIF according to sociodemographic characteristics in this clinical sample. We evaluated respons-es for evidence of unidimensionality, local independence (given a single common factor, differen-tial item functioning, and DIF impact. DIF was evaluated attributable to sex, age (middle aged vs. younger and older, race/ethnicity (White vs. Black or African-American, Asian/Pacific Islander, Hispanic and level of education. We used a multiple group confirmatory factor analysis with covariates approach, a multiple indicators multiple causes (MIMIC model. We confirmed essential unidimensionality but some evidence for multidimensionality is present, particularly for basic activities of daily living items, and many instances of local dependence. The presence of local dependence calls for further review of the meaning and measurement of the physical functioning domain among cancer patients. Nearly every item demonstrated statistically significant DIF. In all group comparisons the impact of DIF was negligible. However, the Hispanic subgroup comparison revealed an impact estimate just below an arbitrary threshold for small impact. Within the limitations of local dependency violations, we conclude that items from a static short form derived from the PROMIS physical functioning item bank displayed trivial and ignorable DIF attributable to sex, race, ethnicity, age, and education among cancer patients.
Cogliser, Claudia C.; Schriesheim, Chester A.
1994-01-01
A method of testing semantic differential scales for bipolarity is developed using a new conception of bipolarity that does not require unidimensionality. Assessment of Fielder's Least Preferred Coworker instrument with 63 college student subjects using multidimensional scaling revealed its significant departures from bipolarity. (SLD)
Kim, Jihye
2010-01-01
In DIF studies, a Type I error refers to the mistake of identifying non-DIF items as DIF items, and a Type I error rate refers to the proportion of Type I errors in a simulation study. The possibility of making a Type I error in DIF studies is always present and high possibility of making such an error can weaken the validity of the assessment.…
Nonparametric tests for censored data
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.
Tian, Wei; Cai, Li; Thissen, David; Xin, Tao
2013-01-01
In item response theory (IRT) modeling, the item parameter error covariance matrix plays a critical role in statistical inference procedures. When item parameters are estimated using the EM algorithm, the parameter error covariance matrix is not an automatic by-product of item calibration. Cai proposed the use of Supplemented EM algorithm for…
Substance use disorder symptoms: evidence of differential item functioning by age.
Conrad, Kendon J; Dennis, Michael L; Bezruczko, Nikolaus; Funk, Rodney R; Riley, Barth B
2007-01-01
This study examined the applicability of substance abuse diagnostic criteria for adolescents, young adults, and adults using the Global Appraisal of Individual Need's Substance Problems Scale (SPS) from 7,408 clients. Rasch analysis was used to: 1) evaluate whether the SPS operationalized a single reliable dimension, and 2) examine the extent to which the severity of each symptom and the overall test functioned the same or differently by age. Rasch analysis indicated that the SPS was unidimensional with a person reliability of .84. Eight symptoms were significantly different between adolescents and adults. Young adult calibrations tended to fall between adolescents and adults. Differential test functioning was clinically negligible for adolescents but resulted in about 7% more adults being classified as high need. These findings have theoretical implications for screening and treatment of adolescents vs. adults. SPS can be used across age groups though age-specific calibrations enable greater precision of measurement.
Nispen, R.M.A. van; Knol, D.L.; Langelaan, M.; Rens, G.H.M.B. van
2011-01-01
Background: For the Low Vision Quality Of Life questionnaire (LVQOL) it is unknown whether the psychometric properties are satisfactory when an item response theory (IRT) perspective is considered. This study evaluates some essential psychometric properties of the LVQOL questionnaire in an IRT model
Chiesi, Francesca; Ciancaleoni, Matteo; Galli, Silvia; Morsanyi, Kinga; Primi, Caterina
2012-01-01
Item Response Theory (IRT) models were applied to investigate the psychometric properties of the Arthur and Day's Advanced Progressive Matrices-Short Form (APM-SF; 1994) [Arthur and Day (1994). "Development of a short form for the Raven Advanced Progressive Matrices test." "Educational and Psychological Measurement, 54," 395-403] in order to test…
CURRENT STATUS OF NONPARAMETRIC STATISTICS
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Orlov A. I.
2015-02-01
Full Text Available Nonparametric statistics is one of the five points of growth of applied mathematical statistics. Despite the large number of publications on specific issues of nonparametric statistics, the internal structure of this research direction has remained undeveloped. The purpose of this article is to consider its division into regions based on the existing practice of scientific activity determination of nonparametric statistics and classify investigations on nonparametric statistical methods. Nonparametric statistics allows to make statistical inference, in particular, to estimate the characteristics of the distribution and testing statistical hypotheses without, as a rule, weakly proven assumptions about the distribution function of samples included in a particular parametric family. For example, the widespread belief that the statistical data are often have the normal distribution. Meanwhile, analysis of results of observations, in particular, measurement errors, always leads to the same conclusion - in most cases the actual distribution significantly different from normal. Uncritical use of the hypothesis of normality often leads to significant errors, in areas such as rejection of outlying observation results (emissions, the statistical quality control, and in other cases. Therefore, it is advisable to use nonparametric methods, in which the distribution functions of the results of observations are imposed only weak requirements. It is usually assumed only their continuity. On the basis of generalization of numerous studies it can be stated that to date, using nonparametric methods can solve almost the same number of tasks that previously used parametric methods. Certain statements in the literature are incorrect that nonparametric methods have less power, or require larger sample sizes than parametric methods. Note that in the nonparametric statistics, as in mathematical statistics in general, there remain a number of unresolved problems
Nonparametric statistical methods using R
Kloke, John
2014-01-01
A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.The book first gives an overview of the R language and basic statistical c
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Paula Elosua
2006-08-01
Full Text Available This report analyses the differential item functioning (DIF in the Programme for Indicators of Student Achievement PISA2000. The items studied are coming from the Reading Comprehension Test. We analyzed the released items from this year because we wanted to join the detection of DIF and its understanding. The reference group is the sample of United Kingdom and the focal group is the Spanish sample. The procedures of detection are Mantel-Haenszel, Logistic Regression and the standardized mean difference, and their extensions for polytomous items. Two items were flagged and the post-hoc analysis didn’t explain the causes of DIF entirely. Este trabajo analiza el funcionamiento diferencial del ítem (FDI de la prueba de comprensión lectora de la evaluación PISA2000 entre la muestras del Reino Unido y España. Se estudian los ítems liberados con el fin de aunar las fases de detección del FDI con la comprensión de sus causas. En la fase de detección se comparan los resultados de los procedimientos Mantel-Haenszel, Regresión Logística y Medias Estandarizadas en sus versiones para ítems dicotómicos y politómicos. Los resultados muestran que dos ítems presentan funcionamiento diferencial aunque el estudio post-hoc llevado a cabo sobre su contenido no ha podido precisar sus causas.
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Alireza Ahmadi
2016-01-01
Full Text Available Differential Item Functioning (DIF exists when examinees of equal ability from different groups have different probabilities of successful performance in a certain item. This study examined gender differential item functioning across the PhD Entrance Exam of TEFL (PEET in Iran, using both logistic regression (LR and one-parameter item response theory (1-p IRT models. The PEET is a national test consisting of a centralized written examination designed to provide information on the eligibility of PhD applicants of TEFL to enter PhD programs. The 2013 administration of this test provided score data for a sample of 999 Iranian PhD applicants consisting of 397 males and 602 females. First, the data were subjected to DIF analysis through logistic regression (LR model. Then, to triangulate the findings, a 1-p IRT procedure was applied. The results indicated (1 more items flagged for DIF by LR than by 1-p IRT (2 DIF cancellation (the number of DIF items were equal for both males and females, as revealed through LR, (3 equal number of uniform and non-uniform DIF, as tracked via LR, and (4 female superiority in the test performance, as revealed via IRT analysis. Overall, the findings of the study indicated that PEET suffers from DIF. As such, test developers and policymakers (like NOET & MSRT are recommended to take these findings into serious consideration and exercise care in fair test practice by dedicating effort to more unbiased test development and decision making.
Zampetakis, Leonidas A.; Bakatsaki, Maria; Litos, Charalambos; Kafetsios, Konstantinos G.; Moustakis, Vassilis
2017-01-01
Over the past years the percentage of female entrepreneurs has increased, yet it is still far below of that for males. Although various attempts have been made to explain differences in mens’ and women’s entrepreneurial attitudes and intentions, the extent to which those differences are due to self-report biases has not been yet considered. The present study utilized Differential Item Functioning (DIF) to compare men and women’s reporting on entrepreneurial intentions. DIF occurs in situations where members of different groups show differing probabilities of endorsing an item despite possessing the same level of the ability that the item is intended to measure. Drawing on the theory of planned behavior (TPB), the present study investigated whether constructs such as entrepreneurial attitudes, perceived behavioral control, subjective norms and intention would show gender differences and whether these gender differences could be explained by DIF. Using DIF methods on a dataset of 1800 Greek participants (50.4% female) indicated that differences at the item-level are almost non-existent. Moreover, the differential test functioning (DTF) analysis, which allows assessing the overall impact of DIF effects with all items being taken into account simultaneously, suggested that the effect of DIF across all the items for each scale was negligible. Future research should consider that measurement invariance can be assumed when using TPB constructs for the study of entrepreneurial motivation independent of gender. PMID:28386244
Laitusis, Cara Cahalan; Maneckshana, Behroz; Monfils, Lora; Ahlgrim-Delzell, Lynn
2009-01-01
The purpose of this study was to examine Differential Item Functioning (DIF) by disability groups on an on-demand performance assessment for students with severe cognitive impairments. Researchers examined the presence of DIF for two comparisons. One comparison involved students with severe cognitive impairments who served as the reference group…
Laitusis, Cara Cahalan; Maneckshana, Behroz; Monfils, Lora; Ahlgrim-Delzell, Lynn
2009-01-01
The purpose of this study was to examine Differential Item Functioning (DIF) by disability groups on an on-demand performance assessment for students with severe cognitive impairments. Researchers examined the presence of DIF for two comparisons. One comparison involved students with severe cognitive impairments who served as the reference group…
Benítez, Isabel; Padilla, José-Luis
2014-01-01
Differential item functioning (DIF) can undermine the validity of cross-lingual comparisons. While a lot of efficient statistics for detecting DIF are available, few general findings have been found to explain DIF results. The objective of the article was to study DIF sources by using a mixed method design. The design involves a quantitative phase…
Robitzsch, Alexander; Rupp, Andre A.
2009-01-01
This article describes the results of a simulation study to investigate the impact of missing data on the detection of differential item functioning (DIF). Specifically, it investigates how four methods for dealing with missing data (listwise deletion, zero imputation, two-way imputation, response function imputation) interact with two methods of…
Aryadoust, Vahid
2012-01-01
This article investigates a version of the International English Language Testing System (IELTS) listening test for evidence of differential item functioning (DIF) based on gender, nationality, age, and degree of previous exposure to the test. Overall, the listening construct was found to be underrepresented, which is probably an important cause…
Zwick, Rebecca
The Mantel Haenszel (MH; 1959) approach of Holland and Thayer (1988) is a well-established method for assessing differential item functioning (DIF). The formula for the variance of the MH DIF statistic is based on work by Phillips and Holland (1987) and Robins, Breslow, and Greenland (1986). Recent simulation studies showed that the MH variances…
Paek, Insu
2009-01-01
Three statistical testing procedures well-known in the maximum likelihood approach are the Wald, likelihood ratio (LR), and score tests. Although well-known, the application of these three testing procedures in the logistic regression method to investigate differential item function (DIF) has not been rigorously made yet. Employing a variety of…
The Probability of Exceedance as a Nonparametric Person-Fit Statistic for Tests of Moderate Length
Tendeiro, Jorge N.; Meijer, Rob R.
2013-01-01
To classify an item score pattern as not fitting a nonparametric item response theory (NIRT) model, the probability of exceedance (PE) of an observed response vector x can be determined as the sum of the probabilities of all response vectors that are, at most, as likely as x, conditional on the test
Mohanty, Praggyan Pam; Naveh-Benjamin, Moshe; Ratneshwar, Srinivasan
2016-02-01
The effects of two types of semantic memory support-meaningfulness of an item and relatedness between items-in mitigating age-related deficits in item and associative, memory are examined in a marketing context. In Experiment 1, participants studied less (vs. more) meaningful brand logo graphics (pictures) paired with meaningful brand names (words) and later were assessed by item (old/new) and associative (intact/recombined) memory recognition tests. Results showed that meaningfulness of items eliminated age deficits in item memory, while equivalently boosting associative memory for older and younger adults. Experiment 2, in which related and unrelated brand logo graphics and brand name pairs served as stimuli, revealed that relatedness between items eliminated age deficits in associative memory, while improving to the same degree item memory in older and younger adults. Experiment 2 also provided evidence for a probable boundary condition that could reconcile seemingly contradictory extant results. Overall, these experiments provided evidence that although the two types of semantic memory support can improve both item and associative memory in older and younger adults, older adults' memory deficits can be eliminated when the type of support provided is compatible with the type of information required to perform well on the test. (c) 2016 APA, all rights reserved).
Semi- and Nonparametric ARCH Processes
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Oliver B. Linton
2011-01-01
Full Text Available ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about the general properties of such processes, including stationary conditions, ergodic conditions and mixing conditions. The last part is on the estimation methods in ARCH/GARCH processes.
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Pieter Schaap
2011-03-01
Full Text Available Orientation: For a number of years, eliminating a language component in testing by using nonverbal cognitive tests has been proposed as a possible solution to the effect of groups’ languages (mother tongues or first languages on test performance. This is particularly relevant in South Africa with its 11 official languages.Research purpose: The aim of the study was to determine the differential item functioning (DIF and structural equivalence of a nonverbal cognitive ability test (the PiB/SpEEx Observance test [401] for five South African language groups.Motivation for study: Cultural and language group sensitive tests can lead to unfair discrimination and is a contentious workplace issue in South Africa today. Misconceptions about psychometric testing in industry can cause tests to lose credibility if industries do not use a scientifically sound test-by-test evaluation approach.Research design, approach and method: The researcher used a quasi-experimental design and factor analytic and logistic regression techniques to meet the research aims. The study used a convenience sample drawn from industry and an educational institution.Main findings: The main findings of the study show structural equivalence of the test at a holistic level and nonsignificant DIF effect sizes for most of the comparisons that the researcher made.Practical/managerial implications: This research shows that the PIB/SpEEx Observance Test (401 is not completely language insensitive. One should see it rather as a language-reduced test when people from different language groups need testing.Contribution/value-add: The findings provide supporting evidence that nonverbal cognitive tests are plausible alternatives to verbal tests when one compares people from different language groups.
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Tennant Alan
2007-07-01
Full Text Available Abstract Background A previous study had identified 45 items assessing the impact of atopic dermatitis (AD on the whole family. From these it was intended to develop two separate scales, one assessing impact on carers and the other determining the effect on the child. Methods The 45 items were included in three clinical trials designed to test the efficacy of a new topical treatment (pimecrolimus, Elidel cream 1% in the treatment of AD in infants and children and in validation studies in the UK, US, Germany, France and the Netherlands. Rasch analyses were undertaken to determine whether an internationally valid, unidimensional scale could be developed that would inform on the direct impact of AD on the child. Results Rasch analyses applied to the data from the trials indicated that the draft measure consisted of two scales, one assessing the QoL of the carer and the other (consisting of 12 items measuring the impact of AD on the child. Three of the 12 potential items failed to fit the measurement model in Europe and five in the US. In addition, four items exhibiting differential item functioning (DIF by country were identified. After removing the misfitting items and controlling for DIF it was possible to derive a scale; The Childhood Impact of Atopic Dermatitis (CIAD with good item fit for each trial analysis. Analysis of the validation data from each of the different countries confirmed that the CIAD had adequate internal consistency, reproducibility and construct validity. The CIAD demonstrated the benefits of treatment with Elidel over placebo in the European trial. A similar (non-significant trend was found for the US trials. Conclusion The study represents a novel method of dealing with the problem of DIF associated with different cultures. Such problems are likely to arise in any multinational study involving patient-reported outcome measures, as items in the scales are likely to be valued differently in different cultures. However, where
Nonparametric estimation of ultrasound pulses
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Leeman, Sidney
1994-01-01
An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori...
Testing discontinuities in nonparametric regression
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
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Fermino Fernandes Sisto
2008-01-01
Full Text Available Nesta pesquisa buscou-se evidência de validade de construto relacionada ao funcionamento dos itens para diferenciar sexos em um instrumento de agressividade. Participaram 445 universitários, de ambos os sexos, dos cursos de Engenharia, Computação e Psicologia. A escala de agressividade composta por 81 itens foi aplicada coletivamente, em sala de aula, nos estudantes que consentiram em participar do estudo. Os itens do instrumento foram analisados por meio do modelo Rasch. Vinte e oito itens apresentaram funcionamento diferencial, sendo 15 condutas mais características de pessoas do sexo feminino e outras 13 mais características do masculino. Os índices de precisão foram de 0,99 para os itens e 0,86 para as pessoas. Conclui-se que a agressividade pode ser medida separadamente em razão do sexo.In this research evidences of construct validity were searched analyzing the differential functioning items related to aggressiveness. The participants were 445 college students of both genders, attending the courses of Engineering, Computing and Psychology. The scale of aggressiveness composed by 81 items was collectively applied, in the classroom, to the students who consented to participate in the study. The items of the instrument were studied by means of the Rasch model. Twenty-eight items presented differential functioning item, 15 were characterized as typical for females and 13 for males. The reliability coefficients were 0.99 to the items and 0.86 to the persons. It was concluded that the aggressiveness can be measured separately on the basis of gender.
Wong, Kwan Yin
The aim of this study is to investigate the effect of gender differences of 15-year-old students on scientific literacy and their impacts on students’ motivation to pursue science education and careers (Future-oriented Science Motivation) in Hong Kong. The data for this study was collected from the Program for International Student Assessment in Hong Kong (HKPISA). It was carried out in 2006. A total of 4,645 students were randomly selected from 146 secondary schools including government, aided and private schools by two-stage stratified sampling method for the assessment. HKPISA 2006, like most of other large-scale international assessments, presents its assessment frameworks in multidimensional subscales. To fulfill the requirements of this multidimensional assessment framework, this study deployed new approaches to model and investigate gender differences in cognitive and affective latent traits of scientific literacy by using multidimensional differential item functioning (MDIF) and multilevel mediation (MLM). Compared with mean score difference t-test, MDIF improves the precision of each subscales measure at item level and the gender differences in science performance can be accurately estimated. In the light of Eccles et al (1983) Expectancy-value Model of Achievement-related Choices (Eccles’ Model), MLM examines the pattern of gender effects on Future-oriented Science Motivation mediated through cognitive and affective factors. As for MLM investigation, Single-Group Confirmatory Factor Analysis (Single-Group CFA) was used to confirm the applicability and validity of six affective factors which was, originally prepared by OECD. These six factors are Science Self-concept, Personal Value of Science, Interest in Science Learning, Enjoyment of Science Learning, Instrumental Motivation to Learn Science and Future-oriented Science Motivation. Then, Multiple Group CFA was used to verify measurement invariance of these factors across gender groups. The results of
Directory of Open Access Journals (Sweden)
Lisa M Lix
Full Text Available Self-reported health status measures, like the Short Form 36-item Health Survey (SF-36, can provide rich information about the overall health of a population and its components, such as physical, mental, and social health. However, differential item functioning (DIF, which arises when population sub-groups with the same underlying (i.e., latent level of health have different measured item response probabilities, may compromise the comparability of these measures. The purpose of this study was to test for DIF on the SF-36 physical functioning (PF and mental health (MH sub-scale items in a Canadian population-based sample.Study data were from the prospective Canadian Multicentre Osteoporosis Study (CaMos, which collected baseline data in 1996-1997. DIF was tested using a multiple indicators multiple causes (MIMIC method. Confirmatory factor analysis defined the latent variable measurement model for the item responses and latent variable regression with demographic and health status covariates (i.e., sex, age group, body weight, self-perceived general health produced estimates of the magnitude of DIF effects.The CaMos cohort consisted of 9423 respondents; 69.4% were female and 51.7% were less than 65 years. Eight of 10 items on the PF sub-scale and four of five items on the MH sub-scale exhibited DIF. Large DIF effects were observed on PF sub-scale items about vigorous and moderate activities, lifting and carrying groceries, walking one block, and bathing or dressing. On the MH sub-scale items, all DIF effects were small or moderate in size.SF-36 PF and MH sub-scale scores were not comparable across population sub-groups defined by demographic and health status variables due to the effects of DIF, although the magnitude of this bias was not large for most items. We recommend testing and adjusting for DIF to ensure comparability of the SF-36 in population-based investigations.
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
Nonparametric Inference for Periodic Sequences
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.
Nonparametric Econometrics: The np Package
Directory of Open Access Journals (Sweden)
Tristen Hayﬁeld
2008-07-01
Full Text Available We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of signiﬁcance and consistent model speciﬁcation tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Choi, Youn-Jeng; Alexeev, Natalia; Cohen, Allan S.
2015-01-01
The purpose of this study was to explore what may be contributing to differences in performance in mathematics on the Trends in International Mathematics and Science Study 2007. This was done by using a mixture item response theory modeling approach to first detect latent classes in the data and then to examine differences in performance on items…
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
Directory of Open Access Journals (Sweden)
Yoon Jung Choi, MD
2016-03-01
Conclusion: We anticipate that our abridged GDS, composed of five items, will facilitate a rapid, yet effective assessment of patients in primary care centers. Its use will benefit both patients and medical professionals by minimizing the length of time required to conduct the assessment, and by allowing early diagnosis and care of patients. However, further research with a larger population is required to verify its efficacy.
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Nonparametric identification of copula structures
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.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
A contingency table approach to nonparametric testing
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 statistics for social and behavioral sciences
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
The Rasch Model and Missing Data, with an Emphasis on Tailoring Test Items.
de Gruijter, Dato N. M.
Many applications of educational testing have a missing data aspect (MDA). This MDA is perhaps most pronounced in item banking, where each examinee responds to a different subtest of items from a large item pool and where both person and item parameter estimates are needed. The Rasch model is emphasized, and its non-parametric counterpart (the…
Polytomous latent scales for the investigation of the ordering of items
Ligtvoet, R.; van der Ark, L.A.; Bergsma, W. P.; Sijtsma, K.
2011-01-01
We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning that the order of the items is the same for each measurement value on the latent scale. This ordering prope
Directory of Open Access Journals (Sweden)
Wagner Bandeira Andriola
2000-01-01
Full Text Available Este estudo objetivou determinar o funcionamento diferencial de 30 analogias destinadas à avaliação do raciocínio verbal, considerando a variável sexo. Utilizou-se uma amostra de 730 alunos do Ensino Médio, com idade média de 17,74 anos (dp= 3,12 anos. A maioria procedia de escolas públicas (58,5% e era do sexo feminino (53,2%. Os grupos organizados para a investigação foram compostos por homens (n=342 e mulheres (n=388. Os parâmetros métricos dos itens foram determinados pelo modelo TRI de dois parâmetros logísticos. Para a verificação do DIF foram comparados os parâmetros métricos dos itens. Os resultados indicaram a presença de cinco itens com DIF.This research aimed the determination of the differential item functioning (DIF in 30 analogies used for the verbal reasoning assessment in students, taking into account the sex variable. A sample of 730 high school students, whose average age was 17,74 years (sd = 3,12 years was used. The majority was composed by students from public schools (58,4% and females (53,3%. The groups which participated in the study of DIF were composed by men (n= 342 and women (n= 388. The metric parameters of the items were determined according to the TRI model of two logistics parameters. For the determination of the DIF the method of comparation of the metric parameters of the items was used. The results indicated the presence of five items with DIF.
Multilevel Modeling of Item Position Effects
Albano, Anthony D.
2013-01-01
In many testing programs it is assumed that the context or position in which an item is administered does not have a differential effect on examinee responses to the item. Violations of this assumption may bias item response theory estimates of item and person parameters. This study examines the potentially biasing effects of item position. A…
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.
Nonparametric Bayesian inference in biostatistics
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...
Nonparametric Regression with Common Shocks
Directory of Open Access Journals (Sweden)
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
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...... for complex networks can be derived and point out relevant literature....
An asymptotically optimal nonparametric adaptive controller
Institute of Scientific and Technical Information of China (English)
郭雷; 谢亮亮
2000-01-01
For discrete-time nonlinear stochastic systems with unknown nonparametric structure, a kernel estimation-based nonparametric adaptive controller is constructed based on truncated certainty equivalence principle. Global stability and asymptotic optimality of the closed-loop systems are established without resorting to any external excitations.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
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....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... 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...
Bayesian nonparametric duration model with censorship
Directory of Open Access Journals (Sweden)
Joseph Hakizamungu
2007-10-01
Full Text Available This paper is concerned with nonparametric i.i.d. durations models censored observations and we establish by a simple and unified approach the general structure of a bayesian nonparametric estimator for a survival function S. For Dirichlet prior distributions, we describe completely the structure of the posterior distribution of the survival function. These results are essentially supported by prior and posterior independence properties.
Bootstrap Estimation for Nonparametric Efficiency Estimates
1995-01-01
This paper develops a consistent bootstrap estimation procedure to obtain confidence intervals for nonparametric measures of productive efficiency. Although the methodology is illustrated in terms of technical efficiency measured by output distance functions, the technique can be easily extended to other consistent nonparametric frontier models. Variation in estimated efficiency scores is assumed to result from variation in empirical approximations to the true boundary of the production set. ...
Huxley, Peter John; Chan, Kara; Chiu, Marcus; Ma, Yanni; Gaze, Sarah; Evans, Sherrill
2016-03-01
China's future major health problem will be the management of chronic diseases - of which mental health is a major one. An instrument is needed to measure mental health inclusion outcomes for mental health services in Hong Kong and mainland China as they strive to promote a more inclusive society for their citizens and particular disadvantaged groups. To report on the analysis of structural equivalence and item differentiation in two mentally unhealthy and one healthy sample in the United Kingdom and Hong Kong. The mental health sample in Hong Kong was made up of non-governmental organisation (NGO) referrals meeting the selection/exclusion criteria (being well enough to be interviewed, having a formal psychiatric diagnosis and living in the community). A similar sample in the United Kingdom meeting the same selection criteria was obtained from a community mental health organisation, equivalent to the NGOs in Hong Kong. Exploratory factor analysis and logistic regression were conducted. The single-variable, self-rated 'overall social inclusion' differs significantly between all of the samples, in the way we would expect from previous research, with the healthy population feeling more included than the serious mental illness (SMI) groups. In the exploratory factor analysis, the first two factors explain between a third and half of the variance, and the single variable which enters into all the analyses in the first factor is having friends to visit the home. All the regression models were significant; however, in Hong Kong sample, only one-fifth of the total variance is explained. The structural findings imply that the social and community opportunities profile-Chinese version (SCOPE-C) gives similar results when applied to another culture. As only one-fifth of the variance of 'overall inclusion' was explained in the Hong Kong sample, it may be that the instrument needs to be refined using different or additional items within the structural domains of inclusion.
Why preferring parametric forecasting to nonparametric methods?
Jabot, Franck
2015-05-07
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major......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....
A Nonparametric Approach for Assessing Goodness-of-Fit of IRT Models in a Mixed Format Test
Liang, Tie; Wells, Craig S.
2015-01-01
Investigating the fit of a parametric model plays a vital role in validating an item response theory (IRT) model. An area that has received little attention is the assessment of multiple IRT models used in a mixed-format test. The present study extends the nonparametric approach, proposed by Douglas and Cohen (2001), to assess model fit of three…
Recent Advances and Trends in Nonparametric Statistics
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
Correlated Non-Parametric Latent Feature Models
Doshi-Velez, Finale
2012-01-01
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.
A Censored Nonparametric Software Reliability Model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This paper analyses the effct of censoring on the estimation of failure rate, and presents a framework of a censored nonparametric software reliability model. The model is based on nonparametric testing of failure rate monotonically decreasing and weighted kernel failure rate estimation under the constraint of failure rate monotonically decreasing. Not only does the model have the advantages of little assumptions and weak constraints, but also the residual defects number of the software system can be estimated. The numerical experiment and real data analysis show that the model performs well with censored data.
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...... breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major...
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the trea
Decompounding random sums: A nonparametric approach
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Pitts, Susan M.
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...
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the
A Nonparametric Analogy of Analysis of Covariance
Burnett, Thomas D.; Barr, Donald R.
1977-01-01
A nonparametric test of the hypothesis of no treatment effect is suggested for a situation where measures of the severity of the condition treated can be obtained and ranked both pre- and post-treatment. The test allows the pre-treatment rank to be used as a concomitant variable. (Author/JKS)
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...
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
A Framework for Dimensionality Assessment for Multidimensional Item Response Models
Svetina, Dubravka; Levy, Roy
2014-01-01
A framework is introduced for considering dimensionality assessment procedures for multidimensional item response models. The framework characterizes procedures in terms of their confirmatory or exploratory approach, parametric or nonparametric assumptions, and applicability to dichotomous, polytomous, and missing data. Popular and emerging…
Nonparametric tests for pathwise properties of semimartingales
Cont, Rama; 10.3150/10-BEJ293
2011-01-01
We propose two nonparametric tests for investigating the pathwise properties of a signal modeled as the sum of a L\\'{e}vy process and a Brownian semimartingale. Using a nonparametric threshold estimator for the continuous component of the quadratic variation, we design a test for the presence of a continuous martingale component in the process and a test for establishing whether the jumps have finite or infinite variation, based on observations on a discrete-time grid. We evaluate the performance of our tests using simulations of various stochastic models and use the tests to investigate the fine structure of the DM/USD exchange rate fluctuations and SPX futures prices. In both cases, our tests reveal the presence of a non-zero Brownian component and a finite variation jump component.
Nonparametric Transient Classification using Adaptive Wavelets
Varughese, Melvin M; Stephanou, Michael; Bassett, Bruce A
2015-01-01
Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind ...
A Bayesian nonparametric meta-analysis model.
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G
2015-03-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
Nonparametric Bayes analysis of social science data
Kunihama, Tsuyoshi
Social science data often contain complex characteristics that standard statistical methods fail to capture. Social surveys assign many questions to respondents, which often consist of mixed-scale variables. Each of the variables can follow a complex distribution outside parametric families and associations among variables may have more complicated structures than standard linear dependence. Therefore, it is not straightforward to develop a statistical model which can approximate structures well in the social science data. In addition, many social surveys have collected data over time and therefore we need to incorporate dynamic dependence into the models. Also, it is standard to observe massive number of missing values in the social science data. To address these challenging problems, this thesis develops flexible nonparametric Bayesian methods for the analysis of social science data. Chapter 1 briefly explains backgrounds and motivations of the projects in the following chapters. Chapter 2 develops a nonparametric Bayesian modeling of temporal dependence in large sparse contingency tables, relying on a probabilistic factorization of the joint pmf. Chapter 3 proposes nonparametric Bayes inference on conditional independence with conditional mutual information used as a measure of the strength of conditional dependence. Chapter 4 proposes a novel Bayesian density estimation method in social surveys with complex designs where there is a gap between sample and population. We correct for the bias by adjusting mixture weights in Bayesian mixture models. Chapter 5 develops a nonparametric model for mixed-scale longitudinal surveys, in which various types of variables can be induced through latent continuous variables and dynamic latent factors lead to flexibly time-varying associations among variables.
Bayesian nonparametric estimation for Quantum Homodyne Tomography
Naulet, Zacharie; Barat, Eric
2016-01-01
We estimate the quantum state of a light beam from results of quantum homodyne tomography noisy measurements performed on identically prepared quantum systems. We propose two Bayesian nonparametric approaches. The first approach is based on mixture models and is illustrated through simulation examples. The second approach is based on random basis expansions. We study the theoretical performance of the second approach by quantifying the rate of contraction of the posterior distribution around ...
NONPARAMETRIC ESTIMATION OF CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS
Directory of Open Access Journals (Sweden)
Orlov A. I.
2015-10-01
Full Text Available The article is devoted to the nonparametric point and interval estimation of the characteristics of the probabilistic distribution (the expectation, median, variance, standard deviation, variation coefficient of the sample results. Sample values are regarded as the implementation of independent and identically distributed random variables with an arbitrary distribution function having the desired number of moments. Nonparametric analysis procedures are compared with the parametric procedures, based on the assumption that the sample values have a normal distribution. Point estimators are constructed in the obvious way - using sample analogs of the theoretical characteristics. Interval estimators are based on asymptotic normality of sample moments and functions from them. Nonparametric asymptotic confidence intervals are obtained through the use of special output technology of the asymptotic relations of Applied Statistics. In the first step this technology uses the multidimensional central limit theorem, applied to the sums of vectors whose coordinates are the degrees of initial random variables. The second step is the conversion limit multivariate normal vector to obtain the interest of researcher vector. At the same considerations we have used linearization and discarded infinitesimal quantities. The third step - a rigorous justification of the results on the asymptotic standard for mathematical and statistical reasoning level. It is usually necessary to use the necessary and sufficient conditions for the inheritance of convergence. This article contains 10 numerical examples. Initial data - information about an operating time of 50 cutting tools to the limit state. Using the methods developed on the assumption of normal distribution, it can lead to noticeably distorted conclusions in a situation where the normality hypothesis failed. Practical recommendations are: for the analysis of real data we should use nonparametric confidence limits
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.
Introduction to nonparametric statistics for the biological sciences using R
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...
Nonparametric forecasting of low-dimensional dynamical systems.
Berry, Tyrus; Giannakis, Dimitrios; Harlim, John
2015-03-01
This paper presents a nonparametric modeling approach for forecasting stochastic dynamical systems on low-dimensional manifolds. The key idea is to represent the discrete shift maps on a smooth basis which can be obtained by the diffusion maps algorithm. In the limit of large data, this approach converges to a Galerkin projection of the semigroup solution to the underlying dynamics on a basis adapted to the invariant measure. This approach allows one to quantify uncertainties (in fact, evolve the probability distribution) for nontrivial dynamical systems with equation-free modeling. We verify our approach on various examples, ranging from an inhomogeneous anisotropic stochastic differential equation on a torus, the chaotic Lorenz three-dimensional model, and the Niño-3.4 data set which is used as a proxy of the El Niño Southern Oscillation.
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Overstreet, Michael F; Healy, Alice F; Neath, Ian
2017-01-01
University of Colorado (CU) students were tested for both order and item information in their semantic memory for the "CU Fight Song". Following an earlier study by Overstreet and Healy [(2011). Item and order information in semantic memory: Students' retention of the "CU fight song" lyrics. Memory & Cognition, 39, 251-259. doi: 10.3758/s13421-010-0018-3 ], a symmetrical bow-shaped serial position function (with both primacy and recency advantages) was found for reconstructing the order of the nine lines in the song, whereas a function with no primacy advantage was found for recalling a missing word from each line. This difference between order and item information was found even though students filled in missing words without any alternatives provided and missing words came from the beginning, middle, or end of each line. Similar results were found for CU students' recall of the sequence of Harry Potter book titles and the lyrics of the Scooby Doo theme song. These findings strengthen the claim that the pronounced serial position function in semantic memory occurs largely because of the retention of order, rather than item, information.
A nonparametric and diversified portfolio model
Shirazi, Yasaman Izadparast; Sabiruzzaman, Md.; Hamzah, Nor Aishah
2014-07-01
Traditional portfolio models, like mean-variance (MV) suffer from estimation error and lack of diversity. Alternatives, like mean-entropy (ME) or mean-variance-entropy (MVE) portfolio models focus independently on the issue of either a proper risk measure or the diversity. In this paper, we propose an asset allocation model that compromise between risk of historical data and future uncertainty. In the new model, entropy is presented as a nonparametric risk measure as well as an index of diversity. Our empirical evaluation with a variety of performance measures shows that this model has better out-of-sample performances and lower portfolio turnover than its competitors.
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 pointed...... out, and methods to prevent bias are presented. The techniques are evaluated by comparing their speed and accuracy on the simple case of estimating auto-correlation functions for the response of a single degree-of-freedom system loaded with white noise....
Lottery spending: a non-parametric analysis.
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Lottery spending: a non-parametric analysis.
Directory of Open Access Journals (Sweden)
Skip Garibaldi
Full Text Available We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Nonparametric inferences for kurtosis and conditional kurtosis
Institute of Scientific and Technical Information of China (English)
XIE Xiao-heng; HE You-hua
2009-01-01
Under the assumption of strictly stationary process, this paper proposes a nonparametric model to test the kurtosis and conditional kurtosis for risk time series. We apply this method to the daily returns of S&P500 index and the Shanghai Composite Index, and simulate GARCH data for verifying the efficiency of the presented model. Our results indicate that the risk series distribution is heavily tailed, but the historical information can make its future distribution light-tailed. However the far future distribution's tails are little affected by the historical data.
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
Preliminary results on nonparametric facial occlusion detection
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Daniel LÓPEZ SÁNCHEZ
2016-10-01
Full Text Available The problem of face recognition has been extensively studied in the available literature, however, some aspects of this field require further research. The design and implementation of face recognition systems that can efficiently handle unconstrained conditions (e.g. pose variations, illumination, partial occlusion... is still an area under active research. This work focuses on the design of a new nonparametric occlusion detection technique. In addition, we present some preliminary results that indicate that the proposed technique might be useful to face recognition systems, allowing them to dynamically discard occluded face parts.
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
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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.
Directory of Open Access Journals (Sweden)
Katya Luciane Oliveira
2012-01-01
Full Text Available Este estudo teve por objetivos investigar o ajuste de um Teste de Cloze ao modelo Rasch e avaliar a dificuldade na resposta ao item em razão do gênero das pessoas (DIF. Participaram da pesquisa 573 alunos das 5ª a 8ª séries do ensino fundamental de escolas públicas estaduais dos estados de São Paulo e Minas Gerais. O teste de Cloze foi aplicado de forma coletiva. A análise do instrumento evidenciou um bom ajuste ao modelo Rasch, bem como os itens foram respondidos conforme o padrão esperado, demonstrando um bom ajuste, também. Quanto ao DIF, apenas três itens indicaram diferenciar o gênero. Com base nos dados, identificou-se que houve equilíbrio nas respostas dadas pelos meninos e meninas.The objectives of the present study were to investigate the adaptation of a Cloze test to the Rasch Model as well as to evaluate the Differential Item Functioning (DIF in relation to gender. The sample was composed by 573 students from 5th to 8th grades of public schools in the state of São Paulo. The cloze test was applied collectively. The analysis of the instrument revealed its adaptation to Rash Model and that the items were responded according to the expected pattern, showing good adjustment, as well. Regarding DIF, only three items were differentiated by gender. Based on the data, results indicated a balance in the answers given by boys and girls.
Bayesian Nonparametric Clustering for Positive Definite Matrices.
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.
Examining item difficulty and response time on perceptual ability test items.
Yang, Chien-Lin; O'Neill, Thomas R; Kramer, Gene A
2002-01-01
This study examined item calibration stability in relation to response time and the levels of item difficulty between different response time groups on a sample of 389 examinees responding to six different subtest items of the Perceptual Ability Test (PAT). The results indicated that no Differential Item Functioning (DIF) was found and a significant correlation coefficient of item difficulty was formed between slow and fast responders. Three distinct levels of difficulty emerged among the six subtests across groups. Slow responders spent significantly more time than fast responders on the four most difficult subtests. A positive significant relationship was found between item difficulty and response time across groups on the overall perceptual ability test items. Overall, this study found that: 1) the same underlying construct is being measured across groups, 2) the PAT scores were equally useful across groups, 3) different sources of item difficulty may exist among the six subtests, and 4) more difficult test items may require more time to answer.
Nonparametric dark energy reconstruction from supernova data.
Holsclaw, Tracy; Alam, Ujjaini; Sansó, Bruno; Lee, Herbert; Heitmann, Katrin; Habib, Salman; Higdon, David
2010-12-10
Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.
Local Component Analysis for Nonparametric Bayes Classifier
Khademi, Mahmoud; safayani, Meharn
2010-01-01
The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with co...
Nonparametric k-nearest-neighbor entropy estimator.
Lombardi, Damiano; Pant, Sanjay
2016-01-01
A nonparametric k-nearest-neighbor-based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering nonuniform probability densities in the region of k-nearest neighbors around each sample point. It aims to improve the classical estimators in three situations: first, when the dimensionality of the random variable is large; second, when near-functional relationships leading to high correlation between components of the random variable are present; and third, when the marginal variances of random variable components vary significantly with respect to each other. Heuristics on the error of the proposed and classical estimators are presented. Finally, the proposed estimator is tested for a variety of distributions in successively increasing dimensions and in the presence of a near-functional relationship. Its performance is compared with a classical estimator, and a significant improvement is demonstrated.
Nonparametric estimation of location and scale parameters
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.
Nonparametric Maximum Entropy Estimation on Information Diagrams
Martin, Elliot A; Meinke, Alexander; Děchtěrenko, Filip; Davidsen, Jörn
2016-01-01
Maximum entropy estimation is of broad interest for inferring properties of systems across many different disciplines. In this work, we significantly extend a technique we previously introduced for estimating the maximum entropy of a set of random discrete variables when conditioning on bivariate mutual informations and univariate entropies. Specifically, we show how to apply the concept to continuous random variables and vastly expand the types of information-theoretic quantities one can condition on. This allows us to establish a number of significant advantages of our approach over existing ones. Not only does our method perform favorably in the undersampled regime, where existing methods fail, but it also can be dramatically less computationally expensive as the cardinality of the variables increases. In addition, we propose a nonparametric formulation of connected informations and give an illustrative example showing how this agrees with the existing parametric formulation in cases of interest. We furthe...
Nonparametric estimation of employee stock options
Institute of Scientific and Technical Information of China (English)
FU Qiang; LIU Li-an; LIU Qian
2006-01-01
We proposed a new model to price employee stock options (ESOs). The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modify BlackScholes formula. The model overcomes the limits of Black-Scholes formula in handling option prices with varied volatility. It disposes the effects of ESOs self-characteristics such as non-tradability, the longer term for expiration, the early exercise feature, the restriction on shorting selling and the employee's risk aversion on risk neutral pricing condition, and can be applied to ESOs valuation with the explanatory variable in no matter the certainty case or random case.
On Parametric (and Non-Parametric Variation
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Ricardo Primi
2010-12-01
Full Text Available Parte do Sistema Nacional de Avaliação das Instituições de Educação Superior considera o desempenho dos estudantes por meio do ENADE. Neste artigo efetuou-se uma análise dos itens da prova do ENADE de psicologia aplicada em 2006 tentando-se detectar itens com funcionamento diferencial (DIF, isto é, itens com problema de equivalência ao medir ingressantes e concluintes e estudantes de instituições públicas e privada. Analisou-se uma amostra de 26.613 estudantes ingressantes e concluintes representativa de todos os cursos do país. Empregou-se a análise de Rasch e regressão logística para se detectar o DIF. Onze itens dos 30 que compunham a prova apresentaram DIF. Dois tipos de DIF ocorreram, um tipo em itens com baixa discriminação e outro em itens com alta discriminação. O subgrupo mais relevante tende a favorecer alunos de instituições públicas. Discute-se também a questão da discriminação elevada como indicativo de DIF.Part of the National Assessment of Institutions of Higher Education considers student performance through ENADE. In this article we performed differential item function analysis of the ENADE that took place in 2006 trying to detect items with problems in measurement equivalence in the assessment of freshman and senior students and from public and private institutions. We analyzed a sample of 26,613 freshmen and seniors representative of all the courses in the country. We used the Rasch analysis and logistic regression to detect DIF. Eleven of the 30 items composing the test showed DIF. Two types of DIF were observed, one occurring in less discriminating items and the other in more discriminating items. The most relevant subgroup of items tends to favor students from public institutions. We also discuss the issue of discrimination parameter being an indicator of DIF.
Nonparametric inference of network structure and dynamics
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
A nonparametric dynamic additive regression model for longitudinal data
DEFF Research Database (Denmark)
Martinussen, Torben; Scheike, Thomas 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...
Nonparametric Bayesian inference for multidimensional compound Poisson processes
S. Gugushvili; F. van der Meulen; P. Spreij
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, whic
Directory of Open Access Journals (Sweden)
Linda Kwakkenbos
Full Text Available OBJECTIVE: The Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F is commonly used to assess fatigue in rheumatic diseases, and has shown to discriminate better across levels of the fatigue spectrum than other commonly used measures. The aim of this study was to assess the cross-language measurement equivalence of the English, French, and Dutch versions of the FACIT-F in systemic sclerosis (SSc patients. METHODS: The FACIT-F was completed by 871 English-speaking Canadian, 238 French-speaking Canadian and 230 Dutch SSc patients. Confirmatory factor analysis was used to assess the factor structure in the three samples. The Multiple-Indicator Multiple-Cause (MIMIC model was utilized to assess differential item functioning (DIF, comparing English versus French and versus Dutch patient responses separately. RESULTS: A unidimensional factor model showed good fit in all samples. Comparing French versus English patients, statistically significant, but small-magnitude DIF was found for 3 of 13 items. French patients had 0.04 of a standard deviation (SD lower latent fatigue scores than English patients and there was an increase of only 0.03 SD after accounting for DIF. For the Dutch versus English comparison, 4 items showed small, but statistically significant, DIF. Dutch patients had 0.20 SD lower latent fatigue scores than English patients. After correcting for DIF, there was a reduction of 0.16 SD in this difference. CONCLUSIONS: There was statistically significant DIF in several items, but the overall effect on fatigue scores was minimal. English, French and Dutch versions of the FACIT-F can be reasonably treated as having equivalent scoring metrics.
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
施沛德; 王海燕; 张利华
2000-01-01
For regression analysis, some useful Information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literat黵e, but the optimal rates of global convergence have not been obtained yet. Because of the possible Information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression f unction based on right-censored response data, and proves, under some regularity condi-tions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtai
2nd Conference of the International Society for Nonparametric Statistics
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...
Item Purification Does Not Always Improve DIF Detection: A Counterexample with Angoff's Delta Plot
Magis, David; Facon, Bruno
2013-01-01
Item purification is an iterative process that is often advocated as improving the identification of items affected by differential item functioning (DIF). With test-score-based DIF detection methods, item purification iteratively removes the items currently flagged as DIF from the test scores to get purified sets of items, unaffected by DIF. The…
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…
Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.
Chairez, Isaac
2016-04-05
This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.
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.
Bayesian nonparametric adaptive control using Gaussian processes.
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.
Nonparametric methods in actigraphy: An update
Gonçalves, Bruno S.B.; Cavalcanti, Paula R.A.; Tavares, Gracilene R.; Campos, Tania F.; Araujo, John F.
2014-01-01
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. PMID:26483921
Nonparametric Detection of Geometric Structures Over Networks
Zou, Shaofeng; Liang, Yingbin; Poor, H. Vincent
2017-10-01
Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exist, all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary and unknown. The goal is to design statistically consistent tests with probability of errors converging to zero as the network size becomes asymptotically large. Kernel-based tests are proposed based on maximum mean discrepancy that measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space. Detection of an anomalous interval over a line network is first studied. Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent. It is also shown that certain necessary conditions must hold to guarantee any test to be universally consistent. Comparison of sufficient and necessary conditions yields that the proposed test is order-level optimal and nearly optimal respectively in terms of minimum and maximum sizes of candidate anomalous intervals. Generalization of the results to other networks is further developed. Numerical results are provided to demonstrate the performance of the proposed tests.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of
A non-parametric approach to investigating fish population dynamics
National Research Council Canada - National Science Library
Cook, R.M; Fryer, R.J
2001-01-01
.... Using a non-parametric model for the stock-recruitment relationship it is possible to avoid defining specific functions relating recruitment to stock size while also providing a natural framework to model process error...
Non-parametric approach to the study of phenotypic stability.
Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P
2016-02-19
The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.
Nonparametric Bayesian Modeling for Automated Database Schema Matching
Energy Technology Data Exchange (ETDEWEB)
Ferragut, Erik M [ORNL; Laska, Jason A [ORNL
2015-01-01
The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.
PV power forecast using a nonparametric PV model
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
other aspects, the properties of a method for parameter estimation in stochastic differential equations is considered within the field of heat dynamics of buildings. In the second paper a lack-of-fit test for stochastic differential equations is presented. The test can be applied to both linear and non-linear...... 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...... stochastic differential equations. Some applications are presented in the papers. In the summary report references are made to a number of other applications. Resumé på dansk: Nærværende afhandling består af ti artikler publiceret i perioden 1996-1999 samt et sammendrag og en perspektivering heraf. I...
Directory of Open Access Journals (Sweden)
Kutlay Sehim
2006-03-01
Full Text Available Abstract Background The Middlesex Elderly Assessment of Mental State (MEAMS was developed as a screening test to detect cognitive impairment in the elderly. It includes 12 subtests, each having a 'pass score'. A series of tasks were undertaken to adapt the measure for use in the adult population in Turkey and to determine the validity of existing cut points for passing subtests, given the wide range of educational level in the Turkish population. This study focuses on identifying and validating the scoring system of the MEAMS for Turkish adult population. Methods After the translation procedure, 350 normal subjects and 158 acquired brain injury patients were assessed by the Turkish version of MEAMS. Initially, appropriate pass scores for the normal population were determined through ANOVA post-hoc tests according to age, gender and education. Rasch analysis was then used to test the internal construct validity of the scale and the validity of the cut points for pass scores on the pooled data by using Differential Item Functioning (DIF analysis within the framework of the Rasch model. Results Data with the initially modified pass scores were analyzed. DIF was found for certain subtests by age and education, but not for gender. Following this, pass scores were further adjusted and data re-fitted to the model. All subtests were found to fit the Rasch model (mean item fit 0.184, SD 0.319; person fit -0.224, SD 0.557 and DIF was then found to be absent. Thus the final pass scores for all subtests were determined. Conclusion The MEAMS offers a valid assessment of cognitive state for the adult Turkish population, and the revised cut points accommodate for age and education. Further studies are required to ascertain the validity in different diagnostic groups.
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
For regression analysis, some useful information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literature, but the optimal rates of global convergence have not been obtained yet. Because of the possible information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression function based on right_censored response data, and proves, under some regularity conditions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtain the asymptotic optimality of AIC, AICC, GCV, Cp and FPE criteria in the process of selecting the parameters.
Rediscovery of Good-Turing estimators via Bayesian nonparametrics.
Favaro, Stefano; Nipoti, Bernardo; Teh, Yee Whye
2016-03-01
The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good-Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library.
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......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...... rejects both the Cobb-Douglas and the Translog functional form, while a recently developed nonparametric kernel regression method with a fully nonparametric panel data specification delivers plausible results. On average, the nonparametric regression results are similar to results that are obtained from...
Methodology in robust and nonparametric statistics
Jurecková, Jana; Picek, Jan
2012-01-01
Introduction and SynopsisIntroductionSynopsisPreliminariesIntroductionInference in Linear ModelsRobustness ConceptsRobust and Minimax Estimation of LocationClippings from Probability and Asymptotic TheoryProblemsRobust Estimation of Location and RegressionIntroductionM-EstimatorsL-EstimatorsR-EstimatorsMinimum Distance and Pitman EstimatorsDifferentiable Statistical FunctionsProblemsAsymptotic Representations for L-Estimators
Aucoin, Julia W
2005-01-01
Professional development specialists have had little opportunity to learn how to write test items to meet the expectations of today's graduate nurse. Schools of nursing have moved away from knowledge-level test items and have had to develop more application and analysis items to prepare graduates for the National Council Licensure Examination (NCLEX). This same type of question can be used effectively to support a competence assessment system and document critical thinking skills.
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.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
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.
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...
DEFF Research Database (Denmark)
Petersen, Morten Aa.; Gamper, Eva-Maria; Costantini, Anna
2016-01-01
OBJECTIVE: To improve measurement precision, the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Group is developing an item bank for computerized adaptive testing (CAT) of emotional functioning (EF). The item bank will be within the conceptual framework...... 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...
Nonparametric estimation of a convex bathtub-shaped hazard function.
Jankowski, Hanna K; Wellner, Jon A
2009-11-01
In this paper, we study the nonparametric maximum likelihood estimator (MLE) of a convex hazard function. We show that the MLE is consistent and converges at a local rate of n(2/5) at points x(0) where the true hazard function is positive and strictly convex. Moreover, we establish the pointwise asymptotic distribution theory of our estimator under these same assumptions. One notable feature of the nonparametric MLE studied here is that no arbitrary choice of tuning parameter (or complicated data-adaptive selection of the tuning parameter) is required.
Assessing Differential Item Functioning in Performance Tests.
Zwick, Rebecca; And Others
Although the belief has been expressed that performance assessments are intrinsically more fair than multiple-choice measures, some forms of performance assessment may in fact be more likely than conventional tests to tap construct-irrelevant factors. As performance assessment grows in popularity, it will be increasingly important to monitor the…
Item Response Theory Using Hierarchical Generalized Linear Models
Directory of Open Access Journals (Sweden)
Hamdollah Ravand
2015-03-01
Full Text Available Multilevel models (MLMs are flexible in that they can be employed to obtain item and person parameters, test for differential item functioning (DIF and capture both local item and person dependence. Papers on the MLM analysis of item response data have focused mostly on theoretical issues where applications have been add-ons to simulation studies with a methodological focus. Although the methodological direction was necessary as a first step to show how MLMs can be utilized and extended to model item response data, the emphasis needs to be shifted towards providing evidence on how applications of MLMs in educational testing can provide the benefits that have been promised. The present study uses foreign language reading comprehension data to illustrate application of hierarchical generalized models to estimate person and item parameters, differential item functioning (DIF, and local person dependence in a three-level model.
Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders
DEFF Research Database (Denmark)
Nielsen, Morten Ørregaard
2009-01-01
In this paper a nonparametric variance ratio testing approach is proposed for determining the number of cointegrating relations in fractionally integrated systems. The test statistic is easily calculated without prior knowledge of the integration order of the data, the strength of the cointegrating...
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...... but that this dependence vanishes after 2-3 years....
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects
Qian, Minghui; Hu, Ridong; Chen, Jianwei
2016-01-01
Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…
Uniform Consistency for Nonparametric Estimators in Null Recurrent Time Series
DEFF Research Database (Denmark)
Gao, Jiti; Kanaya, Shin; Li, Degui
2015-01-01
This paper establishes uniform consistency results for nonparametric kernel density and regression estimators when time series regressors concerned are nonstationary null recurrent Markov chains. Under suitable regularity conditions, we derive uniform convergence rates of the estimators. Our...... results can be viewed as a nonstationary extension of some well-known uniform consistency results for stationary time series....
Non-parametric Bayesian inference for inhomogeneous Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
With reference to a specific data set, we consider how to perform a flexible non-parametric Bayesian analysis of an inhomogeneous point pattern modelled by a Markov point process, with a location dependent first order term and pairwise interaction only. A priori we assume that the first order term...
Investigating the cultural patterns of corruption: A nonparametric analysis
Halkos, George; Tzeremes, Nickolaos
2011-01-01
By using a sample of 77 countries our analysis applies several nonparametric techniques in order to reveal the link between national culture and corruption. Based on Hofstede’s cultural dimensions and the corruption perception index, the results reveal that countries with higher levels of corruption tend to have higher power distance and collectivism values in their society.
Coverage Accuracy of Confidence Intervals in Nonparametric Regression
Institute of Scientific and Technical Information of China (English)
Song-xi Chen; Yong-song Qin
2003-01-01
Point-wise confidence intervals for a nonparametric regression function with random design points are considered. The confidence intervals are those based on the traditional normal approximation and the empirical likelihood. Their coverage accuracy is assessed by developing the Edgeworth expansions for the coverage probabilities. It is shown that the empirical likelihood confidence intervals are Bartlett correctable.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of consump
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...
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
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.
Evaluation of item candidates: the PROMIS qualitative item review.
DeWalt, Darren A; Rothrock, Nan; Yount, Susan; Stone, Arthur A
2007-05-01
One of the PROMIS (Patient-Reported Outcome Measurement Information System) network's primary goals is the development of a comprehensive item bank for patient-reported outcomes of chronic diseases. For its first set of item banks, PROMIS chose to focus on pain, fatigue, emotional distress, physical function, and social function. An essential step for the development of an item pool is the identification, evaluation, and revision of extant questionnaire items for the core item pool. In this work, we also describe the systematic process wherein items are classified for subsequent statistical processing by the PROMIS investigators. Six phases of item development are documented: identification of extant items, item classification and selection, item review and revision, focus group input on domain coverage, cognitive interviews with individual items, and final revision before field testing. Identification of items refers to the systematic search for existing items in currently available scales. Expert item review and revision was conducted by trained professionals who reviewed the wording of each item and revised as appropriate for conventions adopted by the PROMIS network. Focus groups were used to confirm domain definitions and to identify new areas of item development for future PROMIS item banks. Cognitive interviews were used to examine individual items. Items successfully screened through this process were sent to field testing and will be subjected to innovative scale construction procedures.
Directory of Open Access Journals (Sweden)
Akhtar R. Siddique
2000-03-01
Full Text Available This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices. This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices.
Hoijtink, H; Molenaar, IW
1997-01-01
In this paper it will be shown that a certain class of constrained latent class models may be interpreted as a special case of nonparametric multidimensional item response models. The parameters of this latent class model will be estimated using an application of the Gibbs sampler. It will be illust
Roberson-Nay, Roxann; Strong, David R.; Nay, William T.; Beidel, Deborah C.; Turner, Samuel M.
2007-01-01
An abbreviated version of the Social Phobia and Anxiety Inventory (SPAI) was developed using methods based in nonparametric item response theory. Participants included a nonclinical sample of 1,482 undergraduates (52% female, mean age = 19.4 years) as well as a clinical sample of 105 individuals (56% female, mean age = 36.4 years) diagnosed with…
Comparison of Rank Analysis of Covariance and Nonparametric Randomized Blocks Analysis.
Porter, Andrew C.; McSweeney, Maryellen
The relative power of three possible experimental designs under the condition that data is to be analyzed by nonparametric techniques; the comparison of the power of each nonparametric technique to its parametric analogue; and the comparison of relative powers using nonparametric and parametric techniques are discussed. The three nonparametric…
Nonparametric inference procedures for multistate life table analysis.
Dow, M M
1985-01-01
Recent generalizations of the classical single state life table procedures to the multistate case provide the means to analyze simultaneously the mobility and mortality experience of 1 or more cohorts. This paper examines fairly general nonparametric combinatorial matrix procedures, known as quadratic assignment, as an analysis technic of various transitional patterns commonly generated by cohorts over the life cycle course. To some degree, the output from a multistate life table analysis suggests inference procedures. In his discussion of multstate life table construction features, the author focuses on the matrix formulation of the problem. He then presents several examples of the proposed nonparametric procedures. Data for the mobility and life expectancies at birth matrices come from the 458 member Cayo Santiago rhesus monkey colony. The author's matrix combinatorial approach to hypotheses testing may prove to be a useful inferential strategy in several multidimensional demographic areas.
Non-parametric estimation of Fisher information from real data
Shemesh, Omri Har; Miñano, Borja; Hoekstra, Alfons G; Sloot, Peter M A
2015-01-01
The Fisher Information matrix is a widely used measure for applications ranging from statistical inference, information geometry, experiment design, to the study of criticality in biological systems. Yet there is no commonly accepted non-parametric algorithm to estimate it from real data. In this rapid communication we show how to accurately estimate the Fisher information in a nonparametric way. We also develop a numerical procedure to minimize the errors by choosing the interval of the finite difference scheme necessary to compute the derivatives in the definition of the Fisher information. Our method uses the recently published "Density Estimation using Field Theory" algorithm to compute the probability density functions for continuous densities. We use the Fisher information of the normal distribution to validate our method and as an example we compute the temperature component of the Fisher Information Matrix in the two dimensional Ising model and show that it obeys the expected relation to the heat capa...
International Conference on Robust Rank-Based and Nonparametric Methods
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...
Nonparametric instrumental regression with non-convex constraints
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.
Combined parametric-nonparametric identification of block-oriented systems
Mzyk, Grzegorz
2014-01-01
This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.
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......A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered...... 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 Regression Estimation for Multivariate Null Recurrent Processes
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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.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric......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...
Right-Censored Nonparametric Regression: A Comparative Simulation Study
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Dursun Aydın
2016-11-01
Full Text Available This paper introduces the operating of the selection criteria for right-censored nonparametric regression using smoothing spline. In order to transform the response variable into a variable that contains the right-censorship, we used the KaplanMeier weights proposed by [1], and [2]. The major problem in smoothing spline method is to determine a smoothing parameter to obtain nonparametric estimates of the regression function. In this study, the mentioned parameter is chosen based on censored data by means of the criteria such as improved Akaike information criterion (AICc, Bayesian (or Schwarz information criterion (BIC and generalized crossvalidation (GCV. For this purpose, a Monte-Carlo simulation study is carried out to illustrate which selection criterion gives the best estimation for censored data.
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...
Poverty and life cycle effects: A nonparametric analysis for Germany
Stich, Andreas
1996-01-01
Most empirical studies on poverty consider the extent of poverty either for the entire society or for separate groups like elderly people.However, these papers do not show what the situation looks like for persons of a certain age. In this paper poverty measures depending on age are derived using the joint density of income and age. The density is nonparametrically estimated by weighted Gaussian kernel density estimation. Applying the conditional density of income to several poverty measures ...
Nonparametric estimation of Fisher information from real data
Har-Shemesh, Omri; Quax, Rick; Miñano, Borja; Hoekstra, Alfons G.; Sloot, Peter M. A.
2016-02-01
The Fisher information matrix (FIM) is a widely used measure for applications including statistical inference, information geometry, experiment design, and the study of criticality in biological systems. The FIM is defined for a parametric family of probability distributions and its estimation from data follows one of two paths: either the distribution is assumed to be known and the parameters are estimated from the data or the parameters are known and the distribution is estimated from the data. We consider the latter case which is applicable, for example, to experiments where the parameters are controlled by the experimenter and a complicated relation exists between the input parameters and the resulting distribution of the data. Since we assume that the distribution is unknown, we use a nonparametric density estimation on the data and then compute the FIM directly from that estimate using a finite-difference approximation to estimate the derivatives in its definition. The accuracy of the estimate depends on both the method of nonparametric estimation and the difference Δ θ between the densities used in the finite-difference formula. We develop an approach for choosing the optimal parameter difference Δ θ based on large deviations theory and compare two nonparametric density estimation methods, the Gaussian kernel density estimator and a novel density estimation using field theory method. We also compare these two methods to a recently published approach that circumvents the need for density estimation by estimating a nonparametric f divergence and using it to approximate the FIM. We use the Fisher information of the normal distribution to validate our method and as a more involved example we compute the temperature component of the FIM in the two-dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.
ANALYSIS OF TIED DATA: AN ALTERNATIVE NON-PARAMETRIC APPROACH
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I. C. A. OYEKA
2012-02-01
Full Text Available This paper presents a non-parametric statistical method of analyzing two-sample data that makes provision for the possibility of ties in the data. A test statistic is developed and shown to be free of the effect of any possible ties in the data. An illustrative example is provided and the method is shown to compare favourably with its competitor; the Mann-Whitney test and is more powerful than the latter when there are ties.
Nonparametric test for detecting change in distribution with panel data
Pommeret, Denys; Ghattas, Badih
2011-01-01
This paper considers the problem of comparing two processes with panel data. A nonparametric test is proposed for detecting a monotone change in the link between the two process distributions. The test statistic is of CUSUM type, based on the empirical distribution functions. The asymptotic distribution of the proposed statistic is derived and its finite sample property is examined by bootstrap procedures through Monte Carlo simulations.
A Bayesian nonparametric method for prediction in EST analysis
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Prünster Igor
2007-09-01
Full Text Available Abstract Background Expressed sequence tags (ESTs analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library. Results In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b the number of new unique genes to be observed in a future sample; c the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries, previously studied with frequentist methods, are analyzed in detail. Conclusion The Bayesian nonparametric approach we undertake yields valuable tools for gene capture and prediction in EST libraries. The estimators we obtain do not feature the kind of drawbacks associated with frequentist estimators and are reliable for any size of the additional sample.
Fusion of Hard and Soft Information in Nonparametric Density Estimation
2015-06-10
estimation exploiting, in concert, hard and soft information. Although our development, theoretical and numerical, makes no distinction based on sample...Fusion of Hard and Soft Information in Nonparametric Density Estimation∗ Johannes O. Royset Roger J-B Wets Department of Operations Research...univariate density estimation in situations when the sample ( hard information) is supplemented by “soft” information about the random phenomenon. These
Nonparametric estimation for hazard rate monotonously decreasing system
Institute of Scientific and Technical Information of China (English)
Han Fengyan; Li Weisong
2005-01-01
Estimation of density and hazard rate is very important to the reliability analysis of a system. In order to estimate the density and hazard rate of a hazard rate monotonously decreasing system, a new nonparametric estimator is put forward. The estimator is based on the kernel function method and optimum algorithm. Numerical experiment shows that the method is accurate enough and can be used in many cases.
Non-parametric versus parametric methods in environmental sciences
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Muhammad Riaz
2016-01-01
Full Text Available This current report intends to highlight the importance of considering background assumptions required for the analysis of real datasets in different disciplines. We will provide comparative discussion of parametric methods (that depends on distributional assumptions (like normality relative to non-parametric methods (that are free from many distributional assumptions. We have chosen a real dataset from environmental sciences (one of the application areas. The findings may be extended to the other disciplines following the same spirit.
The Role of Item Models in Automatic Item Generation
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…
Identifying Unbiased Items for Screening Preschoolers for Disruptive Behavior Problems.
Studts, Christina R; Polaha, Jodi; van Zyl, Michiel A
2016-10-25
OBJECTIVE : Efficient identification and referral to behavioral services are crucial in addressing early-onset disruptive behavior problems. Existing screening instruments for preschoolers are not ideal for pediatric primary care settings serving diverse populations. Eighteen candidate items for a new brief screening instrument were examined to identify those exhibiting measurement bias (i.e., differential item functioning, DIF) by child characteristics. METHOD : Parents/guardians of preschool-aged children (N = 900) from four primary care settings completed two full-length behavioral rating scales. Items measuring disruptive behavior problems were tested for DIF by child race, sex, and socioeconomic status using two approaches: item response theory-based likelihood ratio tests and ordinal logistic regression. RESULTS : Of 18 items, eight were identified with statistically significant DIF by at least one method. CONCLUSIONS : The bias observed in 8 of 18 items made them undesirable for screening diverse populations of children. These items were excluded from the new brief screening tool.
TIS/RP Group
2001-01-01
The TIS-RP group informs users that shipping of small radioactive items is normally guaranteed within 24 hours from the time the material is handed in at the TIS-RP service. This time is imposed by the necessary procedures (identification of the radionuclides, determination of dose rate and massive objects require a longer procedure and will therefore take longer.
Item Banking with Embedded Standards
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…
Item Banking with Embedded Standards
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…
Stucky, Brian D; Gottfredson, Nisha C; Panter, A T; Daye, Charles E; Allen, Walter R; Wightman, Linda F
2011-04-01
The Everyday Discrimination Scale (EDS), a widely used measure of daily perceived discrimination, is purported to be unidimensional, to function well among African Americans, and to have adequate construct validity. Two separate studies and data sources were used to examine and cross-validate the psychometric properties of the EDS. In Study 1, an exploratory factor analysis was conducted on a sample of African American law students (N = 589), providing strong evidence of local dependence, or nuisance multidimensionality within the EDS. In Study 2, a separate nationally representative community sample (N = 3,527) was used to model the identified local dependence in an item factor analysis (i.e., bifactor model). Next, item response theory (IRT) calibrations were conducted to obtain item parameters. A five-item, revised-EDS was then tested for gender differential item functioning (in an IRT framework). Based on these analyses, a summed score to IRT-scaled score translation table is provided for the revised-EDS. Our results indicate that the revised-EDS is unidimensional, with minimal differential item functioning, and retains predictive validity consistent with the original scale.
TIS/RP Group
2001-01-01
The TIS-RP group informs users that shipping of small radioactive items is normally guaranteed within 24 hours from the time the material is handed in at the TIS-RP service. This time is imposed by the necessary procedures (identification of the radionuclides, determination of dose rate, preparation of the package and related paperwork). Large and massive objects require a longer procedure and will therefore take longer.
a Multivariate Downscaling Model for Nonparametric Simulation of Daily Flows
Molina, J. M.; Ramirez, J. A.; Raff, D. A.
2011-12-01
A multivariate, stochastic nonparametric framework for stepwise disaggregation of seasonal runoff volumes to daily streamflow is presented. The downscaling process is conditional on volumes of spring runoff and large-scale ocean-atmosphere teleconnections and includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. The non-parametric and assumption-free character of the framework allows consideration of the random nature and nonlinearities of daily flows, which parametric models are unable to account for adequately. This paper examines statistical links between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a periodicity analysis of climate patterns to detect coherences of their cyclic behavior in the frequency domain. We explore the use of such relationships and selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices, NPGO, SOI and PDO, respectively) in the proposed data-driven framework by means of a combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. In addition, we present a new and simple algorithm, based on nonparametric resampling, that overcomes the common limitation of lack of preservation of historical correlation between daily flows across months. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and produces synthetic series that are statistically indistinguishable from the observations. We present evidence showing that both
Panel data nonparametric estimation of production risk and risk preferences
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
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...... 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...
Digital spectral analysis parametric, non-parametric and advanced methods
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
Nonparametric statistics a step-by-step approach
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
Categorical and nonparametric data analysis choosing the best statistical technique
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
Nonparametric statistical structuring of knowledge systems using binary feature matches
DEFF Research Database (Denmark)
Mørup, Morten; Glückstad, Fumiko Kano; Herlau, Tue
2014-01-01
statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary......Structuring knowledge systems with binary features is often based on imposing a similarity measure and clustering objects according to this similarity. Unfortunately, such analyses can be heavily influenced by the choice of similarity measure. Furthermore, it is unclear at which level clusters have...
Testing for a constant coefficient of variation in nonparametric regression
Dette, Holger; Marchlewski, Mareen; Wagener, Jens
2010-01-01
In the common nonparametric regression model Y_i=m(X_i)+sigma(X_i)epsilon_i we consider the problem of testing the hypothesis that the coefficient of the scale and location function is constant. The test is based on a comparison of the observations Y_i=\\hat{sigma}(X_i) with their mean by a smoothed empirical process, where \\hat{sigma} denotes the local linear estimate of the scale function. We show weak convergence of a centered version of this process to a Gaussian process under the null ...
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...
Faculty development on item writing substantially improves item quality.
Naeem, N.; Vleuten, C.P.M. van der; Alfaris, E.A.
2012-01-01
The quality of items written for in-house examinations in medical schools remains a cause of concern. Several faculty development programs are aimed at improving faculty's item writing skills. The purpose of this study was to evaluate the effectiveness of a faculty development program in item develo
IRT Item Parameter Scaling for Developing New Item Pools
Kang, Hyeon-Ah; Lu, Ying; Chang, Hua-Hua
2017-01-01
Increasing use of item pools in large-scale educational assessments calls for an appropriate scaling procedure to achieve a common metric among field-tested items. The present study examines scaling procedures for developing a new item pool under a spiraled block linking design. The three scaling procedures are considered: (a) concurrent…
Using Mathematica to build Non-parametric Statistical Tables
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Gloria Perez Sainz de Rozas
2003-01-01
Full Text Available In this paper, I present computational procedures to obtian statistical tables. The tables of the asymptotic distribution and the exact distribution of Kolmogorov-Smirnov statistic Dn for one population, the table of the distribution of the runs R, the table of the distribution of Wilcoxon signed-rank statistic W+ and the table of the distribution of Mann-Whitney statistic Ux using Mathematica, Version 3.9 under Window98. I think that it is an interesting cuestion because many statistical packages give the asymptotic significance level in the statistical tests and with these porcedures one can easily calculate the exact significance levels and the left-tail and right-tail probabilities with non-parametric distributions. I have used mathematica to make these calculations because one can use symbolic language to solve recursion relations. It's very easy to generate the format of the tables, and it's possible to obtain any table of the mentioned non-parametric distributions with any precision, not only with the standard parameters more used in Statistics, and without transcription mistakes. Furthermore, using similar procedures, we can generate tables for the following distribution functions: Binomial, Poisson, Hypergeometric, Normal, x2 Chi-Square, T-Student, F-Snedecor, Geometric, Gamma and Beta.
1st Conference of the International Society for Nonparametric Statistics
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...
Non-parametric Morphologies of Mergers in the Illustris Simulation
Bignone, Lucas A; Sillero, Emanuel; Pedrosa, Susana E; Pellizza, Leonardo J; Lambas, Diego G
2016-01-01
We study non-parametric morphologies of mergers events in a cosmological context, using the Illustris project. We produce mock g-band images comparable to observational surveys from the publicly available Illustris simulation idealized mock images at $z=0$. We then measure non parametric indicators: asymmetry, Gini, $M_{20}$, clumpiness and concentration for a set of galaxies with $M_* >10^{10}$ M$_\\odot$. We correlate these automatic statistics with the recent merger history of galaxies and with the presence of close companions. Our main contribution is to assess in a cosmological framework, the empirically derived non-parametric demarcation line and average time-scales used to determine the merger rate observationally. We found that 98 per cent of galaxies above the demarcation line have a close companion or have experienced a recent merger event. On average, merger signatures obtained from the $G-M_{20}$ criteria anticorrelate clearly with the elapsing time to the last merger event. We also find that the a...
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.
Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes
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.
Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization
Lee, Kyungbook; Song, Seok Goo
2016-10-01
Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events (M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.
A non-parametric framework for estimating threshold limit values
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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.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
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-Douglas a......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...... to estimate production functions without the specification of a functional form. Therefore, they avoid possible misspecification errors due to the use of an unsuitable functional form. In this paper, we use parametric and non-parametric methods to identify the optimal size of Polish crop farms...
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study
Directory of Open Access Journals (Sweden)
Anestis Antoniadis
2001-06-01
Full Text Available Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced.
Computing Economies of Scope Using Robust Partial Frontier Nonparametric Methods
Directory of Open Access Journals (Sweden)
Pedro Carvalho
2016-03-01
Full Text Available This paper proposes a methodology to examine economies of scope using the recent order-α nonparametric method. It allows us to investigate economies of scope by comparing the efficient order-α frontiers of firms that produce two or more goods with the efficient order-α frontiers of firms that produce only one good. To accomplish this, and because the order-α frontiers are irregular, we suggest to linearize them by the DEA estimator. The proposed methodology uses partial frontier nonparametric methods that are more robust than the traditional full frontier methods. By using a sample of 67 Portuguese water utilities for the period 2002–2008 and, also, a simulated sample, we prove the usefulness of the approach adopted and show that if only the full frontier methods were used, they would lead to different results. We found evidence of economies of scope in the provision of water supply and wastewater services simultaneously by water utilities in Portugal.
Bayesian nonparametric dictionary learning for compressed sensing MRI.
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.
Institute of Scientific and Technical Information of China (English)
张龙; 涂冬波
2015-01-01
Differential item functioning( DIF)is a statistical technique to ensure a fair test. Multi-level scoring i-tems are indispensable for educational measurement and psychometrics,but there is still no published articles com-pletely described DIF detection method for multi-level scoring items in generally,this article class the non-paramet-ric polytomous DIF detection methods and parameters polytomous DIF detection methods,these two categories of such methods were described and compared,and follow-up development were discussed.%项目功能差异是确保测验公平的统计技术手段。多级计分题目为教育测量和心理测量中不可或缺的题型，而目前还未见有公开发表的文章较为全面地将常用多级计分题DIF检测方法作一概括，该文从参数类与非参数类2个视角对多级计分DIF检验方法进行论述与比较，为实践应用者在方法选用上提供借鉴及支持，最后对多级计分DIF检验进行讨论。
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...... estimator. A simulation study that serves two purposes is provided. First, it illustrates in details how to implement our proposed nonparametric estimator. Secondly, it facilitates a comparison of the nonparametric estimator to a parametric counterpart based on the estimator of Lu and Liang (2008...
Item Overexposure in Computerized Classification Tests Using Sequential Item Selection
Directory of Open Access Journals (Sweden)
Alan Huebner
2012-06-01
Full Text Available Computerized classification tests (CCTs often use sequential item selection which administers items according to maximizing psychometric information at a cut point demarcating passing and failing scores. This paper illustrates why this method of item selection leads to the overexposure of a significant number of items, and the performances of three different methods for controlling maximum item exposure rates in CCTs are compared. Specifically, the Sympson-Hetter, restricted, and item eligibility methods are examined in two studies realistically simulating different types of CCTs and are evaluated based upon criteria including classification accuracy, the number of items exceeding the desired maximum exposure rate, and test overlap. The pros and cons of each method are discussed from a practical perspective.
Egberink, Iris J. L.; Meijer, Rob 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) w
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)
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)
Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression
Elosua, Paula; Wells, Craig
2013-01-01
The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…
Noble, Tracy; Kachchaf, Rachel; Rosebery, Ann; Warren, Beth; O'Connor, Mary Catherine; Wang, Yang
2014-01-01
Little research has examined individual linguistic features that influence English language learners (ELLs) test performance. Furthermore, research has yet to explore the relationship between the science strand of test items and the types of linguistic features the items include. Utilizing Differential Item Functioning, this study examines ELL…
Robust Depth-Weighted Wavelet for Nonparametric Regression Models
Institute of Scientific and Technical Information of China (English)
Lu LIN
2005-01-01
In the nonpaxametric regression models, the original regression estimators including kernel estimator, Fourier series estimator and wavelet estimator are always constructed by the weighted sum of data, and the weights depend only on the distance between the design points and estimation points. As a result these estimators are not robust to the perturbations in data. In order to avoid this problem, a new nonparametric regression model, called the depth-weighted regression model, is introduced and then the depth-weighted wavelet estimation is defined. The new estimation is robust to the perturbations in data, which attains very high breakdown value close to 1/2. On the other hand, some asymptotic behaviours such as asymptotic normality are obtained. Some simulations illustrate that the proposed wavelet estimator is more robust than the original wavelet estimator and, as a price to pay for the robustness, the new method is slightly less efficient than the original method.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P
2016-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: 1. Deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, that not only remove limitations that seriously degrade the inference on large networks, but also reveal s...
A Non-Parametric Spatial Independence Test Using Symbolic Entropy
Directory of Open Access Journals (Sweden)
López Hernández, Fernando
2008-01-01
Full Text Available In the present paper, we construct a new, simple, consistent and powerful test forspatial independence, called the SG test, by using symbolic dynamics and symbolic entropyas a measure of spatial dependence. We also give a standard asymptotic distribution of anaffine transformation of the symbolic entropy under the null hypothesis of independencein the spatial process. The test statistic and its standard limit distribution, with theproposed symbolization, are invariant to any monotonuous transformation of the data.The test applies to discrete or continuous distributions. Given that the test is based onentropy measures, it avoids smoothed nonparametric estimation. We include a MonteCarlo study of our test, together with the well-known Moran’s I, the SBDS (de Graaffet al, 2001 and (Brett and Pinkse, 1997 non parametric test, in order to illustrate ourapproach.
Analyzing single-molecule time series via nonparametric Bayesian inference.
Hines, Keegan E; Bankston, John R; Aldrich, Richard W
2015-02-03
The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.
Prior processes and their applications nonparametric Bayesian estimation
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...
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......-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation...... 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...
Nonparametric Estimation of Distributions in Random Effects Models
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.
Curve registration by nonparametric goodness-of-fit testing
Dalalyan, Arnak
2011-01-01
The problem of curve registration appears in many different areas of applications ranging from neuroscience to road traffic modeling. In the present work, we propose a nonparametric testing framework in which we develop a generalized likelihood ratio test to perform curve registration. We first prove that, under the null hypothesis, the resulting test statistic is asymptotically distributed as a chi-squared random variable. This result, often referred to as Wilks' phenomenon, provides a natural threshold for the test of a prescribed asymptotic significance level and a natural measure of lack-of-fit in terms of the p-value of the chi squared test. We also prove that the proposed test is consistent, i.e., its power is asymptotically equal to 1. Some numerical experiments on synthetic datasets are reported as well.
Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.
Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z
2017-03-01
A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.
Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees
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Savic Vladimir
2010-01-01
Full Text Available Nonparametric belief propagation (NBP is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST created by breadth first search (BFS method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks. Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.
Revealing components of the galaxy population through nonparametric techniques
Bamford, Steven P; Nichol, Robert C; Miller, Christopher J; Wasserman, Larry; Genovese, Christopher R; Freeman, Peter E
2008-01-01
The distributions of galaxy properties vary with environment, and are often multimodal, suggesting that the galaxy population may be a combination of multiple components. The behaviour of these components versus environment holds details about the processes of galaxy development. To release this information we apply a novel, nonparametric statistical technique, identifying four components present in the distribution of galaxy H$\\alpha$ emission-line equivalent-widths. We interpret these components as passive, star-forming, and two varieties of active galactic nuclei. Independent of this interpretation, the properties of each component are remarkably constant as a function of environment. Only their relative proportions display substantial variation. The galaxy population thus appears to comprise distinct components which are individually independent of environment, with galaxies rapidly transitioning between components as they move into denser environments.
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...... 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...... of stochasticity associated with Lithuanian family farm performance. The former technique showed that the farms differed in terms of the mean values and variance of the efficiency scores over time with some clear patterns prevailing throughout the whole research period. The fuzzy Free Disposal Hull showed...
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
Nonparametric reconstruction of the Om diagnostic to test LCDM
Escamilla-Rivera, Celia
2015-01-01
Cosmic acceleration is usually related with the unknown dark energy, which equation of state, w(z), is constrained and numerically confronted with independent astrophysical data. In order to make a diagnostic of w(z), the introduction of a null test of dark energy can be done using a diagnostic function of redshift, Om. In this work we present a nonparametric reconstruction of this diagnostic using the so-called Loess-Simex factory to test the concordance model with the advantage that this approach offers an alternative way to relax the use of priors and find a possible 'w' that reliably describe the data with no previous knowledge of a cosmological model. Our results demonstrate that the method applied to the dynamical Om diagnostic finds a preference for a dark energy model with equation of state w =-2/3, which correspond to a static domain wall network.
Evaluation of Nonparametric Probabilistic Forecasts of Wind Power
DEFF Research Database (Denmark)
Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008;
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...... of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind......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...
Equity and efficiency in private and public education: a nonparametric comparison
L. Cherchye; K. de Witte; E. Ooghe; I. Nicaise
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Non-parametric tests of productive efficiency with errors-in-variables
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
Equity and efficiency in private and public education: a nonparametric comparison
Cherchye, L.; de Witte, K.; Ooghe, E.; Nicaise, I.
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
Bayesian item fit analysis for unidimensional item response theory models.
Sinharay, Sandip
2006-11-01
Assessing item fit for unidimensional item response theory models for dichotomous items has always been an issue of enormous interest, but there exists no unanimously agreed item fit diagnostic for these models, and hence there is room for further investigation of the area. This paper employs the posterior predictive model-checking method, a popular Bayesian model-checking tool, to examine item fit for the above-mentioned models. An item fit plot, comparing the observed and predicted proportion-correct scores of examinees with different raw scores, is suggested. This paper also suggests how to obtain posterior predictive p-values (which are natural Bayesian p-values) for the item fit statistics of Orlando and Thissen that summarize numerically the information in the above-mentioned item fit plots. A number of simulation studies and a real data application demonstrate the effectiveness of the suggested item fit diagnostics. The suggested techniques seem to have adequate power and reasonable Type I error rate, and psychometricians will find them promising.
Semi-parametric regression: Efficiency gains from modeling the nonparametric part
Yu, Kyusang; Park, Byeong U; 10.3150/10-BEJ296
2011-01-01
It is widely admitted that structured nonparametric modeling that circumvents the curse of dimensionality is important in nonparametric estimation. In this paper we show that the same holds for semi-parametric estimation. We argue that estimation of the parametric component of a semi-parametric model can be improved essentially when more structure is put into the nonparametric part of the model. We illustrate this for the partially linear model, and investigate efficiency gains when the nonparametric part of the model has an additive structure. We present the semi-parametric Fisher information bound for estimating the parametric part of the partially linear additive model and provide semi-parametric efficient estimators for which we use a smooth backfitting technique to deal with the additive nonparametric part. We also present the finite sample performances of the proposed estimators and analyze Boston housing data as an illustration.
Controlling for rater effects when comparing survey items with incomplete Likert data.
Schulz, E M; Sun, A
2001-01-01
The rating scale model (Andrich, 1978) was applied to data from a survey that directed students to rate their satisfaction with college services on a five point Likert scale. Because students used different services, and students were directed to rate only the services they used, the items were differentially exposed to a person factor that we call "pleasability." Differential exposure to pleasability makes items' average rating a biased measure of their performance. In contrast, item parameter estimates in the rating scale model corrected for differential exposure to pleasability. Compared to items' average ratings, item parameter estimates in the rating scale model did a better job of predicting which item received the higher rating when any two items were rated by the same rater.
Directory of Open Access Journals (Sweden)
朱小姝 Xiao-Shu Zhu
2011-03-01
Full Text Available 大型教育評量研究常採用多階段抽樣的設計（multi-stage sampling design），透過對母群體之抽樣單位進行分層以抽取受測者。此外，還會採用複雜題本設計（complex booklet design）的方式將題目組成多份測驗題本。在此情況下，欲確保公正測量出不同受測群體的能力，關鍵在於能夠有效偵測所採用的題目是否具差別試題功能（differential item functioning, DIF）。本文旨在介紹探討在大型教育評量複雜設計之下能用以偵測差別試題功能的建模方法，並應用六種可用於偵測DIF 的多階層廣義線性模式（hierarchical generalized linear models, HGLMs），再透過電腦模擬比較它們偵測DIF 的效力。接著又將這些模式應用到國際數學與科學教育成就趨勢調查研究（TIMSS）的實證數據上，藉以探測是否存在一致性的性別DIF（uniform gender DIF）。 Many educational surveys employ a multi-stage sampling design for students, which makes use of stratification and/or clustering of population units, as well as a complex booklet design for items from an item pool. In these surveys, the reliable detection of item bias or differential item functioning (DIF across student groups is a key component for ensuring fair representations of different student groups. In this paper, we describe several modeling approaches that can be useful for detecting DIF in educational surveys. We illustrate the key ideas by investigating the performance of six hierarchical generalized linear models (HGLMs using a small simulation study and by applying them to real data from the Trends in Mathematics and Science Study (TIMSS study where we use them to investigate potential uniform gender DIF.
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:
GRE Verbal Analogy Items: Examinee Reasoning on Items.
Duran, Richard P.; And Others
Information about how Graduate Record Examination (GRE) examinees solve verbal analogy problems was obtained in this study through protocol analysis. High- and low-ability subjects who had recently taken the GRE General Test were asked to "think aloud" as they worked through eight analogy items. These items varied factorially on the…
A Mixed Effects Randomized Item Response Model
Fox, J.-P.; Wyrick, Cheryl
2008-01-01
The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear…
Computerized adaptive testing with item cloning
Glas, Cornelis A.W.; van der Linden, Willem J.
2003-01-01
To increase the number of items available for adaptive testing and reduce the cost of item writing, the use of techniques of item cloning has been proposed. An important consequence of item cloning is possible variability between the item parameters. To deal with this variability, a multilevel item
A Nonparametric Approach to Estimate Classification Accuracy and Consistency
Lathrop, Quinn N.; Cheng, Ying
2014-01-01
When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…
Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel
2017-10-01
This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.
Item Veto: Dangerous Constitutional Tinkering.
Bellamy, Calvin
1989-01-01
In theory, the item veto would empower the President to remove wasteful and unnecessary projects from legislation. Yet, despite its history at the state level, the item veto is a loosely defined concept that may not work well at the federal level. Much more worrisome is the impact on the balance of power. (Author/CH)
Continuous Online Item Calibration: Parameter Recovery and Item Utilization.
Ren, Hao; van der Linden, Wim J; Diao, Qi
2017-06-01
Parameter recovery and item utilization were investigated for different designs for online test item calibration. The design was adaptive in a double sense: it assumed both adaptive testing of examinees from an operational pool of previously calibrated items and adaptive assignment of field-test items to the examinees. Four criteria of optimality for the assignment of the field-test items were used, each of them based on the information in the posterior distributions of the examinee's ability parameter during adaptive testing as well as the sequentially updated posterior distributions of the field-test item parameters. In addition, different stopping rules based on target values for the posterior standard deviations of the field-test parameters and the size of the calibration sample were used. The impact of each of the criteria and stopping rules on the statistical efficiency of the estimates of the field-test parameters and on the time spent by the items in the calibration procedure was investigated. Recommendations as to the practical use of the designs are given.
Nonparametric predictive inference for combining diagnostic tests with parametric copula
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.
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2017-01-18
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.
The Utility of Nonparametric Transformations for Imputation of Survey Data
Directory of Open Access Journals (Sweden)
Robbins Michael W.
2014-12-01
Full Text Available Missing values present a prevalent problem in the analysis of establishment survey data. Multivariate imputation algorithms (which are used to fill in missing observations tend to have the common limitation that imputations for continuous variables are sampled from Gaussian distributions. This limitation is addressed here through the use of robust marginal transformations. Specifically, kernel-density and empirical distribution-type transformations are discussed and are shown to have favorable properties when used for imputation of complex survey data. Although such techniques have wide applicability (i.e., they may be easily applied in conjunction with a wide array of imputation techniques, the proposed methodology is applied here with an algorithm for imputation in the USDA’s Agricultural Resource Management Survey. Data analysis and simulation results are used to illustrate the specific advantages of the robust methods when compared to the fully parametric techniques and to other relevant techniques such as predictive mean matching. To summarize, transformations based upon parametric densities are shown to distort several data characteristics in circumstances where the parametric model is ill fit; however, no circumstances are found in which the transformations based upon parametric models outperform the nonparametric transformations. As a result, the transformation based upon the empirical distribution (which is the most computationally efficient is recommended over the other transformation procedures in practice.
Nonparametric identification of structural modifications in Laplace domain
Suwała, G.; Jankowski, Ł.
2017-02-01
This paper proposes and experimentally verifies a Laplace-domain method for identification of structural modifications, which (1) unlike time-domain formulations, allows the identification to be focused on these parts of the frequency spectrum that have a high signal-to-noise ratio, and (2) unlike frequency-domain formulations, decreases the influence of numerical artifacts related to the particular choice of the FFT exponential window decay. In comparison to the time-domain approach proposed earlier, advantages of the proposed method are smaller computational cost and higher accuracy, which leads to reliable performance in more difficult identification cases. Analytical formulas for the first- and second-order sensitivity analysis are derived. The approach is based on a reduced nonparametric model, which has the form of a set of selected structural impulse responses. Such a model can be collected purely experimentally, which obviates the need for design and laborious updating of a parametric model, such as a finite element model. The approach is verified experimentally using a 26-node lab 3D truss structure and 30 identification cases of a single mass modification or two concurrent mass modifications.
A New Non-Parametric Approach to Galaxy Morphological Classification
Lotz, J M; Madau, P; Lotz, Jennifer M.; Primack, Joel; Madau, Piero
2003-01-01
We present two new non-parametric methods for quantifying galaxy morphology: the relative distribution of the galaxy pixel flux values (the Gini coefficient or G) and the second-order moment of the brightest 20% of the galaxy's flux (M20). We test the robustness of G and M20 to decreasing signal-to-noise and spatial resolution, and find that both measures are reliable to within 10% at average signal-to-noise per pixel greater than 3 and resolutions better than 1000 pc and 500 pc, respectively. We have measured G and M20, as well as concentration (C), asymmetry (A), and clumpiness (S) in the rest-frame near-ultraviolet/optical wavelengths for 150 bright local "normal" Hubble type galaxies (E-Sd) galaxies and 104 0.05 < z < 0.25 ultra-luminous infrared galaxies (ULIRGs).We find that most local galaxies follow a tight sequence in G-M20-C, where early-types have high G and C and low M20 and late-type spirals have lower G and C and higher M20. The majority of ULIRGs lie above the normal galaxy G-M20 sequence...
Nonparametric Bayes modeling for case control studies with many predictors.
Zhou, Jing; Herring, Amy H; Bhattacharya, Anirban; Olshan, Andrew F; Dunson, David B
2016-03-01
It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Nonparametric estimation of quantum states, processes and measurements
Lougovski, Pavel; Bennink, Ryan
Quantum state, process, and measurement estimation methods traditionally use parametric models, in which the number and role of relevant parameters is assumed to be known. When such an assumption cannot be justified, a common approach in many disciplines is to fit the experimental data to multiple models with different sets of parameters and utilize an information criterion to select the best fitting model. However, it is not always possible to assume a model with a finite (countable) number of parameters. This typically happens when there are unobserved variables that stem from hidden correlations that can only be unveiled after collecting experimental data. How does one perform quantum characterization in this situation? We present a novel nonparametric method of experimental quantum system characterization based on the Dirichlet Process (DP) that addresses this problem. Using DP as a prior in conjunction with Bayesian estimation methods allows us to increase model complexity (number of parameters) adaptively as the number of experimental observations grows. We illustrate our approach for the one-qubit case and show how a probability density function for an unknown quantum process can be estimated.
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
Non-parametric and least squares Langley plot methods
Directory of Open Access Journals (Sweden)
P. W. Kiedron
2015-04-01
Full Text Available Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer–Lambert–Beer law V=V>/i>0e−τ ·m, where a plot of ln (V voltage vs. m air mass yields a straight line with intercept ln (V0. This ln (V0 subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The eleven techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln (V0's are smoothed and interpolated with median and mean moving window filters.
Pivotal Estimation of Nonparametric Functions via Square-root Lasso
Belloni, Alexandre; Wang, Lie
2011-01-01
In a nonparametric linear regression model we study a variant of LASSO, called square-root LASSO, which does not require the knowledge of the scaling parameter $\\sigma$ of the noise or bounds for it. This work derives new finite sample upper bounds for prediction norm rate of convergence, $\\ell_1$-rate of converge, $\\ell_\\infty$-rate of convergence, and sparsity of the square-root LASSO estimator. A lower bound for the prediction norm rate of convergence is also established. In many non-Gaussian noise cases, we rely on moderate deviation theory for self-normalized sums and on new data-dependent empirical process inequalities to achieve Gaussian-like results provided log p = o(n^{1/3}) improving upon results derived in the parametric case that required log p = O(log n). In addition, we derive finite sample bounds on the performance of ordinary least square (OLS) applied tom the model selected by square-root LASSO accounting for possible misspecification of the selected model. In particular, we provide mild con...
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.
Examining Differential Math Performance by Gender and Opportunity to Learn
Albano, Anthony D.; Rodriguez, Michael C.
2013-01-01
Although a substantial amount of research has been conducted on differential item functioning in testing, studies have focused on detecting differential item functioning rather than on explaining how or why it may occur. Some recent work has explored sources of differential functioning using explanatory and multilevel item response models. This…
Faculty development on item writing substantially improves item quality.
Naeem, Naghma; van der Vleuten, Cees; Alfaris, Eiad Abdelmohsen
2012-08-01
The quality of items written for in-house examinations in medical schools remains a cause of concern. Several faculty development programs are aimed at improving faculty's item writing skills. The purpose of this study was to evaluate the effectiveness of a faculty development program in item development. An objective method was developed and used to assess improvement in faculty's competence to develop high quality test items. This was a quasi experimental study with a pretest-midtest-posttest design. A convenience sample of 51 faculty members participated. Structured checklists were used to assess the quality of test items at each phase of the study. Group scores were analyzed using repeated measures analysis of variance. The results showed a significant increase in participants' mean scores on Multiple Choice Questions, Short Answer Questions and Objective Structured Clinical Examination checklists from pretest to posttest (p development are generally lacking in quality. It also provides evidence of the value of faculty development in improving the quality of items generated by faculty.
Institute of Scientific and Technical Information of China (English)
LINGNeng-xiang; DUXue-qiao
2005-01-01
In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.
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...
Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models
De Blasi, Pierpaolo; Lau, John W; 10.3150/09-BEJ233
2011-01-01
This paper develops nonparametric estimation for discrete choice models based on the mixed multinomial logit (MMNL) model. It has been shown that MMNL models encompass all discrete choice models derived under the assumption of random utility maximization, subject to the identification of an unknown distribution $G$. Noting the mixture model description of the MMNL, we employ a Bayesian nonparametric approach, using nonparametric priors on the unknown mixing distribution $G$, to estimate choice probabilities. We provide an important theoretical support for the use of the proposed methodology by investigating consistency of the posterior distribution for a general nonparametric prior on the mixing distribution. Consistency is defined according to an $L_1$-type distance on the space of choice probabilities and is achieved by extending to a regression model framework a recent approach to strong consistency based on the summability of square roots of prior probabilities. Moving to estimation, slightly different te...
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.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models
Fan, Jianqing; Song, Rui
2011-01-01
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under the nonparametric additive models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, an iterative nonparametric independence screening (INIS) is also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data a...
Nonparametric TOA estimators for low-resolution IR-UWB digital receiver
Institute of Scientific and Technical Information of China (English)
Yanlong Zhang; Weidong Chen
2015-01-01
Nonparametric time-of-arrival (TOA) estimators for im-pulse radio ultra-wideband (IR-UWB) signals are proposed. Non-parametric detection is obviously useful in situations where de-tailed information about the statistics of the noise is unavailable or not accurate. Such TOA estimators are obtained based on condi-tional statistical tests with only a symmetry distribution assumption on the noise probability density function. The nonparametric es-timators are attractive choices for low-resolution IR-UWB digital receivers which can be implemented by fast comparators or high sampling rate low resolution analog-to-digital converters (ADCs), in place of high sampling rate high resolution ADCs which may not be available in practice. Simulation results demonstrate that nonparametric TOA estimators provide more effective and robust performance than typical energy detection (ED) based estimators.
Nonparametric statistical tests for the continuous data: the basic concept and the practical use.
Nahm, Francis Sahngun
2016-02-01
Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of sample means is normally distributed. However, parametric test can be misleading when this assumption is not satisfied. In this circumstance, nonparametric tests are the alternative methods available, because they do not required the normality assumption. Nonparametric tests are the statistical methods based on signs and ranks. In this article, we will discuss about the basic concepts and practical use of nonparametric tests for the guide to the proper use.
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2008-01-01
Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.
A robust nonparametric method for quantifying undetected extinctions.
Chisholm, Ryan A; Giam, Xingli; Sadanandan, Keren R; Fung, Tak; Rheindt, Frank E
2016-06-01
How many species have gone extinct in modern times before being described by science? To answer this question, and thereby get a full assessment of humanity's impact on biodiversity, statistical methods that quantify undetected extinctions are required. Such methods have been developed recently, but they are limited by their reliance on parametric assumptions; specifically, they assume the pools of extant and undetected species decay exponentially, whereas real detection rates vary temporally with survey effort and real extinction rates vary with the waxing and waning of threatening processes. We devised a new, nonparametric method for estimating undetected extinctions. As inputs, the method requires only the first and last date at which each species in an ensemble was recorded. As outputs, the method provides estimates of the proportion of species that have gone extinct, detected, or undetected and, in the special case where the number of undetected extant species in the present day is assumed close to zero, of the absolute number of undetected extinct species. The main assumption of the method is that the per-species extinction rate is independent of whether a species has been detected or not. We applied the method to the resident native bird fauna of Singapore. Of 195 recorded species, 58 (29.7%) have gone extinct in the last 200 years. Our method projected that an additional 9.6 species (95% CI 3.4, 19.8) have gone extinct without first being recorded, implying a true extinction rate of 33.0% (95% CI 31.0%, 36.2%). We provide R code for implementing our method. Because our method does not depend on strong assumptions, we expect it to be broadly useful for quantifying undetected extinctions. © 2016 Society for Conservation Biology.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P.
2017-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
Akhtar, Naveed; Mian, Ajmal
2017-10-03
We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.
Non-parametric combination and related permutation tests for neuroimaging.
Winkler, Anderson M; Webster, Matthew A; Brooks, Jonathan C; Tracey, Irene; Smith, Stephen M; Nichols, Thomas E
2016-04-01
In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.
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
Kong, Xiangrong; Mas, Valeria; Archer, Kellie J
2008-02-26
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. 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. 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 reported to be relevant to renal diseases. Further study on the
The Influence of Item Formats when Locating a Student on a Learning Progression in Science
Directory of Open Access Journals (Sweden)
Jing Chen
2016-07-01
Full Text Available Learning progressions are used to describe how students’ understanding of a topic progresses over time. This study evaluates the effectiveness of different item formats for placing students into levels along a learning progression for carbon cycling. The item formats investigated were Constructed Response (CR items and two types of two-tier items: (1 Ordered Multiple-Choice (OMC followed by CR items and (2 Multiple True or False (MTF followed by CR items. Our results suggest that estimates of students’ learning progression level based on OMC and MTF responses are moderately predictive of their level based on CR responses. With few exceptions, CR items were effective for differentiating students among learning progression levels. Based on the results, we discuss how to design and best use items in each format to more accurately measure students’ level along learning progressions in science.
Institute of Scientific and Technical Information of China (English)
LIU Yong-jian; DUAN Chuan; TIAN Meng-liang; HU Er-liang; HUANG Yu-bi
2010-01-01
Analysis of multi-environment trials (METs) of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction (GEI) to 14 maize (Zea mays L.) genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEI and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability.Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX 168 as desirable and ND 108, CM 12, CN36, and NK6661 as undesirable genotypes.
A novel nonparametric confidence interval for differences of proportions for correlated binary data.
Duan, Chongyang; Cao, Yingshu; Zhou, Lizhi; Tan, Ming T; Chen, Pingyan
2016-11-16
Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance. © The Author(s) 2016.
Counting Frequencies from Zotero Items
Directory of Open Access Journals (Sweden)
Spencer Roberts
2013-04-01
Full Text Available In Counting Frequencies you learned how to count the frequency of specific words in a list using python. In this lesson, we will expand on that topic by showing you how to get information from Zotero HTML items, save the content from those items, and count the frequencies of words. It may be beneficial to look over the previous lesson before we begin.
DIF Analysis for Pretest Items in Computer-Adaptive Testing.
Zwick, Rebecca; And Others
A simulation study of methods of assessing differential item functioning (DIF) in computer-adaptive tests (CATs) was conducted by Zwick, Thayer, and Wingersky (in press, 1993). Results showed that modified versions of the Mantel-Haenszel and standardization methods work well with CAT data. DIF methods were also investigated for nonadaptive…
Evaluation of the PROMIS physical function item bank in orthopaedic patients.
Hung, Man; Clegg, Daniel O; Greene, Tom; Saltzman, Charles L
2011-06-01
The patient-reported outcomes measurement information system (PROMIS) physical function item bank v1 (PPFIB) contains 124 item response theory (IRT) calibrated items (Rose et al. 2008. J Clin Epidemiol 61:17–33).We report the psychometric properties of these items within an outpatient, orthopaedic patient population. In particular, we investigated whether a single unidimensional IRT scale can adequately define physical function of patients presenting with primarily upper or lower extremity orthopaedic complaints. We conducted a prospective study at an orthopaedic outpatient clinic to collect data from 865 adult patients with all 124 PROMIS physical function items and seven demographic items. Items were evaluated by a Rasch model. Total variance (60.6%) across the 124 items was explained by a single Rasch dimension. The variance explained by the second dimension was 7.7%, reflecting differential item functioning in the upper and lower extremity patients. The upper extremity physical function items had a pronounced ceiling effect. A single physical function dimension accounts for most of the item variance in the PPFIB, suggesting that the items are measuring predominantly one single construct. Separate subscales for lower versus upper extremities, especially with additional items at the upper trait level of the upper extremity subscale, may further enhance evaluation of physical function in orthopaedic patients.
Directory of Open Access Journals (Sweden)
Jun Zhang
2013-12-01
Full Text Available Divergence functions are the non-symmetric “distance” on the manifold, Μθ, of parametric probability density functions over a measure space, (Χ,μ. Classical information geometry prescribes, on Μθ: (i a Riemannian metric given by the Fisher information; (ii a pair of dual connections (giving rise to the family of α-connections that preserve the metric under parallel transport by their joint actions; and (iii a family of divergence functions ( α-divergence defined on Μθ x Μθ, which induce the metric and the dual connections. Here, we construct an extension of this differential geometric structure from Μθ (that of parametric probability density functions to the manifold, Μ, of non-parametric functions on X, removing the positivity and normalization constraints. The generalized Fisher information and α-connections on M are induced by an α-parameterized family of divergence functions, reflecting the fundamental convex inequality associated with any smooth and strictly convex function. The infinite-dimensional manifold, M, has zero curvature for all these α-connections; hence, the generally non-zero curvature of M can be interpreted as arising from an embedding of Μθ into Μ. Furthermore, when a parametric model (after a monotonic scaling forms an affine submanifold, its natural and expectation parameters form biorthogonal coordinates, and such a submanifold is dually flat for α = ± 1, generalizing the results of Amari’s α-embedding. The present analysis illuminates two different types of duality in information geometry, one concerning the referential status of a point (measurable function expressed in the divergence function (“referential duality” and the other concerning its representation under an arbitrary monotone scaling (“representational duality”.
Australian Item Bank Program: Social Science Item Bank.
Australian Council for Educational Research, Hawthorn.
After vigorous review, editing, and trial testing, this item bank was compiled to help secondary school teachers construct objective tests in the social sciences. Anthropology, economics, ethnic and cultural studies, geography, history, legal studies, politics, and sociology are among the topics represented. The bank consists of multiple choice…
A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator
Energy Technology Data Exchange (ETDEWEB)
Huang, Shengzhi; Huang, Qiang; Leng, Guoyong; Chang, Jianxia
2016-06-01
This study develops a nonparametric multivariate drought index, namely, the Nonparametric Multivariate Standardized Drought Index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing Nonparametric Multivariate Drought Index, we use the nonparametric kernel estimator to derive the joint distribution of precipitation and streamflow, thus providing additional insights in drought index development. The proposed NMSDI are applied in the Wei River Basin (WRB), based on which the drought evolution characteristics are investigated. Results indicate: (1) generally, NMSDI captures the drought onset similar to Standardized Precipitation Index (SPI) and drought termination and persistence similar to Standardized Streamflow Index (SSFI). The drought events identified by NMSDI match well with historical drought records in the WRB. The performances are also consistent with that by an existing Multivariate Standardized Drought Index (MSDI) at various timescales, confirming the validity of the newly constructed NMSDI in drought detections (2) An increasing risk of drought has been detected for the past decades, and will be persistent to a certain extent in future in most areas of the WRB; (3) the identified change points of annual NMSDI are mainly concentrated in the early 1970s and middle 1990s, coincident with extensive water use and soil reservation practices. This study highlights the nonparametric multivariable drought index, which can be used for drought detections and predictions efficiently and comprehensively.
Modelling sequentially scored item responses
Akkermans, W.
2000-01-01
The sequential model can be used to describe the variable resulting from a sequential scoring process. In this paper two more item response models are investigated with respect to their suitability for sequential scoring: the partial credit model and the graded response model. The investigation is c
Gender Differences in Figural Matrices: The Moderating Role of Item Design Features
Arendasy, Martin E.; Sommer, Markus
2012-01-01
There is a heated debate on whether observed gender differences in some figural matrices in adults can be attributed to gender differences in inductive reasoning/G[subscript f] or differential item functioning and/or test bias. Based on previous studies we hypothesized that three specific item design features moderate the effect size of the gender…
Gender Differences in Figural Matrices: The Moderating Role of Item Design Features
Arendasy, Martin E.; Sommer, Markus
2012-01-01
There is a heated debate on whether observed gender differences in some figural matrices in adults can be attributed to gender differences in inductive reasoning/G[subscript f] or differential item functioning and/or test bias. Based on previous studies we hypothesized that three specific item design features moderate the effect size of the gender…
Assessment of Preference for Edible and Leisure Items in Individuals with Dementia
Ortega, Javier Virues; Iwata, Brian A.; Nogales-Gonzalez, Celia; Frades, Belen
2012-01-01
We conducted 2 studies on reinforcer preference in patients with dementia. Results of preference assessments yielded differential selections by 14 participants. Unlike prior studies with individuals with intellectual disabilities, all participants showed a noticeable preference for leisure items over edible items. Results of a subsequent analysis…
Psychometric Consequences of Subpopulation Item Parameter Drift
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…
Psychometric Consequences of Subpopulation Item Parameter Drift
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…
Unidimensional Interpretations for Multidimensional Test Items
Kahraman, Nilufer
2013-01-01
This article considers potential problems that can arise in estimating a unidimensional item response theory (IRT) model when some test items are multidimensional (i.e., show a complex factorial structure). More specifically, this study examines (1) the consequences of model misfit on IRT item parameter estimates due to unintended minor item-level…
Generalizability theory and item response theory
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
Generalizability theory and item response theory
Glas, C.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 s
15 CFR 742.15 - Encryption items.
2010-01-01
... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Encryption items. 742.15 Section 742... BASED CONTROLS § 742.15 Encryption items. Encryption items can be used to maintain the secrecy of... export and reexport of encryption items. As the President indicated in Executive Order 13026 and in his...
Item-Writing Guidelines for Physics
Regan, Tom
2015-01-01
A teacher learning how to write test questions (test items) will almost certainly encounter item-writing guidelines--lists of item-writing do's and don'ts. Item-writing guidelines usually are presented as applicable across all assessment settings. Table I shows some guidelines that I believe to be generally applicable and two will be briefly…
Unidimensional Interpretations for Multidimensional Test Items
Kahraman, Nilufer
2013-01-01
This article considers potential problems that can arise in estimating a unidimensional item response theory (IRT) model when some test items are multidimensional (i.e., show a complex factorial structure). More specifically, this study examines (1) the consequences of model misfit on IRT item parameter estimates due to unintended minor item-level…
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...... updating approach and be integrated in the reliability analysis by a third-order polynomial chaos expansion approximation. Although Classical Bayesian updating approaches are often used because of its parametric formulation, non-parametric approaches are better alternatives for multi-parametric updating...... with a non-conjugating formulation. The results in this paper show the influence on the time dependent updated reliability when non-parametric and classical Bayesian approaches are used. Further, the influence on the reliability of the number of updated parameters is illustrated....
Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures
Li, Quanbao; Wei, Fajie; Zhou, Shenghan
2017-05-01
The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.
Non-parametric seismic hazard analysis in the presence of incomplete data
Yazdani, Azad; Mirzaei, Sajjad; Dadkhah, Koroush
2017-01-01
The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.
Oude Voshaar, Martijn A.H.; Ten Klooster, Peter M.; Vonkeman, Harald E.; van de Laar, Mart A.F.J.
2017-01-01
Objective: Traditional patient-reported physical function instruments often poorly differentiate patients with mild-to-moderate disability. We describe the development and psychometric evaluation of a generic item bank for measuring everyday activity limitations in outpatient populations. Study
Rush, Bonnie R; Rankin, David C; White, Brad J
2016-09-29
Failure to adhere to standard item-writing guidelines may render examination questions easier or more difficult than intended. Item complexity describes the cognitive skill level required to obtain a correct answer. Higher cognitive examination items promote critical thinking and are recommended to prepare students for clinical training. This study evaluated faculty-authored examinations to determine the impact of item-writing flaws and item complexity on the difficulty and discrimination value of examination items used to assess third year veterinary students. The impact of item-writing flaws and item complexity (cognitive level I-V) on examination item difficulty and discrimination value was evaluated on 1925 examination items prepared by clinical faculty for third year veterinary students. The mean (± SE) percent correct (83.3 % ± 17.5) was consistent with target values in professional education, and the mean discrimination index (0.18 ± 0.17) was slightly lower than recommended (0.20). More than one item-writing flaw was identified in 37.3 % of questions. The most common item-writing flaws were awkward stem structure, implausible distractors, longest response is correct, and responses are series of true-false statements. Higher cognitive skills (complexity level III-IV) were required to correctly answer 38.4 % of examination items. As item complexity increased, item difficulty and discrimination values increased. The probability of writing discriminating, difficult examination items decreased when implausible distractors and all of the above were used, and increased if the distractors were comprised of a series of true/false statements. Items with four distractors were not more difficult or discriminating than items with three distractors. Preparation of examination questions targeting higher cognitive levels will increase the likelihood of constructing discriminating items. Use of implausible distractors to complete a five-option multiple choice
Modern nonparametric, robust and multivariate methods festschrift in honour of Hannu Oja
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.
Multivariate nonparametric regression and visualization with R and applications to finance
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
Directory of Open Access Journals (Sweden)
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.
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.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
Wei, Tianlan; Chesnut, Steven R.; Barnard-Brak, Lucy; Stevens, Tara; Olivárez, Arturo, Jr.
2014-01-01
As the United States has begun to lag behind other developed countries in performance on mathematics and science, researchers have sought to explain this with theories of teaching, knowledge, and motivation. We expand this examination by further analyzing a measure of interest that has been linked to student performance in mathematics and…
Wei, Tianlan; Chesnut, Steven R.; Barnard-Brak, Lucy; Stevens, Tara; Olivárez, Arturo, Jr.
2014-01-01
As the United States has begun to lag behind other developed countries in performance on mathematics and science, researchers have sought to explain this with theories of teaching, knowledge, and motivation. We expand this examination by further analyzing a measure of interest that has been linked to student performance in mathematics and…
Caution warranted in extrapolating from Boston Naming Test item gradation construct.
Beattey, Robert A; Murphy, Hilary; Cornwell, Melinda; Braun, Thomas; Stein, Victoria; Goldstein, Martin; Bender, Heidi Allison
2017-01-01
The Boston Naming Test (BNT) was designed to present items in order of difficulty based on word frequency. Changes in word frequencies over time, however, would frustrate extrapolation in clinical and research settings based on the theoretical construct because performance on the BNT might reflect changes in ecological frequency of the test items, rather than performance across items of increasing difficulty. This study identifies the ecological frequency of BNT items at the time of publication using the American Heritage Word Frequency Book and determines changes in frequency over time based on the frequency distribution of BNT items across a current corpus, the Corpus of Contemporary American English. Findings reveal an uneven distribution of BNT items across 2 corpora and instances of negligible differentiation in relative word frequency across test items. As BNT items are not presented in order from least to most frequent, clinicians and researchers should exercise caution in relying on the BNT as presenting items in increasing order of difficulty. A method is proposed for distributing confrontation-naming items to be explicitly measured against test items that are normally distributed across the corpus of a given language.
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
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any func
Sharing the cost of redundant items
DEFF Research Database (Denmark)
Hougaard, Jens Leth; Moulin, Hervé
2014-01-01
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......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...... additive in costs....
Unfair items detection in educational measurement
Bakman, Yefim
2012-01-01
Measurement professionals cannot come to an agreement on the definition of the term 'item fairness'. In this paper a continuous measure of item unfairness is proposed. The more the unfairness measure deviates from zero, the less fair the item is. If the measure exceeds the cutoff value, the item is identified as definitely unfair. The new approach can identify unfair items that would not be identified with conventional procedures. The results are in accord with experts' judgments on the item qualities. Since no assumptions about scores distributions and/or correlations are assumed, the method is applicable to any educational test. Its performance is illustrated through application to scores of a real test.
Nonparametric estimation of population density for line transect sampling using FOURIER series
Crain, B.R.; Burnham, K.P.; Anderson, D.R.; Lake, J.L.
1979-01-01
A nonparametric, robust density estimation method is explored for the analysis of right-angle distances from a transect line to the objects sighted. The method is based on the FOURIER series expansion of a probability density function over an interval. With only mild assumptions, a general population density estimator of wide applicability is obtained.
A non-parametric peak finder algorithm and its application in searches for new physics
Chekanov, S
2011-01-01
We have developed an algorithm for non-parametric fitting and extraction of statistically significant peaks in the presence of statistical and systematic uncertainties. Applications of this algorithm for analysis of high-energy collision data are discussed. In particular, we illustrate how to use this algorithm in general searches for new physics in invariant-mass spectra using pp Monte Carlo simulations.
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...
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...
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Madsen, Kristoffer H.; Siebner, Hartwig Roman
2014-01-01
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian...
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 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 p...
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...
Jang, Eunice Eunhee; Roussos, Louis
2007-01-01
This article reports two studies to illustrate methodologies for conducting a conditional covariance-based nonparametric dimensionality assessment using data from two forms of the Test of English as a Foreign Language (TOEFL). Study 1 illustrates how to assess overall dimensionality of the TOEFL including all three subtests. Study 2 is aimed at…
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.
Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Comparison of reliability techniques of parametric and non-parametric method
Directory of Open Access Journals (Sweden)
C. Kalaiselvan
2016-06-01
Full Text Available Reliability of a product or system is the probability that the product performs adequately its intended function for the stated period of time under stated operating conditions. It is function of time. The most widely used nano ceramic capacitor C0G and X7R is used in this reliability study to generate the Time-to failure (TTF data. The time to failure data are identified by Accelerated Life Test (ALT and Highly Accelerated Life Testing (HALT. The test is conducted at high stress level to generate more failure rate within the short interval of time. The reliability method used to convert accelerated to actual condition is Parametric method and Non-Parametric method. In this paper, comparative study has been done for Parametric and Non-Parametric methods to identify the failure data. The Weibull distribution is identified for parametric method; Kaplan–Meier and Simple Actuarial Method are identified for non-parametric method. The time taken to identify the mean time to failure (MTTF in accelerating condition is the same for parametric and non-parametric method with relative deviation.
Non-parametric Tuning of PID Controllers A Modified Relay-Feedback-Test Approach
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...
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...
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...
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 ...
A Comparison of Shewhart Control Charts based on Normality, Nonparametrics, and Extreme-Value Theory
Ion, R.A.; Does, R.J.M.M.; Klaassen, C.A.J.
2000-01-01
Several control charts for individual observations are compared. The traditional ones are the well-known Shewhart control charts with estimators for the spread based on the sample standard deviation and the average of the moving ranges. The alternatives are nonparametric control charts, based on emp
Non-parametric production analysis of pesticides use in the 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
Agasisti, Tommaso
2011-01-01
The objective of this paper is an efficiency analysis concerning higher education systems in European countries. Data have been extracted from OECD data-sets (Education at a Glance, several years), using a non-parametric technique--data envelopment analysis--to calculate efficiency scores. This paper represents the first attempt to conduct such an…
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models.
Fan, Jianqing; Feng, Yang; Song, Rui
2011-06-01
A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under general nonparametric models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, a data-driven thresholding and an iterative nonparametric independence screening (INIS) are also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data analysis demonstrate that the proposed procedure works well with moderate sample size and large dimension and performs better than competing methods.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models.
Fan, Jianqing; Ma, Yunbei; Dai, Wei
2014-01-01
The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high dimensional varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications.
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...
Measuring the influence of networks on transaction costs using a non-parametric regression technique
DEFF Research Database (Denmark)
Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Do Former College Athletes Earn More at Work? A Nonparametric Assessment
Henderson, Daniel J.; Olbrecht, Alexandre; Polachek, Solomon W.
2006-01-01
This paper investigates how students' collegiate athletic participation affects their subsequent labor market success. By using newly developed techniques in nonparametric regression, it shows that on average former college athletes earn a wage premium. However, the premium is not uniform, but skewed so that more than half the athletes actually…
Nonparametric Tests of Collectively Rational Consumption Behavior : An Integer Programming Procedure
Cherchye, L.J.H.; de Rock, B.; Sabbe, J.; Vermeulen, F.M.P.
2008-01-01
We present an IP-based nonparametric (revealed preference) testing proce- dure for rational consumption behavior in terms of general collective models, which include consumption externalities and public consumption. An empiri- cal application to data drawn from the Russia Longitudinal Monitoring
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...
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...
Wei, Jiawei; Carroll, Raymond J; Maity, Arnab
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.
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...
Using automatic item generation to create multiple-choice test items.
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.
Item calibration in incomplete testing designs
Eggen, Theo J.H.M.; Verhelst, Norman D.
2011-01-01
This study discusses the justifiability of item parameter estimation in incomplete testing designs in item response theory. Marginal maximum likelihood (MML) as well as conditional maximum likelihood (CML) procedures are considered in three commonly used incomplete designs: random incomplete, multis
Greenberg, Ariela Caren
Differential item functioning (DIF) and differential distractor functioning (DDF) are methods used to screen for item bias (Camilli & Shepard, 1994; Penfield, 2008). Using an applied empirical example, this mixed-methods study examined the congruency and relationship of DIF and DDF methods in screening multiple-choice items. Data for Study I were drawn from item responses of 271 female and 236 male low-income children on a preschool science assessment. Item analyses employed a common statistical approach of the Mantel-Haenszel log-odds ratio (MH-LOR) to detect DIF in dichotomously scored items (Holland & Thayer, 1988), and extended the approach to identify DDF (Penfield, 2008). Findings demonstrated that the using MH-LOR to detect DIF and DDF supported the theoretical relationship that the magnitude and form of DIF and are dependent on the DDF effects, and demonstrated the advantages of studying DIF and DDF in multiple-choice items. A total of 4 items with DIF and DDF and 5 items with only DDF were detected. Study II incorporated an item content review, an important but often overlooked and under-published step of DIF and DDF studies (Camilli & Shepard). Interviews with 25 female and 22 male low-income preschool children and an expert review helped to interpret the DIF and DDF results and their comparison, and determined that a content review process of studied items can reveal reasons for potential item bias that are often congruent with the statistical results. Patterns emerged and are discussed in detail. The quantitative and qualitative analyses were conducted in an applied framework of examining the validity of the preschool science assessment scores for evaluating science programs serving low-income children, however, the techniques can be generalized for use with measures across various disciplines of research.
Processing Polarity Items: Contrastive Licensing Costs
Saddy, Douglas; Drenhaus, Heiner; Frisch, Stefan
2004-01-01
We describe an experiment that investigated the failure to license polarity items in German using event-related brain potentials (ERPs). The results reveal distinct processing reflexes associated with failure to license positive polarity items in comparison to failure to license negative polarity items. Failure to license both negative and…
Towards an authoring system for item construction
Rikers, Jos H.A.N.
1988-01-01
The process of writing test items is analyzed, and a blueprint is presented for an authoring system for test item writing to reduce invalidity and to structure the process of item writing. The developmental methodology is introduced, and the first steps in the process are reported. A historical revi
Generalized Full-Information Item Bifactor Analysis
Cai, Li; Yang, Ji Seung; Hansen, Mark
2011-01-01
Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single-group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of…
Item Response Methods for Educational Assessment.
Mislevy, Robert J.; Rieser, Mark R.
Multiple matrix sampling (MMS) theory indicates how data may be gathered to most efficiently convey information about levels of attainment in a population, but standard analyses of these data require random sampling of items from a fixed pool of items. This assumption proscribes the retirement of flawed or obsolete items from the pool as well as…
Matrix Sampling of Test Items. ERIC Digest.
Childs, Ruth A.; Jaciw, Andrew P.
This Digest describes matrix sampling of test items as an approach to achieving broad coverage while minimizing testing time per student. Matrix sampling involves developing a complete set of items judged to cover the curriculum, then dividing the items into subsets and administering one subset to each student. Matrix sampling, by limiting the…
The Multidimensionality of Verbal Analogy Items
Ullstadius, Eva; Carlstedt, Berit; Gustafsson, Jan-Eric
2008-01-01
The influence of general and verbal ability on each of 72 verbal analogy test items were investigated with new factor analytical techniques. The analogy items together with the Computerized Swedish Enlistment Battery (CAT-SEB) were given randomly to two samples of 18-year-old male conscripts (n = 8566 and n = 5289). Thirty-two of the 72 items had…
Optimal item pool design for computerized adaptive tests with polytomous items using GPCM
Directory of Open Access Journals (Sweden)
Xuechun Zhou
2014-09-01
Full Text Available Computerized adaptive testing (CAT is a testing procedure with advantages in improving measurement precision and increasing test efficiency. An item pool with optimal characteristics is the foundation for a CAT program to achieve those desirable psychometric features. This study proposed a method to design an optimal item pool for tests with polytomous items using the generalized partial credit model (G-PCM. It extended a method for approximating optimality with polytomous items being described succinctly for the purpose of pool design. Optimal item pools were generated using CAT simulations with and without practical constraints of content balancing and item exposure control. The performances of the item pools were evaluated against an operational item pool. The results indicated that the item pools designed with stratification based on discrimination parameters performed well with an efficient use of the less discriminative items within the target accuracy levels. The implications for developing item pools are also discussed.
Emergency Power For Critical Items
Young, William R.
2009-07-01
Natural disasters, such as hurricanes, floods, tornados, and tsunami, are becoming a greater problem as climate change impacts our environment. Disasters, whether natural or man made, destroy lives, homes, businesses and the natural environment. Such disasters can happen with little or no warning, leaving hundreds or even thousands of people without medical services, potable water, sanitation, communications and electrical services for up to several weeks. In our modern world, the need for electricity has become a necessity. Modern building codes and new disaster resistant building practices are reducing the damage to homes and businesses. Emergency gasoline and diesel generators are becoming common place for power outages. Generators need fuel, which may not be available after a disaster, but Photovoltaic (solar-electric) systems supply electricity without petroleum fuel as they are powered by the sun. Photovoltaic (PV) systems can provide electrical power for a home or business. PV systems can operate as utility interactive or stand-alone with battery backup. Determining your critical load items and sizing the photovoltaic system for those critical items, guarantees their operation in a disaster.
Intact Transition Epitope Mapping (ITEM)
Yefremova, Yelena; Opuni, Kwabena F. M.; Danquah, Bright D.; Thiesen, Hans-Juergen; Glocker, Michael O.
2017-08-01
Intact transition epitope mapping (ITEM) enables rapid and accurate determination of protein antigen-derived epitopes by either epitope extraction or epitope excision. Upon formation of the antigen peptide-containing immune complex in solution, the entire mixture is electrosprayed to translate all constituents as protonated ions into the gas phase. There, ions from antibody-peptide complexes are separated from unbound peptide ions according to their masses, charges, and shapes either by ion mobility drift or by quadrupole ion filtering. Subsequently, immune complexes are dissociated by collision induced fragmentation and the ion signals of the "complex-released peptides," which in effect are the epitope peptides, are recorded in the time-of-flight analyzer of the mass spectrometer. Mixing of an antibody solution with a solution in which antigens or antigen-derived peptides are dissolved is, together with antigen proteolysis, the only required in-solution handling step. Simplicity of sample handling and speed of analysis together with very low sample consumption makes ITEM faster and easier to perform than other experimental epitope mapping methods.
Miller, G Edward; Gesn, Paul Randall; Rotou, Jourania
2005-01-01
In state assessment programs that employ Rasch-based common item linking procedures, the linking constant is usually estimated with only those common items not identified as exhibiting item difficulty parameter drift. Since state assessments typically contain a fixed number of items, an item classified as exhibiting parameter drift during the linking process remains on the exam as a scorable item even if it is removed from the common item set. Under the assumption that item parameter drift has occurred for one or more of the common items, the expected effect of including or excluding the "affected" item(s) in the estimation of the linking constant is derived in this article. If the item parameter drift is due solely to factors not associated with a change in examinee achievement, no linking error will (be expected to) occur given that the linking constant is estimated only with the items not identified as "affected"; linking error will (be expected to) occur if the linking constant is estimated with all common items. However, if the item parameter drift is due solely to change in examinee achievement, the opposite is true: no linking error will (be expected to) occur if the linking constant is estimated with all common items; linking error will (be expected to) occur if the linking constant is estimated only with the items not identified as "affected".