Directory of Open Access Journals (Sweden)
Nadim Nachar
2008-03-01
Full Text Available It is often difficult, particularly when conducting research in psychology, to have access to large normally distributed samples. Fortunately, there are statistical tests to compare two independent groups that do not require large normally distributed samples. The Mann-Whitney U is one of these tests. In the following work, a summary of this test is presented. The explanation of the logic underlying this test and its application are presented. Moreover, the forces and weaknesses of the Mann-Whitney U are mentioned. One major limit of the Mann-Whitney U is that the type I error or alpha (? is amplified in a situation of heteroscedasticity.
Mann-Whitney Type Tests for Microarray Experiments: The R Package gMWT
Directory of Open Access Journals (Sweden)
Daniel Fischer
2015-06-01
Full Text Available We present the R package gMWT which is designed for the comparison of several treatments (or groups for a large number of variables. The comparisons are made using certain probabilistic indices (PI. The PIs computed here tell how often pairs or triples of observations coming from different groups appear in a specific order of magnitude. Classical two and several sample rank test statistics such as the Mann-Whitney-Wilcoxon, Kruskal-Wallis, or Jonckheere-Terpstra test statistics are simple functions of these PI. Also new test statistics for directional alternatives are provided. The package gMWT can be used to calculate the variable-wise PI estimates, to illustrate their multivariate distribution and mutual dependence with joint scatterplot matrices, and to construct several classical and new rank tests based on the PIs. The aim of the paper is first to briefly explain the theory that is necessary to understand the behavior of the estimated PIs and the rank tests based on them. Second, the use of the package is described and illustrated with simulated and real data examples. It is stressed that the package provides a new flexible toolbox to analyze large gene or microRNA expression data sets, collected on microarrays or by other high-throughput technologies. The testing procedures can be used in an eQTL analysis, for example, as implemented in the package GeneticTools.
Climate Verification Using Running Mann Whitney Z Statistics
A robust method previously used to detect observed intra- to multi-decadal (IMD) climate regimes was adapted to test whether climate models could reproduce IMD variations in U.S. surface temperatures during 1919-2008. This procedure, called the running Mann Whitney Z (MWZ) method, samples data ranki...
Time Series Analysis Based on Running Mann Whitney Z Statistics
A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...
[Do we always correctly interpret the results of statistical nonparametric tests].
Moczko, Jerzy A
2014-01-01
Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman tests create a group of commonly used tests to analyze the results of clinical and laboratory data. These tests are considered to be extremely flexible and their asymptotic relative efficiency exceeds 95 percent. Compared with the corresponding parametric tests they do not require checking the fulfillment of the conditions such as the normality of data distribution, homogeneity of variance, the lack of correlation means and standard deviations, etc. They can be used both in the interval and or-dinal scales. The article presents an example Mann-Whitney test, that does not in any case the choice of these four nonparametric tests treated as a kind of gold standard leads to correct inference.
Teaching Nonparametric Statistics Using Student Instrumental Values.
Anderson, Jonathan W.; Diddams, Margaret
Nonparametric statistics are often difficult to teach in introduction to statistics courses because of the lack of real-world examples. This study demonstrated how teachers can use differences in the rankings and ratings of undergraduate and graduate values to discuss: (1) ipsative and normative scaling; (2) uses of the Mann-Whitney U-test; and…
Directory of Open Access Journals (Sweden)
Donald W. Zimmerman
2004-01-01
Full Text Available It is well known that the two-sample Student t test fails to maintain its significance level when the variances of treatment groups are unequal, and, at the same time, sample sizes are unequal. However, introductory textbooks in psychology and education often maintain that the test is robust to variance heterogeneity when sample sizes are equal. The present study discloses that, for a wide variety of non-normal distributions, especially skewed distributions, the Type I error probabilities of both the t test and the Wilcoxon-Mann-Whitney test are substantially inflated by heterogeneous variances, even when sample sizes are equal. The Type I error rate of the t test performed on ranks replacing the scores (rank-transformed data is inflated in the same way and always corresponds closely to that of the Wilcoxon-Mann-Whitney test. For many probability densities, the distortion of the significance level is far greater after transformation to ranks and, contrary to known asymptotic properties, the magnitude of the inflation is an increasing function of sample size. Although nonparametric tests of location also can be sensitive to differences in the shape of distributions apart from location, the Wilcoxon-Mann-Whitney test and rank-transformation tests apparently are influenced mainly by skewness that is accompanied by specious differences in the means of ranks.
On Cooper's Nonparametric Test.
Schmeidler, James
1978-01-01
The basic assumption of Cooper's nonparametric test for trend (EJ 125 069) is questioned. It is contended that the proper assumption alters the distribution of the statistic and reduces its usefulness. (JKS)
Dickhaus, Thorsten
2018-01-01
This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
Nonparametric 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.
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
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
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Nonparametric Statistics Test Software Package.
1983-09-01
25 I1l,lCELL WRITE (NCF,12 ) IvE (I ,RCCT(I) 122 FORMAT(IlXt 3(H5 9 1) IF( IeLT *NCELL) WRITE (NOF1123 J PARTV(I1J 123 FORMAT( Xll----’,FIo.3J 25 CONT...the user’s entries. Its purpose is to write two types of files needed by the program Crunch: the data file, and the option file. 211 Iuill rateLchiavar...data file and communicate the choice of test and test parameters to Crunch. After a data file is written, Lochinvar prompts the writing of the
A Nonparametric Test for Seasonal Unit Roots
Kunst, Robert M.
2009-01-01
Abstract: We consider a nonparametric test for the null of seasonal unit roots in quarterly time series that builds on the RUR (records unit root) test by Aparicio, Escribano, and Sipols. We find that the test concept is more promising than a formalization of visual aids such as plots by quarter. In order to cope with the sensitivity of the original RUR test to autocorrelation under its null of a unit root, we suggest an augmentation step by autoregression. We present some evidence on the siz...
Nonparametric tests for equality of psychometric functions.
García-Pérez, Miguel A; Núñez-Antón, Vicente
2017-12-07
Many empirical studies measure psychometric functions (curves describing how observers' performance varies with stimulus magnitude) because these functions capture the effects of experimental conditions. To assess these effects, parametric curves are often fitted to the data and comparisons are carried out by testing for equality of mean parameter estimates across conditions. This approach is parametric and, thus, vulnerable to violations of the implied assumptions. Furthermore, testing for equality of means of parameters may be misleading: Psychometric functions may vary meaningfully across conditions on an observer-by-observer basis with no effect on the mean values of the estimated parameters. Alternative approaches to assess equality of psychometric functions per se are thus needed. This paper compares three nonparametric tests that are applicable in all situations of interest: The existing generalized Mantel-Haenszel test, a generalization of the Berry-Mielke test that was developed here, and a split variant of the generalized Mantel-Haenszel test also developed here. Their statistical properties (accuracy and power) are studied via simulation and the results show that all tests are indistinguishable as to accuracy but they differ non-uniformly as to power. Empirical use of the tests is illustrated via analyses of published data sets and practical recommendations are given. The computer code in MATLAB and R to conduct these tests is available as Electronic Supplemental Material.
A NONPARAMETRIC HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING
Temel, Tugrul T.
2001-01-01
This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test. The paper also develops a Mathematica Code for the test algorithm.
A nonparametric test for industrial specialization
Billings, Stephen B.; Johnson, Erik B.
2010-01-01
Urban economists hypothesize that industrial diversity matters for urban growth and development, but metrics for empirically testing this relationship are limited to simple concentration metrics (e.g. location quotient) or summary diversity indices (e.g. Gini, Herfindahl). As shown by recent advances in how we measure localization and specialization, these measures of industrial diversity may be subject to bias under small samples or the Modifiable Areal Unit Problem. Furthermore, empirically...
Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data
CORNELIS A. LOS
2004-01-01
The efficiency of speculative markets, as represented by Fama's 1970 fair game model, is tested on weekly price index data of six Asian stock markets - Hong Kong, Indonesia, Malaysia, Singapore, Taiwan and Thailand - using Sherry's (1992) non-parametric methods. These scientific testing methods were originally developed to analyze the information processing efficiency of nervous systems. In particular, the stationarity and independence of the price innovations are tested over ten years, from ...
Wei, Jiawei; Carroll, Raymond J.; Maity, Arnab
2011-01-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work
A note on Nonparametric Confidence Interval for a Shift Parameter ...
African Journals Online (AJOL)
The method is illustrated using the Cauchy distribution as a location model. The kernel-based method is found to have a shorter interval for the shift parameter between two Cauchy distributions than the one based on the Mann-Whitney test statistic. Keywords: Best Asymptotic Normal; Cauchy distribution; Kernel estimates; ...
Spurious Seasonality Detection: A Non-Parametric Test Proposal
Directory of Open Access Journals (Sweden)
Aurelio F. Bariviera
2018-01-01
Full Text Available This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies.
Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing
DEFF Research Database (Denmark)
Hald, Ditte Høvenhoff
The goal of this thesis is to explore two improvements for functional magnetic resonance imaging (fMRI) analysis; namely our proposed decomposition method and an extension to the non-parametric testing framework. Analysis of fMRI allows researchers to investigate the functional processes...... of the brain, and provides insight into neuronal coupling during mental processes or tasks. The decomposition method is a Gaussian process-based independent components analysis (GPICA), which incorporates a temporal dependency in the sources. A hierarchical model specification is used, featuring both...... instantaneous and convolutive mixing, and the inferred temporal patterns. Spatial maps are seen to capture smooth and localized stimuli-related components, and often identifiable noise components. The implementation is freely available as a GUI/SPM plugin, and we recommend using GPICA as an additional tool when...
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.
Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A
2017-06-30
Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Nonparametric Bayes Classification and Hypothesis Testing on Manifolds
Bhattacharya, Abhishek; Dunson, David
2012-01-01
Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provide simple sufficient conditions for large support and weak and strong posterior consistency in estimating both the joint distribution of the response and predictors and the conditional distribution of the response. Focusing on a Dirichlet process prior for the mixing measure, these conditions hold using von Mises-Fisher kernels when the manifold is the unit hypersphere. In this case, Bayesian methods are developed for efficient posterior computation using slice sampling. Next we develop Bayesian nonparametric methods for testing whether there is a difference in distributions between groups of observations on the manifold having unknown densities. We prove consistency of the Bayes factor and develop efficient computational methods for its calculation. The proposed classification and testing methods are evaluated using simulation examples and applied to spherical data applications. PMID:22754028
Non-parametric 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
On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests
Directory of Open Access Journals (Sweden)
Aaditya Ramdas
2017-01-01
Full Text Available Nonparametric two-sample or homogeneity testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. The literature is old and rich, with a wide variety of statistics having being designed and analyzed, both for the unidimensional and the multivariate setting. Inthisshortsurvey,wefocusonteststatisticsthatinvolvetheWassersteindistance. Usingan entropic smoothing of the Wasserstein distance, we connect these to very different tests including multivariate methods involving energy statistics and kernel based maximum mean discrepancy and univariate methods like the Kolmogorov–Smirnov test, probability or quantile (PP/QQ plots and receiver operating characteristic or ordinal dominance (ROC/ODC curves. Some observations are implicit in the literature, while others seem to have not been noticed thus far. Given nonparametric two-sample testing’s classical and continued importance, we aim to provide useful connections for theorists and practitioners familiar with one subset of methods but not others.
Directory of Open Access Journals (Sweden)
SANGCHAN KANTABUTRA
2009-04-01
Full Text Available This paper examines urban-rural effects on public upper-secondary school efficiency in northern Thailand. In the study, efficiency was measured by a nonparametric technique, data envelopment analysis (DEA. Urban-rural effects were examined through a Mann-Whitney nonparametric statistical test. Results indicate that urban schools appear to have access to and practice different production technologies than rural schools, and rural institutions appear to operate less efficiently than their urban counterparts. In addition, a sensitivity analysis, conducted to ascertain the robustness of the analytical framework, revealed the stability of urban-rural effects on school efficiency. Policy to improve school eff iciency should thus take varying geographical area differences into account, viewing rural and urban schools as different from one another. Moreover, policymakers might consider shifting existing resources from urban schools to rural schools, provided that the increase in overall rural efficiency would be greater than the decrease, if any, in the city. Future research directions are discussed.
Semi-Nonparametric Estimation and Misspecification Testing of Diffusion Models
DEFF Research Database (Denmark)
Kristensen, Dennis
of the estimators and tests under the null are derived, and the power properties are analyzed by considering contiguous alternatives. Test directly comparing the drift and diffusion estimators under the relevant null and alternative are also analyzed. Markov Bootstrap versions of the test statistics are proposed...... to improve on the finite-sample approximations. The finite sample properties of the estimators are examined in a simulation study....
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.
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 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...
A simple non-parametric goodness-of-fit test for elliptical copulas
Directory of Open Access Journals (Sweden)
Jaser Miriam
2017-12-01
Full Text Available In this paper, we propose a simple non-parametric goodness-of-fit test for elliptical copulas of any dimension. It is based on the equality of Kendall’s tau and Blomqvist’s beta for all bivariate margins. Nominal level and power of the proposed test are investigated in a Monte Carlo study. An empirical application illustrates our goodness-of-fit test at work.
von Hirschhausen, Christian R.; Cullmann, Astrid
2005-01-01
Abstract This paper applies parametric and non-parametric and parametric tests to assess the efficiency of electricity distribution companies in Germany. We address traditional issues in electricity sector benchmarking, such as the role of scale effects and optimal utility size, as well as new evidence specific to the situation in Germany. We use labour, capital, and peak load capacity as inputs, and units sold and the number of customers as output. The data cover 307 (out of 553) ...
A nonparametric empirical Bayes framework for large-scale multiple testing.
Martin, Ryan; Tokdar, Surya T
2012-07-01
We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive recursion (PR) marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates. Simulations and real data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the nonnull density can give a much better fit in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions.
Kerschbamer, Rudolf
2015-05-01
This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity.
International Nuclear Information System (INIS)
McIntee, Erin; Viglino, Emilie; Rinke, Caitlin; Kumor, Stephanie; Ni Liqiang; Sigman, Michael E.
2010-01-01
Laser-induced breakdown spectroscopy (LIBS) has been investigated for the discrimination of automobile paint samples. Paint samples from automobiles of different makes, models, and years were collected and separated into sets based on the color, presence or absence of effect pigments and the number of paint layers. Twelve LIBS spectra were obtained for each paint sample, each an average of a five single shot 'drill down' spectra from consecutive laser ablations in the same spot on the sample. Analyses by a nonparametric permutation test and a parametric Wald test were performed to determine the extent of discrimination within each set of paint samples. The discrimination power and Type I error were assessed for each data analysis method. Conversion of the spectral intensity to a log-scale (base 10) resulted in a higher overall discrimination power while observing the same significance level. Working on the log-scale, the nonparametric permutation tests gave an overall 89.83% discrimination power with a size of Type I error being 4.44% at the nominal significance level of 5%. White paint samples, as a group, were the most difficult to differentiate with the power being only 86.56% followed by 95.83% for black paint samples. Parametric analysis of the data set produced lower discrimination (85.17%) with 3.33% Type I errors, which is not recommended for both theoretical and practical considerations. The nonparametric testing method is applicable across many analytical comparisons, with the specific application described here being the pairwise comparison of automotive paint samples.
Robust non-parametric one-sample tests for the analysis of recurrent events.
Rebora, Paola; Galimberti, Stefania; Valsecchi, Maria Grazia
2010-12-30
One-sample non-parametric tests are proposed here for inference on recurring events. The focus is on the marginal mean function of events and the basis for inference is the standardized distance between the observed and the expected number of events under a specified reference rate. Different weights are considered in order to account for various types of alternative hypotheses on the mean function of the recurrent events process. A robust version and a stratified version of the test are also proposed. The performance of these tests was investigated through simulation studies under various underlying event generation processes, such as homogeneous and nonhomogeneous Poisson processes, autoregressive and renewal processes, with and without frailty effects. The robust versions of the test have been shown to be suitable in a wide variety of event generating processes. The motivating context is a study on gene therapy in a very rare immunodeficiency in children, where a major end-point is the recurrence of severe infections. Robust non-parametric one-sample tests for recurrent events can be useful to assess efficacy and especially safety in non-randomized studies or in epidemiological studies for comparison with a standard population. Copyright © 2010 John Wiley & Sons, Ltd.
Parametric and nonparametric Granger causality testing: Linkages between international stock markets
De Gooijer, Jan G.; Sivarajasingham, Selliah
2008-04-01
This study investigates long-term linear and nonlinear causal linkages among eleven stock markets, six industrialized markets and five emerging markets of South-East Asia. We cover the period 1987-2006, taking into account the on-set of the Asian financial crisis of 1997. We first apply a test for the presence of general nonlinearity in vector time series. Substantial differences exist between the pre- and post-crisis period in terms of the total number of significant nonlinear relationships. We then examine both periods, using a new nonparametric test for Granger noncausality and the conventional parametric Granger noncausality test. One major finding is that the Asian stock markets have become more internationally integrated after the Asian financial crisis. An exception is the Sri Lankan market with almost no significant long-term linear and nonlinear causal linkages with other markets. To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VAR filtered residuals and VAR filtered squared residuals for the post-crisis sample. We find quite a few remaining significant bi- and uni-directional causal nonlinear relationships in these series. Finally, after filtering the VAR-residuals with GARCH-BEKK models, we show that the nonparametric test statistics are substantially smaller in both magnitude and statistical significance than those before filtering. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects.
Carroll, Raymond J.
2011-03-01
In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.
A non-parametric consistency test of the ΛCDM model with Planck CMB data
Energy Technology Data Exchange (ETDEWEB)
Aghamousa, Amir; Shafieloo, Arman [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of); Hamann, Jan, E-mail: amir@aghamousa.com, E-mail: jan.hamann@unsw.edu.au, E-mail: shafieloo@kasi.re.kr [School of Physics, The University of New South Wales, Sydney NSW 2052 (Australia)
2017-09-01
Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base ΛCDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base ΛCDM model as cosmology's gold standard.
Nonparametric test of consistency between cosmological models and multiband CMB measurements
Energy Technology Data Exchange (ETDEWEB)
Aghamousa, Amir [Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 790-784 (Korea, Republic of); Shafieloo, Arman, E-mail: amir@apctp.org, E-mail: shafieloo@kasi.re.kr [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of)
2015-06-01
We present a novel approach to test the consistency of the cosmological models with multiband CMB data using a nonparametric approach. In our analysis we calibrate the REACT (Risk Estimation and Adaptation after Coordinate Transformation) confidence levels associated with distances in function space (confidence distances) based on the Monte Carlo simulations in order to test the consistency of an assumed cosmological model with observation. To show the applicability of our algorithm, we confront Planck 2013 temperature data with concordance model of cosmology considering two different Planck spectra combination. In order to have an accurate quantitative statistical measure to compare between the data and the theoretical expectations, we calibrate REACT confidence distances and perform a bias control using many realizations of the data. Our results in this work using Planck 2013 temperature data put the best fit ΛCDM model at 95% (∼ 2σ) confidence distance from the center of the nonparametric confidence set while repeating the analysis excluding the Planck 217 × 217 GHz spectrum data, the best fit ΛCDM model shifts to 70% (∼ 1σ) confidence distance. The most prominent features in the data deviating from the best fit ΛCDM model seems to be at low multipoles 18 < ℓ < 26 at greater than 2σ, ℓ ∼ 750 at ∼1 to 2σ and ℓ ∼ 1800 at greater than 2σ level. Excluding the 217×217 GHz spectrum the feature at ℓ ∼ 1800 becomes substantially less significance at ∼1 to 2σ confidence level. Results of our analysis based on the new approach we propose in this work are in agreement with other analysis done using alternative methods.
Lange, C; Lyon, H; DeMeo, D; Raby, B; Silverman, EK; Weiss, ST
2003-01-01
We introduce a new powerful nonparametric testing strategy for family-based association studies in which multiple quantitative traits are recorded and the phenotype with the strongest genetic component is not known prior to the analysis. In the first stage, using a population-based test based on the
A Non-Parametric Surrogate-based Test of Significance for T-Wave Alternans Detection
Nemati, Shamim; Abdala, Omar; Bazán, Violeta; Yim-Yeh, Susie; Malhotra, Atul; Clifford, Gari
2010-01-01
We present a non-parametric adaptive surrogate test that allows for the differentiation of statistically significant T-Wave Alternans (TWA) from alternating patterns that can be solely explained by the statistics of noise. The proposed test is based on estimating the distribution of noise induced alternating patterns in a beat sequence from a set of surrogate data derived from repeated reshuffling of the original beat sequence. Thus, in assessing the significance of the observed alternating patterns in the data no assumptions are made about the underlying noise distribution. In addition, since the distribution of noise-induced alternans magnitudes is calculated separately for each sequence of beats within the analysis window, the method is robust to data non-stationarities in both noise and TWA. The proposed surrogate method for rejecting noise was compared to the standard noise rejection methods used with the Spectral Method (SM) and the Modified Moving Average (MMA) techniques. Using a previously described realistic multi-lead model of TWA, and real physiological noise, we demonstrate the proposed approach reduces false TWA detections, while maintaining a lower missed TWA detection compared with all the other methods tested. A simple averaging-based TWA estimation algorithm was coupled with the surrogate significance testing and was evaluated on three public databases; the Normal Sinus Rhythm Database (NRSDB), the Chronic Heart Failure Database (CHFDB) and the Sudden Cardiac Death Database (SCDDB). Differences in TWA amplitudes between each database were evaluated at matched heart rate (HR) intervals from 40 to 120 beats per minute (BPM). Using the two-sample Kolmogorov-Smirnov test, we found that significant differences in TWA levels exist between each patient group at all decades of heart rates. The most marked difference was generally found at higher heart rates, and the new technique resulted in a larger margin of separability between patient populations than
Trend Analysis of Pahang River Using Non-Parametric Analysis: Mann Kendalls Trend Test
International Nuclear Information System (INIS)
Nur Hishaam Sulaiman; Mohd Khairul Amri Kamarudin; Mohd Khairul Amri Kamarudin; Ahmad Dasuki Mustafa; Muhammad Azizi Amran; Fazureen Azaman; Ismail Zainal Abidin; Norsyuhada Hairoma
2015-01-01
Flood is common in Pahang especially during northeast monsoon season from November to February. Three river cross station: Lubuk Paku, Sg. Yap and Temerloh were selected as area of this study. The stream flow and water level data were gathered from DID record. Data set for this study were analysed by using non-parametric analysis, Mann-Kendall Trend Test. The results that obtained from stream flow and water level analysis indicate that there are positively significant trend for Lubuk Paku (0.001) and Sg. Yap (<0.0001) from 1972-2011 with the p-value < 0.05. Temerloh (0.178) data from 1963-2011 recorded no trend for stream flow parameter but negative trend for water level parameter. Hydrological pattern and trend are extremely affected by outside factors such as north east monsoon season that occurred in South China Sea and affected Pahang during November to March. There are other factors such as development and management of the areas which can be considered as factors affected the data and results. Hydrological Pattern is important to indicate the river trend such as stream flow and water level. It can be used as flood mitigation by local authorities. (author)
Directory of Open Access Journals (Sweden)
Elżbieta Sandurska
2016-12-01
Full Text Available Introduction: Application of statistical software typically does not require extensive statistical knowledge, allowing to easily perform even complex analyses. Consequently, test selection criteria and important assumptions may be easily overlooked or given insufficient consideration. In such cases, the results may likely lead to wrong conclusions. Aim: To discuss issues related to assumption violations in the case of Student's t-test and one-way ANOVA, two parametric tests frequently used in the field of sports science, and to recommend solutions. Description of the state of knowledge: Student's t-test and ANOVA are parametric tests, and therefore some of the assumptions that need to be satisfied include normal distribution of the data and homogeneity of variances in groups. If the assumptions are violated, the original design of the test is impaired, and the test may then be compromised giving spurious results. A simple method to normalize the data and to stabilize the variance is to use transformations. If such approach fails, a good alternative to consider is a nonparametric test, such as Mann-Whitney, the Kruskal-Wallis or Wilcoxon signed-rank tests. Summary: Thorough verification of the parametric tests assumptions allows for correct selection of statistical tools, which is the basis of well-grounded statistical analysis. With a few simple rules, testing patterns in the data characteristic for the study of sports science comes down to a straightforward procedure.
Nonparametric identification of copula structures
Li, Bo; Genton, Marc G.
2013-01-01
We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric
Bakker, Marjan; Wicherts, Jelte M
2014-09-01
In psychology, outliers are often excluded before running an independent samples t test, and data are often nonnormal because of the use of sum scores based on tests and questionnaires. This article concerns the handling of outliers in the context of independent samples t tests applied to nonnormal sum scores. After reviewing common practice, we present results of simulations of artificial and actual psychological data, which show that the removal of outliers based on commonly used Z value thresholds severely increases the Type I error rate. We found Type I error rates of above 20% after removing outliers with a threshold value of Z = 2 in a short and difficult test. Inflations of Type I error rates are particularly severe when researchers are given the freedom to alter threshold values of Z after having seen the effects thereof on outcomes. We recommend the use of nonparametric Mann-Whitney-Wilcoxon tests or robust Yuen-Welch tests without removing outliers. These alternatives to independent samples t tests are found to have nominal Type I error rates with a minimal loss of power when no outliers are present in the data and to have nominal Type I error rates and good power when outliers are present. PsycINFO Database Record (c) 2014 APA, all rights reserved.
A new non-parametric stationarity test of time series in the time domain
Jin, Lei; Wang, Suojin; Wang, Haiyan
2014-01-01
© 2015 The Royal Statistical Society and Blackwell Publishing Ltd. We propose a new double-order selection test for checking second-order stationarity of a time series. To develop the test, a sequence of systematic samples is defined via Walsh
Zhang, Qingyang
2018-05-16
Differential co-expression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially co-expressed genes is to compare Pearson's correlation coefficients in two phenotypes. However, due to the limitations of Pearson's correlation measure, this approach lacks the power to detect nonlinear changes in gene co-expression which is common in gene regulatory networks. In this work, a new nonparametric procedure is proposed to search differentially co-expressed gene pairs in different phenotypes from large-scale data. Our computational pipeline consisted of two main steps, a screening step and a testing step. The screening step is to reduce the search space by filtering out all the independent gene pairs using distance correlation measure. In the testing step, we compare the gene co-expression patterns in different phenotypes by a recently developed edge-count test. Both steps are distribution-free and targeting nonlinear relations. We illustrate the promise of the new approach by analyzing the Cancer Genome Atlas data and the METABRIC data for breast cancer subtypes. Compared with some existing methods, the new method is more powerful in detecting nonlinear type of differential co-expressions. The distance correlation screening can greatly improve computational efficiency, facilitating its application to large data sets.
Non-Parametric, Closed-Loop Testing of Autonomy in Unmanned Aircraft Systems, Phase I
National Aeronautics and Space Administration — The proposed Phase I program aims to develop new methods to support safety testing for integration of Unmanned Aircraft Systems into the National Airspace (NAS) with...
A new non-parametric stationarity test of time series in the time domain
Jin, Lei
2014-11-07
© 2015 The Royal Statistical Society and Blackwell Publishing Ltd. We propose a new double-order selection test for checking second-order stationarity of a time series. To develop the test, a sequence of systematic samples is defined via Walsh functions. Then the deviations of the autocovariances based on these systematic samples from the corresponding autocovariances of the whole time series are calculated and the uniform asymptotic joint normality of these deviations over different systematic samples is obtained. With a double-order selection scheme, our test statistic is constructed by combining the deviations at different lags in the systematic samples. The null asymptotic distribution of the statistic proposed is derived and the consistency of the test is shown under fixed and local alternatives. Simulation studies demonstrate well-behaved finite sample properties of the method proposed. Comparisons with some existing tests in terms of power are given both analytically and empirically. In addition, the method proposed is applied to check the stationarity assumption of a chemical process viscosity readings data set.
Non-parametric comparison of histogrammed two-dimensional data distributions using the Energy Test
International Nuclear Information System (INIS)
Reid, Ivan D; Lopes, Raul H C; Hobson, Peter R
2012-01-01
When monitoring complex experiments, comparison is often made between regularly acquired histograms of data and reference histograms which represent the ideal state of the equipment. With the larger HEP experiments now ramping up, there is a need for automation of this task since the volume of comparisons could overwhelm human operators. However, the two-dimensional histogram comparison tools available in ROOT have been noted in the past to exhibit shortcomings. We discuss a newer comparison test for two-dimensional histograms, based on the Energy Test of Aslan and Zech, which provides more conclusive discrimination between histograms of data coming from different distributions than methods provided in a recent ROOT release.
A non-parametric test for partial monotonicity in multiple regression
van Beek, M.; Daniëls, H.A.M.
Partial positive (negative) monotonicity in a dataset is the property that an increase in an independent variable, ceteris paribus, generates an increase (decrease) in the dependent variable. A test for partial monotonicity in datasets could (1) increase model performance if monotonicity may be
Brohard, Cheryl
2017-11-01
To test the efficacy of a novel intervention to facilitate advance care planning. . Exploratory, quasiexperimental pilot study with two independent groups. . A large hospice located in the southwestern United States. . A convenience sample of 50 participants with terminal cancer enrolled in hospice. . An autobiographical memory (ABM) intervention used the participants' experiences with cancer and end of life for the purpose of directing advance care planning. . Two domains of advance care planning, decision making and communication, were measured in relation to 11 variables. The ABM intervention was nonthreatening, short in duration, and easily completed with participants as they recalled, without hesitation, specific personal memories of family and friends who had died and their advance care plans. The Mann-Whitney nonparametric test revealed that participants in the experimental group had a higher average rank than those in the control group for communicating the decision about antibiotics, as well as exhibited a trend toward significance for five other advance care planning variables. . Findings showed that directive ABMs may be effective in influencing the decision making and communication of advance care planning for terminally ill patients with cancer. . The current level of understanding about using the ABM intervention suggests that nurses can initiate an advance care planning conversation using this approach.
Kruskal-Wallis Test in Multiple Comparisons
Parys, Dariusz
2009-01-01
In this paper we show that the Kruskal-Wallis test can be transform to quadratic form among the Mann-Whitney or Kendal τ au concordance measures between pairs of treatments. A multiple comparisons procedure based on patterns of transitive ordering among treatments is implement. We also consider the circularity and non-transitive effects. Statystyka testu Kruskala-Wallisa przedstawiona jest w postaci formy kwadratowej z użyciem statystyki Manna-Whitneya lub miar konkordacji τ au Kendalla. N...
Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A
2015-05-01
Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
Mobile Romberg test assessment (mRomberg).
Galán-Mercant, Alejandro; Cuesta-Vargas, Antonio I
2014-09-12
The diagnosis of frailty is based on physical impairments and clinicians have indicated that early detection is one of the most effective methods for reducing the severity of physical frailty. Maybe, an alternative to the classical diagnosis could be the instrumentalization of classical functional testing, as Romberg test or Timed Get Up and Go Test. The aim of this study was (I) to measure and describe the magnitude of accelerometry values in the Romberg test in two groups of frail and non-frail elderly people through instrumentation with the iPhone 4®, (II) to analyse the performances and differences between the study groups, and (III) to analyse the performances and differences within study groups to characterise accelerometer responses to increasingly difficult challenges to balance. This is a cross-sectional study of 18 subjects over 70 years old, 9 frail subjects and 9 non-frail subjects. The non-parametric Mann-Whitney U test was used for between-group comparisons in means values derived from different tasks. The Wilcoxon Signed-Rank test was used to analyse differences between different variants of the test in both independent study groups. The highest difference between groups was found in the accelerometer values with eyes closed and feet parallel: maximum peak acceleration in the lateral axis (p test between frail and non-frail elderly people. In addition, the results indicate that the accelerometry values also were significantly different between the frail and non-frail groups, and that values from the accelerometer accelerometer increased as the test was made more complicated.
Feng, Dai; Cortese, Giuliana; Baumgartner, Richard
2017-12-01
The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann-Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.
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
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
Lee, L.; Helsel, D.
2007-01-01
Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.
Directory of Open Access Journals (Sweden)
D. K. Sari
2017-04-01
Full Text Available The purpose of this study is to obtain a profile of students’ creative thinking skills on quantitative project-based protein testing using local materials. Implementation of the research is using quasi-experimental method pre-test post-test control group design with 40 students involved in Biochemistry lab. The research instrument is pre-test and post-test using creative thinking skills in the form of description and students’ questionnaire. The analysis was performed with SPSS 22.0 program to see the significance normality, U Mann-Whitney test for nonparametric statistics, N-Gain score, and the percentage of student responses to the practicum performed. The research result shows that the pretest rate in the experimental group is 8.25 while in the control group is 6.90. After attending a project-based practicum with local materials, the experimental group obtained the mean of posttest is 37.55 while in control class is 11.18. The students’ improvement on creative thinking skills can be seen from the average of N-Gain in the experimental class with 0.32 (medium category and in the control category with 0.05 (low category. The experimental and control class have different creative thinking skills significantly different fluency, flexibility, novelty, and detail. It can be concluded that quantitative project-based protein testing using local materials can improve students’ creative thinking skills. 71% of total students feel that quantitative project-based protein testing using local materials make them more creative in doing a practicum in the laboratory.
Nonparametric functional mapping of quantitative trait loci.
Yang, Jie; Wu, Rongling; Casella, George
2009-03-01
Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.
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.
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.
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 Transfer Function Models
Liu, Jun M.; Chen, Rong; Yao, Qiwei
2009-01-01
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example. PMID:20628584
Bayesian nonparametric data analysis
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.
Directory of Open Access Journals (Sweden)
Sandvik Leiv
2011-04-01
Full Text Available Abstract Background The number of events per individual is a widely reported variable in medical research papers. Such variables are the most common representation of the general variable type called discrete numerical. There is currently no consensus on how to compare and present such variables, and recommendations are lacking. The objective of this paper is to present recommendations for analysis and presentation of results for discrete numerical variables. Methods Two simulation studies were used to investigate the performance of hypothesis tests and confidence interval methods for variables with outcomes {0, 1, 2}, {0, 1, 2, 3}, {0, 1, 2, 3, 4}, and {0, 1, 2, 3, 4, 5}, using the difference between the means as an effect measure. Results The Welch U test (the T test with adjustment for unequal variances and its associated confidence interval performed well for almost all situations considered. The Brunner-Munzel test also performed well, except for small sample sizes (10 in each group. The ordinary T test, the Wilcoxon-Mann-Whitney test, the percentile bootstrap interval, and the bootstrap-t interval did not perform satisfactorily. Conclusions The difference between the means is an appropriate effect measure for comparing two independent discrete numerical variables that has both lower and upper bounds. To analyze this problem, we encourage more frequent use of parametric hypothesis tests and confidence intervals.
Bayesian nonparametric hierarchical modeling.
Dunson, David B
2009-04-01
In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
Quantal Response: Nonparametric Modeling
2017-01-01
capture the behavior of observed phenomena. Higher-order polynomial and finite-dimensional spline basis models allow for more complicated responses as the...flexibility as these are nonparametric (not constrained to any particular functional form). These should be useful in identifying nonstandard behavior via... deviance ∆ = −2 log(Lreduced/Lfull) is defined in terms of the likelihood function L. For normal error, Lfull = 1, and based on Eq. A-2, we have log
International Nuclear Information System (INIS)
Wesseh, Presley K.; Zoumara, Babette
2012-01-01
This contribution investigates causal interdependence between energy consumption and economic growth in Liberia and proposes application of a bootstrap methodology. To better reflect causality, employment is incorporated as additional variable. The study demonstrates evidence of distinct bidirectional Granger causality between energy consumption and economic growth. Additionally, the results show that employment in Liberia Granger causes economic growth and apply irrespective of the short-run or long-run. Evidence from a Monte Carlo experiment reveals that the asymptotic Granger causality test suffers size distortion problem for Liberian data, suggesting that the bootstrap technique employed in this study is more appropriate. Given the empirical results, implications are that energy expansion policies like energy subsidy or low energy tariff for instance, would be necessary to cope with demand exerted as a result of economic growth in Liberia. Furthermore, Liberia might have the performance of its employment generation on the economy partly determined by adequate energy. Therefore, it seems fully justified that a quick shift towards energy production based on clean energy sources may significantly slow down economic growth in Liberia. Hence, the government’s target to implement a long-term strategy to make Liberia a carbon neutral country, and eventually less carbon dependent by 2050 is understandable. - Highlights: ► Causality between energy consumption and economic growth in Liberia investigated. ► There is bidirectional causality between energy consumption and economic growth. ► Energy expansion policies are necessary to cope with demand from economic growth. ► Asymptotic Granger causality test suffers size distortion problem for Liberian data. ► The bootstrap methodology employed in our study is more appropriate.
International Nuclear Information System (INIS)
Dergiades, Theologos; Martinopoulos, Georgios; Tsoulfidis, Lefteris
2013-01-01
The objective of this paper is to contribute towards the understanding of the linear and non-linear causal linkages between energy consumption and economic activity, making use of annual time series data of Greece for the period 1960–2008. Two are the salient features of our study: first, the total energy consumption has been adjusted for qualitative differences among its constituent components through the thermodynamics of energy conversion. In doing so, we rule out the possibility of a misleading inference with respect to causality due to aggregation bias. Second, the investigation of the causal linkage between economic growth and the adjusted for quality total energy consumption is conducted within a non-linear context. Our empirical results reveal significant unidirectional both linear and non-linear causal linkages running from total useful energy to economic growth. These findings may provide valuable information for the contemplation of more effective energy policies with respect to both the consumption of energy and environmental protection. - Highlights: ► The energy consumption and economic growth nexus is investigated for Greece. ► A quality-adjusted energy series is used in our analysis. ► The causality testing procedure is conducted within a non-linear context. ► A causality running from energy consumption to economic growth is verified
Directory of Open Access Journals (Sweden)
César Merino Soto
2009-05-01
Full Text Available Resumen:La presente investigación hace un estudio psicométrico de un nuevo sistema de calificación de la Prueba Gestáltica del Bendermodificada para niños, que es el Sistema de Calificación Cualitativa (Brannigan y Brunner, 2002, en un muestra de 244 niñosingresantes a primer grado de primaria en cuatro colegios públicos, ubicados en Lima. El enfoque usado es un análisis noparamétrico de ítems mediante el programa Testgraf (Ramsay, 1991. Los resultados indican niveles apropiados deconsistencia interna, identificándose la unidimensionalidad, y el buen nivel discriminativo de las categorías de calificación deeste Sistema Cualitativo. No se hallaron diferencias demográficas respecto al género ni la edad. Se discuten los presenteshallazgos en el contexto del potencial uso del Sistema de Calificación Cualitativa y del análisis no paramétrico de ítems en lainvestigación psicométrica.AbstracThis research designs a psychometric study of a new scoring system of the Bender Gestalt test modified to children: it is theQualitative Scoring System (Brannigan & Brunner, 2002, in a sample of 244 first grade children of primary level, in four public school of Lima. The approach aplied is the nonparametric item analysis using The test graft computer program (Ramsay, 1991. Our findings point to good levels of internal consistency, unidimensionality and good discriminative level ofthe categories of scoring from the Qualitative Scoring System. There are not demographic differences between gender or age.We discuss our findings within the context of the potential use of the Qualitative Scoring System and of the nonparametricitem analysis approach in the psychometric research.
Simple nonparametric checks for model data fit in CAT
Meijer, R.R.
2005-01-01
In this paper, the usefulness of several nonparametric checks is discussed in a computerized adaptive testing (CAT) context. Although there is no tradition of nonparametric scalability in CAT, it can be argued that scalability checks can be useful to investigate, for example, the quality of item
Nonparametric combinatorial sequence models.
Wauthier, Fabian L; Jordan, Michael I; Jojic, Nebojsa
2011-11-01
This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This article presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three biological sequence families which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution over sequence representations induced by the prior. By integrating out the posterior, our method compares favorably to leading binding predictors.
Nonparametric statistics with applications to science and engineering
Kvam, Paul H
2007-01-01
A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provide...
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...
Directory of Open Access Journals (Sweden)
Heloisa Borges
2012-12-01
Full Text Available Esta pesquisa teve o objetivo de avaliar os efeitos do estímulo verbal (EV no tempo do teste de escada (TEsc e nas variáveis cardiorrespiratórias de adultos saudáveis. Trinta e um adultos saudáveis realizaram dois TEsc (com EV e sem EV. Antes e depois de cada teste, foram avaliados os sinais vitais e a Escala de Borg. Os tempos nos TEsc foram comparados por meio do Teste t de Student para amostras pareadas e as diferenças, de acordo com a ordem de realização dos testes utilizando o Teste de Mann-Whitney. Os sinais vitais e a Escala de Borg foram comparados por meio do Teste de Friedman ou ANOVA com post hoc do Teste de Tukey. As variações foram comparadas utilizando o Teste t Student para amostras independentes ou Teste de Mann-Whitney (pThis research aimed to evaluate the effects of verbal stimuli (VS in the time of the Stair climbing Test (SCT and in the cardiorespiratory variables on healthy adults. Thirty-one healthy adults performed two SCT (with VS and without VS. Before and after each test, vital signs and Borg Scale were evaluated. The times in SCT were compared using the Student's t-test for paired samples, and differences were compared according to the order of the testing using the Mann-Whitney Test. The vital signs and Borg Scale were compared by the Friedman Test or ANOVA with post-hoc Tukey Test. The variations of these variables were compared using the Student's t-test for independent samples or Mann-Whitney Test (p<0.05. The time in the SCT without VS was 23.48±8.28 seconds significantly greater than the test with VS that was 21.60±7.18 seconds (p<0.05. All the variables increased after the tests and the Borg Scale was the one that had more variation in SCT with VS, ranging 2.5±1.4 in the test without VS and 3.0±1.8 points in the test with VS (p<0.05. Verbal stimulation improves performance in TEsc and leads to greater sensation of effort.
Uddameri, Venkatesh; Singaraju, Sreeram; Hernandez, E Annette
2018-02-21
Seasonal and cyclic trends in nutrient concentrations at four agricultural drainage ditches were assessed using a dataset generated from a multivariate, multiscale, multiyear water quality monitoring effort in the agriculturally dominant Lower Rio Grande Valley (LRGV) River Watershed in South Texas. An innovative bootstrap sampling-based power analysis procedure was developed to evaluate the ability of Mann-Whitney and Noether tests to discern trends and to guide future monitoring efforts. The Mann-Whitney U test was able to detect significant changes between summer and winter nutrient concentrations at sites with lower depths and unimpeded flows. Pollutant dilution, non-agricultural loadings, and in-channel flow structures (weirs) masked the effects of seasonality. The detection of cyclical trends using the Noether test was highest in the presence of vegetation mainly for total phosphorus and oxidized nitrogen (nitrite + nitrate) compared to dissolved phosphorus and reduced nitrogen (total Kjeldahl nitrogen-TKN). Prospective power analysis indicated that while increased monitoring can lead to higher statistical power, the effect size (i.e., the total number of trend sequences within a time-series) had a greater influence on the Noether test. Both Mann-Whitney and Noether tests provide complementary information on seasonal and cyclic behavior of pollutant concentrations and are affected by different processes. The results from these statistical tests when evaluated in the context of flow, vegetation, and in-channel hydraulic alterations can help guide future data collection and monitoring efforts. The study highlights the need for long-term monitoring of agricultural drainage ditches to properly discern seasonal and cyclical trends.
Development of a Pitch Discrimination Screening Test for Preschool Children.
Abramson, Maria Kulick; Lloyd, Peter J
2016-04-01
There is a critical need for tests of auditory discrimination for young children as this skill plays a fundamental role in the development of speaking, prereading, reading, language, and more complex auditory processes. Frequency discrimination is important with regard to basic sensory processing affecting phonological processing, dyslexia, measurements of intelligence, auditory memory, Asperger syndrome, and specific language impairment. This study was performed to determine the clinical feasibility of the Pitch Discrimination Test (PDT) to screen the preschool child's ability to discriminate some of the acoustic demands of speech perception, primarily pitch discrimination, without linguistic content. The PDT used brief speech frequency tones to gather normative data from preschool children aged 3 to 5 yrs. A cross-sectional study was used to gather data regarding the pitch discrimination abilities of a sample of typically developing preschool children, between 3 and 5 yrs of age. The PDT consists of ten trials using two pure tones of 100-msec duration each, and was administered in an AA or AB forced-choice response format. Data from 90 typically developing preschool children between the ages of 3 and 5 yrs were used to provide normative data. Nonparametric Mann-Whitney U-testing was used to examine the effects of age as a continuous variable on pitch discrimination. The Kruskal-Wallis test was used to determine the significance of age on performance on the PDT. Spearman rank was used to determine the correlation of age and performance on the PDT. Pitch discrimination of brief tones improved significantly from age 3 yrs to age 4 yrs, as well as from age 3 yrs to the age 4- and 5-yrs group. Results indicated that between ages 3 and 4 yrs, children's auditory discrimination of pitch improved on the PDT. The data showed that children can be screened for auditory discrimination of pitch beginning with age 4 yrs. The PDT proved to be a time efficient, feasible tool for
Nonparametric Bayes Modeling of Multivariate Categorical Data.
Dunson, David B; Xing, Chuanhua
2012-01-01
Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.
Decision support using nonparametric statistics
Beatty, Warren
2018-01-01
This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.
Price Overreactions in the Cryptocurrency Market
Caporale, Guglielmo Maria; Plastun, Alex
2018-01-01
This paper examines price overreactions in the case of the following cryptocurrencies: BitCoin, LiteCoin, Ripple and Dash. A number of parametric (t-test, ANOVA, regression analysis with dummy variables) and non-parametric (Mann-Whitney U test) tests confirm the presence of price patterns after overreactions: the next-day price changes in both directions are bigger than after "normal" days. A trading robot approach is then used to establish whether these statistical anomalies can be exploited...
Psychosocial risk factors in medical personnel of a health service in Cartagena de Indias, Colombia
Directory of Open Access Journals (Sweden)
Irma Y. Castillo Á
2011-11-01
Full Text Available Objective: to determine the variables associated with psychosocial risk factors among the doctors of a stateowned social welfare enterprise providing health services in Cartagena. Methodology: a cross-sectional study on a population of 197 doctors from the enterprise’s outpatient and emergency services. The istas21 questionnaire, a Spanish adaptation of the Copenhagen Psychosocial Questionnaire (copsoq, was used to assess psychosocial factors. Statistical analysis was performed using the program SPSS® version 17, and the non-parametric Mann-Whitney U test was applied to estimate the associations between variables. Results: 170 doctors participated in this study; 88.8% of which had favorable exposure to risk factors in the following dimensions: social support and quality of leadership and Double presence. 69.4% showed adverse exposure in the insecurity dimension. In the dimensions Insecurity and Double Presence, general practitioners were in worse conditions than specialists (Mann-Whitney U Prob<0.05. Additionally, doctors from the outpatient service showed more deterioration in the social support and quality of leadership dimensions than those from the emergency service (Mann-Whitney U Prob<0.05. As for the psychological demands dimension, doctors from higher socioeconomic strata showed higher unfavorable scores than those from lower strata (Mann-Whitney U Prob<0.05.
2016-10-01
Whitney U test for evaluating differences in inflammatory mediators between groups (Case vs. Control) and used nonparametric correlations (Spearman’s rho...responses to acute pain. PAIN 2008;140:135–144. [10] Gordon S, Martinez FO. Alternative activation of macrophages: mechanism and functions...Concentrations in Cases vs. Controls. Mediator Case (n=36) Median (Range) Control (n=40) Median (Range) Mann- Whitney U Test (p value) IFN
Nonparametric factor analysis of time series
Rodríguez-Poo, Juan M.; Linton, Oliver Bruce
1998-01-01
We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.
de los Santos, Gonzalo; Reyes, Pablo; del Castillo, Raúl; Fragola, Claudio; Royuela, Ana
2015-11-01
Our objective was to perform translation, cross-cultural adaptation and validation of the sino-nasal outcome test 22 (SNOT-22) to Spanish language. SNOT-22 was translated, back translated, and a pretest trial was performed. The study included 119 individuals divided into 60 cases, who met diagnostic criteria for chronic rhinosinusitis according to the European Position Paper on Rhinosinusitis 2012; and 59 controls, who reported no sino-nasal disease. Internal consistency was evaluated with Cronbach's alpha test, reproducibility with Kappa coefficient, reliability with intraclass correlation coefficient (ICC), validity with Mann-Whitney U test and responsiveness with Wilcoxon test. In cases, Cronbach's alpha was 0.91 both before and after treatment, as for controls, it was 0.90 at their first test assessment and 0.88 at 3 weeks. Kappa coefficient was calculated for each item, with an average score of 0.69. ICC was also performed for each item, with a score of 0.87 in the overall score and an average among all items of 0.71. Median score for cases was 47, and 2 for controls, finding the difference to be highly significant (Mann-Whitney U test, p internal consistency, reliability, reproducibility, validity and responsiveness necessary to be a valid instrument to be used in clinical practice.
Nonparametric predictive inference in reliability
International Nuclear Information System (INIS)
Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.
2002-01-01
We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere
A review of software project testing
Directory of Open Access Journals (Sweden)
Jose Calvo-Manzano Villalón
2016-03-01
Full Text Available In this article a review of software projects based on a taxonomy project is established, allowing the development team or testing personnel to identify the tests to which the project must be subjected for validation. The taxonomy is focused on identifying software projects according to their technology. To establish the taxonomy, a development method comprised of 5 phases was applied. The developed taxonomy is comprised of 10 categories and 35 subcategories and was validated by a group of information technology (IT managers and professionals in the field of IT through the use of a survey. The results obtained from the survey are subjected to the Mann-Whitney U test, which indicates that the taxonomy is validated. The taxonomy can be implemented in development organizations with or without a testing team that provides a classification for technology projects.
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
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...
A nonparametric spatial scan statistic for continuous data.
Jung, Inkyung; Cho, Ho Jin
2015-10-20
Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.
Nonparametric regression using the concept of minimum energy
International Nuclear Information System (INIS)
Williams, Mike
2011-01-01
It has recently been shown that an unbinned distance-based statistic, the energy, can be used to construct an extremely powerful nonparametric multivariate two sample goodness-of-fit test. An extension to this method that makes it possible to perform nonparametric regression using multiple multivariate data sets is presented in this paper. The technique, which is based on the concept of minimizing the energy of the system, permits determination of parameters of interest without the need for parametric expressions of the parent distributions of the data sets. The application and performance of this new method is discussed in the context of some simple example analyses.
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural ...... currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis....
Erythema-index of clinical patch test reactions
DEFF Research Database (Denmark)
Jemec, G B; Johansen, J D
1995-01-01
that the method could be used for the grading of eczematous reactions in a clinical setting as well. OBJECTIVE: To assess the usefulness of the erythema index for the quantification of eczematous reactions using the Derma-Spectrometer (Cortex technology, Hadsund, Denmark) in a clinical setting. METHOD......: The erythema index of 56 patch test reactions ranging from +? to +++, was compared to regional controls and negative patch tests (189). The effects of intrumental application pressure was studied in 5 volunteers. Statistical analysis was carried out using Mann-Whitney and Jonckheere-Terpstra tests. RESULTS......: The erythema-index was significantly higher in all degrees of patch test reactions than in uninvolved regional skin or negative patch tests. It also showed a significant positive trend for higher values in +, ++ and +++ reactions (P
Bayesian nonparametric system reliability using sets of priors
Walter, G.M.; Aslett, L.J.M.; Coolen, F.P.A.
2016-01-01
An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test
Rock, N. M. S.
ROBUST calculates 53 statistics, plus significance levels for 6 hypothesis tests, on each of up to 52 variables. These together allow the following properties of the data distribution for each variable to be examined in detail: (1) Location. Three means (arithmetic, geometric, harmonic) are calculated, together with the midrange and 19 high-performance robust L-, M-, and W-estimates of location (combined, adaptive, trimmed estimates, etc.) (2) Scale. The standard deviation is calculated along with the H-spread/2 (≈ semi-interquartile range), the mean and median absolute deviations from both mean and median, and a biweight scale estimator. The 23 location and 6 scale estimators programmed cover all possible degrees of robustness. (3) Normality: Distributions are tested against the null hypothesis that they are normal, using the 3rd (√ h1) and 4th ( b 2) moments, Geary's ratio (mean deviation/standard deviation), Filliben's probability plot correlation coefficient, and a more robust test based on the biweight scale estimator. These statistics collectively are sensitive to most usual departures from normality. (4) Presence of outliers. The maximum and minimum values are assessed individually or jointly using Grubbs' maximum Studentized residuals, Harvey's and Dixon's criteria, and the Studentized range. For a single input variable, outliers can be either winsorized or eliminated and all estimates recalculated iteratively as desired. The following data-transformations also can be applied: linear, log 10, generalized Box Cox power (including log, reciprocal, and square root), exponentiation, and standardization. For more than one variable, all results are tabulated in a single run of ROBUST. Further options are incorporated to assess ratios (of two variables) as well as discrete variables, and be concerned with missing data. Cumulative S-plots (for assessing normality graphically) also can be generated. The mutual consistency or inconsistency of all these measures
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...
Mura, Maria Chiara; De Felice, Marco; Morlino, Roberta; Fuselli, Sergio
2010-01-01
In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6), concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a) node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b) node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c) node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW) non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%); most important, they suggest a possible procedure to optimize network design.
Directory of Open Access Journals (Sweden)
Maria Chiara Mura
2010-12-01
Full Text Available In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6, concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%; most important, they suggest a possible procedure to optimize network design.
Nonparametric e-Mixture Estimation.
Takano, Ken; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru
2016-12-01
This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.
Lachin, John M
2011-11-10
The power of a chi-square test, and thus the required sample size, are a function of the noncentrality parameter that can be obtained as the limiting expectation of the test statistic under an alternative hypothesis specification. Herein, we apply this principle to derive simple expressions for two tests that are commonly applied to discrete ordinal data. The Wilcoxon rank sum test for the equality of distributions in two groups is algebraically equivalent to the Mann-Whitney test. The Kruskal-Wallis test applies to multiple groups. These tests are equivalent to a Cochran-Mantel-Haenszel mean score test using rank scores for a set of C-discrete categories. Although various authors have assessed the power function of the Wilcoxon and Mann-Whitney tests, herein it is shown that the power of these tests with discrete observations, that is, with tied ranks, is readily provided by the power function of the corresponding Cochran-Mantel-Haenszel mean scores test for two and R > 2 groups. These expressions yield results virtually identical to those derived previously for rank scores and also apply to other score functions. The Cochran-Armitage test for trend assesses whether there is an monotonically increasing or decreasing trend in the proportions with a positive outcome or response over the C-ordered categories of an ordinal independent variable, for example, dose. Herein, it is shown that the power of the test is a function of the slope of the response probabilities over the ordinal scores assigned to the groups that yields simple expressions for the power of the test. Copyright © 2011 John Wiley & Sons, Ltd.
Bayesian Nonparametric Longitudinal Data Analysis.
Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen
2016-01-01
Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
Essays on nonparametric econometrics of stochastic volatility
Zu, Y.
2012-01-01
Volatility is a concept that describes the variation of financial returns. Measuring and modelling volatility dynamics is an important aspect of financial econometrics. This thesis is concerned with nonparametric approaches to volatility measurement and volatility model validation.
Nonparametric methods for volatility density estimation
Es, van Bert; Spreij, P.J.C.; Zanten, van J.H.
2009-01-01
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on
Non-parametric estimation of the individual's utility map
Noguchi, Takao; Sanborn, Adam N.; Stewart, Neil
2013-01-01
Models of risky choice have attracted much attention in behavioural economics. Previous research has repeatedly demonstrated that individuals' choices are not well explained by expected utility theory, and a number of alternative models have been examined using carefully selected sets of choice alternatives. The model performance however, can depend on which choice alternatives are being tested. Here we develop a non-parametric method for estimating the utility map over the wide range of choi...
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...
Directory of Open Access Journals (Sweden)
Özgür ERİKOĞLU
2015-09-01
Full Text Available The aim of this study was to compare physical fitness parameters of male adolescent soccer players and sedentary counterparts. A total of 26 male adolescents participated in this study voluntarily: Active soccer players (n: 3, age x : 13,00 ± 0,00 and sedentary counterparts (n: 13, age x :12,92 ± 0,75. The EUROFIT test battery was used to determine physical fitness. The test battery includes body height and weight measurements, touching the discs, flamingo balan ce, throwing health ball, vertical jumping, sit and reach, sit - up for 30 s, 20 meter sprint run, and 20 meter shuttle run tests. Data were analyzed by Mann Whitney U test. Significance was defined as p.05. In conclusion, children who do sports are more successful on most of the fitness parameters than sedentary children.
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
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
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
Nonparametric conditional predictive regions for time series
de Gooijer, J.G.; Zerom Godefay, D.
2000-01-01
Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2000-01-01
New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2004-01-01
Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric
Non-Parametric Estimation of Correlation Functions
DEFF Research Database (Denmark)
Brincker, Rune; Rytter, Anders; Krenk, Steen
In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are point...
Nonparametric estimation in models for unobservable heterogeneity
Hohmann, Daniel
2014-01-01
Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.
Nonparametric estimation of location and scale parameters
Potgieter, C.J.; Lombard, F.
2012-01-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal
A Bayesian Nonparametric Approach to Factor Analysis
DEFF Research Database (Denmark)
Piatek, Rémi; Papaspiliopoulos, Omiros
2018-01-01
This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality
Directory of Open Access Journals (Sweden)
Zhanchao Li
2013-01-01
Full Text Available The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model and change of sequence distribution law of nonparametric statistical model. On this basis, through the reduction of change point problem, the establishment of basic nonparametric change point model, and asymptotic analysis on test method of basic change point problem, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is created in consideration of the situation that in practice concrete dam crack behavior may have more abnormality points. And the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is used in the actual project, demonstrating the effectiveness and scientific reasonableness of the method established. Meanwhile, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality has a complete theoretical basis and strong practicality with a broad application prospect in actual project.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...... considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...
Network structure exploration via Bayesian nonparametric models
International Nuclear Information System (INIS)
Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z
2015-01-01
Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)
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.
Nonparametric Mixture Models for Supervised Image Parcellation.
Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina
2009-09-01
We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.
Robustifying Bayesian nonparametric mixtures for count data.
Canale, Antonio; Prünster, Igor
2017-03-01
Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure. © 2016, The International Biometric Society.
Application of nonparametric statistics to material strength/reliability assessment
International Nuclear Information System (INIS)
Arai, Taketoshi
1992-01-01
An advanced material technology requires data base on a wide variety of material behavior which need to be established experimentally. It may often happen that experiments are practically limited in terms of reproducibility or a range of test parameters. Statistical methods can be applied to understanding uncertainties in such a quantitative manner as required from the reliability point of view. Statistical assessment involves determinations of a most probable value and the maximum and/or minimum value as one-sided or two-sided confidence limit. A scatter of test data can be approximated by a theoretical distribution only if the goodness of fit satisfies a test criterion. Alternatively, nonparametric statistics (NPS) or distribution-free statistics can be applied. Mathematical procedures by NPS are well established for dealing with most reliability problems. They handle only order statistics of a sample. Mathematical formulas and some applications to engineering assessments are described. They include confidence limits of median, population coverage of sample, required minimum number of a sample, and confidence limits of fracture probability. These applications demonstrate that a nonparametric statistical estimation is useful in logical decision making in the case a large uncertainty exists. (author)
Heydari, Payam; Varmazyar, Sakineh; Nikpey, Ahmad; Variani, Ali Safari; Jafarvand, Mojtaba
2017-03-01
Maximum oxygen consumption shows the maximum oxygen rate of muscle oxygenation that is acceptable in many cases, to measure the fitness between person and the desired job. Given that medical emergencies are important, and difficult jobs in emergency situations require people with high physical ability and readiness for the job, the aim of this study was to evaluate the maximum oxygen consumption, to determine the ability of work type among students of medical emergencies in Qazvin in 2016. This study was a descriptive - analytical, and in cross-sectional type conducted among 36 volunteer students of medical emergencies in Qazvin in 2016. After necessary coordination for the implementation of the study, participants completed health questionnaires and demographic characteristics and then the participants were evaluated with step tests of American College of Sport Medicine (ACSM). Data analysis was done by SPSS version 18 and U-Mann-Whitney tests, Kruskal-Wallis and Pearson correlation coefficient. Average of maximum oxygen consumption of the participants was estimated 3.15±0.50 liters per minute. 91.7% of medical emergencies students were selected as appropriate in terms of maximum oxygen consumption and thus had the ability to do heavy and too heavy work. Average of maximum oxygen consumption evaluated by the U-Mann-Whitney test and Kruskal-Wallis, had significant relationship with age (p<0.05) and weight groups (p<0.001). There was a significant positive correlation between maximum oxygen consumption with weight and body mass index (p<0.001). The results of this study showed that demographic variables of weight and body mass index are the factors influencing the determination of maximum oxygen consumption, as most of the students had the ability to do heavy, and too heavy work. Therefore, people with ability to do average work are not suitable for medical emergency tasks.
Non-parametric smoothing of experimental data
International Nuclear Information System (INIS)
Kuketayev, A.T.; Pen'kov, F.M.
2007-01-01
Full text: Rapid processing of experimental data samples in nuclear physics often requires differentiation in order to find extrema. Therefore, even at the preliminary stage of data analysis, a range of noise reduction methods are used to smooth experimental data. There are many non-parametric smoothing techniques: interval averages, moving averages, exponential smoothing, etc. Nevertheless, it is more common to use a priori information about the behavior of the experimental curve in order to construct smoothing schemes based on the least squares techniques. The latter methodology's advantage is that the area under the curve can be preserved, which is equivalent to conservation of total speed of counting. The disadvantages of this approach include the lack of a priori information. For example, very often the sums of undifferentiated (by a detector) peaks are replaced with one peak during the processing of data, introducing uncontrolled errors in the determination of the physical quantities. The problem is solvable only by having experienced personnel, whose skills are much greater than the challenge. We propose a set of non-parametric techniques, which allows the use of any additional information on the nature of experimental dependence. The method is based on a construction of a functional, which includes both experimental data and a priori information. Minimum of this functional is reached on a non-parametric smoothed curve. Euler (Lagrange) differential equations are constructed for these curves; then their solutions are obtained analytically or numerically. The proposed approach allows for automated processing of nuclear physics data, eliminating the need for highly skilled laboratory personnel. Pursuant to the proposed approach is the possibility to obtain smoothing curves in a given confidence interval, e.g. according to the χ 2 distribution. This approach is applicable when constructing smooth solutions of ill-posed problems, in particular when solving
Decompounding random sums: A nonparametric approach
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Pitts, Susan M.
Observations from sums of random variables with a random number of summands, known as random, compound or stopped sums arise within many areas of engineering and science. Quite often it is desirable to infer properties of the distribution of the terms in the random sum. In the present paper we...... review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...
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.
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.
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.
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.
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
A Bayesian Beta-Mixture Model for Nonparametric IRT (BBM-IRT)
Arenson, Ethan A.; Karabatsos, George
2017-01-01
Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…
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.
Nonparametric predictive pairwise comparison with competing risks
International Nuclear Information System (INIS)
Coolen-Maturi, Tahani
2014-01-01
In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed
Nonparametric 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 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
Nonparametric Bayesian inference for multidimensional compound Poisson processes
Gugushvili, S.; van der Meulen, F.; Spreij, P.
2015-01-01
Given a sample from a discretely observed multidimensional compound Poisson process, we study the problem of nonparametric estimation of its jump size density r0 and intensity λ0. We take a nonparametric Bayesian approach to the problem and determine posterior contraction rates in this context,
Nonparametric analysis of blocked ordered categories data: some examples revisited
Directory of Open Access Journals (Sweden)
O. Thas
2006-08-01
Full Text Available Nonparametric analysis for general block designs can be given by using the Cochran-Mantel-Haenszel (CMH statistics. We demonstrate this with four examples and note that several well-known nonparametric statistics are special cases of CMH statistics.
A Structural Labor Supply Model with Nonparametric Preferences
van Soest, A.H.O.; Das, J.W.M.; Gong, X.
2000-01-01
Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis.This paper presents an example where nonparametric flexibility can be attained
Exact nonparametric confidence bands for the survivor function.
Matthews, David
2013-10-12
A method to produce exact simultaneous confidence bands for the empirical cumulative distribution function that was first described by Owen, and subsequently corrected by Jager and Wellner, is the starting point for deriving exact nonparametric confidence bands for the survivor function of any positive random variable. We invert a nonparametric likelihood test of uniformity, constructed from the Kaplan-Meier estimator of the survivor function, to obtain simultaneous lower and upper bands for the function of interest with specified global confidence level. The method involves calculating a null distribution and associated critical value for each observed sample configuration. However, Noe recursions and the Van Wijngaarden-Decker-Brent root-finding algorithm provide the necessary tools for efficient computation of these exact bounds. Various aspects of the effect of right censoring on these exact bands are investigated, using as illustrations two observational studies of survival experience among non-Hodgkin's lymphoma patients and a much larger group of subjects with advanced lung cancer enrolled in trials within the North Central Cancer Treatment Group. Monte Carlo simulations confirm the merits of the proposed method of deriving simultaneous interval estimates of the survivor function across the entire range of the observed sample. This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. It was begun while the author was visiting the Department of Statistics, University of Auckland, and completed during a subsequent sojourn at the Medical Research Council Biostatistics Unit in Cambridge. The support of both institutions, in addition to that of NSERC and the University of Waterloo, is greatly appreciated.
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.
Students Perception on the Use of Computer Based Test
Nugroho, R. A.; Kusumawati, N. S.; Ambarwati, O. C.
2018-02-01
Teaching nowadays might use technology in order to disseminate science and knowledge. As part of teaching, the way evaluating study progress and result has also benefited from this IT rapid progress. The computer-based test (CBT) has been introduced to replace the more conventional Paper and Pencil Test (PPT). CBT are considered more advantageous than PPT. It is considered as more efficient, transparent, and has the ability of minimising fraud in cognitive evaluation. Current studies have indicated the debate of CBT vs PPT usage. Most of the current research compares the two methods without exploring the students’ perception about the test. This study will fill the gap in the literature by providing students’ perception on the two tests method. Survey approach is conducted to obtain the data. The sample is collected in two identical classes with similar subject in a public university in Indonesia. Mann-Whitney U test used to analyse the data. The result indicates that there is a significant difference between two groups of students regarding CBT usage. Student with different test method prefers to have test other than what they were having. Further discussion and research implication is discussed in the paper.
Bugała, Artur; Bednarek, Karol; Kasprzyk, Leszek; Tomczewski, Andrzej
2017-10-01
The paper presents the most representative - from the three-year measurement time period - characteristics of daily and monthly electricity production from a photovoltaic conversion using modules installed in a fixed and 2-axis tracking construction. Results are presented for selected summer, autumn, spring and winter days. Analyzed measuring stand is located on the roof of the Faculty of Electrical Engineering Poznan University of Technology building. The basic parameters of the statistical analysis like mean value, standard deviation, skewness, kurtosis, median, range, or coefficient of variation were used. It was found that the asymmetry factor can be useful in the analysis of the daily electricity production from a photovoltaic conversion. In order to determine the repeatability of monthly electricity production, occurring between the summer, and summer and winter months, a non-parametric Mann-Whitney U test was used as a statistical solution. In order to analyze the repeatability of daily peak hours, describing the largest value of the hourly electricity production, a non-parametric Kruskal-Wallis test was applied as an extension of the Mann-Whitney U test. Based on the analysis of the electric energy distribution from a prepared monitoring system it was found that traditional forecasting methods of the electricity production from a photovoltaic conversion, like multiple regression models, should not be the preferred methods of the analysis.
International Nuclear Information System (INIS)
Janurová, Kateřina; Briš, Radim
2014-01-01
Medical survival right-censored data of about 850 patients are evaluated to analyze the uncertainty related to the risk of mortality on one hand and compare two basic surgery techniques in the context of risk of mortality on the other hand. Colorectal data come from patients who underwent colectomy in the University Hospital of Ostrava. Two basic surgery operating techniques are used for the colectomy: either traditional (open) or minimally invasive (laparoscopic). Basic question arising at the colectomy operation is, which type of operation to choose to guarantee longer overall survival time. Two non-parametric approaches have been used to quantify probability of mortality with uncertainties. In fact, complement of the probability to one, i.e. survival function with corresponding confidence levels is calculated and evaluated. First approach considers standard nonparametric estimators resulting from both the Kaplan–Meier estimator of survival function in connection with Greenwood's formula and the Nelson–Aalen estimator of cumulative hazard function including confidence interval for survival function as well. The second innovative approach, represented by Nonparametric Predictive Inference (NPI), uses lower and upper probabilities for quantifying uncertainty and provides a model of predictive survival function instead of the population survival function. The traditional log-rank test on one hand and the nonparametric predictive comparison of two groups of lifetime data on the other hand have been compared to evaluate risk of mortality in the context of mentioned surgery techniques. The size of the difference between two groups of lifetime data has been considered and analyzed as well. Both nonparametric approaches led to the same conclusion, that the minimally invasive operating technique guarantees the patient significantly longer survival time in comparison with the traditional operating technique
[Characteristics of allergic conjunctivitis with positive skin prick test].
Yang, S; Jiang, Y; Jin, Y M; Zhang, J Y; Li, Y
2017-09-11
Objective: To observe the clinical characteristics of allergic conjunctivitis, and the correlations with skin prick test results. Methods: A retrospective study. Forty patients with positive skin prick test result were included. Patients underwent an ophthalmologic examination to identify their primary presenting signs and symptoms. The allergy types were divided into 5 groups. All dates were analyzed for the dependence, normality and homogeneity of variance. Chi-square test, Mann-Whitney U test, Kruskal-Wallis H test and Spearman correlation analysis were performed accordingly. Results: Among 40 patients, 18(45.0%) had a clinical diagnosis of seasonal allergic conjunctivitis, 14(35.0%) had perennial allergic conjunctivitis, 5(12.5%) had vernal keratoconjunctivitis, and 2(5.0%) had atopic keratoconjunctivits, and 1(2.5%) had giant papillary conjunctivitis. There was no significant difference in the number of symptoms and signs score among different types of allergic conjunctivitis, the score of itching and hyperemia had a positive relationship with the number of positive allergens ( r =0.74, Ptest of the allergen, the more symptoms and signs encountered in terms of severity. Conclusion: Seasonal allergic conjunctivitis was the most prevalent disorder, the most important clinical characteristics of allergic conjunctivitis are itching and conjunctival congestion, the main allergens are dust and pollens, patients may be sensitive to multiple allergens. (Chin J Ophthalmol, 2017, 53: 689-693) .
Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs
Kuosmanen, T.K.
2005-01-01
Environmental Economics and Natural Resources Group at Wageningen University in The Netherlands Weak disposability of outputs means that firms can abate harmful emissions by decreasing the activity level. Modeling weak disposability in nonparametric production analysis has caused some confusion.
Multi-sample nonparametric treatments comparison in medical ...
African Journals Online (AJOL)
Multi-sample nonparametric treatments comparison in medical follow-up study with unequal observation processes through simulation and bladder tumour case study. P. L. Tan, N.A. Ibrahim, M.B. Adam, J. Arasan ...
A nonparametric mixture model for cure rate estimation.
Peng, Y; Dear, K B
2000-03-01
Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.
Speaker Linking and Applications using Non-Parametric Hashing Methods
2016-09-08
nonparametric estimate of a multivariate density function,” The Annals of Math- ematical Statistics , vol. 36, no. 3, pp. 1049–1051, 1965. [9] E. A. Patrick...Speaker Linking and Applications using Non-Parametric Hashing Methods† Douglas Sturim and William M. Campbell MIT Lincoln Laboratory, Lexington, MA...with many approaches [1, 2]. For this paper, we focus on using i-vectors [2], but the methods apply to any embedding. For the task of speaker QBE and
Dexter, Franklin; Bayman, Emine O; Dexter, Elisabeth U
2017-12-01
We examined type I and II error rates for analysis of (1) mean hospital length of stay (LOS) versus (2) percentage of hospital LOS that are overnight. These 2 end points are suitable for when LOS is treated as a secondary economic end point. We repeatedly resampled LOS for 5052 discharges of thoracoscopic wedge resections and lung lobectomy at 26 hospitals. Unequal variances t test (Welch method) and Fisher exact test both were conservative (ie, type I error rate less than nominal level). The Wilcoxon rank sum test was included as a comparator; the type I error rates did not differ from the nominal level of 0.05 or 0.01. Fisher exact test was more powerful than the unequal variances t test at detecting differences among hospitals; estimated odds ratio for obtaining P < .05 with Fisher exact test versus unequal variances t test = 1.94, with 95% confidence interval, 1.31-3.01. Fisher exact test and Wilcoxon-Mann-Whitney had comparable statistical power in terms of differentiating LOS between hospitals. For studies with LOS to be used as a secondary end point of economic interest, there is currently considerable interest in the planned analysis being for the percentage of patients suitable for ambulatory surgery (ie, hospital LOS equals 0 or 1 midnight). Our results show that there need not be a loss of statistical power when groups are compared using this binary end point, as compared with either Welch method or Wilcoxon rank sum test.
Efficient nonparametric n -body force fields from machine learning
Glielmo, Aldo; Zeni, Claudio; De Vita, Alessandro
2018-05-01
We provide a definition and explicit expressions for n -body Gaussian process (GP) kernels, which can learn any interatomic interaction occurring in a physical system, up to n -body contributions, for any value of n . The series is complete, as it can be shown that the "universal approximator" squared exponential kernel can be written as a sum of n -body kernels. These recipes enable the choice of optimally efficient force models for each target system, as confirmed by extensive testing on various materials. We furthermore describe how the n -body kernels can be "mapped" on equivalent representations that provide database-size-independent predictions and are thus crucially more efficient. We explicitly carry out this mapping procedure for the first nontrivial (three-body) kernel of the series, and we show that this reproduces the GP-predicted forces with meV /Å accuracy while being orders of magnitude faster. These results pave the way to using novel force models (here named "M-FFs") that are computationally as fast as their corresponding standard parametrized n -body force fields, while retaining the nonparametric character, the ease of training and validation, and the accuracy of the best recently proposed machine-learning potentials.
Discrete non-parametric kernel estimation for global sensitivity analysis
International Nuclear Information System (INIS)
Senga Kiessé, Tristan; Ventura, Anne
2016-01-01
This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.
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.
Test Performance Related Dysfunctional Beliefs
Directory of Open Access Journals (Sweden)
Recep TÜTÜNCÜ
2012-11-01
Full Text Available Objective: Examinations by using tests are very frequently used in educational settings and successful studying before the examinations is a complex matter to deal with. In order to understand the determinants of success in exams better, we need to take into account not only emotional and motivational, but also cognitive aspects of the participants such as dysfunctional beliefs. Our aim is to present the relationship between candidates’ characteristics and distorted beliefs/schemata just before an examination. Method: The subjects of the study were 30 female and 30 male physicians who were about to take the medical specialization exam (MSE in Turkey. Dysfunctional Attitude Scale (DAS and Young Schema Questionnaire Short Form (YSQ-SF were applied to the subjects. The statistical analysis was done using the F test, Mann-Whitney, Kruskal-Wallis, chi-square test and spearman’s correlation test. Results: It was shown that some of the DAS and YSQ-SF scores were significantly higher in female gender, in the group who could not pass the exam, who had repetitive examinations, who had their first try taking an examination and who were unemployed at the time of the examination. Conclusion: Our findings indicate that candidates seeking help before MSE examination could be referred for cognitive therapy or counseling even they do not have any psychiatric diagnosis due to clinically significant cognitive distortion. Measurement and treatment of cognitive distortions that have negative impact on MSE performance may improve the cost-effectiveness and mental well being of the young doctors.
Nonparametric NAR-ARCH Modelling of Stock Prices by the Kernel Methodology
Directory of Open Access Journals (Sweden)
Mohamed Chikhi
2018-02-01
Full Text Available This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over 01/03/2000 to 02/02/2017 by testing the nonlinearities through a class of conditional heteroscedastic nonparametric models. The linearity and Gaussianity assumptions are rejected for Orange Stock returns and informational shocks have transitory effects on returns and volatility. The forecasting results show that Orange stock prices are short-term predictable and nonparametric NAR-ARCH model has better performance over parametric MA-APARCH model for short horizons. Plus, the estimates of this model are also better comparing to the predictions of the random walk model. This finding provides evidence for weak form of inefficiency in Paris stock market with limited rationality, thus it emerges arbitrage opportunities.
Statistical Analysis of the Polarimetric Cloud Analysis and Seeding Test (POLCAST) Field Projects
Ekness, Jamie Lynn
The North Dakota farming industry brings in more than $4.1 billion annually in cash receipts. Unfortunately, agriculture sales vary significantly from year to year, which is due in large part to weather events such as hail storms and droughts. One method to mitigate drought is to use hygroscopic seeding to increase the precipitation efficiency of clouds. The North Dakota Atmospheric Research Board (NDARB) sponsored the Polarimetric Cloud Analysis and Seeding Test (POLCAST) research project to determine the effectiveness of hygroscopic seeding in North Dakota. The POLCAST field projects obtained airborne and radar observations, while conducting randomized cloud seeding. The Thunderstorm Identification Tracking and Nowcasting (TITAN) program is used to analyze radar data (33 usable cases) in determining differences in the duration of the storm, rain rate and total rain amount between seeded and non-seeded clouds. The single ratio of seeded to non-seeded cases is 1.56 (0.28 mm/0.18 mm) or 56% increase for the average hourly rainfall during the first 60 minutes after target selection. A seeding effect is indicated with the lifetime of the storms increasing by 41 % between seeded and non-seeded clouds for the first 60 minutes past seeding decision. A double ratio statistic, a comparison of radar derived rain amount of the last 40 minutes of a case (seed/non-seed), compared to the first 20 minutes (seed/non-seed), is used to account for the natural variability of the cloud system and gives a double ratio of 1.85. The Mann-Whitney test on the double ratio of seeded to non-seeded cases (33 cases) gives a significance (p-value) of 0.063. Bootstrapping analysis of the POLCAST set indicates that 50 cases would provide statistically significant results based on the Mann-Whitney test of the double ratio. All the statistical analysis conducted on the POLCAST data set show that hygroscopic seeding in North Dakota does increase precipitation. While an additional POLCAST field
Testing the equality of nonparametric regression curves based on ...
African Journals Online (AJOL)
Abstract. In this work we propose a new methodology for the comparison of two regression functions f1 and f2 in the case of homoscedastic error structure and a fixed design. Our approach is based on the empirical Fourier coefficients of the regression functions f1 and f2 respectively. As our main results we obtain the ...
Schiemann, R.; Erdin, R.; Willi, M.; Frei, C.; Berenguer, M.; Sempere-Torres, D.
2011-05-01
Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications. Various formulations of geostatistical combination (Kriging) methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK) are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages. Furthermore, two variants of Kriging with external drift (KED) are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain). The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases as well as an extended evaluation
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.
Application of nonparametric statistic method for DNBR limit calculation
International Nuclear Information System (INIS)
Dong Bo; Kuang Bo; Zhu Xuenong
2013-01-01
Background: Nonparametric statistical method is a kind of statistical inference method not depending on a certain distribution; it calculates the tolerance limits under certain probability level and confidence through sampling methods. The DNBR margin is one important parameter of NPP design, which presents the safety level of NPP. Purpose and Methods: This paper uses nonparametric statistical method basing on Wilks formula and VIPER-01 subchannel analysis code to calculate the DNBR design limits (DL) of 300 MW NPP (Nuclear Power Plant) during the complete loss of flow accident, simultaneously compared with the DL of DNBR through means of ITDP to get certain DNBR margin. Results: The results indicate that this method can gain 2.96% DNBR margin more than that obtained by ITDP methodology. Conclusions: Because of the reduction of the conservation during analysis process, the nonparametric statistical method can provide greater DNBR margin and the increase of DNBR margin is benefited for the upgrading of core refuel scheme. (authors)
DEFF Research Database (Denmark)
Christensen, Kim; Hounyo, Ulrich; Podolskij, Mark
In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely sampled jump-diffusion process are available. The test...... inference, we propose a new bootstrap approach, which leads to almost correctly sized tests of the null hypothesis. We apply the developed framework to a large cross-section of equity high-frequency data and find that the diurnal pattern accounts for a rather significant fraction of intraday variation...
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.
Shahraki, Mohammad Reza; Ahmadimoghadm, Mahdieh; Shahraki, Ahmad Reza
2015-10-01
Borago officinalis flower (borage) is a known sedative in herbal medicine; the aim of the present study was to evaluate the antinociceptive effect of borage hydroalcoholic extract in formalin test male rats. Fifty-six adult male albino Wistar rats were randomly divided into seven groups: Control groups of A (intact), B (saline), and C (Positive control) plus test groups of D, E, F, and G (n=8). The groups D, E, and F received 6.25, 12.5, and 25 mg/kg, Borago officinalis flower hydroalcholic extract before the test, respectively but group G received 25 mg/kg borage extract and aspirin before the test. A biphasic pain was induced by injection of formalin 1%. The obtained data were analyzed by SPSS software ver. 17 employing statistical tests of Kruskal-Wallis and Mann-Whitney. The results were expressed as mean±SD. Statistical differences were considered significant at Ptest groups of D, E, F, and G significantly decreased compared to groups A and B, but this score did not show any difference compared to group C. Moreover, chronic pain behavior score in group G was significantly lower than all other groups. The results indicated that Borago officinalis hydroalcoholic extract affects the acute and chronic pain behavior response in formaline test male rats.
EXAMINATION OF HANDBALL PLAYERS’ TEAM COHESION
Directory of Open Access Journals (Sweden)
İlyas Görgüt
2017-04-01
Full Text Available The aim of this study was to determine team cohesion of handballplayers who were actively engaged in sport in various categories. The study group consisted of a total of 607 handball players, 317 female and 290 male, selected by random method and from 11 provinces of Turkey according to the some factors. When we examine the age distributions of the participants, 121 athletes appear to be 13 years and under, 309 athletes 14-18 years, 94 athletes 19-23 years, 54 athletes 24-28 years and 29 athletes 29 years and over. In addition, 186 of them expressed their education situation as middle school, 253 of them expressed their education situation as high school and 168 of them expressed their education situation as university. Personal information form and team cohesion scale, developed by Widmeyer et al. (1985 and adapted to Turkish by Moralı (1994, were used as a data collecting tools. The Kolmogorov Smirnov test was used to measure whether the obtained data showed normal distribution or not and nonparametric tests were used to determine the subscale scores because they didn’t show normal disturbance. For binary comparisons Mann Whitney U test, for multiple comparisons Kruskal Wallis variance and for the difference between significant groups Bonferroni Mann Whitney U test were used. As a result of the research, there were significant differences in scale subscale scores in terms of gender, age, educational status, sports experience, income and province variables of handball players.
Adaptive nonparametric Bayesian inference using location-scale mixture priors
Jonge, de R.; Zanten, van J.H.
2010-01-01
We study location-scale mixture priors for nonparametric statistical problems, including multivariate regression, density estimation and classification. We show that a rate-adaptive procedure can be obtained if the prior is properly constructed. In particular, we show that adaptation is achieved if
The nonparametric bootstrap for the current status model
Groeneboom, P.; Hendrickx, K.
2017-01-01
It has been proved that direct bootstrapping of the nonparametric maximum likelihood estimator (MLE) of the distribution function in the current status model leads to inconsistent confidence intervals. We show that bootstrapping of functionals of the MLE can however be used to produce valid
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...
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.
Nonparametric modeling of dynamic functional connectivity in fmri data
DEFF Research Database (Denmark)
Nielsen, Søren Føns Vind; Madsen, Kristoffer H.; Røge, Rasmus
2015-01-01
dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a nonparametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted...
Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.
Storlie, Curtis B; Bondell, Howard D; Reich, Brian J; Zhang, Hao Helen
2011-04-01
Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.
Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support
Czech Academy of Sciences Publication Activity Database
Kalina, Jan; Zvárová, Jana
2017-01-01
Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability
On the robust nonparametric regression estimation for a functional regressor
Azzedine , Nadjia; Laksaci , Ali; Ould-Saïd , Elias
2009-01-01
On the robust nonparametric regression estimation for a functional regressor correspondance: Corresponding author. (Ould-Said, Elias) (Azzedine, Nadjia) (Laksaci, Ali) (Ould-Said, Elias) Departement de Mathematiques--> , Univ. Djillali Liabes--> , BP 89--> , 22000 Sidi Bel Abbes--> - ALGERIA (Azzedine, Nadjia) Departement de Mathema...
A general approach to posterior contraction in nonparametric inverse problems
Knapik, Bartek; Salomond, Jean Bernard
In this paper, we propose a general method to derive an upper bound for the contraction rate of the posterior distribution for nonparametric inverse problems. We present a general theorem that allows us to derive contraction rates for the parameter of interest from contraction rates of the related
Non-parametric analysis of production efficiency of poultry egg ...
African Journals Online (AJOL)
Non-parametric analysis of production efficiency of poultry egg farmers in Delta ... analysis of factors affecting the output of poultry farmers showed that stock ... should be put in place for farmers to learn the best farm practices carried out on the ...
Normative values and the effects of age, gender, and handedness on the Moberg Pick-Up Test.
Amirjani, Nasim; Ashworth, Nigel L; Gordon, Tessa; Edwards, David C; Chan, K Ming
2007-06-01
The Moberg Pick-Up Test is a standardized test for assessing hand dexterity. Although reduction of sensation in the hand occurs with aging, the effect of age on a subject's performance of the Moberg Pick-Up Test has not been examined. The primary goal of this study was to examine the impact of aging and, secondarily, the impact of gender and handedness, on performance of the Moberg Pick-Up Test in 116 healthy subjects. The average time to complete each of the four subsets of the test was analyzed using the Kruskal-Wallis, Mann-Whitney U, and Wilcoxon signed-rank tests. The results show that hand dexterity of the subjects was significantly affected by age, with young subjects being the fastest and elderly subjects the slowest. Women accomplished the test faster than men, and task performance with the dominant hand was faster than with the non-dominant hand. Use of normative values established based on age and gender is a valuable objective tool to gauge hand function in patients with different neurologic disorders.
Sport Tourism Centres from Top Athletes’ Perspective: Differences among Sport Groups
Directory of Open Access Journals (Sweden)
Polanec Anze
2014-09-01
Full Text Available Background: Sport tourism plays an important role in the tourism industry and consequently in the economy. Sport tourism centres as providers of sport services need to be familiar with the basic needs of their customers and tailor their services accordingly. Objectives: The aim of the paper is to determine the models for customizing sport tourism services to address the needs specific for an individual sport. Methods/Approach: A questionnaire has been created and sent electronically or physically to top athletes from Slovenia, Central and Eastern Europe. Respondents were mainly from Slovenia and mostly representatives of national sports federations. The Mann Whitney and the Kruskall-Wallis tests were applied in order to test differences among sport groups. Results: The conducted Mann-Whitney non-parametric tests show that representatives of different sport groups have different perspectives on sport tourism services. Conclusions: The results of the study can be used by sport tourism centres in the process of tailoring their services, planning marketing activities or developing strategic projects.
Statistical decisions under nonparametric a priori information
International Nuclear Information System (INIS)
Chilingaryan, A.A.
1985-01-01
The basic module of applied program package for statistical analysis of the ANI experiment data is described. By means of this module tasks of choosing theoretical model most adequately fitting to experimental data, selection of events of definte type, identification of elementary particles are carried out. For mentioned problems solving, the Bayesian rules, one-leave out test and KNN (K Nearest Neighbour) adaptive density estimation are utilized
kruX: matrix-based non-parametric eQTL discovery.
Qi, Jianlong; Asl, Hassan Foroughi; Björkegren, Johan; Michoel, Tom
2014-01-14
The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure and is freely available from http://krux.googlecode.com.
Directory of Open Access Journals (Sweden)
Leandro Ciulla
2007-10-01
Full Text Available PURPOSE: To compare the effects of the antidepressant drugs duloxetine and fluoxetine on depressive behaviors in rodents. METHODS: Eighteen male Wistar rats were given systemic injections of duloxetine, fluoxetine, or saline prior to a Forced Swimming Test (FST. Immobility and number of stops were measured. RESULTS: Rats given injections of fluoxetine displayed significantly less immobility (p = 0.02 and fewer stops than the control group (p = 0.003. Duloxetine significanlty reduced the number of stops (p = 0.003, but did not effect immobility (p = 0.48. CONCLUSION: Duloxetine and fluoxetine reduced depressive behaviors in the Forced FST. However, our findings suggest that fluoxetine is more effective than duloxetine.OBJETIVO: Comparar o efeito antidepressivo da droga cloridrato de duloxetina com a fluoxetina. MÉTODOS: O teste do nado forçado, teste comportamental que avalia a atividade antidepressiva em ratos, foi utilizado em 18 ratos Wistar, machos adultos, divididos em três grupos iguais: duloxetina, fluoxetina e controle. RESULTADOS: Os dados do teste do nado forçado foram analisados pelo teste One-way ANOVA, Mann Whitney e Kruskall-Wallis.Houve diferença significativa (p = 0,003 entre o número de paradas dos grupos duloxetina e fluoxetina e o grupo controle. CONCLUSÃO: A duloxetina e a fluoxetina tiveram frequência de paradas similares. A fluoxetina mostrou ser mais efetiva que a duloxetina no teste do nado forçado em ratos.
Nurses' Competency and Challenges in Enteral feeding in the ...
African Journals Online (AJOL)
Studies have emphasised the role of nurses in nutritional support. .... Ethical consideration. The study was ... Mann-Whitney U test was applied to make associations ..... based guidelines and critical care nurses knowledge of enteral feeding.
Gundogan, Fatih Cakir; Dinç, Umut Asli; Erdem, Uzeyir; Ozge, Gokhan; Sobaci, Gungor
2010-01-01
To study multifocal electroretinogram (mfERG) and its relation to retinal sensitivity assessed by Humphrey visual field (HVF) analysis in central areolar choroidal dystrophy (CACD). Seven eyes of 4 patients with CACD and 15 normal control subjects were examined. mfERG and central 30/2 HVF were tested for each participant. Ring analysis in mfERG was evaluated. HVF results were evaluated in 5 concentric rings in order to compare the results to concentric ring analysis in mfERG. The differences between control subjects and patients were evaluated by Mann-Whitney U test and the correlations were assessed by Spearman test. Mean Snellen acuity was 0.49+/-0.10 in patients. HVF revealed central scotoma in 6 of 7 eyes (85.7%), whereas a paracentral scotoma extending to fixation point was detected in 1 eye. The retinal sensitivities in 5 concentric rings in HVF were significantly lower (p<0.001 for ring 1 to ring 4, and p=0.017 in ring 5) in CACD patients. Similarly, CACD patients had lower P1/N1 amplitudes (p<0.05) and delayed P1/N1 implicit times (p<0.05). In CACD, in the areas of scotoma detected by HVF, mfERG values were depressed. However, both mfERG and HVF abnormalities were found outside the areas of ophthalmoscopically normal retinal areas.
High throughput nonparametric probability density estimation.
Farmer, Jenny; Jacobs, Donald
2018-01-01
In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity. Such an automated process for univariate data is implemented to achieve this goal by merging the maximum entropy method with single order statistics and maximum likelihood. The only required properties of the random variables are that they are continuous and that they are, or can be approximated as, independent and identically distributed. A quasi-log-likelihood function based on single order statistics for sampled uniform random data is used to empirically construct a sample size invariant universal scoring function. Then a probability density estimate is determined by iteratively improving trial cumulative distribution functions, where better estimates are quantified by the scoring function that identifies atypical fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian or Akaike information criterion. Multiple estimates for the probability density reflect uncertainties due to statistical fluctuations in random samples. Scaled quantile residual plots are also introduced as an effective diagnostic to visualize the quality of the estimated probability densities. Benchmark tests show that estimates for the probability density function (PDF) converge to the true PDF as sample size increases on particularly difficult test probability densities that include cases with discontinuities, multi-resolution scales, heavy tails, and singularities. These results indicate the method has general applicability for high throughput statistical inference.
Diagnostic accuracy of the Rivalta test for feline infectious peritonitis.
Fischer, Yvonne; Sauter-Louis, Carola; Hartmann, Katrin
2012-12-01
The Rivalta test has been used routinely in Europe to diagnose feline infectious peritonitis (FIP) in cats with effusions, but its diagnostic accuracy is uncertain. The objectives of this study were to calculate sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values of the Rivalta test for FIP and to identify correlations between a positive Rivalta test and variables measured in effusion fluid and peripheral blood. In this retrospective study, medical records of cats with effusions were reviewed, and cats with conclusive results for the Rivalta test were included. The prevalence of FIP in this population was determined, and sensitivity, specificity, and PPV and NPV of the Rivalta test were calculated. Variables measured in effusion fluid and peripheral blood were compared between cats that had positive or negative Rivalta tests using the Mann-Whitney U-test and multivariate analysis. Of 851 cats with effusions, 782 had conclusively positive or negative results for the Rivalta test. A definitive final diagnosis was made in 497 of these cats. Prevalence of FIP in cats with effusion and a conclusive Rivalta test result was 34.6%. The Rivalta test had a sensitivity of 91.3%, specificity of 65.5%, PPV of 58.4%, and NPV of 93.4% for the diagnosis of FIP. These values increased when cats with lymphoma or bacterial infections were excluded, or when only cats ≤ 2 years were considered. Increased effusion cholesterol concentration and specific gravity as well as decreased serum albumin:globulin ratio and hyperbilirubinemia were positively correlated with positive Rivalta test results. Sensitivity, specificity, and PPV of the Rivalta test for the diagnosis of FIP were lower than previously reported except when used in young cats. The components in effusions that lead to a positive Rivalta test remain unknown, but the positivity is not simply related to high total protein concentration. © 2012 American Society for Veterinary Clinical Pathology.
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ّFaezeh Salayani
2016-07-01
Results: The mean ages of the intervention and control groups were 35.2±9 and 32.5±11 years, respectively. After the intervention, Mann-Whitney U test did not reflect a significant difference between the intervention and control groups in terms of depression (P=0.14. Moreover, the results of Mann-Whitney test revealed a significant difference between the groups regarding anxiety (P
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben
2017-06-06
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
Chandrasekaran, Varalakshmi; Krupp, Karl; George, Ruja; Madhivanan, Purnima
2007-05-01
Violence against women is a global phenomenon that cuts across all social and economic classes. This study was designed to measure the prevalence and correlates of domestic violence (DV) among women seeking services at a voluntary counseling and testing (VCT) center in Bangalore, India. A cross-sectional survey was conducted among women visiting an human immunodeficiency virus (HIV) VCT center in Bangalore, between September and November 2005. An interviewer-administered questionnaire was used to collect information about violence and other variables. Univariable associations with DV were made using Pearson Chi-squared test for categorical variables and Student t-test or the Mann-Whitney test for continuous variables. Forty-two percent of respondents reported DV, including physical abuse (29%), psychological abuse (69%) and sexual abuse (1%). Among the women who reported violence of any kind, 67% also reported that they were HIV seropositive. The most common reasons reported for DV included financial problems (38%), husband's alcohol use (29%) and woman's HIV status (18%). Older women (P around the world. The findings highlight the need for additional training among health care providers in VCT centers in screening for DV, detection of signs of physical abuse and provisions and referrals for women suffering from domestic partner violence.
Dores, Artemisa R; Barbosa, Fernando; Carvalho, Irene P; Almeida, Isabel; Guerreiro, Sandra; da Rocha, Benedita Martins; Cunha, Gil; Castelo Branco, Miguel; de Sousa, Liliana; Castro Caldas, Alexandre
2017-12-01
The purpose of this study is to present an fMRI paradigm, based on the Williams inhibition test (WIT), to study attentional and inhibitory control and their neuroanatomical substrates. We present an index of the validity of the proposed paradigm and test whether the experimental task discriminates the behavioral performances of healthy participants from those of individuals with acquired brain injury. Stroop and Simon tests present similarities with WIT, but this latter is more demanding. We analyze the BOLD signal in 10 healthy participants performing the WIT. The dorsolateral prefrontal cortex, the inferior prefrontal cortex, the anterior cingulate cortex, and the posterior cingulate cortex were defined for specified region of interest analysis. We additionally compare behavioral data (hits, errors, reaction times) of the healthy participants with those of eight acquired brain injury patients. Data were analyzed with GLM-based random effects and Mann-Whitney tests. Results show the involvement of the defined regions and indicate that the WIT is sensitive to brain lesions. This WIT-based block design paradigm can be used as a research methodology for behavioral and neuroimaging studies of the attentional and inhibitory components of executive functions.
Kuretzki, Carlos Henrique; Campos, Antônio Carlos Ligocki; Malafaia, Osvaldo; Soares, Sandramara Scandelari Kusano de Paula; Tenório, Sérgio Bernardo; Timi, Jorge Rufino Ribas
2016-03-01
The use of information technology is often applied in healthcare. With regard to scientific research, the SINPE(c) - Integrated Electronic Protocols was created as a tool to support researchers, offering clinical data standardization. By the time, SINPE(c) lacked statistical tests obtained by automatic analysis. Add to SINPE(c) features for automatic realization of the main statistical methods used in medicine . The study was divided into four topics: check the interest of users towards the implementation of the tests; search the frequency of their use in health care; carry out the implementation; and validate the results with researchers and their protocols. It was applied in a group of users of this software in their thesis in the strict sensu master and doctorate degrees in one postgraduate program in surgery. To assess the reliability of the statistics was compared the data obtained both automatically by SINPE(c) as manually held by a professional in statistics with experience with this type of study. There was concern for the use of automatic statistical tests, with good acceptance. The chi-square, Mann-Whitney, Fisher and t-Student were considered as tests frequently used by participants in medical studies. These methods have been implemented and thereafter approved as expected. The incorporation of the automatic SINPE (c) Statistical Analysis was shown to be reliable and equal to the manually done, validating its use as a research tool for medical research.
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.
Nonparametric instrumental regression with non-convex constraints
International Nuclear Information System (INIS)
Grasmair, M; Scherzer, O; Vanhems, A
2013-01-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition. (paper)
Nonparametric instrumental regression with non-convex constraints
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.
Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.
Deshwar, Amit G; Vembu, Shankar; Morris, Quaid
2015-01-01
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…
Single versus mixture Weibull distributions for nonparametric satellite reliability
International Nuclear Information System (INIS)
Castet, Jean-Francois; Saleh, Joseph H.
2010-01-01
Long recognized as a critical design attribute for space systems, satellite reliability has not yet received the proper attention as limited on-orbit failure data and statistical analyses can be found in the technical literature. To fill this gap, we recently conducted a nonparametric analysis of satellite reliability for 1584 Earth-orbiting satellites launched between January 1990 and October 2008. In this paper, we provide an advanced parametric fit, based on mixture of Weibull distributions, and compare it with the single Weibull distribution model obtained with the Maximum Likelihood Estimation (MLE) method. We demonstrate that both parametric fits are good approximations of the nonparametric satellite reliability, but that the mixture Weibull distribution provides significant accuracy in capturing all the failure trends in the failure data, as evidenced by the analysis of the residuals and their quasi-normal dispersion.
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...
Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
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Xin Tian
2017-06-01
Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.
Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H.; Maurits, Natasha M.
2016-01-01
In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration
Wang, Yuanjia; Garcia, Tanya P; Ma, Yanyuan
2012-01-01
This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington's Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk
Nonparametric Bayesian models through probit stick-breaking processes.
Rodríguez, Abel; Dunson, David B
2011-03-01
We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.
Exact nonparametric inference for detection of nonlinear determinism
Luo, Xiaodong; Zhang, Jie; Small, Michael; Moroz, Irene
2005-01-01
We propose an exact nonparametric inference scheme for the detection of nonlinear determinism. The essential fact utilized in our scheme is that, for a linear stochastic process with jointly symmetric innovations, its ordinary least square (OLS) linear prediction error is symmetric about zero. Based on this viewpoint, a class of linear signed rank statistics, e.g. the Wilcoxon signed rank statistic, can be derived with the known null distributions from the prediction error. Thus one of the ad...
Investigation of MLE in nonparametric estimation methods of reliability function
International Nuclear Information System (INIS)
Ahn, Kwang Won; Kim, Yoon Ik; Chung, Chang Hyun; Kim, Kil Yoo
2001-01-01
There have been lots of trials to estimate a reliability function. In the ESReDA 20 th seminar, a new method in nonparametric way was proposed. The major point of that paper is how to use censored data efficiently. Generally there are three kinds of approach to estimate a reliability function in nonparametric way, i.e., Reduced Sample Method, Actuarial Method and Product-Limit (PL) Method. The above three methods have some limits. So we suggest an advanced method that reflects censored information more efficiently. In many instances there will be a unique maximum likelihood estimator (MLE) of an unknown parameter, and often it may be obtained by the process of differentiation. It is well known that the three methods generally used to estimate a reliability function in nonparametric way have maximum likelihood estimators that are uniquely exist. So, MLE of the new method is derived in this study. The procedure to calculate a MLE is similar just like that of PL-estimator. The difference of the two is that in the new method, the mass (or weight) of each has an influence of the others but the mass in PL-estimator not
Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.
Du, Pang; Tang, Liansheng
2009-01-30
When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example. Copyright (c) 2008 John Wiley & Sons, Ltd.
Developing and Standardization of a Diagnostic Reading Test
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Tahereh Sima-Shirazi
2004-06-01
Full Text Available Objective: This paper is a report on the development, structure and content of a diagnostic dyslexia reading test. The target population of this test is persian children who have problems in learning reading and may be considered as dyslexic. This diagnostic test is the first reading test developed for the native speakers of persian. Materials & Methods: The theoretical framework of the test is based on two well- established reading tests for the English speaking children, namely Durrell Analysis of Reading and Neale Analysis of Reading Ability. The linguistic content of the subtests is selected from the vocabulary and texts of the textbook used in the primary schools. Both the vocabulary and the sentences of the parrallel passeges were controlled for frequency, phonemic/graphemic regularity, syllable structure, morphology, syntax and semantics. They were also controlled for value judgement by two linguistics and three first grader teachers.The first version of the test is normed on 605 boy and girl first graders from different educational sectors and schools selected randomly.The method used in this research is cross- sectional, descriptive- analytic and the data analysis is based on pearson, and mann-whitney u. Results: Reliability of the test is calculated based on parrallel forms (~ 90% and validity is based on content validity.This test has a supplementary section including spelling, graphem/ phoneme correspondness, nonword reading, irregular word reading, and copy subtests. Conclusion: Considering highreliability and precise validation of the test it can be used to diagnose the dyslexia and related linguistic impairments.
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Zhou Yin
2015-01-01
Full Text Available Background: Safe exclusion and risk stratification are currently recommended for the initial management of patients with acute pulmonary embolism (APE. The aim of this study was to assess the safe exclusion and risk stratification value of D-dimer (DD for APE when tested at the beginning of admission. Materials and Methods: All consecutive Chinese APE patients and controls were recruited from January 2010 to December 2012. All measurements of serum indexes were made in duplicate and blinded to the patients′ status. All the 40 patients with the first episode of APE were confirmed by multi-detector computed tomographic pulmonary angiography. The plasma prothrombin time (PT, activated partial thromboplastin time, thrombin time, fibrinogen, and DD levels were measured within 24 h of admission. We used the Mann-Whitney U-test to determine the differences between groups and drew receiver operator characteristic curve to evaluate the indexes′ value in the APE screening. Results: The PT and DD in the APE group were significantly higher than those in the disease control group (P 1820 μg/L as cut-off value, the sensitivity, specificity, positive and negative predictive value was 82.5%, 75.2%, 56.9%, and 91.6%, respectively. Conclusion: The patients with APE showed significant higher DD levels compared with disease controls, suggesting a negative qualitative DD test result can safely and efficiently exclude APE in primary care.
Ali, Syed S; Wilson, Michael P; Castillo, Edward M; Witucki, Peter; Simmons, Todd T; Vilke, Gary M
2013-02-01
The use of alcohol-based hand sanitizers has recently become widespread. To the authors' knowledge, no previous study has examined whether application of ethanol-based hand sanitizers by the person operating a common breathalyzer machine will affect the accuracy of the readings. This was a prospective study investigating whether the use of hand sanitizer applied according to manufacturer's recommendations (Group I), applied improperly at standard doses (Group II), or applied improperly at high doses (Group III) had an effect on breathalyzer readings of individuals who had not ingested alcohol. The participants of the prospective study were divided into three groups to assess the effect of hand sanitizer on breathalyzer readings. Group I used one pump (1.5 mL) of hand sanitizer (Purell), allowing the hands to dry per manufacturer's recommendations; Group II used one pump (1.5 mL), without allowing the hands to dry; and Group III used two pumps (3 mL), without allowing the hands to dry. Breathalyzer measures for each group are presented as medians with interquartile ranges (IQR) and ranges. Differences between each sequential group (I vs. II and II vs. III) were assessed using a Mann-Whitney U-test (p hand sanitizer may cause false-positive readings with a standard hospital breathalyzer when the operator uses the hand sanitizer correctly. The breathalyzer readings are further elevated if more sanitizer is used or if it is not allowed to dry appropriately. © 2013 by the Society for Academic Emergency Medicine.
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Natália Silveira Corrêa
2009-12-01
Full Text Available Neste trabalho, investigou-se a frequência de micronúcleos em células esfoliadas da mucosa bucal de trabalhadores de sapatarias, na cidade de Pelotas (RS. O estudo constou de 54 trabalhadores de sapatarias expostos à cola e solventes e 54 controles. Avaliou-se a incidência de células com micronúcleos(CMN, binucleadas(CBN, núcleos ligados(CNL e total de anomalias(TA, em 2.000 células por indivíduo. Elaborou-se um banco de dados no programa SPSS "for Windows" pelo teste de Mann-Whitney U, pIn this paper it was investigated the micronuclei frequency in exfoliated oral mucosa cells in shoe shop workers in the city of Pelotas, RS. The study counted on 54 shoe workers exposed to glue and solvents and 54 controls. It was evaluated the incidence of cells with micronucleus (CMN, bi-nucleus (CBN, linked nucleus (CLN and total amount of anomalies (TAA, in 2000 cells per person. A database was created in the SPSS "for Windows" software using the Mann-Whitney U, p<0.05 test. The average of anomalies among shoe workers was 8.69±6.49CMN; 8.85±4.92CBN; 5.78±4.78CNL; 23.31±10.01TA, in the controlled 4.00±5.05CMN; 4.63±4.35CBN; 4.76±5.00CNL; 13.39±9.43TA (p=0.0001; p=0.0001; p=0.144 and p=0.0001 respectively. It was also evaluated the age, gender, time of work, family income, smoke, alcohol beverages, the influence of dermatological, ophthalmological, respiratory and central nervous system (CNS diseases in the number of cell anomalies. These items did not have any influence. It was only observed that among the age group of 15 to 29 years old the number of CNL was bigger than among the age group of 45 to 72. Among those with time of work of 0.1 and 10 years presented a higher CNM than in the other group range.
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loghman rezaei
2018-03-01
Full Text Available Introduction: Nowadays, the main focus of dental studies is on adhesive dental materials; since clinical long-term success of bonded restorations depended more on marginal microleakage minimization. So, the aim of this study was Evaluation of Diode laser irradiation effect on microleakage in class V composite restoration before and after adhesive application. Materials and methods: In this in vitro-experimental study, standard class V cavity was prepared on lingual and buccal surfaces of 60 premolar teeth. For evaluation of microleakage, 60 teeth were divided randomly into four groups A, B, C, D (n=15: A primer + adhesive (Clearfil TM SE Bond, B primer + Diode laser + adhesive (940nm wave-length, 21J total energy, 0.7W power, 30s irradiation time C primer + adhesive + Diode laser D primer + Diode laser + adhesive + Diode laser. Then, restoration was completed by Z250 composite. For data analyzing, we used SPSS 16 software. For statistical analysis, we used Non-parametric Kruskal-Wallis & Mann-Whitney tests at 0.05% significance level. Results: According to non-parametric Kruskal-Wallis test, microleakage scores had not significant difference before and after laser irradiation on gingival margins (p=0.116. But, in occlusal margins the results were significant among the groups (p=0.015. Also according to non-parametric Mann-Whitney tests among the occlusal microleakage scores, group B and D (Diode laser irradiation after primer and Diode laser irradiation after primer and adhesive showed significant results. Conclusion: This study findings showed that in 6th generation adhesives, Diode laser irradiation on self-etch primer before bonding have significant effect on reduction of occlusal marginal microleakage in class V cavities although there was no significant positive effect of Diode laser on gingival margins.
Marcie, S; Costa, A; Lagrange, J L
1995-01-01
During therapeutic irradiation of subdiaphragmatic nodal areas, the gonads are exposed to radiation from the primary beam and scatter. Since young patients have a high probability of cure, limiting exposure to the gonads should be pursued. Primary shielding may be supplemented by additional shields in order to reduce this exposure. Testes dose measurements were performed with thermoluminescent dosimetry (TLD) in 37 patients treated with 25 MV x-rays on subdiaphragmatic nodal areas. Each patient was measured 5 times while under treatment. In 27 cases, an additional shield was placed to protect the testes. For 9 cases, comparative measurements were performed on a phantom with and without additional shielding. The median dose received by gonads was respectively 3% with additional shielding (27 cases) and 5.8% without additional shielding (10 cases, p = 0.001 Mann Whitney test). In the 6 patients for whom the measurements were compared, the differences were also statistically significant (p = 0.028 paired Wilcoxon test). This study confirmed the benefit of additional gonadal shielding during subdiaphragmatic radiation treatment with 25 MV x-rays.
Brink, Anne O'Leary; Jacobs, Anne Burleigh
2011-01-01
This study compared measures of hand sensitivity and handwriting quality in children aged 10 to 12 years identified by their teachers as having nonproficient or proficient handwriting. We hypothesized that children with nonproficient handwriting have decreased kinesthetic sensitivity of the hands and digits. Sixteen subjects without documented motor or cognitive concerns were tested for kinesthetic sensitivity, discriminate tactile awareness, diadochokinesia, stereognosis, and graphesthesia. Eight children were considered to have nonproficient handwriting; 8 had proficient handwriting. Nonparametric Mann-Whitney U tests were used to identify differences between groups on sensory tests. The 2 groups showed a statistically significant difference in handwriting legibility (P = .018). No significant difference was found on tests of kinesthetic sensitivity or other measures of sensation. Children presenting with handwriting difficulty as the only complaint have similar sensitivity in hands and digits as those with proficient handwriting. Failure to detect differences may result from a small sample size.
Pan, Wei
2003-07-22
Recently a class of nonparametric statistical methods, including the empirical Bayes (EB) method, the significance analysis of microarray (SAM) method and the mixture model method (MMM), have been proposed to detect differential gene expression for replicated microarray experiments conducted under two conditions. All the methods depend on constructing a test statistic Z and a so-called null statistic z. The null statistic z is used to provide some reference distribution for Z such that statistical inference can be accomplished. A common way of constructing z is to apply Z to randomly permuted data. Here we point our that the distribution of z may not approximate the null distribution of Z well, leading to possibly too conservative inference. This observation may apply to other permutation-based nonparametric methods. We propose a new method of constructing a null statistic that aims to estimate the null distribution of a test statistic directly. Using simulated data and real data, we assess and compare the performance of the existing method and our new method when applied in EB, SAM and MMM. Some interesting findings on operating characteristics of EB, SAM and MMM are also reported. Finally, by combining the idea of SAM and MMM, we outline a simple nonparametric method based on the direct use of a test statistic and a null statistic.
A Nonparametric Bayesian Approach For Emission Tomography Reconstruction
International Nuclear Information System (INIS)
Barat, Eric; Dautremer, Thomas
2007-01-01
We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
STATCAT, Statistical Analysis of Parametric and Non-Parametric Data
International Nuclear Information System (INIS)
David, Hugh
1990-01-01
1 - Description of program or function: A suite of 26 programs designed to facilitate the appropriate statistical analysis and data handling of parametric and non-parametric data, using classical and modern univariate and multivariate methods. 2 - Method of solution: Data is read entry by entry, using a choice of input formats, and the resultant data bank is checked for out-of- range, rare, extreme or missing data. The completed STATCAT data bank can be treated by a variety of descriptive and inferential statistical methods, and modified, using other standard programs as required
Panel data nonparametric estimation of production risk and risk preferences
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
approaches for obtaining firm-specific measures of risk attitudes. We found that Polish dairy farmers are risk averse regarding production risk and price uncertainty. According to our results, Polish dairy farmers perceive the production risk as being more significant than the risk related to output price......We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...
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
A Bayesian nonparametric approach to causal inference on quantiles.
Xu, Dandan; Daniels, Michael J; Winterstein, Almut G
2018-02-25
We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.
Evaluation of Nonparametric Probabilistic Forecasts of Wind Power
DEFF Research Database (Denmark)
Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008
Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...
Estimation of Stochastic Volatility Models by Nonparametric Filtering
DEFF Research Database (Denmark)
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Chiu, Chun-Huo; Wang, Yi-Ting; Walther, Bruno A; Chao, Anne
2014-09-01
It is difficult to accurately estimate species richness if there are many almost undetectable species in a hyper-diverse community. Practically, an accurate lower bound for species richness is preferable to an inaccurate point estimator. The traditional nonparametric lower bound developed by Chao (1984, Scandinavian Journal of Statistics 11, 265-270) for individual-based abundance data uses only the information on the rarest species (the numbers of singletons and doubletons) to estimate the number of undetected species in samples. Applying a modified Good-Turing frequency formula, we derive an approximate formula for the first-order bias of this traditional lower bound. The approximate bias is estimated by using additional information (namely, the numbers of tripletons and quadrupletons). This approximate bias can be corrected, and an improved lower bound is thus obtained. The proposed lower bound is nonparametric in the sense that it is universally valid for any species abundance distribution. A similar type of improved lower bound can be derived for incidence data. We test our proposed lower bounds on simulated data sets generated from various species abundance models. Simulation results show that the proposed lower bounds always reduce bias over the traditional lower bounds and improve accuracy (as measured by mean squared error) when the heterogeneity of species abundances is relatively high. We also apply the proposed new lower bounds to real data for illustration and for comparisons with previously developed estimators. © 2014, The International Biometric Society.
Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model
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Isaac Mugume
2016-01-01
Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.
Comparison of six different models describing survival of mammalian cells after irradiation
International Nuclear Information System (INIS)
Sontag, W.
1990-01-01
Six different cell-survival models have been compared. All models are based on the similar assumption that irradiated cells are able to exist in one of three states. S A is the state of a totally repaired cell, in state S C the cell contains lethal lesions and in state S b the cell contains potentially lethal lesions i.e. those which either can be repaired or converted into lethal lesions. The differences between the six models lie in the different mathematical relationships between the three states. To test the six models, six different sets of experimental data were used which describe cell survival at different repair times after irradiation with sparsely ionizing irradiation. In order to compare the models, a goodness-of-fit function was used. The differences between the six models were tested by use of the nonparametric Mann-Whitney two sample test. Based on the 95% confidence limit, this required separation into three groups. (orig.)
Differences between early and late onset adult depression
DEFF Research Database (Denmark)
Drachmann Bukh, Jens; Bock, Camilla; Vinberg, Maj
2011-01-01
episode depression were systematically recruited. Characteristics including psychiatric co-morbidity, personality disorders and traits, stressful life events prior to onset, family history, and treatment outcome were assessed by structured interviews and compared by chi-square tests for categorical data...... prevalence of co-morbid personality disorders, higher levels of neuroticism, and a lower prevalence of stressful life events preceding onset compared to patients with later age-of-onset. There were no differences in severity of the depressive episode, treatment outcome or family loading of psychiatric......, t-tests for continuous parametric data and Mann-Whitney U-test for continuous nonparametric data. Logistic and multiple regression analyses were used to adjust the analyses for potentially confounding variables. Results: Patients with early onset of depression were characterised by a higher...
EX-DIVIDEND DATE DAN PERUBAHAN HARGA SAHAM
Directory of Open Access Journals (Sweden)
Novriyanthi Taungke
2015-12-01
Full Text Available The aim of this study is to analyze whether the stock prices decrease at ex-dividend date in Indonesia Stock Exchange (IDX that is determined by amount of dividend drop-off ratio (DDR. This study also attempts to investigate the differences the stock prices decrease at ex-dividend date based on Investment Opportunity Set (IOS. Sample consist of the companiesthat announced the dividend during 2010-2012 periods. By using non-parametric tests, especially Chi-square Test and Mann Whitney Test, the result of this study showed the stock prices decreased less than dividend amount on ex-dividend date. Besides, the non-growth firms experienced decrease more than growth firms.
Lu, Tao
2016-01-01
The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.
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.
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...
A Bayesian nonparametric estimation of distributions and quantiles
International Nuclear Information System (INIS)
Poern, K.
1988-11-01
The report describes a Bayesian, nonparametric method for the estimation of a distribution function and its quantiles. The method, presupposing random sampling, is nonparametric, so the user has to specify a prior distribution on a space of distributions (and not on a parameter space). In the current application, where the method is used to estimate the uncertainty of a parametric calculational model, the Dirichlet prior distribution is to a large extent determined by the first batch of Monte Carlo-realizations. In this case the results of the estimation technique is very similar to the conventional empirical distribution function. The resulting posterior distribution is also Dirichlet, and thus facilitates the determination of probability (confidence) intervals at any given point in the space of interest. Another advantage is that also the posterior distribution of a specified quantitle can be derived and utilized to determine a probability interval for that quantile. The method was devised for use in the PROPER code package for uncertainty and sensitivity analysis. (orig.)
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.
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.
Parametric, nonparametric and parametric modelling of a chaotic circuit time series
Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.
2000-09-01
The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.
Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.
Ghosh, Debashis; Chinnaiyan, Arul M
2009-01-01
In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis. We start by considering the single-gene situation and establishing results on identifiability. We propose 2 nonparametric estimation procedures that have natural links to familiar multiple testing procedures. We then develop multivariate extensions of this methodology to handle genome-wide measurements. The proposed methodologies are compared using simulation studies as well as data from a prostate cancer gene expression study.
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Daiane S. de Pinho
Full Text Available A carqueja (Baccharis trimera(Less. DC. é uma planta medicinal da família Asteraceae muito utilizada como chá no sul do Brasil no tratamento de doenças renais, intestinas, estomacais e principalmente como emagrecedora. O objetivo desde trabalho foi de avaliar a mutagenicidade in vivoe in vitrodo chá e para isso foi realizado o teste de Allium cepaL. e o de aberrações cromossômicas em linfócitos humanos utilizando quatro tratamentos: T1 (água; T2 (20 g/L de carqueja; T3 (200 g/L de carqueja, e T4 (paracetamol, a 400 mg/L. Ambos os procedimentos foram analisados pelo teste Mann-Whitney U. Este estudo evidencia um efeito mutagênico do chá em células vegetais (Allium cepa e em células humanas (aberrações cromossômicas cultivadas, pois em ambos os testes, T2 e T3 obteve-se uma média mais elevada que nos outros tratamentos. Este estudo demonstra que o efeito é dependente da dose, portanto recomenda-se que o chá de carqueja seja consumido com moderação.
International Nuclear Information System (INIS)
Lozhanets, V.V.; Anosov, A.K.
1986-01-01
The nonapeptide delta-sleep inducing peptide (DSIP) causes specific changes in the encephalogram of recipient animals: It prolongs the phase of long-wave or delta sleep. The cellular mechanism of action of DSIP has not yet been explained. To test the hyporhesis that this peptide or its degradation product may be presynaptic regulators of catecholamine release, the action of Leu-enkephaline, DSIP, and amino acids composing DSIP on release of endogenous noradrenalin (NA) from synaptosomes during depolarization was compared. Subcellular fractions from cerebral hemisphere of noninbred male albino rats were isolated. Lactate dehydrogenase activity was determined in the suspension of synaptosomes before and after addition of 0.5% Triton X-100. The results were subjected to statistical analysis, using the Wilcoxon-Mann-Whitney nonparametric test
The influence of creativity on the process of adaptation in the period of teenagers’ crisis
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Chernaya Yu.S.
2017-05-01
Full Text Available this paper studies the influence of regular pictorial creativity class and the environment of creative groups on overcoming the adolescent crisis. Each of 60 students was given a battery of tests. Psychological adaptation, self-esteem and level of aspiration, identity, the subjective sense of loneliness and school anxiety have been studied. The data of descriptive statistics, Mann-Whitney U criterion for nonparametric tests for two independent samples has been processed. It is concluded that adolescents in non-permanent creative groups have a reduced level of neuropsychic adaptation and self-esteem and also high levels of subjective loneliness and frustration in achieving success, compared with adolescents from the constant creative and uncreative groups.
Inaudible functional MRI using a truly mute gradient echo sequence
International Nuclear Information System (INIS)
Marcar, V.L.; Girard, F.; Rinkel, Y.; Schneider, J.F.; Martin, E.
2002-01-01
We performed functional MRI experiments using a mute version of a gradient echo sequence on adult volunteers using either a simple visual stimulus (flicker goggles: 4 subjects) or an auditory stimulus (music: 4 subjects). Because the mute sequence delivers fewer images per unit time than a fast echo planar imaging (EPI) sequence, we explored our data using a parametric ANOVA test and a non-parametric Wilcoxon-Mann-Whitney test in addition to performing a cross-correlation analysis. All three methods were in close agreement regarding the location of the BOLD contrast signal change. We demonstrated that, using appropriate statistical analysis, functional MRI using an MR sequence that is acoustically inaudible to the subject is feasible. Furthermore compared with the ''silent'' event-related procedures involving an EPI protocol, our mGE protocol compares favourably with respect to experiment time and the BOLD signal. (orig.)
Inaudible functional MRI using a truly mute gradient echo sequence
Energy Technology Data Exchange (ETDEWEB)
Marcar, V.L. [University of Zurich, Department of Psychology, Neuropsychology, Treichlerstrasse 10, 8032 Zurich (Switzerland); Girard, F. [GE Medical Systems SA, 283, rue de la Miniere B.P. 34, 78533 Buc Cedex (France); Rinkel, Y.; Schneider, J.F.; Martin, E. [University Children' s Hospital, Neuroradiology and Magnetic Resonance, Department of Diagnostic Imaging, Steinwiesstrasse 75, 8032 Zurich (Switzerland)
2002-11-01
We performed functional MRI experiments using a mute version of a gradient echo sequence on adult volunteers using either a simple visual stimulus (flicker goggles: 4 subjects) or an auditory stimulus (music: 4 subjects). Because the mute sequence delivers fewer images per unit time than a fast echo planar imaging (EPI) sequence, we explored our data using a parametric ANOVA test and a non-parametric Wilcoxon-Mann-Whitney test in addition to performing a cross-correlation analysis. All three methods were in close agreement regarding the location of the BOLD contrast signal change. We demonstrated that, using appropriate statistical analysis, functional MRI using an MR sequence that is acoustically inaudible to the subject is feasible. Furthermore compared with the ''silent'' event-related procedures involving an EPI protocol, our mGE protocol compares favourably with respect to experiment time and the BOLD signal. (orig.)
ÍNDICE DE BASILÉIA NO BRASIL: BANCOS PÚBLICOS X PRIVADOS
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OTÁVIO RIBEIRO DE MEDEIROS
2007-01-01
Full Text Available This paper aims at verifying whether the Basel Inde x of national public and private banks behaves differently according to their shareh older control being private or state-owned. With the purpose of clarifying this qu estion, our methodology utilizes the non-parametric statistical test of Mann-Whitney . By means of this method, we tested whether the Basel Index corresponding to the banks’ shareholder control (public vs. private would be a discrimination fact or, i.e. if there exists a significant difference between the mean value of the Basel Inde x of public vis-à-vis private banks within the period from 2001 and 2006. The emp irical results show that it is not possible to reject the hypothesis that the average Basel Index of public banks is equivalent to that of private banks.
CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions
Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.
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Elias Chaibub Neto
Full Text Available In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson's sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling.
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...
Nonparametric autocovariance estimation from censored time series by Gaussian imputation.
Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K
2009-02-01
One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.
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.
Debt and growth: A non-parametric approach
Brida, Juan Gabriel; Gómez, David Matesanz; Seijas, Maria Nela
2017-11-01
In this study, we explore the dynamic relationship between public debt and economic growth by using a non-parametric approach based on data symbolization and clustering methods. The study uses annual data of general government consolidated gross debt-to-GDP ratio and gross domestic product for sixteen countries between 1977 and 2015. Using symbolic sequences, we introduce a notion of distance between the dynamical paths of different countries. Then, a Minimal Spanning Tree and a Hierarchical Tree are constructed from time series to help detecting the existence of groups of countries sharing similar economic performance. The main finding of the study appears for the period 2008-2016 when several countries surpassed the 90% debt-to-GDP threshold. During this period, three groups (clubs) of countries are obtained: high, mid and low indebted countries, suggesting that the employed debt-to-GDP threshold drives economic dynamics for the selected countries.
Nonparametric estimation of benchmark doses in environmental risk assessment
Piegorsch, Walter W.; Xiong, Hui; Bhattacharya, Rabi N.; Lin, Lizhen
2013-01-01
Summary An important statistical objective in environmental risk analysis is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre-specified benchmark response in a dose-response experiment. In such settings, representations of the risk are traditionally based on a parametric dose-response model. It is a well-known concern, however, that if the chosen parametric form is misspecified, inaccurate and possibly unsafe low-dose inferences can result. We apply a nonparametric approach for calculating benchmark doses, based on an isotonic regression method for dose-response estimation with quantal-response data (Bhattacharya and Kong, 2007). We determine the large-sample properties of the estimator, develop bootstrap-based confidence limits on the BMDs, and explore the confidence limits’ small-sample properties via a short simulation study. An example from cancer risk assessment illustrates the calculations. PMID:23914133
Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees
Directory of Open Access Journals (Sweden)
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.
Multi-Directional Non-Parametric Analysis of Agricultural Efficiency
DEFF Research Database (Denmark)
Balezentis, Tomas
This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... to the Multi-Directional Efficiency Analysis approach when the proposed models were employed to analyse empirical data of Lithuanian family farm performance, we saw substantial differences in efficiencies associated with different inputs. In particular, assets appeared to be the least efficiently used input...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...
Surface roughness of composite resin veneer after application of herbal and non-herbal toothpaste
Nuraini, S.; Herda, E.; Irawan, B.
2017-08-01
The aim of this study was to find out the surface roughness of composite resin veneer after brushing. In this study, 24 specimens of composite resin veneer are divided into three subgroups: brushed without toothpaste, brushed with non-herbal toothpaste, and brushed with herbal toothpaste. Brushing was performed for one set of 5,000 strokes and continued for a second set of 5,000 strokes. Roughness of composite resin veneer was determined using a Surface Roughness Tester. The results were statistically analyzed using Kruskal-Wallis nonparametric test and Post Hoc Mann-Whitney. The results indicate that the highest difference among the Ra values occurred within the subgroup that was brushed with the herbal toothpaste. In conclusion, the herbal toothpaste produced a rougher surface on composite resin veneer compared to non-herbal toothpaste.
Lennon, P; Saunders, J; Fenton, J E
2013-02-01
Anecdotally, infectious mononucleosis is considered a more severe infection than bacterial tonsillitis, requiring a longer hospital stay. However, there is little in the literature comparing the epidemiology of the two conditions. This study aimed to compare the epidemiology of bacterial tonsillitis and infectious mononucleosis, in particular any differences in the length of in-patient stay. The hospital in-patient enquiry system was used to analyse patients admitted with bacterial tonsillitis and infectious mononucleosis between 1990 and 2009 inclusive. There was a total of 3435 cases over the 20 years: 3064 with bacterial tonsillitis and 371 with infectious mononucleosis. The mean length of stay was 3.22 days for bacterial tonsillitis and 4.37 days for infectious mononucleosis. The median length of stay for each condition was compared using the Mann-Whitney U non-parametric test, and a significant difference detected (p mononucleosis have a significantly longer stay in hospital than those with bacterial tonsillitis.
Elgin, Ufuk; Cankaya, Bülent; Simsek, Tulay; Batman, Aygen
2010-01-01
To compare the optic disc topography parameters of children with juvenile diabetes mellitus and normal children using the Heidelberg Retinal Tomograph (HRT III) (Heidelberg Engineering, Heidelberg, Germany). The topographic optic disc parameters (cup volume, cup area, rim volume, rim area, disc area, mean cup-to-disc ratio, and mean cup depth) of 28 non-glaucomatous eyes of 28 children with type 1 diabetes mellitus and 28 eyes of 28 age-matched healthy children were compared using the nonparametric Mann-Whitney U test. No statistically significant differences were found between cup volume (P = .782), cup area (P = .878), rim volume (P = .853), disc area (P = .452), mean cup-to-disc ratio (P = .852), and mean cup depth (P = .711) of eyes of cases with diabetes mellitus and normal subjects. This result suggests that non-glaucomatous eyes of children with type 1 diabetes mellitus and healthy subjects have similar topographic optic disc characteristics. Copyright 2010, SLACK Incorporated.
Transformational leadership and employee satisfaction
Directory of Open Access Journals (Sweden)
Alisa Mujkić
2014-12-01
Full Text Available The main purpose of this paper was to carry out an empirical research on whether transformational leadership, in comparison to other contemporary leadership styles, contributes to higher employee satisfaction levels. In total, 399 respondents took part in this research, which was conducted in companies in Bosnia and Herzegovina and Germany. This was the starting point to identify the dominant leadership style in each of the two countries. Using a nonparametric Mann-Whitney test, it was proved that there is a statistically significant difference in employee satisfaction under transformational leadership as opposed to the transactional and charismatic styles. After a detailed research of the literature, it became apparent that research on this subject is scarce. Accordingly, presenting transformational leadership and its influence on employee satisfaction was a particular challenge.
Ugolini, Alessandro; Mapelli, Andrea; Segù, Marzia; Galante, Domenico; Sidequersky, Fernanda V; Sforza, Chiarella
2017-03-01
The aim of the study was to detect the changes in 3D mandibular motion after orthognathic surgery for skeletal Class III malocclusion. Using a 3D motion analyzer, free mandibular border movements were recorded in nine patients successfully treated for skeletal Class III malocclusion and in nine patients scheduled for orthognathic surgery. Data were compared using Mann-Whitney non-parametric U-test. The results showed no differences between the groups in the total amount of mouth opening, protrusion, and in lateral excursions, but the percentage of mandibular movement explained by condylar translation was significantly increased after surgery (20% vs. 23.6%). During opening, the post-surgery patients showed a more symmetrical mandibular interincisal point and condylar path than pre-surgery patients (p < 0.01). Patients treated with orthognathic surgery for skeletal Class III malocclusion recover a good and symmetric temporomandibular joint function.
Armadi, A. S.; Usman, M.; Suprastiwi, E.
2017-08-01
The aim of this study was to find out the surface roughness of composite resin veneer after brushing. In this study, 24 specimens of composite resin veneer are divided into three subgroups: brushed without toothpaste, brushed with non-herbal toothpaste, and brushed with herbal toothpaste. Brushing was performed for one set of 5,000 strokes and continued for a second set of 5,000 strokes. Roughness of composite resin veneer was determined using a Surface Roughness Tester. The results were statistically analyzed using Kruskal-Wallis nonparametric test and Post Hoc Mann-Whitney. The results indicate that the highest difference among the Ra values occurred within the subgroup that was brushed with the herbal toothpaste. In conclusion, the herbal toothpaste produced a rougher surface on composite resin veneer compared to non-herbal toothpaste.
A new virtual instrument for estimating punch velocity in combat sports.
Urbinati, K S; Scheeren, E; Nohama, P
2013-01-01
For improving the performance in combat sport, especially percussion, it is necessary achieving high velocity in punches and kicks. The aim of this study was to evaluate the applicability of 3D accelerometry in a Virtual Instrumentation System (VIS) designed for estimating punch velocity in combat sports. It was conducted in two phases: (1) integration of the 3D accelerometer with the communication interface and software for processing and visualization, and (2) applicability of the system. Fifteen karate athletes performed five gyaku zuki type punches (with reverse leg) using the accelerometer on the 3rd metacarpal on the back of the hand. It was performed nonparametric Mann-Whitney U-test to determine differences in the mean linear velocity among three punches performed sequentially (p sport.
Assessment of Communications-related Admissions Criteria in a Three-year Pharmacy Program.
Parmar, Jayesh R; Tejada, Frederick R; Lang, Lynn A; Purnell, Miriam; Acedera, Lisa; Ngonga, Ferdinand
2015-08-25
To determine if there is a correlation between TOEFL and other admissions criteria that assess communications skills (ie, PCAT variables: verbal, reading, essay, and composite), interview, and observational scores and to evaluate TOEFL and these admissions criteria as predictors of academic performance. Statistical analyses included two sample t tests, multiple regression and Pearson's correlations for parametric variables, and Mann-Whitney U for nonparametric variables, which were conducted on the retrospective data of 162 students, 57 of whom were foreign-born. The multiple regression model of the other admissions criteria on TOEFL was significant. There was no significant correlation between TOEFL scores and academic performance. However, significant correlations were found between the other admissions criteria and academic performance. Since TOEFL is not a significant predictor of either communication skills or academic success of foreign-born PharmD students in the program, it may be eliminated as an admissions criterion.
Hyperspectral image segmentation using a cooperative nonparametric approach
Taher, Akar; Chehdi, Kacem; Cariou, Claude
2013-10-01
In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.
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...... models for node clustering in complex networks. In particular, we test their ability to predict unseen data and their ability to reproduce clustering across datasets. The three generative models considered are the Infinite Relational Model (IRM), Bayesian Community Detection (BCD), and the Infinite...... between clusters. BCD restricts the between-cluster link probabilities to be strictly lower than within-cluster link probabilities to conform to the community structure typically seen in social networks. IDM only models a single between-cluster link probability, which can be interpreted as a background...
Energy Technology Data Exchange (ETDEWEB)
Takamizawa, Hisashi, E-mail: takamizawa.hisashi@jaea.go.jp; Itoh, Hiroto, E-mail: ito.hiroto@jaea.go.jp; Nishiyama, Yutaka, E-mail: nishiyama.yutaka93@jaea.go.jp
2016-10-15
In order to understand neutron irradiation embrittlement in high fluence regions, statistical analysis using the Bayesian nonparametric (BNP) method was performed for the Japanese surveillance and material test reactor irradiation database. The BNP method is essentially expressed as an infinite summation of normal distributions, with input data being subdivided into clusters with identical statistical parameters, such as mean and standard deviation, for each cluster to estimate shifts in ductile-to-brittle transition temperature (DBTT). The clusters typically depend on chemical compositions, irradiation conditions, and the irradiation embrittlement. Specific variables contributing to the irradiation embrittlement include the content of Cu, Ni, P, Si, and Mn in the pressure vessel steels, neutron flux, neutron fluence, and irradiation temperatures. It was found that the measured shifts of DBTT correlated well with the calculated ones. Data associated with the same materials were subdivided into the same clusters even if neutron fluences were increased.
Simulation-Based Testing of Pager Interruptions During Laparoscopic Cholecystectomy.
Sujka, Joseph A; Safcsak, Karen; Bhullar, Indermeet S; Havron, William S
2018-01-30
To determine if pager interruptions affect operative time, safety, or complications and management of pager issues during a simulated laparoscopic cholecystectomy. Twelve surgery resident volunteers were tested on a Simbionix Lap Mentor II simulator. Each resident performed 6 randomized simulated laparoscopic cholecystectomies; 3 with pager interruptions (INT) and 3 without pager interruptions (NO-INT). The pager interruptions were sent in the form of standardized patient vignettes and timed to distract the resident during dissection of the critical view of safety and clipping of the cystic duct. The residents were graded on a pass/fail scale for eliciting appropriate patient history and management of the pager issue. Data was extracted from the simulator for the following endpoints: operative time, safety metrics, and incidence of operative complications. The Mann-Whitney U test and contingency table analysis were used to compare the 2 groups (INT vs. NO-INT). Level I trauma center; Simulation laboratory. Twelve general surgery residents. There was no significant difference between the 2 groups in any of the operative endpoints as measured by the simulator. However, in the INT group, only 25% of the time did the surgery residents both adequately address the issue and provide effective patient management in response to the pager interruption. Pager interruptions did not affect operative time, safety, or complications during the simulated procedure. However, there were significant failures in the appropriate evaluations and management of pager issues. Consideration for diversion of patient care issues to fellow residents not operating to improve quality and safety of patient care outside the operating room requires further study. Copyright © 2018. Published by Elsevier Inc.
Kuzmickienė, Jurgita; Kaubrys, Gintaras
2016-10-08
BACKGROUND The primary manifestation of Alzheimer's disease (AD) is decline in memory. Dysexecutive symptoms have tremendous impact on functional activities and quality of life. Data regarding frontal-executive dysfunction in mild AD are controversial. The aim of this study was to assess the presence and specific features of executive dysfunction in mild AD based on Cambridge Neuropsychological Test Automated Battery (CANTAB) results. MATERIAL AND METHODS Fifty newly diagnosed, treatment-naïve, mild, late-onset AD patients (MMSE ≥20, AD group) and 25 control subjects (CG group) were recruited in this prospective, cross-sectional study. The CANTAB tests CRT, SOC, PAL, SWM were used for in-depth cognitive assessment. Comparisons were performed using the t test or Mann-Whitney U test, as appropriate. Correlations were evaluated by Pearson r or Spearman R. Statistical significance was set at p<0.05. RESULTS AD and CG groups did not differ according to age, education, gender, or depression. Few differences were found between groups in the SOC test for performance measures: Mean moves (minimum 3 moves): AD (Rank Sum=2227), CG (Rank Sum=623), p<0.001. However, all SOC test time measures differed significantly between groups: SOC Mean subsequent thinking time (4 moves): AD (Rank Sum=2406), CG (Rank Sum=444), p<0.001. Correlations were weak between executive function (SOC) and episodic/working memory (PAL, SWM) (R=0.01-0.38) or attention/psychomotor speed (CRT) (R=0.02-0.37). CONCLUSIONS Frontal-executive functions are impaired in mild AD patients. Executive dysfunction is highly prominent in time measures, but minimal in performance measures. Executive disorders do not correlate with a decline in episodic and working memory or psychomotor speed in mild AD.
Upadhyay, Dinesh Kumar; Mohamed Ibrahim, Mohamed Izham; Mishra, Pranaya; Alurkar, Vijay M
2015-02-12
Patient satisfaction is the ultimate goal of healthcare system which can be achieved from good patient-healthcare professional relationship and quality of healthcare services provided. Study was conducted to determine the baseline satisfaction level of newly diagnosed diabetics and to explore the impact of pharmaceutical care intervention on patients' satisfaction during their follow-ups in a tertiary care teaching hospital in Nepal. An interventional, pre-post non-clinical randomised controlled study was designed among randomly distributed 162 [control group (n = 54), test 1 group (n = 54) and test 2 group (n = 54)] newly diagnosed diabetes mellitus patients by consecutive sampling method for 18 months. Diabetes Patient Satisfaction Questionnaire was used to evaluate patient's satisfaction scores at baseline, three, six, nine and, twelve months' follow-ups. Test groups patients were provided pharmaceutical care whereas control group patients only received their usual care from physician/nurses. The responses were entered in SPSS version 16. Data distribution was not normal on Kolmogorov-Smirnov test. Non-parametric tests i.e. Friedman test, Mann-Whitney U test and Wilcoxon signed rank test were used to find the differences among the groups before and after the intervention (p ≤0.05). There were significant (p patients' satisfaction scores in the test groups on Friedman test. Mann-Whitney U test identified the significant differences in satisfaction scores between test 1 and test 2 groups, control and test 1 groups and, control and test 2 groups at 3-months (p = 0.008), (p satisfaction level of diabetics in the test groups compare to the control group. Diabetic kit demonstration strengthened the satisfaction level among the test 2 group patients. Therefore, pharmacist can act as a counsellor through pharmaceutical care program and assist the patients in managing their disease. This will not only modify the patients' related outcomes and their
A ¤nonparametric dynamic additive regression model for longitudinal data
DEFF Research Database (Denmark)
Martinussen, T.; Scheike, T. H.
2000-01-01
dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models......dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models...
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...
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...
Usefulness of Basophil Activation Tests for Diagnosis of Sugammadex-Induced Anaphylaxis.
Horiuchi, Tatsuo; Yokohama, Akihiko; Orihara, Masaki; Tomita, Yukinari; Tomioka, Akihiro; Yoshida, Nagahide; Takahashi, Kenichiro; Saito, Shigeru; Takazawa, Tomonori
2018-05-01
Sugammadex is used to reverse the effects of neuromuscular blocking agents in many cases of general anesthesia. However, there are several reports of anaphylaxis after its use. Skin testing is the gold standard for detecting the causative agent of anaphylaxis. However, due to the lack of validated protocols for skin testing with sugammadex, the diagnostic accuracy might be inadequate. Recently, the basophil activation test (BAT) has been established as a tool to detect the causative agent of anaphylaxis with high sensitivity and specificity. However, few studies have investigated the utility of the BAT for sugammadex-induced anaphylaxis. Eight patients who presented with immediate hypersensitivity to sugammadex during general anesthesia were included in this study. We conducted skin tests to confirm the diagnosis of sugammadex-induced anaphylaxis. Twenty-one sugammadex-naive individuals who had a negative skin test for allergy to this drug were enrolled as controls. Basophils were selected on a CD3/CRTH2 gate and labeled with CD63 and CD203c. The ratios of activated basophils in the patients were much higher than those in controls: the median values of areas under the curves in the patients and controls for CD203c were 1,265,985 (95% confidence interval [CI], 77,580-5,040,270) and 116,325 (95% CI, -268,605 to 232,690), respectively (Mann-Whitney U test, P sugammadex was 88% (95% CI, 47%-100%), and specificity was 100% (95% CI, 84%-100%), while sensitivity and specificity for CD63 were 75% (95% CI, 35%-97%) and 100% (95% CI, 84%-100%), respectively. The BAT seems to have comparable accuracy to skin tests for the diagnosis of sugammadex-induced anaphylaxis. For this purpose, both CD203c and CD63 can be used to detect activated basophils.
EFEITO DA HEMISFERICIDADE NOS TESTES FÍSICOS PRATICADOS POR JOVENS JOGADORES DO FUTSAL MASCULINO
Directory of Open Access Journals (Sweden)
Nelson Kautzner Marques Junior
2014-04-01
Full Text Available Hemisphericity is strong tendency during mental processing in one of the hemispheres. Athletes that have left hemisphere are the best in analytical tasks and athletes with right hemisphere are the best in motor tasks. The objective of the study was to determine the difference of the physical tests of young futsal players of right hemisphere versus the players of left hemisphere. Were selected 18 futsal payers they practiced the CLEM test for determines the hemisphericity, practiced anthropometric tests and physical tests. The study was composed of 9 futsal players of left hemisphere and 9 of right hemisphere. The age of the players of left hemisphere was of 10,56±2,35 years and of the right hemisphere was of 11,89±2,14. The left hemisphere had a vertical jump of 27,29±2,05 cm, the agility of 5 m of 1,49±0,19 m/s and the velocity of 10 m of 4,44±0,83 m/s. The right hemisphere had a vertical jump of 33,08±0,94 cm, the agility of 5 m of 1,61±0,13 m/s and the velocity of 10 m of 4,44±0,83 m/s. The “t” test detected a significant difference of the vertical jump between the left hemisphere versus the right hemisphere, t (16 = 2,56, p = 0,02, effect size = 3,88 (great. The futsal players of right hemisphere were best in the vertical jump. The Mann-Whitney U test no detected significant difference between the hemispheres (p>0,05, in the test of agility and of velocity. In conclusion, hemisphericity is an important reference for the teacher understands the motor performance of the athletes.
Rogers, Jeffrey N.; Parrish, Christopher E.; Ward, Larry G.; Burdick, David M.
2018-03-01
Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet's stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.
Performance of non-parametric algorithms for spatial mapping of tropical forest structure
Directory of Open Access Journals (Sweden)
Liang Xu
2016-08-01
Full Text Available Abstract Background Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation. Results By using airborne lidar data as the “truth” and focusing on the mean canopy height (MCH as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME and random forest (RF for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size. Conclusions A significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution.
Directory of Open Access Journals (Sweden)
Santiago Ostengo
2011-12-01
considered in a breeding program. It is for that reason that in sugar cane breeding, multienvironmental trials (MET are conducted at the last stage of the selection process. There exist different approaches to study genotype-environment interaction. One of these is the non-parametric technique, a valid and useful tool which allows making an initial exploration that can be easily interpreted. The non-parametric technique called relative consistency of performance enables the classification of genotypes into the following four categories: (i consistently superior; (ii inconsistently superior; (iii inconsistently inferior and (iv consistently inferior. This work aims to evaluate the consistency of performance of TUC 95-10 variety across different agro-ecological environments in the province of Tucumán (Argentina, as regards the variable tons of sugar per hectare and considering different crop ages. Data were obtained from MET of the Sugarcane Breeding Program of Estación Experimental Agroindustrial Obispo Colombres (EEAOC from Tucumán (Argentina, conducted at six sites through four crop ages. Results showed that TUC 95-10, recently released by EEAOC, can be labeled as consistently superior at all ages, i.e. it held the top position in sugar production in all tested environments. Therefore, it can be concluded that TUC 95-10 shows an excellent performance and good adaptation to different agro-ecological environments in Tucumán, at all crop ages.
Wesselink, Christiaan; Heeg, Govert P.; Jansonius, Nomdo M.
Objective: To compare prospectively 2 perimetric progression detection algorithms for glaucoma, the Early Manifest Glaucoma Trial algorithm (glaucoma progression analysis [GPA]) and a nonparametric algorithm applied to the mean deviation (MD) (nonparametric progression analysis [NPA]). Methods:
A local non-parametric model for trade sign inference
Blazejewski, Adam; Coggins, Richard
2005-03-01
We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.
Non-parametric Bayesian networks: Improving theory and reviewing applications
International Nuclear Information System (INIS)
Hanea, Anca; Morales Napoles, Oswaldo; Ababei, Dan
2015-01-01
Applications in various domains often lead to high dimensional dependence modelling. A Bayesian network (BN) is a probabilistic graphical model that provides an elegant way of expressing the joint distribution of a large number of interrelated variables. BNs have been successfully used to represent uncertain knowledge in a variety of fields. The majority of applications use discrete BNs, i.e. BNs whose nodes represent discrete variables. Integrating continuous variables in BNs is an area fraught with difficulty. Several methods that handle discrete-continuous BNs have been proposed in the literature. This paper concentrates only on one method called non-parametric BNs (NPBNs). NPBNs were introduced in 2004 and they have been or are currently being used in at least twelve professional applications. This paper provides a short introduction to NPBNs, a couple of theoretical advances, and an overview of applications. The aim of the paper is twofold: one is to present the latest improvements of the theory underlying NPBNs, and the other is to complement the existing overviews of BNs applications with the NPNBs applications. The latter opens the opportunity to discuss some difficulties that applications pose to the theoretical framework and in this way offers some NPBN modelling guidance to practitioners. - Highlights: • The paper gives an overview of the current NPBNs methodology. • We extend the NPBN methodology by relaxing the conditions of one of its fundamental theorems. • We propose improvements of the data mining algorithm for the NPBNs. • We review the professional applications of the NPBNs.
Nonparametric predictive inference for combined competing risks data
International Nuclear Information System (INIS)
Coolen-Maturi, Tahani; Coolen, Frank P.A.
2014-01-01
The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies
Transition redshift: new constraints from parametric and nonparametric methods
Energy Technology Data Exchange (ETDEWEB)
Rani, Nisha; Mahajan, Shobhit; Mukherjee, Amitabha [Department of Physics and Astrophysics, University of Delhi, New Delhi 110007 (India); Jain, Deepak [Deen Dayal Upadhyaya College, University of Delhi, New Delhi 110015 (India); Pires, Nilza, E-mail: nrani@physics.du.ac.in, E-mail: djain@ddu.du.ac.in, E-mail: shobhit.mahajan@gmail.com, E-mail: amimukh@gmail.com, E-mail: npires@dfte.ufrn.br [Departamento de Física Teórica e Experimental, UFRN, Campus Universitário, Natal, RN 59072-970 (Brazil)
2015-12-01
In this paper, we use the cosmokinematics approach to study the accelerated expansion of the Universe. This is a model independent approach and depends only on the assumption that the Universe is homogeneous and isotropic and is described by the FRW metric. We parametrize the deceleration parameter, q(z), to constrain the transition redshift (z{sub t}) at which the expansion of the Universe goes from a decelerating to an accelerating phase. We use three different parametrizations of q(z) namely, q{sub I}(z)=q{sub 1}+q{sub 2}z, q{sub II} (z) = q{sub 3} + q{sub 4} ln (1 + z) and q{sub III} (z)=½+q{sub 5}/(1+z){sup 2}. A joint analysis of the age of galaxies, strong lensing and supernovae Ia data indicates that the transition redshift is less than unity i.e. z{sub t} < 1. We also use a nonparametric approach (LOESS+SIMEX) to constrain z{sub t}. This too gives z{sub t} < 1 which is consistent with the value obtained by the parametric approach.
Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines
Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.
2011-01-01
Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433
Nonparametric Integrated Agrometeorological Drought Monitoring: Model Development and Application
Zhang, Qiang; Li, Qin; Singh, Vijay P.; Shi, Peijun; Huang, Qingzhong; Sun, Peng
2018-01-01
Drought is a major natural hazard that has massive impacts on the society. How to monitor drought is critical for its mitigation and early warning. This study proposed a modified version of the multivariate standardized drought index (MSDI) based on precipitation, evapotranspiration, and soil moisture, i.e., modified multivariate standardized drought index (MMSDI). This study also used nonparametric joint probability distribution analysis. Comparisons were done between standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), MSDI, and MMSDI, and real-world observed drought regimes. Results indicated that MMSDI detected droughts that SPEI and/or SSMI failed to do. Also, MMSDI detected almost all droughts that were identified by SPEI and SSMI. Further, droughts detected by MMSDI were similar to real-world observed droughts in terms of drought intensity and drought-affected area. When compared to MMSDI, MSDI has the potential to overestimate drought intensity and drought-affected area across China, which should be attributed to exclusion of the evapotranspiration components from estimation of drought intensity. Therefore, MMSDI is proposed for drought monitoring that can detect agrometeorological droughts. Results of this study provide a framework for integrated drought monitoring in other regions of the world and can help to develop drought mitigation.
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
2018-01-30
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. Copyright © 2017 John Wiley & Sons, Ltd.
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.
Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi
2015-02-01
We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.
Nonparametric adaptive age replacement with a one-cycle criterion
International Nuclear Information System (INIS)
Coolen-Schrijner, P.; Coolen, F.P.A.
2007-01-01
Age replacement of technical units has received much attention in the reliability literature over the last four decades. Mostly, the failure time distribution for the units is assumed to be known, and minimal costs per unit of time is used as optimality criterion, where renewal reward theory simplifies the mathematics involved but requires the assumption that the same process and replacement strategy continues over a very large ('infinite') period of time. Recently, there has been increasing attention to adaptive strategies for age replacement, taking into account the information from the process. Although renewal reward theory can still be used to provide an intuitively and mathematically attractive optimality criterion, it is more logical to use minimal costs per unit of time over a single cycle as optimality criterion for adaptive age replacement. In this paper, we first show that in the classical age replacement setting, with known failure time distribution with increasing hazard rate, the one-cycle criterion leads to earlier replacement than the renewal reward criterion. Thereafter, we present adaptive age replacement with a one-cycle criterion within the nonparametric predictive inferential framework. We study the performance of this approach via simulations, which are also used for comparisons with the use of the renewal reward criterion within the same statistical framework
Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution
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Emmanuel Kidando
2017-01-01
Full Text Available Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time. Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions. In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution. In particular, a nonparametric Dirichlet Process Mixture Model (DPMM with stick-breaking process representation was used. The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation. To reduce computational complexity, the modeling process was limited to a maximum of six components. Then, the Markov Chain Monte Carlo (MCMC sampling technique was employed to estimate the parameters’ posterior distribution. Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time. The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components. The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods.
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)
Anelise Ineu Figueiredo
Full Text Available Abstract Introduction: The Multiple Sclerosis Functional Composite (MSFC is a scale that evaluates the functional and cognitive aspects of patients with multiple sclerosis (MS. Objective: To compare the performance of individuals with the relapsing-remitting form of MS (RRMS with a group of healthy subjects using the MSFC. Methods: Twenty subjects were investigated in this study, consisting of 10 patients with clinical diagnosis of RRMS and 10 controls with similar gender and age to the group with the disease. The three tests that comprise the MSFC were used for the evaluation of gait, upper limb motor function and cognition (memory and processing speed. Student's t-test was used to assess data with normal distribution and data with skewed distribution were evaluated using the Mann-Whitney test. Results: The results showed that the patients with RRMS took longer to perform the locomotion test (6.91 ± 2.35 compared to the control group (5.16 ± 1.28. The MS group (22.06 ± 5.44 also showed greater difficulty in performing a task with the dominant upper limb compared to the healthy subjects (17.79 ± 2.96. No statistically significant difference was found between the groups in the performance of cognitive tasks (p = .65. Conclusion: The use of the MSFC tests proved valuable for measuring possible motor and cognitive impairments in patients with RRMS. Thus, it is suggested that this scale is adopted in clinical practice, improving therapies for the treatment of MS patients and thereby providing them a better quality of life.
Colon Transit Time Test in Korean Children with Chronic Functional Constipation
Yoo, Ha Yeong; Kim, Mock Ryeon; Park, Hye Won; Son, Jae Sung
2016-01-01
Purpose Each ethnic group has a unique life style, including diets. Life style affects bowel movement. The aim of this study is to describe the results of colon transit time (CTT) tests in Korean children who had chronic functional constipation based on highly refined data. Methods One hundred ninety (86 males) out of 415 children who performed a CTT test under the diagnosis of chronic constipation according to Rome III criteria at Konkuk University Medical Center from January 2006 through March 2015 were enrolled in this study. Two hundreds twenty-five children were excluded on the basis of CTT test result, defecation diary, and clinical setting. Shapiro-Wilk and Mann-Whitney U, and chi-square tests were used for statistical analysis. Results The median value and interquartile range (IQR) of CTT was 54 (37.5) hours in Encopresis group, and those in non-encopresis group was 40.2 (27.9) hours (pencopresis group and encopresis was statistically significant (p=0.002). The non-encopresis group (n=154, 81.1%) was divided into normal transit subgroup (n=84, 54.5%; median value and IQR of CTT=26.4 [9.6] hours), outlet obstruction subgroup (n=18, 11.7%; 62.4 [15.6] hours), and slow transit subgroup (n=52, 33.8%; 54.6 [21.0] hours]. The encopresis group (n=36, 18.9%) was divided into normal transit subgroup (n=8, 22.2%; median value and IQR of CTT=32.4 [9.9] hours), outlet obstruction subgroup (n=8, 22.2%; 67.8 [34.8] hours), and slow transit subgroup (n=20, 55.6%; 59.4 [62.7]hours). Conclusion This study provided the basic pattern and value of the CTT test in Korean children with chronic constipation. PMID:27064388
Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G
2018-03-01
Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.
International Nuclear Information System (INIS)
Aboalkhair, Ahmad M.; Coolen, Frank P.A.; MacPhee, Iain M.
2014-01-01
Nonparametric predictive inference for system reliability has recently been presented, with specific focus on k-out-of-m:G systems. The reliability of systems is quantified by lower and upper probabilities of system functioning, given binary test results on components, taking uncertainty about component functioning and indeterminacy due to limited test information explicitly into account. Thus far, systems considered were series configurations of subsystems, with each subsystem i a k i -out-of-m i :G system which consisted of only one type of components. Key results are briefly summarized in this paper, and as an important generalization new results are presented for a single k-out-of-m:G system consisting of components of multiple types. The important aspects of redundancy and diversity for such systems are discussed. - Highlights: • New results on nonparametric predictive inference for system reliability. • Prediction of system reliability based on test data for components. • New insights on system redundancy optimization and diversity. • Components that appear inferior in tests may be included to enhance redundancy
Kocabeyoglu, Sibel; Uzun, Salih; Mocan, Mehmet Cem; Bozkurt, Banu; Irkec, Murat; Orhan, Mehmet
2013-10-01
The aim of this study was to compare the visual field test results in healthy children obtained via the Humphrey matrix 24-2 threshold program and standard automated perimetry (SAP) using the Swedish interactive threshold algorithm (SITA)-Standard 24-2 test. This prospective study included 55 healthy children without ocular or systemic disorders who underwent both SAP and frequency doubling technology (FDT) perimetry visual field testing. Visual field test reliability indices, test duration, global indices (mean deviation [MD], and pattern standard deviation [PSD]) were compared between the 2 tests using the Wilcoxon signed-rank test and paired t-test. The performance of the Humphrey field analyzer (HFA) 24-2 SITA-standard and frequency-doubling technology Matrix 24-2 tests between genders were compared with Mann-Whitney U-test. Fifty-five healthy children with a mean age of 12.2 ± 1.9 years (range from 8 years to 16 years) were included in this prospective study. The test durations of SAP and FDT were similar (5.2 ± 0.5 and 5.1 ± 0.2 min, respectively, P = 0.651). MD and the PSD values obtained via FDT Matrix were significantly higher than those obtained via SAP (P tests in terms of MD (r = 0.352, P = 0.008) and PSD (r = 0.329, P = 0.014) was observed. Children were able to complete both the visual test algorithms successfully within 6 min. However, SAP testing appears to be associated with less depression of the visual field indices of healthy children. FDT Matrix and SAP should not be used interchangeably in the follow-up of children.
Directory of Open Access Journals (Sweden)
Sibel Kocabeyoglu
2013-01-01
Full Text Available Aims : The aim of this study was to compare the visual field test results in healthy children obtained via the Humphrey matrix 24-2 threshold program and standard automated perimetry (SAP using the Swedish interactive threshold algorithm (SITA-Standard 24-2 test. Materials and Methods: This prospective study included 55 healthy children without ocular or systemic disorders who underwent both SAP and frequency doubling technology (FDT perimetry visual field testing. Visual field test reliability indices, test duration, global indices (mean deviation [MD], and pattern standard deviation [PSD] were compared between the 2 tests using the Wilcoxon signed-rank test and paired t-test. The performance of the Humphrey field analyzer (HFA 24-2 SITA-standard and frequency-doubling technology Matrix 24-2 tests between genders were compared with Mann-Whitney U-test. Results: Fifty-five healthy children with a mean age of 12.2 ± 1.9 years (range from 8 years to 16 years were included in this prospective study. The test durations of SAP and FDT were similar (5.2 ± 0.5 and 5.1 ± 0.2 min, respectively, P = 0.651. MD and the PSD values obtained via FDT Matrix were significantly higher than those obtained via SAP (P < 0.001, and fixation losses and false negative errors were significantly less with SAP (P < 0.05. A weak positive correlation between the two tests in terms of MD (r = 0.352, P = 0.008 and PSD (r = 0.329, P = 0.014 was observed. Conclusion: Children were able to complete both the visual test algorithms successfully within 6 min. However, SAP testing appears to be associated with less depression of the visual field indices of healthy children. FDT Matrix and SAP should not be used interchangeably in the follow-up of children.
Wang, Yuanjia; Garcia, Tanya P.; Ma, Yanyuan
2012-01-01
This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington’s Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk
Yavuz, Y C; Selcuk, N Y; Altıntepe, L; Güney, I; Yavuz, S
2018-01-01
In chronic hemodialysis patients, the low flow of vascular access may leads to inadequate dialysis, increased rate of hospitalization, morbidity, and mortality. It was found that surveillance should be performed for native arteriovenous (AV) should not be performed for AV graft in various studies. However, surveillance was done in graft AV fistulas in most studies. Doppler ultrasonography (US) was suggested for surveillance of AV fistulas by the last vascular access guideline of National Kidney Foundation Disease Outcomes Quality Initiative (NKF KDOQI). The aim of study is to determine whether glucose pump test (GPT) is used for surveillance of native AV fistulas by using Doppler US as reference. In 93 chronic hemodialysis patients with native AV fistula, blood flow rates were measured by Doppler US and GPT. For GPT, glucose was infused to 16 mL/min by pump and was measured at basal before the infusion and 11 s after the start of the infusion by glucometer. Doppler US was done by an expert radiologist. Used statistical tests were Mann-Whitney U test, Friedman test, regression analysis, and multiple regression analysis. Median values of blood flow rates measured by GPT (707 mL/min) and by Doppler US (700 mL/min) were not different (Z = 0.414, P = 0.678). Results of GPT and Doppler US measurements were positive correlate by regression analysis. The mean GPT value of diabetic patients (n = 39; 908 mL/min) was similar to that of nondiabetic patients (n = 54; 751 mL/min; Z = 1.31, P = 0.188). GPT values measured at three different dialysis session did not differ from each other that by Friedman test (F = 0.92, P = 0.39). This showed that GPT was stable and reliable. Glucose pump test can be used to measure blood flow rate of native AV fistula. GPT is an accurate and reliable test.
Performances of non-parametric statistics in sensitivity analysis and parameter ranking
International Nuclear Information System (INIS)
Saltelli, A.
1987-01-01
Twelve parametric and non-parametric sensitivity analysis techniques are compared in the case of non-linear model responses. The test models used are taken from the long-term risk analysis for the disposal of high level radioactive waste in a geological formation. They describe the transport of radionuclides through a set of engineered and natural barriers from the repository to the biosphere and to man. The output data from these models are the dose rates affecting the maximum exposed individual of a critical group at a given point in time. All the techniques are applied to the output from the same Monte Carlo simulations, where a modified version of Latin Hypercube method is used for the sample selection. Hypothesis testing is systematically applied to quantify the degree of confidence in the results given by the various sensitivity estimators. The estimators are ranked according to their robustness and stability, on the basis of two test cases. The conclusions are that no estimator can be considered the best from all points of view and recommend the use of more than just one estimator in sensitivity analysis
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.
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.
Validity and reliability of Abbreviated Mental Test Score (AMTS) among older Iranian.
Foroughan, Mahshid; Wahlund, Lars-Olof; Jafari, Zahra; Rahgozar, Mehdi; Farahani, Ida G; Rashedi, Vahid
2017-11-01
Cognitive impairment is common among older people and is associated with increased morbidity and mortality. The main aim of this study was to evaluate the validity of the Persian version of the Abbreviated Mental Test Score (AMTS) as a screening tool for dementia. Data were obtained from a cross-sectional study. One hundred and one older adults who were members of Iranian Alzheimer Association and 101 of their siblings were entered into this study by convenient sampling. The Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria for diagnosing dementia and the Mini-Mental State Examination were used as the study tools. The gathered data were analyzed by the Mann-Whitney U-test, the Kruskal-Wallis test, Spearman's rank correlation coefficient, and the receiver-operating characteristic. The AMTS could successfully differentiate the dementia group from the non-dementia group. Scores were significantly correlated with Diagnostic and Statistical Manual of Mental Disorders diagnosis for dementia and Mini-Mental State Examination scores (P < 0.001). Educational level (P < 0.001) and male sex (P = 0.015) were positively associated with AMTS, whereas (P < 0.001) was negatively associated with AMTS. Total Cronbach's α coefficient was 0.90. The scores 6 and 7 showed the optimum balance between sensitivity (99% and 94%, respectively) and specificity (85% and 86%, respectively). The Persian version of the AMTS is a valid cognitive assessment tool for older Iranian adults and can be used for dementia screening in Iran. © 2017 Japanese Psychogeriatric Society.
Al Ben Ali, Abdulaziz; Kang, Kiho; Finkelman, Matthew D; Zandparsa, Roya; Hirayama, Hiroshi
2014-04-01
The purpose of this study was to compare the effect of variations in translucency and background on color differences (ΔE) for different shades of computer-aided design and computer-aided manufacturing (CAD/CAM) lithium disilicate glass ceramics. A pilot study suggested n = 10 as an appropriate sample size for the number of lithium disilicate glass ceramic cylinders per group. High-transparency (HT) and low-transparency (LT) cylinders (diameter, 12 mm; length, 13 mm) were fabricated in three ceramic shades (BL1, A2, C3) using CAD/CAM technology and were cut into specimen disks (thickness, 1.2 mm; diameter, 12 mm) for placement on Natural Die (ND1 and ND4) backgrounds. Four combinations of translucency and background color were evaluated in terms of color differences for the three ceramic shades: group 1 (HT ND1, reference), group 2 (HT ND4), group 3 (LT ND1), and group 4 (LT ND4). A spectrophotometer was used to measure the color differences. Nonparametric tests (Kruskal-Wallis tests) were used to evaluate the color differences among the tested groups, and Mann-Whitney U tests with Bonferroni correction were used as post hoc tests. Furthermore, for each ceramic shade, the HT groups were compared to the LT groups using the Mann-Whitney U test. Significant differences were present among the tested groups of the same ceramic shade (p glass ceramic color among the BL1, A2, and C3 ceramic shades. Changing the underlying color from a lighter background to a darker background resulted in increased color differences. © 2013 by the American College of Prosthodontists.
Desrini, Sufi; Ghiffary, Hifzhan Maulana
2018-04-01
Muntingia calabura L., also known locally as Talok or Kersen, is a plant which has been widely used as traditional medicine in Indonesia. In this study, we evaluated the antibacterial activity of Muntingia calabura L. Leaves ethanolic and n-hexane extract extract on Propionibacterium acnes. Antibacterial activity was determined in the extracts using agar well diffusion method. The antibacterial activities of each extract (2 mg/mL, 8 mg/ml, 20 mg/mL 30 mg/mL, and 40 mg/mL) were tested against to Propionibacterium acnes. Zone of inhibition of ethanolic extract and n-hexane extract was measured, compared, and analyzed by using a statistical programme. The phytochemical analyses of the plants were carried out using thin chromatography layer (TLC). The average diameter zone of inhibition at the concentration of 2 mg/mL of the ethanolic extract is 9,97 mm while n-Hexane extract at the same concentration showed 0 mm. Statistical test used was non-parametric test using Kruskal Wallis test which was continued to the Mann-Whitney to see the magnitude of the difference between concentration among groups. Kruskal-Wallis test revealed a significant value 0,000. Based on the result of Post Hoc test using Mann - Whitney test, there is the statistically significant difference between each concentration of ethanolic extract and n-hexane as well as positive control group (p-value < 0,05). Both extracts have antibacterial activity on P.acne. However, ethanolic extract of Muntingia calabura L. is better in inhibiting Propionibacterium acnes growth than n-hexane extract.
Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change
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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.
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...
Schlenz, Maximiliane Amelie; Schmidt, Alexander; Rehmann, Peter; Niem, Thomas; Wöstmann, Bernd
2018-04-24
To investigate debonding of full crowns made of CAD/CAM composites, CAD/CAM technology was applied to manufacture standardized test abutments to increase the reproducibility of human teeth used in in vitro studies. A virtual test abutment and the corresponding virtual crown were designed and two STL data sets were generated. Sixty-four human third molars and CAD/CAM blocks were milled using a CNC machine. Crowns of four different composite blocks (Lava Ultimate (LU), Brilliant Crios (BC), Cerasmart (CS), Experimental (EX)) were adhesively bonded with their corresponding luting system (LU: Scotchbond Universal/RelyX Ultimate; BC: One Coat 7 Universal/DuoCem; CS: G-PremioBond/G-Cem LinkForce; EX: Experimental-Bond/Experimental-Luting-Cement). Half of the specimens were chemical-cured (CC) and the others were light-cured (LC). Afterwards, specimens were artificially aged in a chewing simulator (WL-tec, 1 million cycles, 50-500 N, 2 Hz, 37 °C). Finally, a dye penetration test was used to detect debonding. For inspection, the specimens were sliced, and penetration depth was measured with a digital microscope. Data were analyzed with the Mann-Whitney U test. No cases of total debonding were observed after cyclic loading. However, the LC specimens showed a significantly lower amount of leakage than the CC ones (p CAD/CAM technology in highly standardized test abutments for in vitro testing. For CAD/CAM composites, light curing should be performed. The success of a restoration depends on the long-term sealing ability of the luting materials, which avoids debonding along with microleakage. For CAD/CAM composites, separate light curing of the adhesive and luting composite is highly recommended.
Nagai, Taro; Takahashi, Yasuhito; Endo, Kenji; Ikegami, Ryo; Ueno, Ryuichi; Yamamoto, Kengo
2018-01-01
Gait dysfunction associated with spasticity and hyperreflexia is a primary symptom in patients with compression of cervical spinal cord. The objective of this study was to link maximum compression ratio (CR) to spatiotemporal/pedobarographic parameters. Quantitative gait analysis was performed by using a pedobarograph in 75 elderly males with a wide range of cervical compression severity. CR values were characterized on T1-weighted magnetic resonance imaging (MRI). Statistical significances in gait analysis parameters (speed, cadence, stride length, step with, and toe-out angle) were evaluated among different CR groups by the non-parametric Kruskal-Wallis test followed by the Mann-Whitney U test using Bonferroni correction. The Spearman test was performed to verify correlations between CR and gait parameters. The Kruskal-Wallis test revealed significant decline in gait speed and stride length and significant increase in toe-out angle with progression of cervical compression myelopathy. The post-hoc Mann-Whitney U test showed significant differences in these parameters between the control group (0.45test revealed that CR was significantly correlated with speed, cadence, stride length, and toe-out angle. Gait speed, stride length, and toe-out angle can serve as useful indexes for evaluating progressive gait abnormality in cervical myelopathy. Our findings suggest that CR≤0.25 is associated with significantly poorer gait performance. Nevertheless, future prospective studies are needed to determine a potential benefit from decompressive surgery in such severe compression patients. Copyright © 2017 Elsevier B.V. All rights reserved.
Sharma, Andy
2017-06-01
The purpose of this study was to showcase an advanced methodological approach to model disability and institutional entry. Both of these are important areas to investigate given the on-going aging of the United States population. By 2020, approximately 15% of the population will be 65 years and older. Many of these older adults will experience disability and require formal care. A probit analysis was employed to determine which disabilities were associated with admission into an institution (i.e. long-term care). Since this framework imposes strong distributional assumptions, misspecification leads to inconsistent estimators. To overcome such a short-coming, this analysis extended the probit framework by employing an advanced semi-nonparamertic maximum likelihood estimation utilizing Hermite polynomial expansions. Specification tests show semi-nonparametric estimation is preferred over probit. In terms of the estimates, semi-nonparametric ratios equal 42 for cognitive difficulty, 64 for independent living, and 111 for self-care disability while probit yields much smaller estimates of 19, 30, and 44, respectively. Public health professionals can use these results to better understand why certain interventions have not shown promise. Equally important, healthcare workers can use this research to evaluate which type of treatment plans may delay institutionalization and improve the quality of life for older adults. Implications for rehabilitation With on-going global aging, understanding the association between disability and institutional entry is important in devising successful rehabilitation interventions. Semi-nonparametric is preferred to probit and shows ambulatory and cognitive impairments present high risk for institutional entry (long-term care). Informal caregiving and home-based care require further examination as forms of rehabilitation/therapy for certain types of disabilities.
Examples of the Application of Nonparametric Information Geometry to Statistical Physics
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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.
Screen Wars, Star Wars, and Sequels: Nonparametric Reanalysis of Movie Profitability
W. D. Walls
2012-01-01
In this paper we use nonparametric statistical tools to quantify motion-picture profit. We quantify the unconditional distribution of profit, the distribution of profit conditional on stars and sequels, and we also model the conditional expectation of movie profits using a non- parametric data-driven regression model. The flexibility of the non-parametric approach accommodates the full range of possible relationships among the variables without prior specification of a functional form, thereb...
Froelich, Markus; Puhani, Patrick
2004-01-01
Based on a nonparametrically estimated model of labor market classifications, this paper makes suggestions for immigration policy using data from western Germany in the 1990s. It is demonstrated that nonparametric regression is feasible in higher dimensions with only a few thousand observations. In sum, labor markets able to absorb immigrants are characterized by above average age and by professional occupations. On the other hand, labor markets for young workers in service occupations are id...
Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices
Hiroyuki Kasahara; Katsumi Shimotsu
2006-01-01
In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...
Rank-based permutation approaches for non-parametric factorial designs.
Umlauft, Maria; Konietschke, Frank; Pauly, Markus
2017-11-01
Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.
Two non-parametric methods for derivation of constraints from radiotherapy dose–histogram data
International Nuclear Information System (INIS)
Ebert, M A; Kennedy, A; Joseph, D J; Gulliford, S L; Buettner, F; Foo, K; Haworth, A; Denham, J W
2014-01-01
Dose constraints based on histograms provide a convenient and widely-used method for informing and guiding radiotherapy treatment planning. Methods of derivation of such constraints are often poorly described. Two non-parametric methods for derivation of constraints are described and investigated in the context of determination of dose-specific cut-points—values of the free parameter (e.g., percentage volume of the irradiated organ) which best reflect resulting changes in complication incidence. A method based on receiver operating characteristic (ROC) analysis and one based on a maximally-selected standardized rank sum are described and compared using rectal toxicity data from a prostate radiotherapy trial. Multiple test corrections are applied using a free step-down resampling algorithm, which accounts for the large number of tests undertaken to search for optimal cut-points and the inherent correlation between dose–histogram points. Both methods provide consistent significant cut-point values, with the rank sum method displaying some sensitivity to the underlying data. The ROC method is simple to implement and can utilize a complication atlas, though an advantage of the rank sum method is the ability to incorporate all complication grades without the need for grade dichotomization. (note)
Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker
2012-08-01
Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.
The 6-min push test is reliable and predicts low fitness in spinal cord injury.
Cowan, Rachel E; Callahan, Morgan K; Nash, Mark S
2012-10-01
The objective of this study is to assess 6-min push test (6MPT) reliability, determine whether the 6MPT is sensitive to fitness differences, and assess if 6MPT distance predicts fitness level in persons with spinal cord injury (SCI) or disease. Forty individuals with SCI who could self-propel a manual wheelchair completed an incremental arm crank peak oxygen consumption assessment and two 6MPTs across 3 d (37% tetraplegia (TP), 63% paraplegia (PP), 85% men, 70% white, 63% Hispanic, mean age = 34 ± 10 yr, mean duration of injury = 13 ± 10 yr, and mean body mass index = 24 ± 5 kg.m). Intraclass correlation and Bland-Altman plots assessed 6MPT distance (m) reliability. Mann-Whitney U test compared 6MPT distance (m) of high and low fitness groups for TP and PP. The fitness status prediction was developed using N = 30 and validated in N = 10 (validation group (VG)). A nonstatistical prediction approach, below or above a threshold distance (TP = 445 m and PP = 604 m), was validated statistically by binomial logistic regression. Accuracy, sensitivity, and specificity were computed to evaluate the threshold approach. Intraclass correlation coefficients exceeded 0.90 for the whole sample and the TP/PP subsets. High fitness persons propelled farther than low fitness persons for both TP/PP (both P < 0.05). Binomial logistic regression (P < 0.008) predicted the same fitness levels in the VG as the threshold approach. In the VG, overall accuracy was 70%. Eighty-six percent of low fitness persons were correctly identified (sensitivity), and 33% of high fitness persons were correctly identified (specificity). The 6MPT may be a useful tool for SCI clinicians and researchers. 6MPT distance demonstrates excellent reliability and is sensitive to differences in fitness level. 6MPT distances less than a threshold distance may be an effective approach to identify low fitness in person with SCI.
Bundros, Joanna; Clifford, Dawn; Silliman, Kathryn; Neyman Morris, Michelle
2016-06-01
Disordered eating is prevalent among college student populations, and Orthorexia nervosa (ON) is being explored as a new type of eating disorder. There is currently no standardized ON diagnostic tool, and the majority of ON research has been conducted among European populations. The present study explored the Bratman Orthorexia Test (BOT) for ON diagnosis, and its relationship to validated tools for assessing disordered eating, body dysmorphic, and obsessive-compulsive tendencies among college students attending a western university. A convenience sample of 448 college students with a mean age of 22 years was recruited to complete an online survey that included the BOT, Eating Attitudes Test-26 (EAT-26), Body Dysmorphic Disorder Questionnaire (BDDQ), Obsessive Compulsive Inventory, Revised (OCI-R) and demographics. Spearman correlation, Mann-Whitney U, Kruskal-Wallis, chi-square, and multiple linear regressions were used for analyses. The average BOT score was 4.71, near the "health fanatic" range, with Hispanic/Latino subjects and overweight/obese students having significantly higher median BOT scores. Gender, age, and college major were not significantly associated with BOT score. Significant positive correlations were observed between total BOT and EAT-26 scores (r = .47, p < 0.01), BOT and BDDQ scores (r = .25, p < 0.01), and BOT and OCI-R scores (r = .19, p < 0.01). ON tendencies may exist among college students and Hispanic/Latino and overweight/obese students may be at increased risk. Further research is needed to determine ON risk factors among diverse student populations in order to inform prevention and treatment approaches on college campuses. Copyright © 2016 Elsevier Ltd. All rights reserved.
An adaptive distance measure for use with nonparametric models
International Nuclear Information System (INIS)
Garvey, D. R.; Hines, J. W.
2006-01-01
Distance measures perform a critical task in nonparametric, locally weighted regression. Locally weighted regression (LWR) models are a form of 'lazy learning' which construct a local model 'on the fly' by comparing a query vector to historical, exemplar vectors according to a three step process. First, the distance of the query vector to each of the exemplar vectors is calculated. Next, these distances are passed to a kernel function, which converts the distances to similarities or weights. Finally, the model output or response is calculated by performing locally weighted polynomial regression. To date, traditional distance measures, such as the Euclidean, weighted Euclidean, and L1-norm have been used as the first step in the prediction process. Since these measures do not take into consideration sensor failures and drift, they are inherently ill-suited for application to 'real world' systems. This paper describes one such LWR model, namely auto associative kernel regression (AAKR), and describes a new, Adaptive Euclidean distance measure that can be used to dynamically compensate for faulty sensor inputs. In this new distance measure, the query observations that lie outside of the training range (i.e. outside the minimum and maximum input exemplars) are dropped from the distance calculation. This allows for the distance calculation to be robust to sensor drifts and failures, in addition to providing a method for managing inputs that exceed the training range. In this paper, AAKR models using the standard and Adaptive Euclidean distance are developed and compared for the pressure system of an operating nuclear power plant. It is shown that using the standard Euclidean distance for data with failed inputs, significant errors in the AAKR predictions can result. By using the Adaptive Euclidean distance it is shown that high fidelity predictions are possible, in spite of the input failure. In fact, it is shown that with the Adaptive Euclidean distance prediction
Directory of Open Access Journals (Sweden)
Aidan G. O’Keeffe
2017-12-01
Full Text Available Abstract Background In healthcare research, outcomes with skewed probability distributions are common. Sample size calculations for such outcomes are typically based on estimates on a transformed scale (e.g. log which may sometimes be difficult to obtain. In contrast, estimates of median and variance on the untransformed scale are generally easier to pre-specify. The aim of this paper is to describe how to calculate a sample size for a two group comparison of interest based on median and untransformed variance estimates for log-normal outcome data. Methods A log-normal distribution for outcome data is assumed and a sample size calculation approach for a two-sample t-test that compares log-transformed outcome data is demonstrated where the change of interest is specified as difference in median values on the untransformed scale. A simulation study is used to compare the method with a non-parametric alternative (Mann-Whitney U test in a variety of scenarios and the method is applied to a real example in neurosurgery. Results The method attained a nominal power value in simulation studies and was favourable in comparison to a Mann-Whitney U test and a two-sample t-test of untransformed outcomes. In addition, the method can be adjusted and used in some situations where the outcome distribution is not strictly log-normal. Conclusions We recommend the use of this sample size calculation approach for outcome data that are expected to be positively skewed and where a two group comparison on a log-transformed scale is planned. An advantage of this method over usual calculations based on estimates on the log-transformed scale is that it allows clinical efficacy to be specified as a difference in medians and requires a variance estimate on the untransformed scale. Such estimates are often easier to obtain and more interpretable than those for log-transformed outcomes.
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Sai Kumar .N
2015-08-01
Full Text Available Background: Sacroiliac joint dysfunction (SIJD is a common problem that causes pain and disability. Adductor pull back exercise is widely used for treating sacroiliac joint dysfunction. No yet research has been directly examined the efficacy of adductor pull back exercise for sacroiliac joint dysfunction. The purpose of the study to find the efficacy of adductor pull back exercise on pain and functional disability for subjects with sacroiliac joint dysfunction. Methods: An experimental study design, 40 subjects with unilateral Sacroiliac joint dysfunction were randomized into two groups: study group (n=20, and control group (n=20. Subjects in study group received adductor pull back exercise along with conventional exercise and Subjects in control group received conventional exercise. The duration of treatment was given for two weeks, three times a day, total six days per week. Outcome measures such as pain was measured using Visual analog scale (VAS, and functional disability was measured using Oswestry Disability Index questionnaire (ODI before and after 2 weeks of the treatment in both the groups. Results: When means were analyzed using Independent ‘t’ test as a parametric and Mann Whitney U test as a non-parametric test, there is a statistically significant improvements in means of VAS, and ODI within the groups. When means were compared using Independent ‘t’ and Mann Whitney U test, there is a significant difference in post-means of VAS and ODI between the groups. Conclusion: The present study concludes that the 2 weeks of adductor pull back exercise along with conventional exercise found statistically and clinically significant effect on improving pain, functional disability for subjects with sacroiliac joint dysfunction. Adductors pull back exercise along with conventional exercise techniques shown to have greater percentage of improvement in improving pain and functional disability for subjects with sacroiliac joint dysfunction.
Moyano-Cuevas, J L; Sánchez-Margallo, F M; Sánchez-Peralta, L F; Pagador, J B; Enciso, S; Sánchez-González, P; Gómez-Aguilera, E J; Usón-Gargallo, J
2011-11-01
Laparoscopic surgery is commonly used in many surgical procedures but requires a learning process to develop the necessary skills. Virtual reality simulators play an essential role within the training curricula. This paper aims to determine whether training in SINERGIA VR simulator allows novice surgeons to improve their basic psychomotor laparoscopic skills. Forty-two people participated in this study, including 28 unexperience medical students and 14 expert surgeons who developed previously more than 100 laparoscopic procedures. Medical students made a pre-training test in LapMentor II; then, they trained in SINERGIA and they finally accomplished a post-training test in LapMentor II. Experts just made one trial in LapMentor II. A statistical analysis was carried out and results of pre- and post-training tests of novices were compared with Wilcoxon signed-rank test. Pre- and post-training tests of novices were also compared with results of experts with Mann-Whitney U test. Most metrics provided by LapMentor II and included in this study show significant differences when comparing pre- and post-training tests of novices. Analysis of pre-training test of novices and experts results show significant differences in all analyzed metrics for all studied tasks. On the other hand, LapMentor was not able to distinguish between experts and novices after training in SINERGIA for any metric in the camera manipulation task and for some metrics of the other tasks. Training in SINERGIA VR simulator allows improvement of basic psychomotor laparoscpic skills and transferring them to another virtual simulator. Therefore, it could be used in laparoscopic surgery training programs.
Pereira, Luiz Fernando Ferreira; Mancuzo, Eliane Viana; Rezende, Camila Farnese; Côrrea, Ricardo de Amorim
2015-01-01
To evaluate respiratory muscle strength and six-minute walk test (6MWT) variables in patients with uncontrolled severe asthma (UCSA). This was a cross-sectional study involving UCSA patients followed at a university hospital. The patients underwent 6MWT, spirometry, and measurements of respiratory muscle strength, as well as completing the Asthma Control Test (ACT). The Mann-Whitney test was used in order to analyze 6MWT variables, whereas the Kruskal-Wallis test was used to determine whether there was an association between the use of oral corticosteroids and respiratory muscle strength. We included 25 patients. Mean FEV1 was 58.8 ± 21.8% of predicted, and mean ACT score was 14.0 ± 3.9 points. No significant difference was found between the median six-minute walk distance recorded for the UCSA patients and that predicted for healthy Brazilians (512 m and 534 m, respectively; p = 0.14). During the 6MWT, there was no significant drop in SpO2. Mean MIP and MEP were normal (72.9 ± 15.2% and 67.6 ± 22.2%, respectively). Comparing the patients treated with at least four courses of oral corticosteroids per year and those treated with three or fewer, we found no significant differences in MIP (p = 0.15) or MEP (p = 0.45). Our findings suggest that UCSA patients are similar to normal subjects in terms of 6MWT variables and respiratory muscle strength. The use of oral corticosteroids has no apparent impact on respiratory muscle strength.
National survey on turnaround time of clinical biochemistry tests in 738 laboratories in China.
Zhang, Xiaoyan; Fei, Yang; Wang, Wei; Zhao, Haijian; Wang, Minqi; Chen, Bingquan; Zhou, Jie; Wang, Zhiguo
2018-02-01
This survey was initiated to estimate the current status of turnaround time (TAT) monitoring of clinical biochemistry in China, provide baseline data for establishment of quality specifications and analyze the impact factors of TAT. 738 laboratories were included. Questionnaires involved general information and data of related indicators of TAT during 1 week were provided to participating laboratories. Nine quality indicators were covered, which were medians, 90th and outlier rates of pre-examination, examination, and post-examination TAT. The 25th percentile, median, and 75th percentile of TATs were calculated as optimum, desirable, and minimum quality specifications. Percentages and sigma values were used to describe the outlier rates. Mann-Whitney and Kruskal-Wallis tests were used to identify the potential impacts of TAT. Response rate of this survey was 46.44%. More than 50% of the laboratories indicated they had set up target TATs in three time intervals and monitored TATs generally. The post-examination TAT of most laboratories was 0min, while the pre-examination and examination TAT varied. Sigma values of outlier rates for 45%~60% of laboratories were above 4, while 15%~20% of labs whose sigma values were below 3. Group comparisons suggested nurse or mechanical pipeline transportation, link laboratory information system with hospital information system, and using computer reporting instead of printing report were related to shorter TATs. Despite of the remarkable progresses of TATs in China, there was also room to improve. Laboratories should strengthen the construction of information systems, identify reasons for TAT delay to improve the service quality continuously. © 2017 Wiley Periodicals, Inc.
International Nuclear Information System (INIS)
Zhong, X.; Ichchou, M.; Saidi, A.
2010-01-01
Various parametric skewed distributions are widely used to model the time-to-failure (TTF) in the reliability analysis of mechatronic systems, where many items are unobservable due to the high cost of testing. Estimating the parameters of those distributions becomes a challenge. Previous research has failed to consider this problem due to the difficulty of dependency modeling. Recently the methodology of Bayesian networks (BNs) has greatly contributed to the reliability analysis of complex systems. In this paper, the problem of system reliability assessment (SRA) is formulated as a BN considering the parameter uncertainty. As the quantitative specification of BN, a normal distribution representing the stochastic nature of TTF distribution is learned to capture the interactions between the basic items and their output items. The approximation inference of our continuous BN model is performed by a modified version of nonparametric belief propagation (NBP) which can avoid using a junction tree that is inefficient for the mechatronic case because of the large treewidth. After reasoning, we obtain the marginal posterior density of each TTF model parameter. Other information from diverse sources and expert priors can be easily incorporated in this SRA model to achieve more accurate results. Simulation in simple and complex cases of mechatronic systems demonstrates that the posterior of the parameter network fits the data well and the uncertainty passes effectively through our BN based SRA model by using the modified NBP.
Nonparametric Second-Order Theory of Error Propagation on Motion Groups.
Wang, Yunfeng; Chirikjian, Gregory S
2008-01-01
Error propagation on the Euclidean motion group arises in a number of areas such as in dead reckoning errors in mobile robot navigation and joint errors that accumulate from the base to the distal end of kinematic chains such as manipulators and biological macromolecules. We address error propagation in rigid-body poses in a coordinate-free way. In this paper we show how errors propagated by convolution on the Euclidean motion group, SE(3), can be approximated to second order using the theory of Lie algebras and Lie groups. We then show how errors that are small (but not so small that linearization is valid) can be propagated by a recursive formula derived here. This formula takes into account errors to second-order, whereas prior efforts only considered the first-order case. Our formulation is nonparametric in the sense that it will work for probability density functions of any form (not only Gaussians). Numerical tests demonstrate the accuracy of this second-order theory in the context of a manipulator arm and a flexible needle with bevel tip.
Graziani, Rebecca; Guindani, Michele; Thall, Peter F.
2015-01-01
Summary The effect of a targeted agent on a cancer patient's clinical outcome putatively is mediated through the agent's effect on one or more early biological events. This is motivated by pre-clinical experiments with cells or animals that identify such events, represented by binary or quantitative biomarkers. When evaluating targeted agents in humans, central questions are whether the distribution of a targeted biomarker changes following treatment, the nature and magnitude of this change, and whether it is associated with clinical outcome. Major difficulties in estimating these effects are that a biomarker's distribution may be complex, vary substantially between patients, and have complicated relationships with clinical outcomes. We present a probabilistically coherent framework for modeling and estimation in this setting, including a hierarchical Bayesian nonparametric mixture model for biomarkers that we use to define a functional profile of pre-versus-post treatment biomarker distribution change. The functional is similar to the receiver operating characteristic used in diagnostic testing. The hierarchical model yields clusters of individual patient biomarker profile functionals, and we use the profile as a covariate in a regression model for clinical outcome. The methodology is illustrated by analysis of a dataset from a clinical trial in prostate cancer using imatinib to target platelet-derived growth factor, with the clinical aim to improve progression-free survival time. PMID:25319212
Lee, Soojeong; Rajan, Sreeraman; Jeon, Gwanggil; Chang, Joon-Hyuk; Dajani, Hilmi R; Groza, Voicu Z
2017-06-01
Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients. This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities. The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE. The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates. Copyright © 2015 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Antonio Canale
2017-06-01
Full Text Available msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric inference introduced by Canale and Dunson (2016. The method, based on mixtures of multiscale beta dictionary densities, overcomes the drawbacks of Pólya trees and inherits many of the advantages of Dirichlet process mixture models. The key idea is that an infinitely-deep binary tree is introduced, with a beta dictionary density assigned to each node of the tree. Using a multiscale stick-breaking characterization, stochastically decreasing weights are assigned to each node. The result is an infinite mixture model. The package msBP implements a series of basic functions to deal with this family of priors such as random densities and numbers generation, creation and manipulation of binary tree objects, and generic functions to plot and print the results. In addition, it implements the Gibbs samplers for posterior computation to perform multiscale density estimation and multiscale testing of group differences described in Canale and Dunson (2016.
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.
NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
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Richard C. Zink
2012-07-01
Full Text Available Analysis of covariance serves two important purposes in a randomized clinical trial. First, there is a reduction of variance for the treatment effect which provides more powerful statistical tests and more precise confidence intervals. Second, it provides estimates of the treatment effect which are adjusted for random imbalances of covariates between the treatment groups. The nonparametric analysis of covariance method of Koch, Tangen, Jung, and Amara (1998 defines a very general methodology using weighted least-squares to generate covariate-adjusted treatment effects with minimal assumptions. This methodology is general in its applicability to a variety of outcomes, whether continuous, binary, ordinal, incidence density or time-to-event. Further, its use has been illustrated in many clinical trial settings, such as multi-center, dose-response and non-inferiority trials.NParCov3 is a SAS/IML macro written to conduct the nonparametric randomization-based covariance analyses of Koch et al. (1998. The software can analyze a variety of outcomes and can account for stratification. Data from multiple clinical trials will be used for illustration.
Steenhaut, Kevin; Lapage, Koen; Bové, Thierry; De Hert, Stefan; Moerman, Annelies
2017-12-01
An increasing number of NIRS devices are used to provide measurements of peripheral tissue oxygen saturation (S t O 2 ). The aim of the present study is to test the hypothesis that despite technological differences between devices, similar trend values will be obtained during a vascular occlusion test. The devices compared are NIRO-200NX, which measures S t O 2 and oxyhemoglobin by spatially resolved spectroscopy and the Beer-Lambert law, respectively, and INVOS 5100C and Foresight Elite, which both measure S t O 2 with the Beer-Lambert law, enhanced with the spatial resolution technique. Forty consenting adults scheduled for CABG surgery were recruited. The respective sensors of the three NIRS devices were applied over the brachioradial muscle. Before induction of anesthesia, 3 min of ischemia were induced by inflating a blood pressure cuff at the upper arm, whereafter cuff pressure was rapidly released. Tissue oxygenation measurements included baseline, minimum and maximum values, desaturation and resaturation slopes, and rise time. Comparisons between devices were performed with the Kruskal-Wallis test with post hoc Mann-Whitney pairwise comparisons. Agreement was evaluated using Bland-Altman plots. Oxyhemoglobin measured with NIRO responded faster than the other NIRS technologies to changes in peripheral tissue oxygenation (20 vs. 27-40 s, p ≤ 0.01). When comparing INVOS with Foresight, oxygenation changes were prompter (upslope 311 [92-523]%/min vs. 114[65-199]%/min, p ≤ 0.01) and more pronounced (minimum value 36 [21-48] vs. 45 [40-51]%, p ≤ 0.01) with INVOS. Significant differences in tissue oxygen saturation measurements were observed, both within the same device as between different devices using the same measurement technology.
de Pinho, Lucinéia; Moura, Paulo Henrique Tolentino; Silveira, Marise Fagundes; de Botelho, Ana Cristina Carvalho; Caldeira, Antônio Prates
2013-07-18
In light of its epidemic proportions in developed and developing countries, obesity is considered a serious public health issue. In order to increase knowledge concerning the ability of health care professionals in caring for obese adolescents and adopt more efficient preventive and control measures, a questionnaire was developed and validated to assess non-dietitian health professionals regarding their Knowledge of Nutrition in Obese Adolescents (KNOA). The development and evaluation of a questionnaire to assess the knowledge of primary care practitioners with respect to nutrition in obese adolescents was carried out in five phases, as follows: 1) definition of study dimensions 2) development of 42 questions and preliminary evaluation of the questionnaire by a panel of experts; 3) characterization and selection of primary care practitioners (35 dietitians and 265 non-dietitians) and measurement of questionnaire criteria by contrasting the responses of dietitians and non-dietitians; 4) reliability assessment by question exclusion based on item difficulty (too easy and too difficult for non-dietitian practitioners), item discrimination, internal consistency and reproducibility index determination; and 5) scoring the completed questionnaires. Dietitians obtained higher scores than non-dietitians (Mann-Whitney U test, P validity of the questionnaire criteria. Items were discriminated by correlating the score for each item with the total score, using a minimum of 0.2 as a correlation coefficient cutoff value. Item difficulty was controlled by excluding questions answered correctly by more than 90% of the non-dietitian subjects (too easy) or by less than 10% of them (too difficult). The final questionnaire contained 26 of the original 42 questions, increasing Cronbach's α value from 0.788 to 0.807. Test-retest agreement between respondents was classified as good to very good (Kappa test, >0.60). The KNOA questionnaire developed for primary care practitioners is a
Wagner, Christina; Stock, Veronika; Merk, Susanne; Schmidlin, Patrick R; Roos, Malgorzata; Eichberger, Marlis; Stawarczyk, Bogna
2018-02-01
To investigate the retention loads of differently fabricated secondary telescopic polyetheretherketone (PEEK) crowns on cobalt-chromium primary crowns with different tapers. Cobalt-chromium primary crowns with 0°, 1°, and 2° tapers were constructed, milled, and sintered. Corresponding secondary crowns were fabricated by milling, pressing from pellets, and pressing from granules. For these nine test groups, the pull-off tests of each crown combination were performed 20 times, and the retention loads were measured (Zwick 1445, 50 mm/min). Data were analyzed using linear regression, covariance analysis, mixed models, Kruskal-Wallis, and Mann-Whitney U-test, together with the Benferroni-Holm correction. The mixed models covariance analysis reinforced stable retention load values (p = 0.162) for each single test sequence. There was no interaction between the groups and the separation cycles (p = 0.179). Milled secondary crowns with 0° showed the lowest mean retention load values compared to all tested groups (p = 0.003) followed by those pressed form pellets with 1°. Regarding the different tapers, no effect of manufacturing method on the results was observed within 1° and 2° groups (p = 0.540; p = 0.052); however, among the 0° groups, the milled ones showed significantly the lowest retention load values (p = 0.002). Among the manufacturing methods, both pressed groups showed no impact of taper on the retention load values (p > 0.324 and p > 0.123, respectively), whereas among the milled secondary crowns, the 0° taper showed significantly lower retention load values than the 1° and 2° taper (p test sequence; however, data with thermo-mechanical aging are still required. In addition, further developments in CAD/CAM manufacturing of PEEK materials for telescopic crowns are warranted, especially for 0°. © 2016 by the American College of Prosthodontists.
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Pizzimenti, G.
2004-12-01
Full Text Available Ninety percent of the olive trees are destined to oil production in the Mediterranean basin. Within a wide study of Southern Italian olive oils, with a particular reference to Sicily, several varieties of olive have been studied. The result of an analytical study carried out with two cultivars "Nocellara del Belice" and "Cerasuola" for six consecutive years is described in this paper. Parameters related to olive oil purity and shelf-life together with the composition (% of sterols and fatty acids of the single oils are shown. Data were analyzed by the univariate non-parametric Mann-Whitney test and the multivariate procedure of Factor Analysis.Un noventa por ciento de los olivos de la cuenca mediterránea son destinados a la producción de aceite. Dentro de un amplio estudio de los aceites de oliva del sur de Italia, con especial atención a Sicilia, se han estudiado distintas variedades de olivos. El resultado de un estudio analítico llevado a cabo con dos cultivares "Nocellara del Belice" y "Cerasuola" es descrito en este trabajo. Se muestran distintos parámetros relacionados con la pureza del aceite y la estabilidad junto con la composición (% esterólica y de ácidos grasos de los ácidos individuales. Los datas obtenidos se analizan tanto por métodos paramétricos univariantes (test de Mann-Whitney como por procedimientos multivariantes de análisis factorial.
Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.
Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui
2006-01-01
This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.
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.
Jacquot, Cyril; Seo, Andrew; Miller, Peter M; Lezama, Niara; Criss, Valli R; Colvin, Camilla; Luban, Naomi L C; Wong, Edward C C
2017-11-01
Directed donation is associated with a higher prevalence of donations that are positive for infectious disease markers; however, little is known about the positive rates among parental-directed, non-parental-directed, and allogeneic donations. We reviewed blood-collection records from January 1997 through December 2008, including infectious disease results, among parental, non-parental, and community donations. Infectious disease rates were compared by Mann-Whitney U test. In total, 1532 parental, 4910 non-parental, and 17,423 community donations were examined. Among parental donors, the median rate of positive infectious disease testing was 8.66% (interquartile range (IQR), 4.49%) for first-time donors and 1.26% (IQR, 5.86%) for repeat donors; among non-parental donors, the rate was 1.09% (IQR, 0.98%) for first-time donors and 0% (IQR, 0.83%) for repeat donors; and, among community donors, the rate was 2.95% (IQR, 1.50%) for first-time donors and 0.45% (IQR, 0.82%) for repeat donors. The mean rate of positive infectious disease testing for first-time parental donors was significantly higher (7.63%), whereas all repeat donors had similar rates. However, the rate of positive infectious disease testing among first-time non-parental donors was significantly lower than that in the other groups, especially for the period from 2001 through 2008. First-time non-parental and community donors had significantly higher infectious disease risk than the respective repeat donors. First-time parental donors had the highest rates of positive infectious disease testing. We suggest that first-time parental blood donation should be discouraged. Repeat community donors or first-time non-parental donors provide a safer alternative. These findings can foster better patient education, donor selection, and possibly a reduced risk of infectious disease. © 2017 AABB.
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Emily R Adams
Full Text Available BACKGROUND: The Direct Agglutination Test (DAT has a high diagnostic accuracy and remains, in some geographical areas, part of the diagnostic algorithm for Visceral Leishmaniasis (VL. However, subjective interpretation of results introduces potential for inter-reader variation. We report an assessment of inter-laboratory agreement and propose a pictorial-based approach to standardize reading of the DAT. METHODOLOGY: In preparation for a comparative evaluation of immunochromatographic diagnostics for VL, a proficiency panel of 15 well-characterized sera, DAT-antigen from a single batch and common protocol was sent to nine laboratories in Latin-America, East-Africa and Asia. Agreement (i.e., equal titre or within 1 titer with the reading by the reference laboratory was computed. Due to significant inter-laboratory disagreement on-site refresher training was provided to all technicians performing DAT. Photos of training plates were made, and end-titres agreed upon by experienced users of DAT within the Visceral-Leishmaniasis Laboratory-Network (VL-LN. RESULTS: Pre-training, concordance in DAT results with reference laboratories was only 50%, although agreement on negative sera was high (94%. After refresher training concordance increased to 84%; agreement on negative controls increased to 98%. Variance in readings significantly decreased after training from 3.3 titres to an average of 1.0 titre (two-sample Wilcoxon rank-sum (Mann-Whitney test (z = -3,624 and p = 0.0003. CONCLUSION: The most probable explanation for disagreement was subjective endpoint reading. Using pictorials as training materials may be a useful tool to reduce disparity in results and promote more standardized reading of DAT, without compromising diagnostic sensitivity.
Nonparametric Bayesian density estimation on manifolds with applications to planar shapes.
Bhattacharya, Abhishek; Dunson, David B
2010-12-01
Statistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article proposes a broad class of mixture models constructed using suitable kernels on a general compact metric space and then on the planar shape space in particular. Following a Bayesian approach with a nonparametric prior on the mixing distribution, conditions are obtained under which the Kullback-Leibler property holds, implying large support and weak posterior consistency. Gibbs sampling methods are developed for posterior computation, and the methods are applied to problems in density estimation and classification with shape-based predictors. Simulation studies show improved estimation performance relative to existing approaches.
Nonparametric Analysis of Right Censored Data with Multiple Comparisons
Shih, Hwei-Weng
1982-01-01
This report demonstrates the use of a computer program written in FORTRAN for the Burroughs B6800 computer at Utah State University to perform Breslow's (1970) generalization of the Kruskal-Wallis test for right censored data. A pairwise multiple comparison procedure using Bonferroni's inequality is also introduced and demonstrated. Comparisons are also made with a parametric F test and the original Kruskal-Wallis test. Application of these techniques to two data sets indicate that there is l...
An Evaluation of Parametric and Nonparametric Models of Fish Population Response.
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Haas, Timothy C.; Peterson, James T.; Lee, Danny C.
1999-11-01
Predicting the distribution or status of animal populations at large scales often requires the use of broad-scale information describing landforms, climate, vegetation, etc. These data, however, often consist of mixtures of continuous and categorical covariates and nonmultiplicative interactions among covariates, complicating statistical analyses. Using data from the interior Columbia River Basin, USA, we compared four methods for predicting the distribution of seven salmonid taxa using landscape information. Subwatersheds (mean size, 7800 ha) were characterized using a set of 12 covariates describing physiography, vegetation, and current land-use. The techniques included generalized logit modeling, classification trees, a nearest neighbor technique, and a modular neural network. We evaluated model performance using out-of-sample prediction accuracy via leave-one-out cross-validation and introduce a computer-intensive Monte Carlo hypothesis testing approach for examining the statistical significance of landscape covariates with the non-parametric methods. We found the modular neural network and the nearest-neighbor techniques to be the most accurate, but were difficult to summarize in ways that provided ecological insight. The modular neural network also required the most extensive computer resources for model fitting and hypothesis testing. The generalized logit models were readily interpretable, but were the least accurate, possibly due to nonlinear relationships and nonmultiplicative interactions among covariates. Substantial overlap among the statistically significant (P<0.05) covariates for each method suggested that each is capable of detecting similar relationships between responses and covariates. Consequently, we believe that employing one or more methods may provide greater biological insight without sacrificing prediction accuracy.
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Rabia Ece OMAY
2013-06-01
Full Text Available In this study, relationship between gross domestic product (GDP per capita and sulfur dioxide (SO2 and particulate matter (PM10 per capita is modeled for Turkey. Nonparametric fixed effect panel data analysis is used for the modeling. The panel data covers 12 territories, in first level of Nomenclature of Territorial Units for Statistics (NUTS, for period of 1990-2001. Modeling of the relationship between GDP and SO2 and PM10 for Turkey, the non-parametric models have given good results.
Nonparametric method for failures diagnosis in the actuating subsystem of aircraft control system
Terentev, M. N.; Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.
2018-02-01
In this paper we design a nonparametric method for failures diagnosis in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on analytical nonparametric one-step-ahead state prediction approach. This makes it possible to predict the behavior of unidentified and failure dynamic systems, to weaken the requirements to control signals, and to reduce the diagnostic time and problem complexity.
Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.
2018-03-01
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
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Jinchao Feng
2018-03-01
Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.
Fronczyk, Kassandra; Kottas, Athanasios
2014-03-01
We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature. © 2013, The International Biometric Society.
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.
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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
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Constantinescu, C C; Yoder, K K; Normandin, M D; Morris, E D [Department of Radiology, Indiana University School of Medicine, Indianapolis, IN (United States); Kareken, D A [Department of Neurology, Indiana University School of Medicine, Indianapolis, IN (United States); Bouman, C A [Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN (United States); O' Connor, S J [Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN (United States)], E-mail: emorris@iupui.edu
2008-03-07
We previously developed a model-independent technique (non-parametric ntPET) for extracting the transient changes in neurotransmitter concentration from paired (rest and activation) PET studies with a receptor ligand. To provide support for our method, we introduced three hypotheses of validation based on work by Endres and Carson (1998 J. Cereb. Blood Flow Metab. 18 1196-210) and Yoder et al (2004 J. Nucl. Med. 45 903-11), and tested them on experimental data. All three hypotheses describe relationships between the estimated free (synaptic) dopamine curves (F{sup DA}(t)) and the change in binding potential ({delta}BP). The veracity of the F{sup DA}(t) curves recovered by nonparametric ntPET is supported when the data adhere to the following hypothesized behaviors: (1) {delta}BP should decline with increasing DA peak time, (2) {delta}BP should increase as the strength of the temporal correlation between F{sup DA}(t) and the free raclopride (F{sup RAC}(t)) curve increases, (3) {delta}BP should decline linearly with the effective weighted availability of the receptor sites. We analyzed regional brain data from 8 healthy subjects who received two [{sup 11}C]raclopride scans: one at rest, and one during which unanticipated IV alcohol was administered to stimulate dopamine release. For several striatal regions, nonparametric ntPET was applied to recover F{sup DA}(t), and binding potential values were determined. Kendall rank-correlation analysis confirmed that the F{sup DA}(t) data followed the expected trends for all three validation hypotheses. Our findings lend credence to our model-independent estimates of F{sup DA}(t). Application of nonparametric ntPET may yield important insights into how alterations in timing of dopaminergic neurotransmission are involved in the pathologies of addiction and other psychiatric disorders.
Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders
DEFF Research Database (Denmark)
Nielsen, Morten Ørregaard
2009-01-01
of a particular model and is invariant to short-run dynamics. Nor does it require the choice of any smoothing parameters that change the test statistic without being reflected in the asymptotic distribution. Furthermore, a consistent estimator of the cointegration space can be obtained from the procedure....... The asymptotic distribution theory for the proposed test is non-standard but easily tabulated. Monte Carlo simulations demonstrate excellent finite sample properties, even rivaling those of well-specified parametric tests. The proposed methodology is applied to the term structure of interest rates, where...
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Ricardo Furtado
2009-02-01
Full Text Available OBJECTIVE: To develop a Brazilian version of the gesture behavior test (GBT for patients with chronic low back pain. METHODS: Translation of GBT into Portuguese was performed by a rheumatologist fluent in the language of origin (French and skilled in the validation of questionnaires. This translated version was back-translated into French by a native-speaking teacher of the language. The two translators then created a final consensual version in Portuguese. Cultural adaptation was carried out by two rheumatologists, one educated patient and the native-speaking French teacher. Thirty patients with chronic low back pain and fifteen healthcare professionals involved in the education of patients with low back pain through back schools (gold-standard were evaluated. Reproducibility was initially tested by two observers (inter-observer; the procedures were also videotaped for later evaluation by one of the observers (intra-observer. For construct validation, we compared patients' scores against the scores of the healthcare professionals. RESULTS: Modifications were made to the GBT for cultural reasons. The Spearman's correlation coefficient and the intra-class coefficient, which was employed to measure reproducibility, ranged between 0.87 and 0.99 and 0.94 to 0.99, respectively (p < 0.01. With regard to validation, the Mann-Whitney test revealed a significant difference (p < 0.01 between the averages for healthcare professionals (26.60; SD 2.79 and patients (16.30; SD 6.39. There was a positive correlation between the GBT score and the score on the Roland Morris Disability Questionnaire (r= 0.47. CONCLUSIONS: The Brazilian version of the GBT proved to be a reproducible and valid instrument. In addition, according to the questionnaire results, more disabled patients exhibited more protective gesture behavior related to low-back.
Moazami, Fariborz; Bahrampour, Ehsan; Azar, Mohammad Reza; Jahedi, Farzad; Moattari, Marzieh
2014-03-05
The importance of using technologies such as e-learning in different disciplines is discussed in the literature. Researchers have measured the effectiveness of e-learning in a number of fields.Considering the lack of research on the effectiveness of online learning in dental education particularly in Iran, the advantages of these learning methods and the positive university atmosphere regarding the use of online learning. This study, therefore, aims to compare the effects of two methods of teaching (virtual versus traditional) on student learning. This post-test only design study approached 40, fifth year dental students of Shiraz University of Medical Sciences. From this group, 35 students agreed to participate. These students were randomly allocated into two groups, experimental (virtual learning) and comparison (traditional learning). To ensure similarity between groups, we compared GPAs of all participants by the Mann-Whitney U test (P > 0.05). The experimental group received a virtual learning environment courseware package specifically designed for this study, whereas the control group received the same module structured in a traditional lecture form. The virtual learning environment consisted of online and offline materials. Two identical valid, reliable post-tests that consisted of 40 multiple choice questions (MCQs) and 4 essay questions were administered immediately (15 min) after the last session and two months later to assess for knowledge retention. Data were analyzed by SPSS version 20. A comparison of the mean knowledge score of both groups showed that virtual learning was more effective than traditional learning (effect size = 0.69). The newly designed virtual learning package is feasible and will result in more effective learning in comparison with lecture-based training. However further studies are needed to generalize the findings of this study.
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Leonardo A. Simões
2010-02-01
Full Text Available CONTEXTUALIZAÇÃO: A sarcopenia é considerada o fator mais significativo na redução da força muscular periférica e respiratória e pode ocasionar incapacidades progressivas, perda de independência e interferir na capacidade funcional dos idosos. OBJETIVOS: Caracterizar a força dos músculos respiratórios (pressão inspiratória máxima - PImax e pressão expiratória máxima - PEmax e de membros inferiores (MMII, bem como as possíveis correlações existentes com a capacidade funcional dos idosos. MÉTODOS: Sessenta e cinco idosos, com 71,7±4,9 anos; foram avaliados por dinamometria isocinética para flexores e extensores dos joelhos, manovacuometria analógica para os músculos respiratórios pelo teste de caminhada de 6 minutos para capacidade funcional. Foram utilizados os testes Mann-Whitney e t de Student para comparação entre os gêneros. As correlações foram calculadas pelo coeficiente de correlação de Pearson. Para todos os testes foi considerado pBACKGROUND: Sarcopenia is the most significant factor in the decline of peripheral and respiratory muscle strength. It can lead to progressive disability, loss of independence and impaired functional capacity. OBJECTIVES: To determine the strength of respiratory muscles (maximal inspiratory pressure - MIP and maximal expiratory pressure - MEP and lower limb muscles, and to explore the possible relationships between these variables and the functional capacity of the elderly. METHODS: Sixty-five elderly patients (71.7±4.9 years old took part in the study. Isokinetic dynamometry was used to assess the knee flexors and extensors, an analog vacuum manometer was used to assess the respiratory muscles, and the six-minute walking test was used as an outcome of functional capacity. The Mann-Whitney test and Student's t-test were used for gender comparison. The relationships were investigated using Pearson's correlation. The significance level was p<0.05. RESULTS: The lower limb and
Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression
Yoo, W.W.; Ghosal, S
2016-01-01
In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and its mixed partial derivatives. We use a random series of tensor product of B-splines with normal basis coefficients as a
Does Private Tutoring Work? The Effectiveness of Private Tutoring: A Nonparametric Bounds Analysis
Hof, Stefanie
2014-01-01
Private tutoring has become popular throughout the world. However, evidence for the effect of private tutoring on students' academic outcome is inconclusive; therefore, this paper presents an alternative framework: a nonparametric bounds method. The present examination uses, for the first time, a large representative data-set in a European setting…
A structural nonparametric reappraisal of the CO2 emissions-income relationship
Azomahou, T.T.; Goedhuys - Degelin, Micheline; Nguyen-Van, P.
Relying on a structural nonparametric estimation, we show that co2 emissions clearly increase with income at low income levels. For higher income levels, we observe a decreasing relationship, though not significant. We also find thatco2 emissions monotonically increases with energy use at a
Assessing pupil and school performance by non-parametric and parametric techniques
de Witte, K.; Thanassoulis, E.; Simpson, G.; Battisti, G.; Charlesworth-May, A.
2010-01-01
This paper discusses the use of the non-parametric free disposal hull (FDH) and the parametric multi-level model (MLM) as alternative methods for measuring pupil and school attainment where hierarchical structured data are available. Using robust FDH estimates, we show how to decompose the overall
Nonparametric Estimation of Interval Reliability for Discrete-Time Semi-Markov Systems
DEFF Research Database (Denmark)
Georgiadis, Stylianos; Limnios, Nikolaos
2016-01-01
In this article, we consider a repairable discrete-time semi-Markov system with finite state space. The measure of the interval reliability is given as the probability of the system being operational over a given finite-length time interval. A nonparametric estimator is proposed for the interval...
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...
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...
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
Analyzing cost efficient production behavior under economies of scope : A nonparametric methodology
Cherchye, L.J.H.; de Rock, B.; Vermeulen, F.M.P.
2008-01-01
In designing a production model for firms that generate multiple outputs, we take as a starting point that such multioutput production refers to economies of scope, which in turn originate from joint input use and input externalities. We provide a nonparametric characterization of cost-efficient
Analyzing Cost Efficient Production Behavior Under Economies of Scope : A Nonparametric Methodology
Cherchye, L.J.H.; de Rock, B.; Vermeulen, F.M.P.
2006-01-01
In designing a production model for firms that generate multiple outputs, we take as a starting point that such multi-output production refers to economies of scope, which in turn originate from joint input use and input externalities. We provide a nonparametric characterization of cost efficient
The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models
GROENEBOOM, PIET; JONGBLOED, GEURT; WELLNER, JON A.
2008-01-01
In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M-estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the ‘Aspect problem’ in quantum physics.
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
A non-parametric Bayesian approach to decompounding from high frequency data
Gugushvili, Shota; van der Meulen, F.H.; Spreij, Peter
2016-01-01
Given a sample from a discretely observed compound Poisson process, we consider non-parametric estimation of the density f0 of its jump sizes, as well as of its intensity λ0. We take a Bayesian approach to the problem and specify the prior on f0 as the Dirichlet location mixture of normal densities.
Mittag, Kathleen Cage
Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…
Data analysis with small samples and non-normal data nonparametrics and other strategies
Siebert, Carl F
2017-01-01
Written in everyday language for non-statisticians, this book provides all the information needed to successfully conduct nonparametric analyses. This ideal reference book provides step-by-step instructions to lead the reader through each analysis, screenshots of the software and output, and case scenarios to illustrate of all the analytic techniques.
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...
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 comparative study of non-parametric models for identification of ...
African Journals Online (AJOL)
However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...
A non-parametric hierarchical model to discover behavior dynamics from tracks
Kooij, J.F.P.; Englebienne, G.; Gavrila, D.M.
2012-01-01
We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by
Verrelst, Jochem; Rivera, Juan Pablo; Veroustraete, Frank; Muñoz-Marí, Jordi; Clevers, J.G.P.W.; Camps-Valls, Gustau; Moreno, José
2015-01-01
Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC),
Directory of Open Access Journals (Sweden)
Anderson Freitas
2009-01-01
Full Text Available OBJETIVO: Avaliar isolada e comparativamente placas do tipo sistema dinâmico do quadril (DHS de dois fabricantes nacionais, analisar estatisticamente seus resultados e demonstrar a falta de determinantes para sua fabricação. MÉTODOS: Foram realizados ensaios estáticos de flexão em cinco placas DHS do fabricante I (grupo I e em igual quantidade do mesmo modelo do fabricante II (grupo II, sendo todas fabricadas em aço inoxidável austenítico ASTM F 138, com quatro furos e angulação de 135º. Utilizou-se máquina servohidráulica MTS, modelo Test Star II®, com capacidade de carga de 10 toneladas e controle de deslocamento. Foram obtidos dados da carga aplicada (P em função do deslocamento vertical do pistão (L, cuja velocidade foi 5 mm/min. Os ensaios foram interrompidos após atingir a deflexão vertical máxima especificada pelas normas dos ensaios. RESULTADOS: Grupo I: resistência de flexão, 161,4 ± 17,2 kgf rigidez, 64,5 ± 1,8 kgf/mm, ductilidade, > 25,4 mm. Grupo II: resistência de flexão, 124,7 ± 4,4, rigidez 59,6 ± 2,3, ductilidade > 25,4 mm. Para análise estatística foi adotado o teste de Mann-Whitney e a determinação de significância foi de 5% (pOBJECTIVE: To evaluate, both individually and comparatively, dynamic hip system-type plates marketed by two local manufacturers, to statistically analyze its results and show the lack of parameters for its manufacturing. METHODS: Static tests of flexion were carried out in five DHS plates of the manufacturer I (I group I and in equal quantity of the same model of the manufacturer II (I group II, being all made in stainless austenitic ASTM F 138 steel, with four holes and a 135º angle. A servo-hydraulic MTS machine, Test Star II model, was used with a load capacity of 10 tons and dislocation control. The data were obtained from the applied load (P as a function of the vertical dislocation of the piston (L, whose speed was 5mm/min. The tests were shutdown after reaching
Konietschke, Frank; Libiger, Ondrej; Hothorn, Ludwig A
2012-01-01
Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible
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...
Touchette, Timothy M.
2013-01-01
This doctoral thesis contributes to the literature on helicopter parents, and their relation to student development theory. A secondary examination of approximately 1800 randomized results from the 2007 National Survey of Student Engagement (NSSE) was tested using the following statistical tests: Mann-Whitney Test, Wilcoxon Signed Rank Test,…
Subpixel Snow Cover Mapping from MODIS Data by Nonparametric Regression Splines
Akyurek, Z.; Kuter, S.; Weber, G. W.
2016-12-01
Spatial extent of snow cover is often considered as one of the key parameters in climatological, hydrological and ecological modeling due to its energy storage, high reflectance in the visible and NIR regions of the electromagnetic spectrum, significant heat capacity and insulating properties. A significant challenge in snow mapping by remote sensing (RS) is the trade-off between the temporal and spatial resolution of satellite imageries. In order to tackle this issue, machine learning-based subpixel snow mapping methods, like Artificial Neural Networks (ANNs), from low or moderate resolution images have been proposed. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression tool that can build flexible models for high dimensional and complex nonlinear data. Although MARS is not often employed in RS, it has various successful implementations such as estimation of vertical total electron content in ionosphere, atmospheric correction and classification of satellite images. This study is the first attempt in RS to evaluate the applicability of MARS for subpixel snow cover mapping from MODIS data. Total 16 MODIS-Landsat ETM+ image pairs taken over European Alps between March 2000 and April 2003 were used in the study. MODIS top-of-atmospheric reflectance, NDSI, NDVI and land cover classes were used as predictor variables. Cloud-covered, cloud shadow, water and bad-quality pixels were excluded from further analysis by a spatial mask. MARS models were trained and validated by using reference fractional snow cover (FSC) maps generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also developed. The mutual comparison of obtained MARS and ANN models was accomplished on independent test areas. The MARS model performed better than the ANN model with an average RMSE of 0.1288 over the independent test areas; whereas the average RMSE of the ANN model
Nonparametric reconstruction of the dark energy equation of state
Energy Technology Data Exchange (ETDEWEB)
Heitmann, Katrin [Los Alamos National Laboratory; Holsclaw, Tracy [Los Alamos National Laboratory; Alam, Ujjaini [Los Alamos National Laboratory; Habib, Salman [Los Alamos National Laboratory; Higdon, David [Los Alamos National Laboratory; Sanso, Bruno [UC SANTA CRUZ; Lee, Herbie [UC SANTA CRUZ
2009-01-01
The major aim of ongoing and upcoming cosmological surveys is to unravel the nature of dark energy. In the absence of a compelling theory to test, a natural approach is to first attempt to characterize the nature of dark energy in detail, the hope being that this will lead to clues about the underlying fundamental theory. A major target in this characterization is the determination of the dynamical properties of the dark energy equation of state w. The discovery of a time variation in w(z) could then lead to insights about the dynamical origin of dark energy. This approach requires a robust and bias-free method for reconstructing w(z) from data, which does not rely on restrictive expansion schemes or assumed functional forms for w(z). We present a new non parametric reconstruction method for the dark energy equation of state based on Gaussian Process models. This method reliably captures nontrivial behavior of w(z) and provides controlled error bounds. We demollstrate the power of the method on different sets of simulated supernova data. The GP model approach is very easily extended to include diverse cosmological probes.
Nonparametric testing for DNA copy number induced differential mRNA gene expression
van Wieringen, W.N.; van de Wiel, M.A.
2009-01-01
The central dogma of molecular biology relates DNA with mRNA. Array CGH measures DNA copy number and gene expression microarrays measure the amount of mRNA. Methods that integrate data from these two platforms may uncover meaningful biological relationships that further our understanding of cancer.
The Nonlinear Dynamic Relationship of Exchange Rates: Parametric and Nonparametric Causality testing
Bekiros, S.D.; Diks, C.
2007-01-01
The present study investigates the long-term linear and nonlinear causal linkages among six currencies, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD and USD/CAD. The prime motivation for choosing these exchange rates comes from the fact that they are the most liquid and widely traded, covering
Carroll, Raymond J.; Delaigle, Aurore; Hall, Peter
2011-01-01
In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment
A Nonparametric Multidimensional IRT Approach with Applications to Ability Estimation and Test Bias.
1988-04-01
VA 22314 800 N. Quincy Street Attn: TC Arlington, VA 22217-5000 (12 Copies) Dr. Hans Crombag Dr. Stephen Dunbar University of Leyden Lindquist...CenterEducation Research Center for Measurement Boerhaavelaan 2 University of Iowa 2334 EN Leyden Iowa City, IA 52242 The NETHERLANDS Dr. James A. Earles Mr...William Montague Naval Air Station NPRDC Code 13 Pensacola, FL 32508 San Diego, CA 92152-6800 Dr. Gary Marco Ms. Kathleen Moreno Stop 31-E Navy Personnel R
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
Skype Synchronous Interaction Effectiveness in a Quantitative Management Science Course
Strang, Kenneth David
2012-01-01
An experiment compared asynchronous versus synchronous instruction in an online quantitative course. Mann-Whitney U-tests, correlation, analysis of variance, t tests, and multivariate analysis of covariance (MANCOVA) were utilized to test the hypothesis that more high-quality online experiential learning interactions would increase grade.…
Evaluation of various filling techniques in distal canals of mandibular ...
African Journals Online (AJOL)
Evaluation of various filling techniques in distal canals of mandibular molars instrumented with different single-file nickel-titanium systems. ... Comparisons between groups were applied using Student's t-test or one-way ANOVA for normally distributed data. The Mann-Whitney U-test or Kruskal-Wallis test was used when ...
DEFF Research Database (Denmark)
Jensen, Hanne Irene; Plesner, Karin; Kvorning, Nina
2016-01-01
significantly after a course of treatment which in most cases consisted of both medical, physiotherapeutic and psychological treatment as well as health-oriented education. The chi-square test, Mann-Whitney U-test, the Kruskal-Wallis and Wilcoxon Signed-rank test were used for analyses. CONCLUSIONS: In order...
DEFF Research Database (Denmark)
van Straaten, EC; Fazekas, F; Rostrup, Egill
2006-01-01
and Mann-Whitney tests. In addition, the punctate and confluent lesion type with comparable WMH volume were compared with respect to the clinical data using Student t test and chi2 test. Direct comparison of visual ratings with volumetry was done using curve fitting. RESULTS: Visual and volumetric...
Indian Academy of Sciences (India)
Gerardo Santos López
), I580 (E2), L938 (NS2), A962 (NS2), non A1176 (NS3), non A 1647 (NS4), R2774 (NS5B), Direct sequencing, Chi-squared test, Fisher exact probability test, Mann-Whitney U test, Japan. Takahashi et al. 2001, 1b, 15 patients with HCC + ...
Hadron Energy Reconstruction for ATLAS Barrel Combined Calorimeter Using Non-Parametrical Method
Kulchitskii, Yu A
2000-01-01
Hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter in the framework of the non-parametrical method is discussed. The non-parametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to fast energy reconstruction in a first level trigger. The reconstructed mean values of the hadron energies are within \\pm1% of the true values and the fractional energy resolution is [(58\\pm 3)%{\\sqrt{GeV}}/\\sqrt{E}+(2.5\\pm0.3)%]\\bigoplus(1.7\\pm0.2) GeV/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74\\pm0.04. Results of a study of the longitudinal hadronic shower development are also presented.
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Directory of Open Access Journals (Sweden)
Xibin Zhang
2016-04-01
Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.
Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection
Kumar, Sricharan; Srivistava, Ashok N.
2012-01-01
Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J.
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
Bayesian Non-Parametric Mixtures of GARCH(1,1 Models
Directory of Open Access Journals (Sweden)
John W. Lau
2012-01-01
Full Text Available Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. Further, the number and times of regime changes are not always obvious. This article outlines a nonparametric mixture of GARCH models that is able to estimate the number and time of volatility regime changes by mixing over the Poisson-Kingman process. The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel. Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. The methodology is illustrated on the Standard and Poor's 500 financial index.
Bornkamp, Björn; Ickstadt, Katja
2009-03-01
In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose-response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose-response analysis.
Promotion time cure rate model with nonparametric form of covariate effects.
Chen, Tianlei; Du, Pang
2018-05-10
Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model. The covariate effect function is estimated by smoothing splines via the optimization of a penalized profile likelihood. Point-wise interval estimates are also derived from the Bayesian interpretation of the penalized profile likelihood. Asymptotic convergence rates are established for the proposed estimates. Simulations show excellent performance of the proposed nonparametric method, which is then applied to a melanoma study. Copyright © 2018 John Wiley & Sons, Ltd.
Filippi, Sarah; Holmes, Chris C; Nieto-Barajas, Luis E
2016-11-16
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.
Riihimäki, Jaakko; Sund, Reijo; Vehtari, Aki
2010-06-01
Effective utilisation of limited resources is a challenge for health care providers. Accurate and relevant information extracted from the length of stay distributions is useful for management purposes. Patient care episodes can be reconstructed from the comprehensive health registers, and in this paper we develop a Bayesian approach to analyse the length of care episode after a fractured hip. We model the large scale data with a flexible nonparametric multilayer perceptron network and with a parametric Weibull mixture model. To assess the performances of the models, we estimate expected utilities using predictive density as a utility measure. Since the model parameters cannot be directly compared, we focus on observables, and estimate the relevances of patient explanatory variables in predicting the length of stay. To demonstrate how the use of the nonparametric flexible model is advantageous for this complex health care data, we also study joint effects of variables in predictions, and visualise nonlinearities and interactions found in the data.
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems.
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks
Gray-Davies, Tristan; Holmes, Chris C.; Caron, François
2018-01-01
We present a novel Bayesian nonparametric regression model for covariates X and continuous response variable Y ∈ ℝ. The model is parametrized in terms of marginal distributions for Y and X and a regression function which tunes the stochastic ordering of the conditional distributions F (y|x). By adopting an approximate composite likelihood approach, we show that the resulting posterior inference can be decoupled for the separate components of the model. This procedure can scale to very large datasets and allows for the use of standard, existing, software from Bayesian nonparametric density estimation and Plackett-Luce ranking estimation to be applied. As an illustration, we show an application of our approach to a US Census dataset, with over 1,300,000 data points and more than 100 covariates. PMID:29623150
Yau, Christopher; Holmes, Chris
2011-07-01
We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. This allows us to perform full Gibbs sampling to obtain posterior distributions over parameters of interest including an explicit measure of each covariate's relevance and a distribution over the number of potential clusters present in the data. This also allows for individual cluster specific variable selection. We demonstrate improved inference on a number of canonical problems.
DEFF Research Database (Denmark)
Carrao, Hugo; Sepulcre, Guadalupe; Horion, Stéphanie Marie Anne F
2013-01-01
This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric...... and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC...... for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009...
Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric
2017-01-01
This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches
Wang, Wenshuo; Xi, Junqiang; Zhao, Ding
2017-01-01
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number...
Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality
Li, Zhanchao; Gu, Chongshi; Wu, Zhongru
2013-01-01
The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model ...
Adaptive nonparametric estimation for L\\'evy processes observed at low frequency
Kappus, Johanna
2013-01-01
This article deals with adaptive nonparametric estimation for L\\'evy processes observed at low frequency. For general linear functionals of the L\\'evy measure, we construct kernel estimators, provide upper risk bounds and derive rates of convergence under regularity assumptions. Our focus lies on the adaptive choice of the bandwidth, using model selection techniques. We face here a non-standard problem of model selection with unknown variance. A new approach towards this problem is proposed, ...
Bootstrapping the economy -- a non-parametric method of generating consistent future scenarios
Müller, Ulrich A; Bürgi, Roland; Dacorogna, Michel M
2004-01-01
The fortune and the risk of a business venture depends on the future course of the economy. There is a strong demand for economic forecasts and scenarios that can be applied to planning and modeling. While there is an ongoing debate on modeling economic scenarios, the bootstrapping (or resampling) approach presented here has several advantages. As a non-parametric method, it directly relies on past market behaviors rather than debatable assumptions on models and parameters. Simultaneous dep...
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
DEFF Research Database (Denmark)
Tan, Qihua; Thomassen, Mads; Burton, Mark
2017-01-01
the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....
Essays on parametric and nonparametric modeling and estimation with applications to energy economics
Gao, Weiyu
My dissertation research is composed of two parts: a theoretical part on semiparametric efficient estimation and an applied part in energy economics under different dynamic settings. The essays are related in terms of their applications as well as the way in which models are constructed and estimated. In the first essay, efficient estimation of the partially linear model is studied. We work out the efficient score functions and efficiency bounds under four stochastic restrictions---independence, conditional symmetry, conditional zero mean, and partially conditional zero mean. A feasible efficient estimation method for the linear part of the model is developed based on the efficient score. A battery of specification test that allows for choosing between the alternative assumptions is provided. A Monte Carlo simulation is also conducted. The second essay presents a dynamic optimization model for a stylized oilfield resembling the largest developed light oil field in Saudi Arabia, Ghawar. We use data from different sources to estimate the oil production cost function and the revenue function. We pay particular attention to the dynamic aspect of the oil production by employing petroleum-engineering software to simulate the interaction between control variables and reservoir state variables. Optimal solutions are studied under different scenarios to account for the possible changes in the exogenous variables and the uncertainty about the forecasts. The third essay examines the effect of oil price volatility on the level of innovation displayed by the U.S. economy. A measure of innovation is calculated by decomposing an output-based Malmquist index. We also construct a nonparametric measure for oil price volatility. Technical change and oil price volatility are then placed in a VAR system with oil price and a variable indicative of monetary policy. The system is estimated and analyzed for significant relationships. We find that oil price volatility displays a significant
Directory of Open Access Journals (Sweden)
Ibsen Chivatá Cárdenas
2008-05-01
Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions
Dai, Wenlin; Tong, Tiejun; Zhu, Lixing
2017-01-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Smooth semi-nonparametric (SNP) estimation of the cumulative incidence function.
Duc, Anh Nguyen; Wolbers, Marcel
2017-08-15
This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event. The time to event distributions conditional on the event type are modeled using smooth semi-nonparametric densities. One strength of this approach is that it can handle arbitrary censoring and truncation while relying on mild parametric assumptions. A stepwise forward algorithm for model estimation and adaptive selection of smooth semi-nonparametric polynomial degrees is presented, implemented in the statistical software R, evaluated in a sequence of simulation studies, and applied to data from a clinical trial in cryptococcal meningitis. The simulations demonstrate that the proposed method frequently outperforms both parametric and nonparametric alternatives. They also support the use of 'ad hoc' asymptotic inference to derive confidence intervals. An extension to regression modeling is also presented, and its potential and challenges are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Dai, Wenlin
2017-09-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Mize, T W; Sundararaj, K P; Leite, R S; Huang, Y
2015-06-01
Both gingival tissue destruction and regeneration are associated with chronic periodontitis, although the former overwhelms the latter. Studies have shown that transforming growth factor beta 1 (TGF-β1), a growth factor largely involved in tissue regeneration and remodeling, is upregulated in chronic periodontitis. However, the gingival expression of connective tissue growth factor (CTGF or CCN2), a TGF-β1-upregulated gene, in patients with periodontitis remains undetermined. Although both CTGF/CCN2 and TGF-b1 increase the production of extracellular matrix, they have many different biological functions. Therefore, it is important to delineate the impact of periodontitis on gingival CTGF/CCN2 expression. Periodontal tissue specimens were collected from seven individuals without periodontitis (group 1) and from 14 with periodontitis (group 2). The expression of CTGF and TGFβ1 mRNAs were quantified using real-time PCR. Analysis using the nonparametric Mann-Whitney U-test showed that the levels of expression of both CTGF/CCN2 and TGFβ1 mRNAs were significantly increased in individuals with periodontitis compared with individuals without periodontitis. Furthermore, analysis using a nonparametric correlation (Spearman r) test showed a positive correlation between TGFβ1 and CTGF/CCN2 mRNAs. The gingival expression levels of CTGF/CCN2 and TGFβ1 mRNAs in individuals with periodontitis are upregulated and correlated. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
DEFF Research Database (Denmark)
Gaardbo, J; Ronit, A; Hartling, H
2012-01-01
161+), and regulatory T cells (Tregs, CD4+CD25+CD127lowFoxP3+) were evaluated using flow cytometry. For statistics Kruskal-Wallis test followed by Mann-Whitney U test were used. Data are given as medians. Summary of results: LTNP had higher frequency of activated CD4+ and CD8+cells compared to VC (3...
Hypernatraemic dehydration in infants with acute gastroenteritis at ...
African Journals Online (AJOL)
[3] In this event, rapid correction of the Na with hypotonic intravenous fluids can lead .... Independent Samples t-test or the Mann-. Whitney test was used ... Seventy-six percent of the hypernatraemic cohort were exclusively formula fed with only ...
Analysis of Turkish Prospective Science Teachers' Perceptions on Technology in Education
Koksal, Mustafa Serdar; Yaman, Suleyman; Saka, Yavuz
2016-01-01
Purpose of this study was to determine and analyze Turkish pre-service science teachers' perceptions on technology in terms of learning style, computer competency level, possession of a computer, and gender. The study involved 264 Turkish pre-service science teachers. Analyses were conducted through four-way ANOVA, t-tests, Mann Whitney U test and…
Injury history, sex, and performance on the functional movement screen and Y balance test.
Chimera, Nicole J; Smith, Craig A; Warren, Meghan
2015-05-01
Research is limited regarding the effects of injury or surgery history and sex on the Functional Movement Screen (FMS) and Y Balance Test (YBT). To determine if injury or surgery history or sex affected results on the FMS and YBT. Cross-sectional study. Athletic training facilities. A total of 200 National Collegiate Athletic Association Division I female (n = 92; age = 20.0 ± 1.4 years, body mass index = 22.8 ± 3.1 kg/m(2)) and male (n = 108; age = 20.0 ± 1.5 years, body mass index = 27.0 ± 4.6 kg/m(2)) athletes were screened; 170 completed the FMS, and 190 completed the YBT. A self-reported questionnaire identified injury or surgery history and sex. The FMS assessed movement during the patterns of deep squat, hurdle step, in-line lunge, shoulder mobility, impingement-clearing test, straight-leg raise, trunk stability push-up, press-up clearing test, rotary stability, and posterior-rocking clearing test. The YBT assessed balance while participants reached in anterior, posteromedial, and posterolateral directions. The FMS composite score (CS; range, 0-21) and movement pattern score (range, 0-3), the YBT CS (% lower extremity length), and YBT anterior, posteromedial, and posterolateral asymmetry (difference between limbs in centimeters). Independent-samples t tests established differences in mean FMS CS, YBT CS, and YBT asymmetry. The Mann-Whitney U test identified differences in FMS movement patterns. We found lower overall FMS CSs for the following injuries or surgeries: hip (injured = 12.7 ± 3.1, uninjured = 14.4 ± 2.3; P = .005), elbow (injured = 12.1 ± 2.8, uninjured = 14.3 ± 2.4; P = .02), and hand (injured = 12.3 ± 2.9, uninjured = 14.3 ± 2.3; P = .006) injuries and shoulder surgery (surgery = 12.0 ± 1.0, no surgery = 14.3 ± 2.4; P lunge: P lunge: P = .01). Female athletes performed worse in FMS movement patterns for trunk (P in the lunge (P = .008), shoulder mobility (P < .001), and straight-leg raise (P < .001). Anterior asymmetry was greater
Munguía-Izquierdo, Diego; Legaz-Arrese, Alejandro
2012-11-01
To evaluate the reliability, standard error of the mean (SEM), clinical significant change, and known group validity of 2 assessments of endurance strength to low loads in patients with fibromyalgia syndrome (FS). Cross-sectional reliability and comparative study. University Pablo de Olavide, Seville, Spain. Middle-aged women with FS (n=95) and healthy women (n=64) matched for age, weight, and body mass index (BMI) were recruited for the study. Not applicable. The endurance strength to low loads tests of the upper and lower extremities and anthropometric measures (BMI) were used for the evaluations. The differences between the readings (tests 1 and 2) and the SDs of the differences, intraclass correlation coefficient (ICC) model (2,1), 95% confidence interval for the ICC, coefficient of repeatability, intrapatient SD, SEM, Wilcoxon signed-rank test, and Bland-Altman plots were used to examine reliability. A Mann-Whitney U test was used to analyze the differences in test values between the patient group and the control group. We hypothesized that patients with FS would have an endurance strength to low loads performance in lower and upper extremities at least twice as low as that of the healthy controls. Satisfactory test-retest reliability and SEMs were found for the lower extremity, dominant arm, and nondominant arm tests (ICC=.973-.979; P.05 for all). The Bland-Altman plots showed 95% limits of agreement for the lower extremity (4.7 to -4.5), dominant arm (3.8 to -4.4), and nondominant arm (3.9 to -4.1) tests. The endurance strength to low loads test scores for the patients with FS were 4-fold lower than for the controls in all performed tests (P<.001 for all). The endurance strength to low loads tests showed good reliability and known group validity and can be recommended for evaluating endurance strength to low loads in patients with FS. For individual evaluation, however, an improved score of at least 4 and 5 repetitions for the upper and lower extremities
Non-parametric order statistics method applied to uncertainty propagation in fuel rod calculations
International Nuclear Information System (INIS)
Arimescu, V.E.; Heins, L.
2001-01-01
Advances in modeling fuel rod behavior and accumulations of adequate experimental data have made possible the introduction of quantitative methods to estimate the uncertainty of predictions made with best-estimate fuel rod codes. The uncertainty range of the input variables is characterized by a truncated distribution which is typically a normal, lognormal, or uniform distribution. While the distribution for fabrication parameters is defined to cover the design or fabrication tolerances, the distribution of modeling parameters is inferred from the experimental database consisting of separate effects tests and global tests. The final step of the methodology uses a Monte Carlo type of random sampling of all relevant input variables and performs best-estimate code calculations to propagate these uncertainties in order to evaluate the uncertainty range of outputs of interest for design analysis, such as internal rod pressure and fuel centerline temperature. The statistical method underlying this Monte Carlo sampling is non-parametric order statistics, which is perfectly suited to evaluate quantiles of populations with unknown distribution. The application of this method is straightforward in the case of one single fuel rod, when a 95/95 statement is applicable: 'with a probability of 95% and confidence level of 95% the values of output of interest are below a certain value'. Therefore, the 0.95-quantile is estimated for the distribution of all possible values of one fuel rod with a statistical confidence of 95%. On the other hand, a more elaborate procedure is required if all the fuel rods in the core are being analyzed. In this case, the aim is to evaluate the following global statement: with 95% confidence level, the expected number of fuel rods which are not exceeding a certain value is all the fuel rods in the core except only a few fuel rods. In both cases, the thresholds determined by the analysis should be below the safety acceptable design limit. An indirect
Directory of Open Access Journals (Sweden)
M. Eugenia Ponce de León-Castañeda
2012-09-01
selected 88 questions of the three levels of knowledge from tests of Anatomy, Psychology, Physiology and Surgery. The same test was randomized and applied to 13 groups of second and fourth grade. The evaluation and the analysis were performed with an electronic system. The U of Mann-Whitney was applied to identify differences and percentiles with inter-quartiles rank for the dispersion. Results. 310 students of second grade and 247 of fourth grade were answered and analyses. The reliability of the test was of 0.9009 and 0.9102, respectively. Significant differences were identified (p = 0.000 in the global examination and the answers of Surgery and Psychology, considering right answers and level of knowledge and Psychology. Considering right answers and level of knowledge (memory and understanding. There were no differences in Anatomy (p = 0.527 and Physiology (p = 0.203. There median of right answers was 39 and 43, respectively. The dispersion of items in the global analysis and by subject maintained an inter-quartiles rank between 3 and 4. Conclusions. It is important to include as many items of high cognitive levels in assessments to facilitate meaningful learning.
Clinical and demographic correlates of unilateral spatial neglect ...
African Journals Online (AJOL)
Information on age, gender, stroke laterality, time after stroke and motor function assessed using modified motor assessment scale were also documented. Prevalence of USN was determined while differences in prevalence by demographic and clinical variables were analyzed using Chi-square and Mann Whitney U tests ...
Miranda Garduño, Luis Miguel; Bermúdez Rocha, Rocío; Gómez Pérez, Francisco J; Aguilar Salinas, Carlos A
2011-01-01
An ankle/arm index 51 years, cardiovascular outcomes, and amputation. With the Mann Whitney U test we found that a relationship exists between pathological and amputation iliotibial band (p < 0.05). Diabetic patients have a high prevalence of pathological ankle/arm index.
Colonisation and community structure of benthic diatoms on artificial ...
African Journals Online (AJOL)
This was undertaken using tiles as artificial substrates so that we could study how the communities developed after the flood disturbance. The diatom community structure was assessed over a 28-day period following a flood event in October 2012. The Mann Whitney test indicated that there was a statistically significant ...
2016-06-01
instruments into the root canal system, manufacturers recommend creating a glide path to reduce the risk of instrument fracture due to taper lock . This...Results The two groups had almost identical mean and standard...The groups had identical median values of 85 seconds, and there was no significant difference between the groups (Mann-Whitney U test; p=0.15; two
Heart Rate Variability and Drawing Impairment in Hypoxemic COPD
Incalzi, Raffaele Antonelli; Corsonello, Andrea; Trojano, Luigi; Pedone, Claudio; Acanfora, Domenico; Spada, Aldo; D'Addio, Gianni; Maestri, Roberto; Rengo, Franco; Rengo, Giuseppe
2009-01-01
We studied 54 patients with hypoxemic chronic obstructive pulmonary disease (COPD). The Mini Mental State Examination and the Mental Deterioration Battery were used for neuropsychological assessment. Heart rate variability (HRV) was assessed based on 24-h Holter ECG recording. Mann-Whitney test was used to compare HRV parameters of patients…
Kılıc, Hatice; Kanbay, Asiye; Karalezlı, Ayşegul; Babaoglu, Elif; Hasanoglu, H Canan; Erel, Ozcan; Ates, Can
2018-01-01
To investigate the relationship between serum values of magnesium and the parameters of the pulmonary function tests (PFT) in patients with chronic asthma. This study recruited 50 patients with chronic stable asthma and 40 healthy individuals as a control group. Data on age, sex, severity of asthma, PFT, and details of drug therapy were obtained from each group. Serum magnesium, potassium, phosphorus, calcium, and sodium levels were also measured. To evaluate differences between groups, the Student t test or Mann-Whitney U test was performed for continuous variables, and the χ2 test for categorical variables. In the asthma group, 10% (n = 9) of the patients had hypomagnesemia and 5.5% (n = 5) had hypophosphatemia. Patients with asthma were divided into two groups: the hypomagnesemic group (n = 9) and the normomagnesemic group (n = 41). Forced expiratory volume in 1 s (FEV1), FEV1%, peak expiratory flow (PEF), and PEF% were lower in the hypomagnesemic group than in the normomagnesemic group (p = 0.02). Multiple logistic regression analysis revealed a statistically significant association between hypomagnesemia and PFT in the hypomagnesemic asthmatic group. The correlations of age with FEV1, FEV1%, PEF, and PEF% were as follows: p = 0.00, r = 0.29; p = 0.00, r = 0.43; p = 0.03, r = 0.22; p = 0.00, r = 0.38; and p = 0.03, r = 0.22, respectively. The correlation of serum magnesium levels with PFT (FEV1, FEV1%, PEF, PEF%) were as follows: p = 0.001, r = 0.29; p = 0.001, r = 0.43; p = 0.03, r = 0.22; and p = 0.001, r = 0.38, respectively. The other electrolytes were within the normal range in both groups. In this study, hypomagnesemia and hypophosphatemia were found to be the most common electrolyte abnormalities in patients with chronic stable asthma. FEV1, FEV1%, PEF, and PEF% were significantly lower in asthmatic patients with hypomagnesemia compared to asthmatic patients with normomagnesemia. ©2018 The Author(s). Published by S. Karger AG, Basel.
Curceac, S.; Ternynck, C.; Ouarda, T.
2015-12-01
Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed
PERBANDINGAN KINERJA KEUANGAN PEMERINTAH KABUPATEN/KOTA DI PROVINSI JAWA BARAT TAHUN 2009-2013
Directory of Open Access Journals (Sweden)
Jatnika Dwi Asri
2016-08-01
Full Text Available Abstract. This study aims to analyze the comparison of financial performance as well as analyze whether there are differences in financial performance between districts/cities in West Java Province during 2009-2013. The research used the descriptive method with quantitative data analysis technique of financial ratios as well as T test and Mann-Whitney test. The results showed that city government has a higher level of independence ratio than the district. The district effectiveness ratio is very effective with an average of 120.06%. T and Mann-Whitney test results showed no difference between effectiveness ratio, while independence ratio showed the difference. Keyword: Financial performance; Effectiveness Ratio; Independence Ratio Abstrak.Penelitian ini bertujuan untuk menganalisis perbandingan kinerja keuangan serta menganalisis apakah terdapat perbedaan kinerja keuangan antara kabupaten/kota di Provinsi Jawa Barat selama 2009-2013. Penelitian menggunakan metode deskriptif dengan teknik analisis data kuantitatif rasio keuangan serta uji T dan uji Mann-Whitney. Hasil penelitian menunjukkan pemerintah kota mempunyai tingkat rasio kemandirian lebih tinggi dibandingkan kabupaten. Rasio efektivitas kabupaten/kota sangat efektif dengan rata-rata 120.06%. Hasil uji T dan Mann-Whitney menunjukkan tidak terdapat perbedaan antara rasio efektivitas, sedangkan rasio kemandirian menunjukkan perbedaan. Kata Kunci: Kinerja Keuangan; Rasio Efektivitas; Rasio Kemandirian
Nonparametric bootstrap analysis with applications to demographic effects in demand functions.
Gozalo, P L
1997-12-01
"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt
Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.
2018-02-01
In this paper we design a nonparametric method for failures detection and localization in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on algebraic solvability conditions for the aircraft model identification problem. This makes it possible to significantly increase the efficiency of detection and localization problem solution by completely eliminating errors, associated with aircraft model uncertainties.
Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation
DEFF Research Database (Denmark)
Katebi, S.D.; Blanke, M.; Katebi, M.R.
This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second...... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...
Energy Technology Data Exchange (ETDEWEB)
Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)
Bhattacharya, Abhishek; Dunson, David B
2012-08-01
This article considers a broad class of kernel mixture density models on compact metric spaces and manifolds. Following a Bayesian approach with a nonparametric prior on the location mixing distribution, sufficient conditions are obtained on the kernel, prior and the underlying space for strong posterior consistency at any continuous density. The prior is also allowed to depend on the sample size n and sufficient conditions are obtained for weak and strong consistency. These conditions are verified on compact Euclidean spaces using multivariate Gaussian kernels, on the hypersphere using a von Mises-Fisher kernel and on the planar shape space using complex Watson kernels.
International Nuclear Information System (INIS)
Peterson, James R.; Haas, Timothy C.; Lee, Danny C.
2000-01-01
Natural resource professionals are increasingly required to develop rigorous statistical models that relate environmental data to categorical responses data. Recent advances in the statistical and computing sciences have led to the development of sophisticated methods for parametric and nonparametric analysis of data with categorical responses. The statistical software package CATDAT was designed to make some of these relatively new and powerful techniques available to scientists. The CATDAT statistical package includes 4 analytical techniques: generalized logit modeling; binary classification tree; extended K-nearest neighbor classification; and modular neural network
Non-parametric system identification from non-linear stochastic response
DEFF Research Database (Denmark)
Rüdinger, Finn; Krenk, Steen
2001-01-01
An estimation method is proposed for identification of non-linear stiffness and damping of single-degree-of-freedom systems under stationary white noise excitation. Non-parametric estimates of the stiffness and damping along with an estimate of the white noise intensity are obtained by suitable...... of the energy at mean-level crossings, which yields the damping relative to white noise intensity. Finally, an estimate of the noise intensity is extracted by estimating the absolute damping from the autocovariance functions of a set of modified phase plane variables at different energy levels. The method...
Directory of Open Access Journals (Sweden)
Vangelis Sakkalis
2008-01-01
Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.
Cliff´s Delta Calculator: A non-parametric effect size program for two groups of observations
Directory of Open Access Journals (Sweden)
Guillermo Macbeth
2011-05-01
Full Text Available The Cliff´s Delta statistic is an effect size measure that quantifies the amount of difference between two non-parametric variables beyond p-values interpretation. This measure can be understood as a useful complementary analysis for the corresponding hypothesis testing. During the last two decades the use of effect size measures has been strongly encouraged by methodologists and leading institutions of behavioral sciences. The aim of this contribution is to introduce the Cliff´s Delta Calculator software that performs such analysis and offers some interpretation tips. Differences and similarities with the parametric case are analysed and illustrated. The implementation of this free program is fully described and compared with other calculators. Alternative algorithmic approaches are mathematically analysed and a basic linear algebra proof of its equivalence is formally presented. Two worked examples in cognitive psychology are commented. A visual interpretation of Cliff´s Delta is suggested. Availability, installation and applications of the program are presented and discussed.
Teste de esforco cardiopulmonar na insuficiencia cardiaca de fracao de ejecao normal
Directory of Open Access Journals (Sweden)
Jose Antonio Caldas Teixeira
2014-01-01
Full Text Available INTRODUÇÃO: O teste de esforço cardiopulmonar (TECP fornece dados que orientam tratamento, prognóstico e tomadas de decisões. Entretanto, seu uso na insuficiência cardíaca de fração de ejeção normal (ICFEN ainda não está bem esclarecido, em especial considerando novas variáveis que vêm despontando. OBJETIVOS: Comparar o comportamento das principais variáveis diagnósticas e prognósticas do TECP entre dois grupos: pacientes com insuficiência cardíaca de fração de ejeção reduzida (ICFER e pacientes com ICFEN. MÉTODOS: Foram avaliados 36 pacientes com insuficiência cardíaca em classe funcional II-III da NYAH: 20 com ICFEN e 16 com ICFER do ambulatório de insuficiência cardíaca do Hospital Universitário Antônio Pedro (UFF. Os pacientes do Grupo ICFER selecionados foram os com FE < 35% e os do grupo ICFEN seguiram os critérios diagnósticos da Sociedade Europeia de Cardiologia de 2007. Realizou-se TECP, em esteira com protocolo de rampa, com analisador de gases VO2000. Foram aplicados teste t de Student, Mann-Whitney, teste de Fisher, modelo linear generalizado e de Cochran-Mantel-Haenszel para as análises estatísticas. RESULTADOS: O grupo ICFEN apresentou níveis mais elevados da pressão arterial em repouso, na resposta ao esforço, na potência circulatória e ventilatória, além de um maior tempo de recuperação da cinética do consumo de oxigênio. Não houve diferença em relação a outras variáveis prognósticas do TECP para o grupo ICFER. CONCLUSÕES: A pressão arterial de repouso e em esforço, a potência circulatória e ventilatória e a cinética de recuperação do VO2 (T1/2 foram as variáveis que apresentaram maior valor discriminativo entre os grupos pelo TECP.
Directory of Open Access Journals (Sweden)
A.M. Rojas-Serey
2009-06-01
Full Text Available Objetivo. Conocer la orientación empática de los alumnos de la carrera de kinesiología de dos escuelas de la región metropolitana. Sujetos y métodos. Este trabajo corresponde a una investigación analítica de corte transversal realizada entre los meses de marzo y noviembre del año 2006. Participaron 274 alumnos de un universo de 351 correspondientes a los niveles I, III y V de la carrera de kinesiología de la Universidad de Chile y la Universidad Mayor. Se aplicó la escala de empatía médica de Jefferson (EEMJ. Para el análisis de los datos, se utilizó la prueba U no paramétrica de Wilcoxon-Mann-Whitney y la prueba no paramétrica de Kruskal-Wallis. Resultados. Se obtuvieron mayores puntuaciones en la EEMJ con significación estadística en el tercer y quinto nivel de la carrera con relación al primero (p Aim. To know the empathetic orientation of the physical therapist's students in two schools of the metropolitan region. Subjects and methods. This work is an analytical cross-sectional research, carried out between the months on March and November, 2006. 274 out of 351 students participated, who belonged to the levels I, III and V of the Universidad de Chile and Universidad Mayor Physical Therapy Programs. The Jefferson Scale of Physician Empathy (JSPE was applied. For data analysis, the non-parametrical Wilcoxon-Mann-Whitney test and the non-parametrical Kruskal-Wallis test were used. Results. Higher scores in the JSPE with statistical significance were obtained in the third and fifth level of the program compared with the first one (p < 0.05. No significant differences were found in the scores related to gender. Conclusions. There exists higher scores obtained in the JSPE in students who are in more advanced levels of the Physical Therapy Program, being this difference significant in both universities. The scores obtained in the JSPE do not have statistical significance in relation to the variable gender in both universities.
Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach
International Nuclear Information System (INIS)
Wang, H.; Ang, B.W.; Wang, Q.W.; Zhou, P.
2017-01-01
Evaluating economy-wide energy performance is an integral part of assessing the effectiveness of a country's energy efficiency policy. Non-parametric frontier approach has been widely used by researchers for such a purpose. This paper proposes an extended non-parametric frontier approach to studying economy-wide energy efficiency and productivity performances by accounting for sectoral heterogeneity. Relevant techniques in index number theory are incorporated to quantify the driving forces behind changes in the economy-wide energy productivity index. The proposed approach facilitates flexible modelling of different sectors' production processes, and helps to examine sectors' impact on the aggregate energy performance. A case study of China's economy-wide energy efficiency and productivity performances in its 11th five-year plan period (2006–2010) is presented. It is found that sectoral heterogeneities in terms of energy performance are significant in China. Meanwhile, China's economy-wide energy productivity increased slightly during the study period, mainly driven by the technical efficiency improvement. A number of other findings have also been reported. - Highlights: • We model economy-wide energy performance by considering sectoral heterogeneity. • The proposed approach can identify sectors' impact on the aggregate energy performance. • Obvious sectoral heterogeneities are identified in evaluating China's energy performance.
Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance
Bhattacharjee, A.; Sutradhar, A.
2012-12-01
Modern close loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a frequency domain nonparametric identification of the nonlinear glucose-insulin process in an insulin dependent diabetes mellitus patient that captures the process dynamics in presence of uncertainties and parameter variations. An online frequency domain kernel estimation method has been proposed that uses the input-output data from the 19th order first principle model of the patient in intravenous route. Volterra equations up to second order kernels with extended input vector for a Hammerstein model are solved online by adaptive recursive least square (ARLS) algorithm. The frequency domain kernels are estimated using the harmonic excitation input data sequence from the virtual patient model. A short filter memory length of M = 2 was found sufficient to yield acceptable accuracy with lesser computation time. The nonparametric models are useful for closed loop control, where the frequency domain kernels can be directly used as the transfer function. The validation results show good fit both in frequency and time domain responses with nominal patient as well as with parameter variations.
Ryu, Duchwan
2010-09-28
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.
MEASURING DARK MATTER PROFILES NON-PARAMETRICALLY IN DWARF SPHEROIDALS: AN APPLICATION TO DRACO
International Nuclear Information System (INIS)
Jardel, John R.; Gebhardt, Karl; Fabricius, Maximilian H.; Williams, Michael J.; Drory, Niv
2013-01-01
We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 ≤ r ≤ 700 pc. The profile for r ≥ 20 pc is well fit by a power law with slope α = –1.0 ± 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.
Non-parametric transformation for data correlation and integration: From theory to practice
Energy Technology Data Exchange (ETDEWEB)
Datta-Gupta, A.; Xue, Guoping; Lee, Sang Heon [Texas A& M Univ., College Station, TX (United States)
1997-08-01
The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require a priori functional relationship between response and predictor variables which is the case with traditional multiple regression. The transformations are very general, computationally efficient and can easily handle mixed data types for example, continuous variables such as porosity, permeability and categorical variables such as rock type, lithofacies. The power of the non-parametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we utilize these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its fullest potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus, greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes.
Directory of Open Access Journals (Sweden)
Urbi Garay
2016-03-01
Full Text Available We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return and betas (to a choice set of explanatory factors in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers.
Park, Taeyoung; Jeong, Jong-Hyeon; Lee, Jae Won
2012-08-15
There is often an interest in estimating a residual life function as a summary measure of survival data. For ease in presentation of the potential therapeutic effect of a new drug, investigators may summarize survival data in terms of the remaining life years of patients. Under heavy right censoring, however, some reasonably high quantiles (e.g., median) of a residual lifetime distribution cannot be always estimated via a popular nonparametric approach on the basis of the Kaplan-Meier estimator. To overcome the difficulties in dealing with heavily censored survival data, this paper develops a Bayesian nonparametric approach that takes advantage of a fully model-based but highly flexible probabilistic framework. We use a Dirichlet process mixture of Weibull distributions to avoid strong parametric assumptions on the unknown failure time distribution, making it possible to estimate any quantile residual life function under heavy censoring. Posterior computation through Markov chain Monte Carlo is straightforward and efficient because of conjugacy properties and partial collapse. We illustrate the proposed methods by using both simulated data and heavily censored survival data from a recent breast cancer clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project. Copyright © 2012 John Wiley & Sons, Ltd.
A semi-nonparametric mixture model for selecting functionally consistent proteins.
Yu, Lianbo; Doerge, Rw
2010-09-28
High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein.
Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.
Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.
Akhmadaliev, S Z; Ambrosini, G; Amorim, A; Anderson, K; Andrieux, M L; Aubert, Bernard; Augé, E; Badaud, F; Baisin, L; Barreiro, F; Battistoni, G; Bazan, A; Bazizi, K; Belymam, A; Benchekroun, D; Berglund, S R; Berset, J C; Blanchot, G; Bogush, A A; Bohm, C; Boldea, V; Bonivento, W; Bosman, M; Bouhemaid, N; Breton, D; Brette, P; Bromberg, C; Budagov, Yu A; Burdin, S V; Calôba, L P; Camarena, F; Camin, D V; Canton, B; Caprini, M; Carvalho, J; Casado, M P; Castillo, M V; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Chadelas, R; Chalifour, M; Chekhtman, A; Chevalley, J L; Chirikov-Zorin, I E; Chlachidze, G; Citterio, M; Cleland, W E; Clément, C; Cobal, M; Cogswell, F; Colas, Jacques; Collot, J; Cologna, S; Constantinescu, S; Costa, G; Costanzo, D; Crouau, M; Daudon, F; David, J; David, M; Davidek, T; Dawson, J; De, K; de La Taille, C; Del Peso, J; Del Prete, T; de Saintignon, P; Di Girolamo, B; Dinkespiler, B; Dita, S; Dodd, J; Dolejsi, J; Dolezal, Z; Downing, R; Dugne, J J; Dzahini, D; Efthymiopoulos, I; Errede, D; Errede, S; Evans, H; Eynard, G; Fassi, F; Fassnacht, P; Ferrari, A; Ferrer, A; Flaminio, Vincenzo; Fournier, D; Fumagalli, G; Gallas, E; Gaspar, M; Giakoumopoulou, V; Gianotti, F; Gildemeister, O; Giokaris, N; Glagolev, V; Glebov, V Yu; Gomes, A; González, V; González de la Hoz, S; Grabskii, V; Graugès-Pous, E; Grenier, P; Hakopian, H H; Haney, M; Hébrard, C; Henriques, A; Hervás, L; Higón, E; Holmgren, Sven Olof; Hostachy, J Y; Hoummada, A; Huston, J; Imbault, D; Ivanyushenkov, Yu M; Jézéquel, S; Johansson, E K; Jon-And, K; Jones, R; Juste, A; Kakurin, S; Karyukhin, A N; Khokhlov, Yu A; Khubua, J I; Klioukhine, V I; Kolachev, G M; Kopikov, S V; Kostrikov, M E; Kozlov, V; Krivkova, P; Kukhtin, V V; Kulagin, M; Kulchitskii, Yu A; Kuzmin, M V; Labarga, L; Laborie, G; Lacour, D; Laforge, B; Lami, S; Lapin, V; Le Dortz, O; Lefebvre, M; Le Flour, T; Leitner, R; Leltchouk, M; Li, J; Liablin, M V; Linossier, O; Lissauer, D; Lobkowicz, F; Lokajícek, M; Lomakin, Yu F; López-Amengual, J M; Lund-Jensen, B; Maio, A; Makowiecki, D S; Malyukov, S N; Mandelli, L; Mansoulié, B; Mapelli, Livio P; Marin, C P; Marrocchesi, P S; Marroquim, F; Martin, P; Maslennikov, A L; Massol, N; Mataix, L; Mazzanti, M; Mazzoni, E; Merritt, F S; Michel, B; Miller, R; Minashvili, I A; Miralles, L; Mnatzakanian, E A; Monnier, E; Montarou, G; Mornacchi, Giuseppe; Moynot, M; Muanza, G S; Nayman, P; Némécek, S; Nessi, Marzio; Nicoleau, S; Niculescu, M; Noppe, J M; Onofre, A; Pallin, D; Pantea, D; Paoletti, R; Park, I C; Parrour, G; Parsons, J; Pereira, A; Perini, L; Perlas, J A; Perrodo, P; Pilcher, J E; Pinhão, J; Plothow-Besch, Hartmute; Poggioli, Luc; Poirot, S; Price, L; Protopopov, Yu; Proudfoot, J; Puzo, P; Radeka, V; Rahm, David Charles; Reinmuth, G; Renzoni, G; Rescia, S; Resconi, S; Richards, R; Richer, J P; Roda, C; Rodier, S; Roldán, J; Romance, J B; Romanov, V; Romero, P; Rossel, F; Rusakovitch, N A; Sala, P; Sanchis, E; Sanders, H; Santoni, C; Santos, J; Sauvage, D; Sauvage, G; Sawyer, L; Says, L P; Schaffer, A C; Schwemling, P; Schwindling, J; Seguin-Moreau, N; Seidl, W; Seixas, J M; Selldén, B; Seman, M; Semenov, A; Serin, L; Shaldaev, E; Shochet, M J; Sidorov, V; Silva, J; Simaitis, V J; Simion, S; Sissakian, A N; Snopkov, R; Söderqvist, J; Solodkov, A A; Soloviev, A; Soloviev, I V; Sonderegger, P; Soustruznik, K; Spanó, F; Spiwoks, R; Stanek, R; Starchenko, E A; Stavina, P; Stephens, R; Suk, M; Surkov, A; Sykora, I; Takai, H; Tang, F; Tardell, S; Tartarelli, F; Tas, P; Teiger, J; Thaler, J; Thion, J; Tikhonov, Yu A; Tisserant, S; Tokar, S; Topilin, N D; Trka, Z; Turcotte, M; Valkár, S; Varanda, M J; Vartapetian, A H; Vazeille, F; Vichou, I; Vinogradov, V; Vorozhtsov, S B; Vuillemin, V; White, A; Wielers, M; Wingerter-Seez, I; Wolters, H; Yamdagni, N; Yosef, C; Zaitsev, A; Zitoun, R; Zolnierowski, Y
2002-01-01
This paper discusses hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter (consisting of a lead-liquid argon electromagnetic part and an iron-scintillator hadronic part) in the framework of the nonparametrical method. The nonparametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to an easy use in a first level trigger. The reconstructed mean values of the hadron energies are within +or-1% of the true values and the fractional energy resolution is [(58+or-3)%/ square root E+(2.5+or-0.3)%](+)(1.7+or-0.2)/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74+or-0.04 and agrees with the prediction that e/h >1.66 for this electromagnetic calorimeter. Results of a study of the longitudinal hadronic shower development are also presented. The data have been taken in the H8 beam...
Impulse response identification with deterministic inputs using non-parametric methods
International Nuclear Information System (INIS)
Bhargava, U.K.; Kashyap, R.L.; Goodman, D.M.
1985-01-01
This paper addresses the problem of impulse response identification using non-parametric methods. Although the techniques developed herein apply to the truncated, untruncated, and the circulant models, we focus on the truncated model which is useful in certain applications. Two methods of impulse response identification will be presented. The first is based on the minimization of the C/sub L/ Statistic, which is an estimate of the mean-square prediction error; the second is a Bayesian approach. For both of these methods, we consider the effects of using both the identity matrix and the Laplacian matrix as weights on the energy in the impulse response. In addition, we present a method for estimating the effective length of the impulse response. Estimating the length is particularly important in the truncated case. Finally, we develop a method for estimating the noise variance at the output. Often, prior information on the noise variance is not available, and a good estimate is crucial to the success of estimating the impulse response with a nonparametric technique
Macmillan, N A; Creelman, C D
1996-06-01
Can accuracy and response bias in two-stimulus, two-response recognition or detection experiments be measured nonparametrically? Pollack and Norman (1964) answered this question affirmatively for sensitivity, Hodos (1970) for bias: Both proposed measures based on triangular areas in receiver-operating characteristic space. Their papers, and especially a paper by Grier (1971) that provided computing formulas for the measures, continue to be heavily cited in a wide range of content areas. In our sample of articles, most authors described triangle-based measures as making fewer assumptions than measures associated with detection theory. However, we show that statistics based on products or ratios of right triangle areas, including a recently proposed bias index and a not-yetproposed but apparently plausible sensitivity index, are consistent with a decision process based on logistic distributions. Even the Pollack and Norman measure, which is based on non-right triangles, is approximately logistic for low values of sensitivity. Simple geometric models for sensitivity and bias are not nonparametric, even if their implications are not acknowledged in the defining publications.
Directory of Open Access Journals (Sweden)
Navid Haghighat
2017-12-01
Full Text Available This paper focuses on evaluating airline service quality from the perspective of passengers' view. Until now a lot of researches has been performed in airline service quality evaluation in the world but a little research has been conducted in Iran, yet. In this study, a framework for measuring airline service quality in Iran is proposed. After reviewing airline service quality criteria, SSQAI model was selected because of its comprehensiveness in covering airline service quality dimensions. SSQAI questionnaire items were redesigned to adopt with Iranian airlines requirements and environmental circumstances in the Iran's economic and cultural context. This study includes fuzzy decision-making theory, considering the possible fuzzy subjective judgment of the evaluators during airline service quality evaluation. Fuzzy TOPSIS have been applied for ranking airlines service quality performances. Three major Iranian airlines which have the most passenger transfer volumes in domestic and foreign flights were chosen for evaluation in this research. Results demonstrated Mahan airline has got the best service quality performance rank in gaining passengers' satisfaction with delivery of high-quality services to its passengers, among the three major Iranian airlines. IranAir and Aseman airlines placed in the second and third rank, respectively, according to passenger's evaluation. Statistical analysis has been used in analyzing passenger responses. Due to the abnormality of data, Non-parametric tests were applied. To demonstrate airline ranks in every criterion separately, Friedman test was performed. Variance analysis and Tukey test were applied to study the influence of increasing in age and educational level of passengers on degree of their satisfaction from airline's service quality. Results showed that age has no significant relation to passenger satisfaction of airlines, however, increasing in educational level demonstrated a negative impact on
Directory of Open Access Journals (Sweden)
Valtr Ludvík
2016-12-01
Full Text Available Background: The Movement Assessment Battery for Children - 2nd edition (MABC-2 is used for the assessment of motor proficiency and identification of motor impairments in 3-16 year old children. Although there are some gender differences in the motor development of children, in the MABC-2 test the same tasks and norms are used for both genders. Objective: The aim of the study was to determine gender differences in performance of motor tasks involved in the MABC-2 test in adolescents aged 15 to 16. Methods: Participants (N = 121, 50 boys and 71 girls, mean age 16.0 ± 0.5 years randomly recruited from schools were assessed using the MABC-2 test. The Mann-Whitney U test and effect size r were used to analyse gender differences in performance outcome in the particular motor tasks of the MABC-2 test. Results: As compared to the boys, the girls achieved a significantly shorter time of completion of the unimanual coordination task executed with their preferred hand (p < .001, r = .33 and significantly fewer errors in the graphomotor task (p = .001, r = .29. On the other hand, the boys achieved significantly better results than the girls in the aiming and catching tasks (p ≤ .030, r = .20-.33. Performance in the dynamic balance tasks was not significantly different between genders. The girls demonstrated a significantly longer duration of static balance in one-leg standing as compared to the boys (p = .011, r = .23. For the motor tasks some statistical differences were found, however the effect size of the gender on performance was small or medium. Conclusions: The findings of the study suggest that gender could be a significant factor of performance in the motor tasks associated with object control such as aiming and catching. Other domains, such as manual dexterity and balance, seem to be influenced by gender to a small extent.
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Niyousha Mortaza
Full Text Available Use of functional knee braces has been suggested to provide protection and to improve kinetic performance of the knee in Anterior cruciate ligament(ACL-injured patients. However, many athletes might refrain from wearing the braces because of the fear of performance hindrance in the playing field. The aim of this study was to examine the effect of three functional knee brace/sleeves upon the isokinetic and functional performance of ACL-deficient and healthy subjects. Six anterior cruciate ligament deficient (29.0 ± 5.3 yrs., 175.2 ± 5.4 cm, and 73.0 ± 10.0 kg and six healthy male subjects (27.2 ± 3.7 yrs., 176.4 ± 6.4 cm, and 70.3 ± 6.9 kg were selected. The effect of a custom-made functional knee brace, and two neoprene knee sleeves, one with four metal supports and one without support were examined via the use of isokinetic and functional tests in four sets (non-braced,wearing functional knee brace,and wearing the sleeves. Cross-over hop and single leg vertical jump test were performed and jump height, and hop distance were recorded. Peak torque to body weight ratio and average power in two isokinetic velocities(60°.s(-1,180°.s(-1 were recorded and the brace/sleeves effect was calculated as the changes in peak torque measured in the brace/sleeves conditions, expressed as a percentage of peak torque measured in non-braced condition. Frequency content of the isokinetic torque-time curves was also analyzed. Wilcoxon signed rank test was used to compare the measured values in four test conditions within each control and ACL-deficient group,and Mann-Whitney U test was used for the comparison between the two groups. No significant differences in peak torque, average power, torque-time curve frequency content, vertical-jump and hop measurements were found within the experimental and the non-braced conditions (p>0.05. Although the examined functional knee brace/sleeves had no significant effect on the knee muscle performance, there have been
Information and communication strategies for increasing information literacy in students
Directory of Open Access Journals (Sweden)
Haddadian, F
2013-04-01
Full Text Available The study reviews the effects of Information and Communication Technology (ICT on learning and information literacy of students. Experimental method involving experimental and control groups was used. Pre-test and post-test were run to investigate the effectiveness of ICT. The statistical population of the research consisted of all male third year students of middle school (school year 89-90 in the city of Arak. After pre-certification testing and applying random cluster sampling, 64 students were selected and placed into two experimental and control groups. Data collection instruments were Educational Improvement Test and Standardized Information Literacy Questionnaire. Collected data were analysed using analysis of covariance method, t-test, and non-parametric Mann-Whitney U test. Findings showed that general hypotheses of the research were true: ICT has a significant effect on learning rate of students, and there is a significant difference between the experimental group and control group regarding information literacy and its features. Based on the results of this study, we recommend educational authorities to apply ICT in educational canters in order to improve students’ learning and educational quality.
Effect of Luting Cements On the Bond Strength to Turkom-Cera All-Ceramic Material
Al–Makramani, Bandar M. A.; Razak, Abdul A. A.; Abu–Hassan, Mohamed I.; Al–Sanabani, Fuad A.; Albakri, Fahad M.
2018-01-01
BACKGROUND: The selection of the appropriate luting cement is a key factor for achieving a strong bond between prepared teeth and dental restorations. AIM: To evaluate the shear bond strength of Zinc phosphate cement Elite, glass ionomer cement Fuji I, resin-modified glass ionomer cement Fuji Plus and resin luting cement Panavia-F to Turkom-Cera all-ceramic material. MATERIALS AND METHODS: Turkom-Cera was used to form discs 10mm in diameter and 3 mm in thickness (n = 40). The ceramic discs were wet ground, air - particle abraded with 50 - μm aluminium oxide particles and randomly divided into four groups (n = 10). The luting cement was bonded to Turkom-Cera discs as per manufacturer instructions. The shear bond strengths were determined using the universal testing machine at a crosshead speed of 0.5 mm/min. The data were analysed using the tests One Way ANOVA, the nonparametric Kruskal - Wallis test and Mann - Whitney Post hoc test. RESULTS: The shear bond strength of the Elite, Fuji I, Fuji Plus and Panavia F groups were: 0.92 ± 0.42, 2.04 ± 0.78, 4.37 ± 1.18, and 16.42 ± 3.38 MPa, respectively. There was the statistically significant difference between the four luting cement tested (p < 0.05). CONCLUSION: the phosphate-containing resin cement Panavia-F exhibited shear bond strength value significantly higher than all materials tested. PMID:29610618
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Fahni Haris
2017-11-01
Full Text Available Various solutions have been recommended for cleanse the wound, however normal saline is favored. Aqueous guava leaf extracts have material that known for antibacterial in the diabetic wound care especially for cleansing. Guava leaf available in Indonesia, but there is unresolved debate about its use. This study use quasi-experimental with pre-test post-test design. Sample in this study consist 19 outpatients who had diabetic chronic wounds care in clinic Kitamura, Pontianak. Analysis of quantitative data was tested with non-parametric analysis, Wilcoxon test and Mann Whitney test to determine the effect of aqueous guava leaves extract in reducing bacterial. Results sowed that he number of bacteria colonies after cleansing the wound using aqueous guava leaves extract was decreased. P-value on first day until seventh day for 10% aqueous guava leaves was p=0.008 (p0.05, but 20% aqueous guava leaves extract most effective than 10% aqueous guava leaves extract.
Basaglia-Pappas, S; Laterza, M; Borg, C; Richard-Mornas, A; Favre, E; Thomas-Antérion, C
2013-05-01
In mild Alzheimer's disease (AD), a deficit in episodic memory, particularly autobiographical memory, is clearly established. Several recent studies have also shown impaired semantic memory from the onset of the disease. Musical memory capacities may be especially preserved and listening to music might encourage autobiographical recall. The aim of this study was to explore recall of popular songs in AD. We tested 12 patients with mild AD and 12 control subjects. We created a tool made up of old French popular songs: POP 10. This tool is a questionnaire composed of several subtests: melodic free recall, chorus free recall, melodic recognition, chorus recognition, semantic knowledge, autobiographical recall about the song, and autobiographical recall about the interpreter. We used non-parametric tests, the Mann-Whitney test (M-W), the Friedman test, and the a posteriori Wilcoxon test. Results of AD patients were rather similar to those of control participants for melodic memory. Concerning chorus memory (except recognition), semantic knowledge, and autobiographical recall about the interpreter, results of AD patients were significantly weaker than those of control participants. The most important result concerned autobiographical recall about the song: we found no impairment-related differences between the two groups. Our findings demonstrate that popular songs can be excellent stimuli for reminiscence, such as the ability to produce an autobiographical memory related to a song. Thus, we confirm that musical semantic knowledge associated with a song may be relatively preserved in the early stages of AD. This leads to new possibilities for cognitive stimulation.
Energy Technology Data Exchange (ETDEWEB)
Mora, Paloma [Hospital Universitari de Bellvitge, Department of Radiology, L' Hospitalet de Llobregat (Spain); Majos, Carles; Aguilera, Carles [Hospital Universitari de Bellvitge, Department of Radiology, Institut de Diagnostic per la Imatge (IDI), Centre Bellvitge, L' Hospitalet de Llobregat (Spain); Centro de Investigacion en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Valles (Spain); Castaner, Sara; Sanchez, Juan J. [Hospital Universitari de Bellvitge, Department of Radiology, Institut de Diagnostic per la Imatge (IDI), Centre Bellvitge, L' Hospitalet de Llobregat (Spain); Gabarros, Andreu [Hospital Universitari de Bellvitge, Department of Neurosurgery, L' Hospitalet de Llobregat (Spain); Muntane, Amadeo [Hospital Universitari de Bellvitge, Department of Radiology, L' Hospitalet de Llobregat (Spain); Arus, Carles [Centro de Investigacion en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Valles (Spain); Universitat Autonoma de Barcelona, Department de Bioquimica i Biologia Molecular, Unitat de Bioquimica de Biociencies, Cerdanyola del Valles (Spain); Universitat Autonoma de Barcelona, Institut de Biotecnologia i de Biomedicina, Cerdanyola del Valles (Spain)
2014-11-15
To assess whether {sup 1}H-MRS may be useful to reinforce the radiological suspicion of PCNSL. In this retrospective study, we included 546 patients with untreated brain tumours in which single-voxel spectroscopy at TE 30 ms and 136 ms had been performed. The patients were split into two subgroups: ''training set'' and ''test set.'' Differences between PCNSL and five other types of intracranial tumours were assessed in the test set of patients using the Mann-Whitney U nonparametric test and cut-off values for pair-wise comparisons defined by constructing receiver operating characteristic curves. These thresholds were used to construct classifiers for binary comparison between PCNSL and non-PCNSL. The performance of the obtained classifiers was assessed in the independent test set of patients. Significant differences were found between PCNSL and the other groups evaluated. All bilateral comparisons performed in the test set obtained accuracy values above 70 % (71-89 %). Lipids were found to be useful to discriminate between PCNSL and glioblastoma/metastasis at short TE. Myo-inositol resonance was found to be very consistent for discriminating between PCNSL and astrocytomas at short TE. {sup 1}H-MRS is useful to reinforce diagnostic suspicion of PCNSL on MRI. (orig.)
International Nuclear Information System (INIS)
Mora, Paloma; Majos, Carles; Aguilera, Carles; Castaner, Sara; Sanchez, Juan J.; Gabarros, Andreu; Muntane, Amadeo; Arus, Carles
2014-01-01
To assess whether 1 H-MRS may be useful to reinforce the radiological suspicion of PCNSL. In this retrospective study, we included 546 patients with untreated brain tumours in which single-voxel spectroscopy at TE 30 ms and 136 ms had been performed. The patients were split into two subgroups: ''training set'' and ''test set.'' Differences between PCNSL and five other types of intracranial tumours were assessed in the test set of patients using the Mann-Whitney U nonparametric test and cut-off values for pair-wise comparisons defined by constructing receiver operating characteristic curves. These thresholds were used to construct classifiers for binary comparison between PCNSL and non-PCNSL. The performance of the obtained classifiers was assessed in the independent test set of patients. Significant differences were found between PCNSL and the other groups evaluated. All bilateral comparisons performed in the test set obtained accuracy values above 70 % (71-89 %). Lipids were found to be useful to discriminate between PCNSL and glioblastoma/metastasis at short TE. Myo-inositol resonance was found to be very consistent for discriminating between PCNSL and astrocytomas at short TE. 1 H-MRS is useful to reinforce diagnostic suspicion of PCNSL on MRI. (orig.)
Sánchez Socarrás, Violeida; Aguilar Martínez, Alicia; Vaqué Crusellas, Cristina; Milá Villarroel, Raimon; González Rivas, Fabián
2016-01-01
To design and validate a questionnaire to assess the level of knowledge regarding eating disorders in college students. Observational, prospective, and longitudinal study, with the design of the questionnaire based on a conceptual review and validation by a cognitive pre-test and pilot test-retest, with analysis of the psychometric properties in each application. University Foundation of Bages, Barcelona. Marco community care. A total of 140 students from Health Sciences; 53 women and 87 men with a mean age of 21.87 years; 28 participated in the pre-test and 112 in the test-retests, 110 students completed the study. Validity and stability study using Cronbach α and Pearson product-moment correlation coefficient statistics; relationship skills with sex and type of study, non-parametric statistical Mann-Whitney and Kruskal-Wallis tests; for demographic variables, absolute or percentage frequencies, as well as mean, central tendency and standard deviation as measures of dispersion were calculated. The statistical significance level was 95% confidence. The questionnaire was obtained that had 10 questions divided into four dimensions (classification, demographics characteristics of patients, risk factors and clinical manifestations of eating disorders). The scale showed good internal consistency in its final version (Cronbach α=0.724) and adequate stability (Pearson correlation 0.749). The designed tool can be accurately used to assess Health Sciences students' knowledge of eating disorders. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.
Gitau, Tabither M; Micklesfield, Lisa K; Pettifor, John M; Norris, Shane A
2014-01-01
This cross-sectional study of urban high schools in Johannesburg, South Africa, sought to examine eating attitudes, body image and self-esteem among male adolescents (n = 391). Anthropometric measurements, Eating Attitudes Test-26 (EAT-26), Rosenberg self-esteem, body image satisfaction and perception of females were collected at age 13, 15 and 17 years. Descriptive analysis was done to describe the sample, and non-parametric Wilcoxon Mann-Whitney test was used to test for significant differences between data that were not normally distributed (EAT-26). Spearman's rank correlation coefficient analyses were conducted to test for associations between self-esteem scores and eating attitudes, body mass indices and body image satisfaction scores. To assess the differences between groups that were normally distributed chi-square tests were carried out. Ethnic differences significantly affected adolescent boys' body mass index (BMI), eating attitudes and self-esteem; White boys had higher self-esteem, BMI and normal eating attitudes than the Black boys did. BMI was positively associated with self-esteem (p = 0.01, r = 0.134) and negatively with dieting behaviour in White boys (p = 0.004, r = -0.257), and with lower EAT-26 bulimic and oral control scores in Black boys. In conclusion, the findings highlight ethnic differences and a need to better understand cultural differences that influence adolescent attitudes and behaviour.
Directory of Open Access Journals (Sweden)
Yin Wang
2014-01-01
Full Text Available We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy.
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.
Zhang, Tingting; Kou, S C
2010-01-01
Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.
A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion
Directory of Open Access Journals (Sweden)
Xiaoqian Zhu
2014-01-01
Full Text Available It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach.
Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke
2017-01-01
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Semi-nonparametric estimates of interfuel substitution in US energy demand
Energy Technology Data Exchange (ETDEWEB)
Serletis, A.; Shahmoradi, A. [University of Calgary, Calgary, AB (Canada). Dept. of Economics
2008-09-15
This paper focuses on the demand for crude oil, natural gas, and coal in the United States in the context of two globally flexible functional forms - the Fourier and the Asymptotically Ideal Model (AIM) - estimated subject to full regularity, using methods suggested over 20 years ago by Gallant and Golub (Gallant, A. Ronald and Golub, Gene H. Imposing Curvature Restrictions on Flexible Functional Forms. Journal of Econometrics 26 (1984), 295-321) and recently used by Serletis and Shahmoradi (Serletis, A., Shahmoradi, A., 2005. Semi-nonparametric estimates of the demand for money in the United States. Macroeconomic Dynamics 9, 542-559) in the monetary demand systems literature. We provide a comparison in terms of a full set of elasticities and also a policy perspective, using (for the first time) parameter estimates that are consistent with global regularity.
Semi-nonparametric estimates of interfuel substitution in U.S. energy demand
Energy Technology Data Exchange (ETDEWEB)
Serletis, Apostolos [Department of Economics, University of Calgary, Calgary, Alberta (Canada); Shahmoradi, Asghar [Faculty of Economics, University of Tehran, Tehran (Iran)
2008-09-15
This paper focuses on the demand for crude oil, natural gas, and coal in the United States in the context of two globally flexible functional forms - the Fourier and the Asymptotically Ideal Model (AIM) - estimated subject to full regularity, using methods suggested over 20 years ago by Gallant and Golub [Gallant, A. Ronald and Golub, Gene H. Imposing Curvature Restrictions on Flexible Functional Forms. Journal of Econometrics 26 (1984), 295-321] and recently used by Serletis and Shahmoradi [Serletis, A., Shahmoradi, A., 2005. Semi-nonparametric estimates of the demand for money in the United States. Macroeconomic Dynamics 9, 542-559] in the monetary demand systems literature. We provide a comparison in terms of a full set of elasticities and also a policy perspective, using (for the first time) parameter estimates that are consistent with global regularity. (author)
A Nonparametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets
Carrig, Madeline M.; Manrique-Vallier, Daniel; Ranby, Krista W.; Reiter, Jerome P.; Hoyle, Rick H.
2015-01-01
Complex research questions often cannot be addressed adequately with a single data set. One sensible alternative to the high cost and effort associated with the creation of large new data sets is to combine existing data sets containing variables related to the constructs of interest. The goal of the present research was to develop a flexible, broadly applicable approach to the integration of disparate data sets that is based on nonparametric multiple imputation and the collection of data from a convenient, de novo calibration sample. We demonstrate proof of concept for the approach by integrating three existing data sets containing items related to the extent of problematic alcohol use and associations with deviant peers. We discuss both necessary conditions for the approach to work well and potential strengths and weaknesses of the method compared to other data set integration approaches. PMID:26257437
International Nuclear Information System (INIS)
Morio, Jerome
2011-01-01
Importance sampling (IS) is a useful simulation technique to estimate critical probability with a better accuracy than Monte Carlo methods. It consists in generating random weighted samples from an auxiliary distribution rather than the distribution of interest. The crucial part of this algorithm is the choice of an efficient auxiliary PDF that has to be able to simulate more rare random events. The optimisation of this auxiliary distribution is often in practice very difficult. In this article, we propose to approach the IS optimal auxiliary density with non-parametric adaptive importance sampling (NAIS). We apply this technique for the probability estimation of spatial launcher impact position since it has currently become a more and more important issue in the field of aeronautics.
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
Bayesian nonparametric modeling for comparison of single-neuron firing intensities.
Kottas, Athanasios; Behseta, Sam
2010-03-01
We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.
A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.
Zhao, Yize; Kang, Jian; Yu, Tianwei
2014-06-01
It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.
Assessing T cell clonal size distribution: a non-parametric approach.
Directory of Open Access Journals (Sweden)
Olesya V Bolkhovskaya
Full Text Available Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Assessing T cell clonal size distribution: a non-parametric approach.
Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V
2014-01-01
Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Nonparametric estimation of age-specific reference percentile curves with radial smoothing.
Wan, Xiaohai; Qu, Yongming; Huang, Yao; Zhang, Xiao; Song, Hanping; Jiang, Honghua
2012-01-01
Reference percentile curves represent the covariate-dependent distribution of a quantitative measurement and are often used to summarize and monitor dynamic processes such as human growth. We propose a new nonparametric method based on a radial smoothing (RS) technique to estimate age-specific reference percentile curves assuming the underlying distribution is relatively close to normal. We compared the RS method with both the LMS and the generalized additive models for location, scale and shape (GAMLSS) methods using simulated data and found that our method has smaller estimation error than the two existing methods. We also applied the new method to analyze height growth data from children being followed in a clinical observational study of growth hormone treatment, and compared the growth curves between those with growth disorders and the general population. Copyright © 2011 Elsevier Inc. All rights reserved.
Rock, N. M. S.; Duffy, T. R.
REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.
Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three
Steinhardt, Charles L.; Jermyn, Adam S.
2018-02-01
Telescopes are much more expensive than astronomers, so it is essential to minimize required sample sizes by using the most data-efficient statistical methods possible. However, the most commonly used model-independent techniques for finding the relationship between two variables in astronomy are flawed. In the worst case they can lead without warning to subtly yet catastrophically wrong results, and even in the best case they require more data than necessary. Unfortunately, there is no single best technique for nonparametric regression. Instead, we provide a guide for how astronomers can choose the best method for their specific problem and provide a python library with both wrappers for the most useful existing algorithms and implementations of two new algorithms developed here.
Using nonparametrics to specify a model to measure the value of travel time
DEFF Research Database (Denmark)
Fosgerau, Mogens
2007-01-01
Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source...... of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing...... unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory....
The application of non-parametric statistical method for an ALARA implementation
International Nuclear Information System (INIS)
Cho, Young Ho; Herr, Young Hoi
2003-01-01
The cost-effective reduction of Occupational Radiation Dose (ORD) at a nuclear power plant could not be achieved without going through an extensive analysis of accumulated ORD data of existing plants. Through the data analysis, it is required to identify what are the jobs of repetitive high ORD at the nuclear power plant. In this study, Percentile Rank Sum Method (PRSM) is proposed to identify repetitive high ORD jobs, which is based on non-parametric statistical theory. As a case study, the method is applied to ORD data of maintenance and repair jobs at Kori units 3 and 4 that are pressurized water reactors with 950 MWe capacity and have been operated since 1986 and 1987, respectively in Korea. The results was verified and validated, and PRSM has been demonstrated to be an efficient method of analyzing the data
Directory of Open Access Journals (Sweden)
S. Pashkevich
2018-03-01
Full Text Available The study objective is to evaluate the possibility of using screening methods for determining the effectiveness of health and fitness activities of students in higher education institutions. Materials and methods. The participants in the experiment were 37 first-year students (17 boys and 20 girls of the School of History of H. S. Skovoroda Kharkiv National Pedagogical University. The experiment lasted during the fall semester. Using the Framingham method for analyzing weekly timing, the study conducted a survey among the students on their level of motor activity and performed a functional movement screen testing. To tentatively evaluate the cause and effect relationship between the level of motor activity and the occurrence of a pathological movement pattern, the study used the Spearman’s rank correlation coefficient. The characteristics between the groups were analyzed by using the Mann-Whitney test for comparing the distribution of ordinal variables. Results. The correlation analysis showed that the first-year students’ motor activity was positively related to the results of functional movement screening (R=+0.69, p< 0.05. At the same time, the students (EG1 who mainly had a high level of physical activity at physical education classes showed low values of functional movement evaluation, compared to the students (EG2 participating in extra-curricular physical activity. In EG1, the overall screening score was 10.3±0.7, in EG2 — 14.2±0.9 (p<0.05. Conclusions. The students with insufficient weekly motor activity had risk values of the test (10.3±0.7, which requires further analysis of the causes of a pathological movement pattern. The study results have confirmed the existence of the relationship between motor activity indicators and functional movement evaluation (R=+0.69, p<0.05. This provides a way to use the screening method of determining motor competence for the effectiveness evaluation of health and fitness programs, but further
Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R
Directory of Open Access Journals (Sweden)
Terrance D. Savitsky
2016-08-01
Full Text Available We present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains. This data structure arises from combining periodically published government survey statistics, such as are reported in the Current Population Study (CPS. The CPS publishes monthly, by-state estimates of employment levels, where each state expresses a noisy time series. Published state-level estimates from the CPS are composed from household survey responses in a model-free manner and express high levels of volatility due to insufficient sample sizes. Existing software solutions borrow information over a modeled time-based dependence to extract a de-noised time series for each domain. These solutions, however, ignore the dependence among the domains that may be additionally leveraged to improve estimation efficiency. The growfunctions package offers two fully nonparametric mixture models that simultaneously estimate both a time and domain-indexed dependence structure for a collection of time series: (1 A Gaussian process (GP construction, which is parameterized through the covariance matrix, estimates a latent function for each domain. The covariance parameters of the latent functions are indexed by domain under a Dirichlet process prior that permits estimation of the dependence among functions across the domains: (2 An intrinsic Gaussian Markov random field prior construction provides an alternative to the GP that expresses different computation and estimation properties. In addition to performing denoised estimation of latent functions from published domain estimates, growfunctions allows estimation of collections of functions for observation units (e.g., households, rather than aggregated domains, by accounting for an informative sampling design under which the probabilities for inclusion of observation units are related to the response variable. growfunctions includes plot
Palacios, Julia A; Minin, Vladimir N
2013-03-01
Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method. Copyright © 2013, The International Biometric Society.
Nonparametric identification of nonlinear dynamic systems using a synchronisation-based method
Kenderi, Gábor; Fidlin, Alexander
2014-12-01
The present study proposes an identification method for highly nonlinear mechanical systems that does not require a priori knowledge of the underlying nonlinearities to reconstruct arbitrary restoring force surfaces between degrees of freedom. This approach is based on the master-slave synchronisation between a dynamic model of the system as the slave and the real system as the master using measurements of the latter. As the model synchronises to the measurements, it becomes an observer of the real system. The optimal observer algorithm in a least-squares sense is given by the Kalman filter. Using the well-known state augmentation technique, the Kalman filter can be turned into a dual state and parameter estimator to identify parameters of a priori characterised nonlinearities. The paper proposes an extension of this technique towards nonparametric identification. A general system model is introduced by describing the restoring forces as bilateral spring-dampers with time-variant coefficients, which are estimated as augmented states. The estimation procedure is followed by an a posteriori statistical analysis to reconstruct noise-free restoring force characteristics using the estimated states and their estimated variances. Observability is provided using only one measured mechanical quantity per degree of freedom, which makes this approach less demanding in the number of necessary measurement signals compared with truly nonparametric solutions, which typically require displacement, velocity and acceleration signals. Additionally, due to the statistical rigour of the procedure, it successfully addresses signals corrupted by significant measurement noise. In the present paper, the method is described in detail, which is followed by numerical examples of one degree of freedom (1DoF) and 2DoF mechanical systems with strong nonlinearities of vibro-impact type to demonstrate the effectiveness of the proposed technique.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Directory of Open Access Journals (Sweden)
González Adriana
2016-01-01
Full Text Available Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF. Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting. The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.
Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng
2018-05-01
Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.
Hematologic Profile and Semen Quality of Male Timor Deer (Rusa timorensis) at Various Hierarchies
Samsudewa, D.; Capitan, S. S.; Sevilla, C. C.; Vega, R. S. A.; Ocampo, P. P.
2018-02-01
The aim of this research was to observe hematologic profile i.e. erythrocyte count, hemoglobin and hematocrit and semen quality, i.e. semen volume, sperm motility and sperm abnormality of α-male, β-male and subordinate male Timor deer raised under captivity. Twelve males (51 ± 6 months old; 68.29 ± 8.41kg body weight) at similar antler stages were use in this study. Before and after 43 days of establishment of dominance hierarchy blood were sampled after sedation for erythrocyte count, hemoglobin (mg/dL), and hematocrit (%). Likewise, semen was collected using electroejaculator and were analyzed for semen volume (ml), sperm motility (%) and sperm abnormality (%) to compare male deer at various heirarchies. Wilcoxon signed ranks test and Kruskal-Wallis H test of non-parametric analysis was done. Significant difference was tested with Mann-Whitney U test. The results showed that highest count of erythrocyte shown on α and β-male (1.60 million per µL). The highest increase in hematocrit was observed in β-male (5%) and then followed by S2-male (4%). S2-male had the highest increase in hemoglobin (0.13 g/dL). The highest increase in semen volume was observed in α -male (0.75 ml). Social stress affected negatively the sperm motility and abnormality (P<0.05). The highest decrease was observed in S2-male.
Eslamian, L; Gholami, H; Mortazavi, S A R; Soheilifar, S
2016-11-01
To compare the effectiveness of 5% benzocaine gel and placebo gel on reducing pain caused by fixed orthodontic appliance activation. Thirty subjects (15-25 years) undergoing fixed orthodontics. A randomized, double-blind, placebo-controlled and cross-over clinical trial study was conducted. Subjects were asked to apply a placebo gel and 5% benzocaine gel, exchangeable in two consecutive appointments, twice a day for 3 days and mark their level of pain on a VAS scale. The pain severity was evaluated by means of Mann-Whitney U-test for comparing two gel groups, Kruskal-Wallis nonparametric test for overall differences and post hoc test of Dunnett for paired multiple comparisons. p-value was assigned test indicated that there was no significant difference between overall pain in both groups (mean difference = 0.258 p ˂ 0.21). For both groups, pain intensity was significantly lower at 2, 6 and 24 h compared with pain experienced at days 2, 3 and 7. Benzocaine gel caused a decrease in pain perception at 2 h compared with placebo gel. Peak pain intensity was at 2 h for placebo gel and at 6 h for benzocaine gel, followed by a decline in pain perception from that point to day 7 for both gels. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Body measurements and testosteron level of male Timor deer (Rusa timorensis at various hierarchies
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D. Samsudewa
2017-12-01
Full Text Available The aim of this research was to observe body (neck, chest and scrotum circumferences and testosterone level of α-male, β-male and subordinate male Timor deer reared under captivity after establisment of the dominance hierarchy. Twelve males (51 ± 6 months old; 68.29 ± 8.41 kg body weight and in same antler stages were used in this research. The bucks was grouped into three stall each containing four bucks. ELISA kit and tape measurements were used for plasma Testosterone assay and body measurement, respectively. Data was collected before and 43 days after establishment of the dominance hierarchy. Wilcoxon signed ranks test and Kruskal-Wallis H test of non-parametric analysis was used. Significant difference was tested with Mann-Whitney U test. The results showed no significantly different for body circumferences (neck, chest, scrotum and testosterone level of male Timor deer before establishment of dominance hierarchy. Chest and scrotum circumferences of male Timor deer after establihment of dominance hierarchy showed no significantly different. Significantly difference shown on parameter neck circumference (P<0.05; χ2 = 8.74 and testosteron level (P<0.05; χ2 = 7.87 after establishment of dominance hierarchy. In conclusion, dominance hierarchy affected the testosterone level and body measurement.
Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.
Tekwe, Carmen D; Carroll, Raymond J; Dabney, Alan R
2012-08-01
Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. ctekwe@stat.tamu.edu.
Directory of Open Access Journals (Sweden)
Naniek Widyaningrum
2012-12-01
Design and Method: In this study, green tea leaf extract condensed obtained by maceration using 50% ethanol solution. Cream formula that is made in five concentration ethanolic extract of green tea leaves 1%, 3%, 5%, 7%, and 9% use a modified formula antiacne cream. Cream tested physical properties include homogenity, percent separation, dispersive power and adhesion. During the antibacterial activity was also tested. The data obtained were analyzed statistically using the non-parametric Kruskal-Wallis test followed by Mann Whitney test with a level of 95%. Results: The preparation cream ethanolic leaf green tea extract at various concentrations have good homogenity and not separate, the greater concentration of cream ethanolic extract of green tea leaves get smaller power and energy dispersive adhesion, whereas the inhibitory against Staphylococcus aureus bacteria is getting biger. Conclusion: Cream ethanolic extract of green tea leaves that are comparable with the positive control (Ristra acne creaming the physical properties and the antibacterial activity at a concentration of 7% (Sains Medika, 4(2:147-156.
Kidney Injury Molecule Levels in Type 2 Diabetes Mellitus.
Aslan, Ozgur; Demir, Metin; Koseoglu, Mehmet
2016-11-01
This study was designed to determine the diagnostic role of urinary kidney injury molecule (KIM)-1 levels in renal damage in patients with type 2 diabetes mellitus according to the urinary albumin/creatinine ratio. Patients with type 2 diabetes mellitus admitted to different polyclinics in our hospital enrolled in the study and were subdivided into three groups according to albumin/creatinine ratio - normalbuminuric (n: 20); microalbuminuric (n: 20); albuminuric (n: 18) - and compared with the control group. Urine albumin was analyzed using the immunoturbidimetric method (Architect C16000, Abbott Diagnostics). uKIM-1 was determined using a commercially available enzyme-linked immunosorbent assay test kit (USCN Life Science, Hankou, Wuhan, China). One-sample Kolmogorov-Smirnov test, Spearman correlation and Kruskal-Wallis non-parametric tests were performed. Post hoc comparisons were made using Bonferroni-corrected Mann-Whitney U tests. The differences between the controls and normalbuminuric, microalbuminuric and albuminuric groups were highly significant for KIM-1. Positive correlation was found between KIM-1 and urine microalbumin-urine microalbumin/creatinine (r = 0.479 P diabetic nephropathy. J. Clin. Lab. Anal. 00:1-6, 2016. © 2016 Wiley Periodicals, Inc.
Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data
Tekwe, C. D.
2012-05-24
MOTIVATION: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. RESULTS: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. AVAILABILITY: The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. CONTACT: ctekwe@stat.tamu.edu.
Markeviciute, Greta; Narbutaite, Julija
2015-01-01
The aim of this study was to evaluate the effect of a motivation and practical skills development methods on the oral hygiene of orphans. Sixty eight orphans aged between 7 and 17 years from two orphanages in Kaunas were divided into two groups: practical application group and motivation group. Children were clinically examined by determining their oral hygiene status using Silness-Löe plaque index. Questionnaire was used to estimate the oral hygiene knowledge and practices at baseline and after 3 months. Statistical analysis included: Chi-square test (χ(2)), Fisher's exact test, Student's t-test, nonparametric Mann-Whitney test, Spearman's rho correlation coefficient and Kappa coefficient. All children had a plaque on at least one tooth in both groups: motivation 1.14 (SD 0.51), practical application 1.08 (SD 0.4) (P = 0.58). Girls in both groups showed significantly better oral hygiene than boys (P soft drinks, the statistically significant decline of their use was in both groups (P = 0.004). Educational programs are effective in improving oral hygiene, especially when they're based on practical skills training.
International Nuclear Information System (INIS)
Das, Shiva K.; Zhou Sumin; Zhang, Junan; Yin, F.-F.; Dewhirst, Mark W.; Marks, Lawrence B.
2007-01-01
Purpose: To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis. Methods and Materials: The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a 'boosting' process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases. Results: The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables. Conclusions: Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation
Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran
Ahani, Hossien; Kherad, Mehrzad; Kousari, Mohammad Reza; van Roosmalen, Lieke; Aryanfar, Ramin; Hosseini, Seyyed Mashaallah
2013-05-01
Currently, an important scientific challenge that researchers are facing is to gain a better understanding of climate change at the regional scale, which can be especially challenging in an area with low and highly variable precipitation amounts such as Iran. Trend analysis of the medium-term change using ground station observations of meteorological variables can enhance our knowledge of the dominant processes in an area and contribute to the analysis of future climate projections. Generally, studies focus on the long-term variability of temperature and precipitation and to a lesser extent on other important parameters such as moisture indices. In this study the recent 50-year trends (1955-2005) of precipitation (P), potential evapotranspiration (PET), and aridity index (AI) in monthly time scale were studied over 14 synoptic stations in three large Iran basins using the Mann-Kendall non-parametric test. Additionally, an analysis of the monthly, seasonal and annual trend of each parameter was performed. Results showed no significant trends in the monthly time series. However, PET showed significant, mostly decreasing trends, for the seasonal values, which resulted in a significant negative trend in annual PET at five stations. Significant negative trends in seasonal P values were only found at a number of stations in spring and summer and no station showed significant negative trends in annual P. Due to the varied positive and negative trends in annual P and to a lesser extent PET, almost as many stations with negative as positive trends in annual AI were found, indicating that both drying and wetting trends occurred in Iran. Overall, the northern part of the study area showed an increasing trend in annual AI which meant that the region became wetter, while the south showed decreasing trends in AI.