Decision support using nonparametric statistics

CERN Document Server

Beatty, Warren

2018-01-01

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

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...

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

Science.gov (United States)

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

2015-12-01

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

A Structural Labor Supply Model with Nonparametric Preferences

NARCIS (Netherlands)

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

2000-01-01

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

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....

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

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...

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

CERN Document Server

Siebert, Carl F

2017-01-01

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

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

Science.gov (United States)

Mittag, Kathleen Cage

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

A nonparametric mixture model for cure rate estimation.

Science.gov (United States)

Peng, Y; Dear, K B

2000-03-01

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

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

Science.gov (United States)

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

2018-03-01

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

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

Directory of Open Access Journals (Sweden)

Jinchao Feng

2018-03-01

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

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...

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

NARCIS (Netherlands)

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

2007-01-01

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

2nd Conference of the International Society for Nonparametric Statistics

CERN Document Server

Manteiga, Wenceslao; Romo, Juan

2016-01-01

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

Nonparametric statistical inference

CERN Document Server

Gibbons, Jean Dickinson

2014-01-01

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

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

Science.gov (United States)

Gozalo, P L

1997-12-01

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

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

International Nuclear Information System (INIS)

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

2010-01-01

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

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

Directory of Open Access Journals (Sweden)

Richard C. Zink

2012-07-01

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

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.

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)

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...

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

Science.gov (United States)

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

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

KAUST Repository

Ryu, Duchwan

2010-09-28

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

A contingency table approach to nonparametric testing

CERN Document Server

Rayner, JCW

2000-01-01

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

Nonparametric 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.

Nonparametric tests for censored data

CERN Document Server

Bagdonavicus, Vilijandas; Nikulin, Mikhail

2013-01-01

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

Bayesian nonparametric meta-analysis using Polya tree mixture models.

Science.gov (United States)

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.

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...

Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.

Science.gov (United States)

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.

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

OpenAIRE

Hiroyuki Kasahara; Katsumi Shimotsu

2006-01-01

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

Theory of nonparametric tests

CERN Document Server

Dickhaus, Thorsten

2018-01-01

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

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.

Nonparametric Transfer Function Models

Science.gov (United States)

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

2009-01-01

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

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)

Nonparametric functional mapping of quantitative trait loci.

Science.gov (United States)

Yang, Jie; Wu, Rongling; Casella, George

2009-03-01

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

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

Science.gov (United States)

Fronczyk, Kassandra; Kottas, Athanasios

2014-03-01

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

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

Nonparametric Mixture of Regression Models.

Science.gov (United States)

Huang, Mian; Li, Runze; Wang, Shaoli

2013-07-01

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

Bayesian Nonparametric Longitudinal Data Analysis.

Science.gov (United States)

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

2016-01-01

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

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

DEFF Research Database (Denmark)

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

2004-01-01

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

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

Science.gov (United States)

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

2015-05-01

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

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 Cooper's Nonparametric Test.

Science.gov (United States)

Schmeidler, James

1978-01-01

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

CATDAT - A program for parametric and nonparametric categorical data analysis user's manual, Version 1.0

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

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

Science.gov (United States)

Bhattacharya, Abhishek; Dunson, David B

2010-12-01

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

Nonparametric e-Mixture Estimation.

Science.gov (United States)

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

2016-12-01

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

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

International Nuclear Information System (INIS)

Janurová, Kateřina; Briš, Radim

2014-01-01

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

Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness.

Science.gov (United States)

Tang, Niansheng; Chow, Sy-Miin; Ibrahim, Joseph G; Zhu, Hongtu

2017-12-01

Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters' restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.

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

Science.gov (United States)

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

2016-11-16

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

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 Bayesian inference for multidimensional compound Poisson processes

NARCIS (Netherlands)

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

2015-01-01

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

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.)

Categorical and nonparametric data analysis choosing the best statistical technique

CERN Document Server

Nussbaum, E Michael

2014-01-01

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

Bayesian nonparametric hierarchical modeling.

Science.gov (United States)

Dunson, David B

2009-04-01

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

A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy

Directory of Open Access Journals (Sweden)

Archer Kellie J

2008-02-01

Full Text Available Abstract Background With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN to those with normal functioning allograft. Results The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. Conclusion We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

Science.gov (United States)

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

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

Directory of Open Access Journals (Sweden)

Rabia Ece OMAY

2013-06-01

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

A NONPARAMETRIC HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING

OpenAIRE

Temel, Tugrul T.

2001-01-01

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

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

Nonparametric identification of copula structures

KAUST Repository

Li, Bo; Genton, Marc G.

2013-01-01

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

Simple nonparametric checks for model data fit in CAT

NARCIS (Netherlands)

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

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

Science.gov (United States)

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

2011-04-01

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

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

Science.gov (United States)

Hof, Stefanie

2014-01-01

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

Recent Advances and Trends in Nonparametric Statistics

CERN Document Server

Akritas, MG

2003-01-01

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

A nonparametric spatial scan statistic for continuous data.

Science.gov (United States)

Jung, Inkyung; Cho, Ho Jin

2015-10-20

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

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...

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

Science.gov (United States)

Siciliani, Luigi

2006-01-01

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

Nonparametric Mixture Models for Supervised Image Parcellation.

Science.gov (United States)

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

2009-09-01

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

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

Science.gov (United States)

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

2010-12-30

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

Nonparametric Inference for Periodic Sequences

KAUST Repository

Sun, Ying

2012-02-01

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

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

Science.gov (United States)

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

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

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

Science.gov (United States)

Lee, L.; Helsel, D.

2007-01-01

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

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

Directory of Open Access Journals (Sweden)

Hae-Bum Yun

2011-01-01

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

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

OpenAIRE

von Hirschhausen, Christian R.; Cullmann, Astrid

2005-01-01

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

portfolio optimization based on nonparametric estimation methods

Directory of Open Access Journals (Sweden)

mahsa ghandehari

2017-03-01

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

Robustifying Bayesian nonparametric mixtures for count data.

Science.gov (United States)

Canale, Antonio; Prünster, Igor

2017-03-01

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

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...

Statistical analysis using the Bayesian nonparametric method for irradiation embrittlement of reactor pressure vessels

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.

Network structure exploration via Bayesian nonparametric models

International Nuclear Information System (INIS)

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

2015-01-01

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

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

Science.gov (United States)

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

2006-01-01

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

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin

2017-01-19

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

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun

2017-01-01

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

Nonparametric methods for volatility density estimation

NARCIS (Netherlands)

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

2009-01-01

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

Quantal Response: Nonparametric Modeling

Science.gov (United States)

2017-01-01

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

Speaker Linking and Applications using Non-Parametric Hashing Methods

Science.gov (United States)

2016-09-08

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

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Science.gov (United States)

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

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

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

KAUST Repository

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

2011-01-01

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

Nonparametric Bayesian inference in biostatistics

CERN Document Server

Müller, Peter

2015-01-01

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

Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes

Science.gov (United States)

Zhou, Wei-Xing; Sornette, Didier

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

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...

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.

Single molecule force spectroscopy at high data acquisition: A Bayesian nonparametric analysis

Science.gov (United States)

Sgouralis, Ioannis; Whitmore, Miles; Lapidus, Lisa; Comstock, Matthew J.; Pressé, Steve

2018-03-01

Bayesian nonparametrics (BNPs) are poised to have a deep impact in the analysis of single molecule data as they provide posterior probabilities over entire models consistent with the supplied data, not just model parameters of one preferred model. Thus they provide an elegant and rigorous solution to the difficult problem encountered when selecting an appropriate candidate model. Nevertheless, BNPs' flexibility to learn models and their associated parameters from experimental data is a double-edged sword. Most importantly, BNPs are prone to increasing the complexity of the estimated models due to artifactual features present in time traces. Thus, because of experimental challenges unique to single molecule methods, naive application of available BNP tools is not possible. Here we consider traces with time correlations and, as a specific example, we deal with force spectroscopy traces collected at high acquisition rates. While high acquisition rates are required in order to capture dwells in short-lived molecular states, in this setup, a slow response of the optical trap instrumentation (i.e., trapped beads, ambient fluid, and tethering handles) distorts the molecular signals introducing time correlations into the data that may be misinterpreted as true states by naive BNPs. Our adaptation of BNP tools explicitly takes into consideration these response dynamics, in addition to drift and noise, and makes unsupervised time series analysis of correlated single molecule force spectroscopy measurements possible, even at acquisition rates similar to or below the trap's response times.

Multi-sample nonparametric treatments comparison in medical ...

African Journals Online (AJOL)

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

Nonparametric regression using the concept of minimum energy

International Nuclear Information System (INIS)

Williams, Mike

2011-01-01

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

Nonparametric Bayesian inference for mean residual life functions in survival analysis.

Science.gov (United States)

Poynor, Valerie; Kottas, Athanasios

2018-01-19

Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

Science.gov (United States)

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

2013-04-01

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

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...

Essays on nonparametric econometrics of stochastic volatility

NARCIS (Netherlands)

Zu, Y.

2012-01-01

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

Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes.

Science.gov (United States)

Yau, C; Papaspiliopoulos, O; Roberts, G O; Holmes, C

2011-01-01

We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. The method uses novel MCMC methodology which combines recent retrospective sampling methods with the use of slice sampler variables. The methodology is computationally efficient, both in terms of MCMC mixing properties, and robustness to the length of the time series being investigated. Moreover, the method is easy to implement requiring little or no user-interaction. We apply our methodology to the analysis of genomic copy number variation.

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

CERN Document Server

Boiko, Igor

2013-01-01

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

Nonparametric Bayes Modeling of Multivariate Categorical Data.

Science.gov (United States)

Dunson, David B; Xing, Chuanhua

2012-01-01

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

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

Science.gov (United States)

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

2011-05-01

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

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

International Nuclear Information System (INIS)

Kuosmanen, Timo

2012-01-01

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

European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis

Science.gov (United States)

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.

Nonparametric predictive inference in statistical process control

NARCIS (Netherlands)

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

2000-01-01

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

CATDAT : A Program for Parametric and Nonparametric Categorical Data Analysis : User's Manual Version 1.0, 1998-1999 Progress Report.

Energy Technology Data Exchange (ETDEWEB)

Peterson, James T.

1999-12-01

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.

Nonparametric instrumental regression with non-convex constraints

International Nuclear Information System (INIS)

Grasmair, M; Scherzer, O; Vanhems, A

2013-01-01

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

Nonparametric instrumental regression with non-convex constraints

Science.gov (United States)

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

2013-03-01

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

Nonparametric conditional predictive regions for time series

NARCIS (Netherlands)

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

2000-01-01

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

Nonparametric 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.

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

Science.gov (United States)

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

2018-04-01

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

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

OpenAIRE

W. D. Walls

2012-01-01

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

Nonparametric predictive inference in reliability

International Nuclear Information System (INIS)

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

2002-01-01

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

Nonparametric estimation in models for unobservable heterogeneity

OpenAIRE

Hohmann, Daniel

2014-01-01

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

Nonparametric estimation of location and scale parameters

KAUST Repository

Potgieter, C.J.; Lombard, F.

2012-01-01

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

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...

Examples of the Application of Nonparametric Information Geometry to Statistical Physics

Directory of Open Access Journals (Sweden)

Giovanni Pistone

2013-09-01

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

Nonparametric Estimation of Distributions in Random Effects Models

KAUST Repository

Hart, Jeffrey D.

2011-01-01

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

Nonparametric predictive inference in statistical process control

NARCIS (Netherlands)

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

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

Science.gov (United States)

Kottas, Athanasios; Behseta, Sam

2010-03-01

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

Multivariate nonparametric regression and visualization with R and applications to finance

CERN Document Server

Klemelä, Jussi

2014-01-01

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

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

Science.gov (United States)

Lu, Tao

2016-01-01

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

Bayesian nonparametric system reliability using sets of priors

NARCIS (Netherlands)

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

2016-01-01

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

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

DEFF Research Database (Denmark)

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

2013-01-01

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

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.

Teaching Nonparametric Statistics Using Student Instrumental Values.

Science.gov (United States)

Anderson, Jonathan W.; Diddams, Margaret

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

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

KAUST Repository

Wei, Jiawei

2011-07-01

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

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

Science.gov (United States)

Duc, Anh Nguyen; Wolbers, Marcel

2017-08-15

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

Investigation of MLE in nonparametric estimation methods of reliability function

International Nuclear Information System (INIS)

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

2001-01-01

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

Nonparametric identification of copula structures

KAUST Repository

Li, Bo

2013-06-01

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

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

NARCIS (Netherlands)

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

2003-01-01

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

The nonparametric bootstrap for the current status model

NARCIS (Netherlands)

Groeneboom, P.; Hendrickx, K.

2017-01-01

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

Nonparametric Regression Estimation for Multivariate Null Recurrent Processes

Directory of Open Access Journals (Sweden)

Biqing Cai

2015-04-01

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

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

Science.gov (United States)

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.

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

DEFF Research Database (Denmark)

Effraimidis, Georgios; Dahl, Christian Møller

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

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.

Non-Parametric Kinetic (NPK Analysis of Thermal Oxidation of Carbon Aerogels

Directory of Open Access Journals (Sweden)

Azadeh Seifi

2017-05-01

Full Text Available In recent years, much attention has been paid to aerogel materials (especially carbon aerogels due to their potential uses in energy-related applications, such as thermal energy storage and thermal protection systems. These open cell carbon-based porous materials (carbon aerogels can strongly react with oxygen at relatively low temperatures (~ 400°C. Therefore, it is necessary to evaluate the thermal performance of carbon aerogels in view of their energy-related applications at high temperatures and under thermal oxidation conditions. The objective of this paper is to study theoretically and experimentally the oxidation reaction kinetics of carbon aerogel using the non-parametric kinetic (NPK as a powerful method. For this purpose, a non-isothermal thermogravimetric analysis, at three different heating rates, was performed on three samples each with its specific pore structure, density and specific surface area. The most significant feature of this method, in comparison with the model-free isoconversional methods, is its ability to separate the functionality of the reaction rate with the degree of conversion and temperature by the direct use of thermogravimetric data. Using this method, it was observed that the Nomen-Sempere model could provide the best fit to the data, while the temperature dependence of the rate constant was best explained by a Vogel-Fulcher relationship, where the reference temperature was the onset temperature of oxidation. Moreover, it was found from the results of this work that the assumption of the Arrhenius relation for the temperature dependence of the rate constant led to over-estimation of the apparent activation energy (up to 160 kJ/mol that was considerably different from the values (up to 3.5 kJ/mol predicted by the Vogel-Fulcher relationship in isoconversional methods

USING A DEA MANAGEMENT TOOLTHROUGH A NONPARAMETRIC APPROACH: AN EXAMINATION OF URBAN-RURAL EFFECTS ON THAI SCHOOL EFFICIENCY

Directory of Open Access Journals (Sweden)

SANGCHAN KANTABUTRA

2009-04-01

Full Text Available This paper examines urban-rural effects on public upper-secondary school efficiency in northern Thailand. In the study, efficiency was measured by a nonparametric technique, data envelopment analysis (DEA. Urban-rural effects were examined through a Mann-Whitney nonparametric statistical test. Results indicate that urban schools appear to have access to and practice different production technologies than rural schools, and rural institutions appear to operate less efficiently than their urban counterparts. In addition, a sensitivity analysis, conducted to ascertain the robustness of the analytical framework, revealed the stability of urban-rural effects on school efficiency. Policy to improve school eff iciency should thus take varying geographical area differences into account, viewing rural and urban schools as different from one another. Moreover, policymakers might consider shifting existing resources from urban schools to rural schools, provided that the increase in overall rural efficiency would be greater than the decrease, if any, in the city. Future research directions are discussed.

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

CERN Document Server

Taskinen, Sara

2015-01-01

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

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

Science.gov (United States)

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.

Adaptive nonparametric Bayesian inference using location-scale mixture priors

NARCIS (Netherlands)

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

2010-01-01

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

A Nonparametric Bayesian Approach For Emission Tomography Reconstruction

International Nuclear Information System (INIS)

Barat, Eric; Dautremer, Thomas

2007-01-01

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

International Conference on Robust Rank-Based and Nonparametric Methods

CERN Document Server

McKean, Joseph

2016-01-01

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

Bayesian nonparametric dictionary learning for compressed sensing MRI.

Science.gov (United States)

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

2014-12-01

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

Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.

Science.gov (United States)

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

2015-01-01

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

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Directory of Open Access Journals (Sweden)

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.

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...

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

Science.gov (United States)

Zhao, Zhibiao

2011-06-01

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

Uncertainty in decision models analyzing cost-effectiveness : The joint distribution of incremental costs and effectiveness evaluated with a nonparametric bootstrap method

NARCIS (Netherlands)

Hunink, Maria; Bult, J.R.; De Vries, J; Weinstein, MC

1998-01-01

Purpose. To illustrate the use of a nonparametric bootstrap method in the evaluation of uncertainty in decision models analyzing cost-effectiveness. Methods. The authors reevaluated a previously published cost-effectiveness analysis that used a Markov model comparing initial percutaneous

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...

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

Science.gov (United States)

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

2018-02-01

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

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Directory of Open Access Journals (Sweden)

Alejandro Jara

2011-04-01

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

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

Nonparametric estimation of benchmark doses in environmental risk assessment

Science.gov (United States)

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

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.

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

Directory of Open Access Journals (Sweden)

Mohamed Chikhi

2018-02-01

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

On the robust nonparametric regression estimation for a functional regressor

OpenAIRE

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

2009-01-01

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

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

Directory of Open Access Journals (Sweden)

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.

Bayesian Nonparametric Clustering for Positive Definite Matrices.

Science.gov (United States)

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.

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

Science.gov (United States)

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

2016-01-01

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

Nonparametric combinatorial sequence models.

Science.gov (United States)

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

2011-11-01

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

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

Science.gov (United States)

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

2018-03-01

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

Genomic breeding value estimation using nonparametric additive regression models

Directory of Open Access Journals (Sweden)

Solberg Trygve

2009-01-01

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

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

DEFF Research Database (Denmark)

Høg, Esben

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

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

DEFF Research Database (Denmark)

Høg, Esben

2003-01-01

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

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

Science.gov (United States)

Yau, Christopher; Holmes, Chris

2011-07-01

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

A Nonparametric Test for Seasonal Unit Roots

OpenAIRE

Kunst, Robert M.

2009-01-01

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

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

OpenAIRE

Froelich, Markus; Puhani, Patrick

2004-01-01

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

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 semi-nonparametric mixture model for selecting functionally consistent proteins.

Science.gov (United States)

Yu, Lianbo; Doerge, Rw

2010-09-28

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

1st Conference of the International Society for Nonparametric Statistics

CERN Document Server

Lahiri, S; Politis, Dimitris

2014-01-01

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

On Parametric (and Non-Parametric Variation

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.

Non-parametric estimation of the individual's utility map

OpenAIRE

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

2013-01-01

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

Nonparametric Bayesian models through probit stick-breaking processes.

Science.gov (United States)

Rodríguez, Abel; Dunson, David B

2011-03-01

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

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

CSIR Research Space (South Africa)

Van der Walt, CM

2013-12-01

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

A general approach to posterior contraction in nonparametric inverse problems

NARCIS (Netherlands)

Knapik, Bartek; Salomond, Jean Bernard

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

A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.

Science.gov (United States)

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.

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

Science.gov (United States)

Zhang, Tingting; Kou, S C

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.

A Bayesian Beta-Mixture Model for Nonparametric IRT (BBM-IRT)

Science.gov (United States)

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…

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...

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)

Exact nonparametric inference for detection of nonlinear determinism

OpenAIRE

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

2005-01-01

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

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

Science.gov (United States)

Chen, Tianlei; Du, Pang

2018-05-10

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

Notes on the Implementation of Non-Parametric Statistics within the Westinghouse Realistic Large Break LOCA Evaluation Model (ASTRUM)

International Nuclear Information System (INIS)

Frepoli, Cesare; Oriani, Luca

2006-01-01

In recent years, non-parametric or order statistics methods have been widely used to assess the impact of the uncertainties within Best-Estimate LOCA evaluation models. The bounding of the uncertainties is achieved with a direct Monte Carlo sampling of the uncertainty attributes, with the minimum trial number selected to 'stabilize' the estimation of the critical output values (peak cladding temperature (PCT), local maximum oxidation (LMO), and core-wide oxidation (CWO A non-parametric order statistics uncertainty analysis was recently implemented within the Westinghouse Realistic Large Break LOCA evaluation model, also referred to as 'Automated Statistical Treatment of Uncertainty Method' (ASTRUM). The implementation or interpretation of order statistics in safety analysis is not fully consistent within the industry. This has led to an extensive public debate among regulators and researchers which can be found in the open literature. The USNRC-approved Westinghouse method follows a rigorous implementation of the order statistics theory, which leads to the execution of 124 simulations within a Large Break LOCA analysis. This is a solid approach which guarantees that a bounding value (at 95% probability) of the 95 th percentile for each of the three 10 CFR 50.46 ECCS design acceptance criteria (PCT, LMO and CWO) is obtained. The objective of this paper is to provide additional insights on the ASTRUM statistical approach, with a more in-depth analysis of pros and cons of the order statistics and of the Westinghouse approach in the implementation of this statistical methodology. (authors)

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

Science.gov (United States)

Kumar, Sricharan; Srivistava, Ashok N.

2012-01-01

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

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

Science.gov (United States)

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

2017-06-01

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

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

Science.gov (United States)

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

2017-06-01

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

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

Science.gov (United States)

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

2018-01-01

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

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

Science.gov (United States)

Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

2000-09-01

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

Nonparametric statistics a step-by-step approach

CERN Document Server

Corder, Gregory W

2014-01-01

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

A structural nonparametric reappraisal of the CO2 emissions-income relationship

NARCIS (Netherlands)

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

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

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

DEFF Research Database (Denmark)

Hansen, Martin Bøgsted

2005-01-01

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

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

Science.gov (United States)

Du, Pang; Tang, Liansheng

2009-01-30

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

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

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

DEFF Research Database (Denmark)

Georgiadis, Stylianos; Limnios, Nikolaos

2016-01-01

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

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

NARCIS (Netherlands)

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

2010-01-01

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

Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression

NARCIS (Netherlands)

Yoo, W.W.; Ghosal, S

2016-01-01

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

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

NARCIS (Netherlands)

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

2012-01-01

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

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

Science.gov (United States)

Pan, Wei

2003-07-22

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

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.

International comparisons of the technical efficiency of the hospital sector: panel data analysis of OECD countries using parametric and non-parametric approaches.

Science.gov (United States)

Varabyova, Yauheniya; Schreyögg, Jonas

2013-09-01

There is a growing interest in the cross-country comparisons of the performance of national health care systems. The present work provides a comparison of the technical efficiency of the hospital sector using unbalanced panel data from OECD countries over the period 2000-2009. The estimation of the technical efficiency of the hospital sector is performed using nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Internal and external validity of findings is assessed by estimating the Spearman rank correlations between the results obtained in different model specifications. The panel-data analyses using two-step DEA and one-stage SFA show that countries, which have higher health care expenditure per capita, tend to have a more technically efficient hospital sector. Whether the expenditure is financed through private or public sources is not related to the technical efficiency of the hospital sector. On the other hand, the hospital sector in countries with higher income inequality and longer average hospital length of stay is less technically efficient. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

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...

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

CERN Document Server

Kulchitskii, Yu A

2000-01-01

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

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...

TRANSIT TIMING OBSERVATIONS FROM KEPLER. II. CONFIRMATION OF TWO MULTIPLANET SYSTEMS VIA A NON-PARAMETRIC CORRELATION ANALYSIS

International Nuclear Information System (INIS)

Ford, Eric B.; Moorhead, Althea V.; Morehead, Robert C.; Fabrycky, Daniel C.; Steffen, Jason H.; Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Ragozzine, Darin; Charbonneau, David; Lissauer, Jack J.; Rowe, Jason F.; Borucki, William J.; Bryson, Stephen T.; Burke, Christopher J.; Caldwell, Douglas A.; Welsh, William F.; Allen, Christopher; Batalha, Natalie M.; Buchhave, Lars A.

2012-01-01

We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies is in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the TTVs of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple-planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.

TRANSIT TIMING OBSERVATIONS FROM KEPLER. II. CONFIRMATION OF TWO MULTIPLANET SYSTEMS VIA A NON-PARAMETRIC CORRELATION ANALYSIS

Energy Technology Data Exchange (ETDEWEB)

Ford, Eric B.; Moorhead, Althea V.; Morehead, Robert C. [Astronomy Department, University of Florida, 211 Bryant Space Sciences Center, Gainesville, FL 32611 (United States); Fabrycky, Daniel C. [UCO/Lick Observatory, University of California, Santa Cruz, CA 95064 (United States); Steffen, Jason H. [Fermilab Center for Particle Astrophysics, P.O. Box 500, MS 127, Batavia, IL 60510 (United States); Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Ragozzine, Darin; Charbonneau, David [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Lissauer, Jack J.; Rowe, Jason F.; Borucki, William J.; Bryson, Stephen T.; Burke, Christopher J.; Caldwell, Douglas A. [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Welsh, William F. [Astronomy Department, San Diego State University, San Diego, CA 92182-1221 (United States); Allen, Christopher [Orbital Sciences Corporation/NASA Ames Research Center, Moffett Field, CA 94035 (United States); Batalha, Natalie M. [Department of Physics and Astronomy, San Jose State University, San Jose, CA 95192 (United States); Buchhave, Lars A., E-mail: eford@astro.ufl.edu [Niels Bohr Institute, Copenhagen University, DK-2100 Copenhagen (Denmark); Collaboration: Kepler Science Team; and others

2012-05-10

We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies is in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the TTVs of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple-planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.

Transit Timing Observations from Kepler: II. Confirmation of Two Multiplanet Systems via a Non-parametric Correlation Analysis

Energy Technology Data Exchange (ETDEWEB)

Ford, Eric B.; /Florida U.; Fabrycky, Daniel C.; /Lick Observ.; Steffen, Jason H.; /Fermilab; Carter, Joshua A.; /Harvard-Smithsonian Ctr. Astrophys.; Fressin, Francois; /Harvard-Smithsonian Ctr. Astrophys.; Holman, Matthew J.; /Harvard-Smithsonian Ctr. Astrophys.; Lissauer, Jack J.; /NASA, Ames; Moorhead, Althea V.; /Florida U.; Morehead, Robert C.; /Florida U.; Ragozzine, Darin; /Harvard-Smithsonian Ctr. Astrophys.; Rowe, Jason F.; /NASA, Ames /SETI Inst., Mtn. View /San Diego State U., Astron. Dept.

2012-01-01

We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the transit timing variations of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.

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

Energy Technology Data Exchange (ETDEWEB)

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

2008-03-07

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

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

Science.gov (United States)

Kenderi, Gábor; Fidlin, Alexander

2014-12-01

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

Nonparametric estimation of stochastic differential equations with sparse Gaussian processes.

Science.gov (United States)

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.

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

NARCIS (Netherlands)

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

2008-01-01

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

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

NARCIS (Netherlands)

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

2016-01-01

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

kruX: matrix-based non-parametric eQTL discovery.

Science.gov (United States)

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

2014-01-14

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

A nonparametric mean-variance smoothing method to assess Arabidopsis cold stress transcriptional regulator CBF2 overexpression microarray data.

Science.gov (United States)

Hu, Pingsha; Maiti, Tapabrata

2011-01-01

Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is available from the authors on request.

Exact nonparametric confidence bands for the survivor function.

Science.gov (United States)

Matthews, David

2013-10-12

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

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

NARCIS (Netherlands)

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

2006-01-01

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

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

KAUST Repository

Dai, Wenlin; Tong, Tiejun; Zhu, Lixing

2017-01-01

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

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

KAUST Repository

Dai, Wenlin

2017-09-01

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

A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production

International Nuclear Information System (INIS)

Khoshroo, Alireza; Mulwa, Richard; Emrouznejad, Ali; Arabi, Behrouz

2013-01-01

Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production. In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming. The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. - Highlights: • The focus of this paper is to identify excessive use of energy and optimize energy consumption in grape production. • We measure the efficiency as a function of labor/machinery/chemicals/farmyard manure/diesel-fuel/electricity/water. • Data were obtained from 41 grape

Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data

OpenAIRE

CORNELIS A. LOS

2004-01-01

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

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

Directory of Open Access Journals (Sweden)

Xibin Zhang

2016-04-01

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

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

DEFF Research Database (Denmark)

Linnet, Kristian

2005-01-01

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

Assessing Goodness of Fit in Item Response Theory with Nonparametric Models: A Comparison of Posterior Probabilities and Kernel-Smoothing Approaches

Science.gov (United States)

Sueiro, Manuel J.; Abad, Francisco J.

2011-01-01

The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…

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

Directory of Open Access Journals (Sweden)

Ibsen Chivatá Cárdenas

2008-05-01

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

Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran

Science.gov (United States)

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.

Nonparametric estimation of location and scale parameters

KAUST Repository

Potgieter, C.J.

2012-12-01

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

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

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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.

Hyperspectral image segmentation using a cooperative nonparametric approach

Science.gov (United States)

Taher, Akar; Chehdi, Kacem; Cariou, Claude

2013-10-01

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

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.

Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies.

Science.gov (United States)

Savitsky, Terrance; Vannucci, Marina; Sha, Naijun

2011-02-01

This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data. Our specific interest is in the analysis of data sets with predictors that have an a priori unknown form of possibly nonlinear associations to the response. The modeling approach we describe incorporates Gaussian processes in a generalized linear model framework to obtain a class of nonparametric regression models where the covariance matrix depends on the predictors. We consider, in particular, continuous, categorical and count responses. We also look into models that account for survival outcomes. We explore alternative covariance formulations for the Gaussian process prior and demonstrate the flexibility of the construction. Next, we focus on the important problem of selecting variables from the set of possible predictors and describe a general framework that employs mixture priors. We compare alternative MCMC strategies for posterior inference and achieve a computationally efficient and practical approach. We demonstrate performances on simulated and benchmark data sets.

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...

Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.

Science.gov (United States)

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.

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 ...

Proposing a framework for airline service quality evaluation using Type-2 Fuzzy TOPSIS and non-parametric analysis

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

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.

Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R

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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

Nonparametric inference of network structure and dynamics

Science.gov (United States)

Peixoto, Tiago P.

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

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

OpenAIRE

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

2008-01-01

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

Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality

OpenAIRE

Li, Zhanchao; Gu, Chongshi; Wu, Zhongru

2013-01-01

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

A non-parametric framework for estimating threshold limit values

Directory of Open Access Journals (Sweden)

Ulm Kurt

2005-11-01

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

Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis.

Science.gov (United States)

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.

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 trend estimation in the presence of fractal noise: application to fMRI time-series analysis.

Science.gov (United States)

Afshinpour, Babak; Hossein-Zadeh, Gholam-Ali; Soltanian-Zadeh, Hamid

2008-06-30

Unknown low frequency fluctuations called "trend" are observed in noisy time-series measured for different applications. In some disciplines, they carry primary information while in other fields such as functional magnetic resonance imaging (fMRI) they carry nuisance effects. In all cases, however, it is necessary to estimate them accurately. In this paper, a method for estimating trend in the presence of fractal noise is proposed and applied to fMRI time-series. To this end, a partly linear model (PLM) is fitted to each time-series. The parametric and nonparametric parts of PLM are considered as contributions of hemodynamic response and trend, respectively. Using the whitening property of wavelet transform, the unknown components of the model are estimated in the wavelet domain. The results of the proposed method are compared to those of other parametric trend-removal approaches such as spline and polynomial models. It is shown that the proposed method improves activation detection and decreases variance of the estimated parameters relative to the other methods.

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

OpenAIRE

Kappus, Johanna

2013-01-01

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

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

International Nuclear Information System (INIS)

Veglia, A.

1981-08-01

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

Nonparametric Integrated Agrometeorological Drought Monitoring: Model Development and Application

Science.gov (United States)

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.

A Non-Parametric Delphi Approach to Foster Innovation Policy Debate in Spain

Directory of Open Access Journals (Sweden)

Juan Carlos Salazar-Elena

2016-05-01

Full Text Available The aim of this paper is to identify some changes needed in Spain’s innovation policy to fill the gap between its innovation results and those of other European countries in lieu of sustainable leadership. To do this we apply the Delphi methodology to experts from academia, business, and government. To overcome the shortcomings of traditional descriptive methods, we develop an inferential analysis by following a non-parametric bootstrap method which enables us to identify important changes that should be implemented. Particularly interesting is the support found for improving the interconnections among the relevant agents of the innovation system (instead of focusing exclusively in the provision of knowledge and technological inputs through R and D activities, or the support found for “soft” policy instruments aimed at providing a homogeneous framework to assess the innovation capabilities of firms (e.g., for funding purposes. Attention to potential innovators among small and medium enterprises (SMEs and traditional industries is particularly encouraged by experts.

Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

Directory of Open Access Journals (Sweden)

Xiaochun Sun

Full Text Available Genomic selection (GS procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA and reproducing kernel Hilbert spaces (RKHS regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

Science.gov (United States)

Sun, Xiaochun; Ma, Ping; Mumm, Rita H

2012-01-01

Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

Separating environmental efficiency into production and abatement efficiency. A nonparametric model with application to U.S. power plants

Energy Technology Data Exchange (ETDEWEB)

Hampf, Benjamin

2011-08-15

In this paper we present a new approach to evaluate the environmental efficiency of decision making units. We propose a model that describes a two-stage process consisting of a production and an end-of-pipe abatement stage with the environmental efficiency being determined by the efficiency of both stages. Taking the dependencies between the two stages into account, we show how nonparametric methods can be used to measure environmental efficiency and to decompose it into production and abatement efficiency. For an empirical illustration we apply our model to an analysis of U.S. power plants.

A Bayesian Nonparametric Meta-Analysis Model

Science.gov (United States)

Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.

2015-01-01

In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…

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.

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...

A Bayesian nonparametric approach to causal inference on quantiles.

Science.gov (United States)

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

2018-02-25

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

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....

Debt and growth: A non-parametric approach

Science.gov (United States)

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

2017-11-01

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

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

DEFF Research Database (Denmark)

Frydman, Halina; Gerds, Thomas; Grøn, Randi

2013-01-01

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

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

DEFF Research Database (Denmark)

Ramirez, José Rangel; Sørensen, John Dalsgaard

2011-01-01

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

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

NARCIS (Netherlands)

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

2015-01-01

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

Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling

Science.gov (United States)

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.

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

An Instructional Module on Mokken Scale Analysis

Science.gov (United States)

Wind, Stefanie A.

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Joaquín Texeira Quirós

2013-09-01

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

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

CERN Document Server

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

2002-01-01

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

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

Science.gov (United States)

De Gooijer, Jan G.; Sivarajasingham, Selliah

2008-04-01

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

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Science.gov (United States)

Tokuda, Tomoki; Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Directory of Open Access Journals (Sweden)

Tomoki Tokuda

Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

Science.gov (United States)

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

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.

Two-component mixture cure rate model with spline estimated nonparametric components.

Science.gov (United States)

Wang, Lu; Du, Pang; Liang, Hua

2012-09-01

In some survival analysis of medical studies, there are often long-term survivors who can be considered as permanently cured. The goals in these studies are to estimate the noncured probability of the whole population and the hazard rate of the susceptible subpopulation. When covariates are present as often happens in practice, to understand covariate effects on the noncured probability and hazard rate is of equal importance. The existing methods are limited to parametric and semiparametric models. We propose a two-component mixture cure rate model with nonparametric forms for both the cure probability and the hazard rate function. Identifiability of the model is guaranteed by an additive assumption that allows no time-covariate interactions in the logarithm of hazard rate. Estimation is carried out by an expectation-maximization algorithm on maximizing a penalized likelihood. For inferential purpose, we apply the Louis formula to obtain point-wise confidence intervals for noncured probability and hazard rate. Asymptotic convergence rates of our function estimates are established. We then evaluate the proposed method by extensive simulations. We analyze the survival data from a melanoma study and find interesting patterns for this study. © 2011, The International Biometric Society.

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

OpenAIRE

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

2004-01-01

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

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.

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

Science.gov (United States)

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

2014-07-15

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

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

Science.gov (United States)

Bhattacharjee, A.; Sutradhar, A.

2012-12-01

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

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

Science.gov (United States)

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

2012-08-15

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

Bayesian Analysis for Penalized Spline Regression Using WinBUGS

Directory of Open Access Journals (Sweden)

Ciprian M. Crainiceanu

2005-09-01

Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.

Prior processes and their applications nonparametric Bayesian estimation

CERN Document Server

Phadia, Eswar G

2016-01-01

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

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

Science.gov (United States)

Guhaniyogi, Rajarshi

2017-11-10

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

Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

CERN Document Server

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 ...

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

Science.gov (United States)

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

2012-01-01

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

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

Science.gov (United States)

Maydeu-Olivares, Albert

2005-01-01

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

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

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Riihimäki, Jaakko; Sund, Reijo; Vehtari, Aki

2010-06-01

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

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

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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.

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

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

2017-03-01

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

A comparison of parametric and nonparametric methods for normalising cDNA microarray data.

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Khondoker, Mizanur R; Glasbey, Chris A; Worton, Bruce J

2007-12-01

Normalisation is an essential first step in the analysis of most cDNA microarray data, to correct for effects arising from imperfections in the technology. Loess smoothing is commonly used to correct for trends in log-ratio data. However, parametric models, such as the additive plus multiplicative variance model, have been preferred for scale normalisation, though the variance structure of microarray data may be of a more complex nature than can be accommodated by a parametric model. We propose a new nonparametric approach that incorporates location and scale normalisation simultaneously using a Generalised Additive Model for Location, Scale and Shape (GAMLSS, Rigby and Stasinopoulos, 2005, Applied Statistics, 54, 507-554). We compare its performance in inferring differential expression with Huber et al.'s (2002, Bioinformatics, 18, 96-104) arsinh variance stabilising transformation (AVST) using real and simulated data. We show GAMLSS to be as powerful as AVST when the parametric model is correct, and more powerful when the model is wrong. (c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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

Energy Technology Data Exchange (ETDEWEB)

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

2009-07-01

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

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

Science.gov (United States)

Ponciano, José Miguel

2017-11-22

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

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

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Elias Chaibub Neto

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

Estimation of the lifetime distribution of mechatronic systems in the presence of a covariate: A comparison among parametric, semiparametric and nonparametric models

International Nuclear Information System (INIS)

Bobrowski, Sebastian; Chen, Hong; Döring, Maik; Jensen, Uwe; Schinköthe, Wolfgang

2015-01-01

In practice manufacturers may have lots of failure data of similar products using the same technology basis under different operating conditions. Thus, one can try to derive predictions for the distribution of the lifetime of newly developed components or new application environments through the existing data using regression models based on covariates. Three categories of such regression models are considered: a parametric, a semiparametric and a nonparametric approach. First, we assume that the lifetime is Weibull distributed, where its parameters are modelled as linear functions of the covariate. Second, the Cox proportional hazards model, well-known in Survival Analysis, is applied. Finally, a kernel estimator is used to interpolate between empirical distribution functions. In particular the last case is new in the context of reliability analysis. We propose a goodness of fit measure (GoF), which can be applied to all three types of regression models. Using this GoF measure we discuss a new model selection procedure. To illustrate this method of reliability prediction, the three classes of regression models are applied to real test data of motor experiments. Further the performance of the approaches is investigated by Monte Carlo simulations. - Highlights: • We estimate the lifetime distribution in the presence of a covariate. • Three types of regression models are considered and compared. • A new nonparametric estimator based on our particular data structure is introduced. • We propose a goodness of fit measure and show a new model selection procedure. • A case study with real data and Monte Carlo simulations are performed

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

Science.gov (United States)

Steinhardt, Charles L.; Jermyn, Adam S.

2018-02-01

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

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

Science.gov (United States)

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

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

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

Directory of Open Access Journals (Sweden)

Urbi Garay

2016-03-01

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

Bayesian nonparametric adaptive control using Gaussian processes.

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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.

A Unified Discussion on the Concept of Score Functions Used in the Context of Nonparametric Linkage Analysis

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Lars Ängquist

2008-01-01

Full Text Available In this article we try to discuss nonparametric linkage (NPL score functions within a broad and quite general framework. The main focus of the paper is the structure, derivation principles and interpretations of the score function entity itself. We define and discuss several families of one-locus score function definitions, i.e. the implicit, explicit and optimal ones. Some generalizations and comments to the two-locus, unconditional and conditional, cases are included as well. Although this article mainly aims at serving as an overview, where the concept of score functions are put into a covering context, we generalize the noncentrality parameter (NCP optimal score functions in Ängquist et al. (2007 to facilitate—through weighting—for incorporation of several plausible distinct genetic models. Since the genetic model itself most oftenly is to some extent unknown this facilitates weaker prior assumptions with respect to plausible true disease models without loosing the property of NCP-optimality. Moreover, we discuss general assumptions and properties of score functions in the above sense. For instance, the concept of identical by descent (IBD sharing structures and score function equivalence are discussed in some detail.

Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

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.

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

Science.gov (United States)

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

2012-01-01

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

[Nonparametric method of estimating survival functions containing right-censored and interval-censored data].

Science.gov (United States)

Xu, Yonghong; Gao, Xiaohuan; Wang, Zhengxi

2014-04-01

Missing data represent a general problem in many scientific fields, especially in medical survival analysis. Dealing with censored data, interpolation method is one of important methods. However, most of the interpolation methods replace the censored data with the exact data, which will distort the real distribution of the censored data and reduce the probability of the real data falling into the interpolation data. In order to solve this problem, we in this paper propose a nonparametric method of estimating the survival function of right-censored and interval-censored data and compare its performance to SC (self-consistent) algorithm. Comparing to the average interpolation and the nearest neighbor interpolation method, the proposed method in this paper replaces the right-censored data with the interval-censored data, and greatly improves the probability of the real data falling into imputation interval. Then it bases on the empirical distribution theory to estimate the survival function of right-censored and interval-censored data. The results of numerical examples and a real breast cancer data set demonstrated that the proposed method had higher accuracy and better robustness for the different proportion of the censored data. This paper provides a good method to compare the clinical treatments performance with estimation of the survival data of the patients. This pro vides some help to the medical survival data analysis.

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

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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.

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

Science.gov (United States)

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

2018-05-01

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

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

NARCIS (Netherlands)

Jiang, GJ

1998-01-01

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

A non-parametric peak calling algorithm for DamID-Seq.

Directory of Open Access Journals (Sweden)

Renhua Li

Full Text Available Protein-DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS of double sex (DSX-an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq. One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only. After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1 reads resampling; 2 reads scaling (normalization and computing signal-to-noise fold changes; 3 filtering; 4 Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC. We also used irreproducible discovery rate (IDR analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.

A non-parametric peak calling algorithm for DamID-Seq.

Science.gov (United States)

Li, Renhua; Hempel, Leonie U; Jiang, Tingbo

2015-01-01

Protein-DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS) of double sex (DSX)-an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID) technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq). One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only). After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1) reads resampling; 2) reads scaling (normalization) and computing signal-to-noise fold changes; 3) filtering; 4) Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC). We also used irreproducible discovery rate (IDR) analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.

[Do we always correctly interpret the results of statistical nonparametric tests].

Science.gov (United States)

Moczko, Jerzy A

2014-01-01

Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman tests create a group of commonly used tests to analyze the results of clinical and laboratory data. These tests are considered to be extremely flexible and their asymptotic relative efficiency exceeds 95 percent. Compared with the corresponding parametric tests they do not require checking the fulfillment of the conditions such as the normality of data distribution, homogeneity of variance, the lack of correlation means and standard deviations, etc. They can be used both in the interval and or-dinal scales. The article presents an example Mann-Whitney test, that does not in any case the choice of these four nonparametric tests treated as a kind of gold standard leads to correct inference.

Nonparametric predictive inference for combined competing risks data

International Nuclear Information System (INIS)

Coolen-Maturi, Tahani; Coolen, Frank P.A.

2014-01-01

The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies

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)

Bayesian nonparametric generative models for causal inference with missing at random covariates.

Science.gov (United States)

Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J

2018-03-26

We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients. © 2018, The International Biometric Society.

Benchmark of the non-parametric Bayesian deconvolution method implemented in the SINBAD code for X/γ rays spectra processing

Energy Technology Data Exchange (ETDEWEB)

Rohée, E. [CEA, LIST, Laboratoire Capteurs et Architectures Electroniques, F-91191 Gif-sur-Yvette (France); Coulon, R., E-mail: romain.coulon@cea.fr [CEA, LIST, Laboratoire Capteurs et Architectures Electroniques, F-91191 Gif-sur-Yvette (France); Carrel, F. [CEA, LIST, Laboratoire Capteurs et Architectures Electroniques, F-91191 Gif-sur-Yvette (France); Dautremer, T.; Barat, E.; Montagu, T. [CEA, LIST, Laboratoire de Modélisation et Simulation des Systèmes, F-91191 Gif-sur-Yvette (France); Normand, S. [CEA, DAM, Le Ponant, DPN/STXN, F-75015 Paris (France); Jammes, C. [CEA, DEN, Cadarache, DER/SPEx/LDCI, F-13108 Saint-Paul-lez-Durance (France)

2016-11-11

Radionuclide identification and quantification are a serious concern for many applications as for in situ monitoring at nuclear facilities, laboratory analysis, special nuclear materials detection, environmental monitoring, and waste measurements. High resolution gamma-ray spectrometry based on high purity germanium diode detectors is the best solution available for isotopic identification. Over the last decades, methods have been developed to improve gamma spectra analysis. However, some difficulties remain in the analysis when full energy peaks are folded together with high ratio between their amplitudes, and when the Compton background is much larger compared to the signal of a single peak. In this context, this study deals with the comparison between a conventional analysis based on “iterative peak fitting deconvolution” method and a “nonparametric Bayesian deconvolution” approach developed by the CEA LIST and implemented into the SINBAD code. The iterative peak fit deconvolution is used in this study as a reference method largely validated by industrial standards to unfold complex spectra from HPGe detectors. Complex cases of spectra are studied from IAEA benchmark protocol tests and with measured spectra. The SINBAD code shows promising deconvolution capabilities compared to the conventional method without any expert parameter fine tuning.

Nonparametric predictive inference for reliability of a k-out-of-m:G system with multiple component types

International Nuclear Information System (INIS)

Aboalkhair, Ahmad M.; Coolen, Frank P.A.; MacPhee, Iain M.

2014-01-01

Nonparametric predictive inference for system reliability has recently been presented, with specific focus on k-out-of-m:G systems. The reliability of systems is quantified by lower and upper probabilities of system functioning, given binary test results on components, taking uncertainty about component functioning and indeterminacy due to limited test information explicitly into account. Thus far, systems considered were series configurations of subsystems, with each subsystem i a k i -out-of-m i :G system which consisted of only one type of components. Key results are briefly summarized in this paper, and as an important generalization new results are presented for a single k-out-of-m:G system consisting of components of multiple types. The important aspects of redundancy and diversity for such systems are discussed. - Highlights: • New results on nonparametric predictive inference for system reliability. • Prediction of system reliability based on test data for components. • New insights on system redundancy optimization and diversity. • Components that appear inferior in tests may be included to enhance redundancy

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

International Nuclear Information System (INIS)

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

2010-01-01

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

The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

OpenAIRE

Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric

2017-01-01

This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...

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.

Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies.

Science.gov (United States)

Palacios, Julia A; Minin, Vladimir N

2013-03-01

Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method. Copyright © 2013, The International Biometric Society.

Multilevel Latent Class Analysis: Parametric and Nonparametric Models

Science.gov (United States)

Finch, W. Holmes; French, Brian F.

2014-01-01

Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…

Nonparametric Bayes Classification and Hypothesis Testing on Manifolds

Science.gov (United States)

Bhattacharya, Abhishek; Dunson, David

2012-01-01

Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provide simple sufficient conditions for large support and weak and strong posterior consistency in estimating both the joint distribution of the response and predictors and the conditional distribution of the response. Focusing on a Dirichlet process prior for the mixing measure, these conditions hold using von Mises-Fisher kernels when the manifold is the unit hypersphere. In this case, Bayesian methods are developed for efficient posterior computation using slice sampling. Next we develop Bayesian nonparametric methods for testing whether there is a difference in distributions between groups of observations on the manifold having unknown densities. We prove consistency of the Bayes factor and develop efficient computational methods for its calculation. The proposed classification and testing methods are evaluated using simulation examples and applied to spherical data applications. PMID:22754028

Nonparametric tests for equality of psychometric functions.

Science.gov (United States)

García-Pérez, Miguel A; Núñez-Antón, Vicente

2017-12-07

Many empirical studies measure psychometric functions (curves describing how observers' performance varies with stimulus magnitude) because these functions capture the effects of experimental conditions. To assess these effects, parametric curves are often fitted to the data and comparisons are carried out by testing for equality of mean parameter estimates across conditions. This approach is parametric and, thus, vulnerable to violations of the implied assumptions. Furthermore, testing for equality of means of parameters may be misleading: Psychometric functions may vary meaningfully across conditions on an observer-by-observer basis with no effect on the mean values of the estimated parameters. Alternative approaches to assess equality of psychometric functions per se are thus needed. This paper compares three nonparametric tests that are applicable in all situations of interest: The existing generalized Mantel-Haenszel test, a generalization of the Berry-Mielke test that was developed here, and a split variant of the generalized Mantel-Haenszel test also developed here. Their statistical properties (accuracy and power) are studied via simulation and the results show that all tests are indistinguishable as to accuracy but they differ non-uniformly as to power. Empirical use of the tests is illustrated via analyses of published data sets and practical recommendations are given. The computer code in MATLAB and R to conduct these tests is available as Electronic Supplemental Material.

Nonparametric autocovariance estimation from censored time series by Gaussian imputation.

Science.gov (United States)

Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K

2009-02-01

One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.

Assessing T cell clonal size distribution: a non-parametric approach.

Science.gov (United States)

Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V

2014-01-01

Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.

Survival Analysis

CERN Document Server

Miller, Rupert G

2011-01-01

A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.

A local non-parametric model for trade sign inference

Science.gov (United States)

Blazejewski, Adam; Coggins, Richard

2005-03-01

We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.

Rank-based permutation approaches for non-parametric factorial designs.

Science.gov (United States)

Umlauft, Maria; Konietschke, Frank; Pauly, Markus

2017-11-01

Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.

Does the high–tech industry consistently reduce CO{sub 2} emissions? Results from nonparametric additive regression model

Energy Technology Data Exchange (ETDEWEB)

Xu, Bin [School of Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013 (China); Research Center of Applied Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013 (China); Lin, Boqiang, E-mail: bqlin@xmu.edu.cn [Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Xiamen, Fujian 361005 (China)

2017-03-15

China is currently the world's largest carbon dioxide (CO{sub 2}) emitter. Moreover, total energy consumption and CO{sub 2} emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO{sub 2} emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO{sub 2} emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Based on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO{sub 2} emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO{sub 2} emissions in the three regions should be given adequate attention in developing effective abatement policies. - Highlights: • The nonlinear effect of the high–tech industry on CO{sub 2} emissions was investigated. • The high–tech industry yields an inverted “U–shaped” effect in the eastern region. • The high–tech industry has a positive “U–shaped” nonlinear effect in other regions. • The linear impact

Economic decision making and the application of nonparametric prediction models

Science.gov (United States)

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2008-01-01

Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

A nonparametric approach to calculate critical micelle concentrations: the local polynomial regression method

Energy Technology Data Exchange (ETDEWEB)

Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)

2004-02-01

The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

KAUST Repository

Tekwe, C. D.; Carroll, R. J.; Dabney, A. R.

2012-01-01

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

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.

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.

Nonparametric method for failures detection and localization in the actuating subsystem of aircraft control system

Science.gov (United States)

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.

Nonparametric estimation of age-specific reference percentile curves with radial smoothing.

Science.gov (United States)

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.

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)

Nonparametric estimation of the heterogeneity of a random medium using compound Poisson process modeling of wave multiple scattering.

Science.gov (United States)

Le Bihan, Nicolas; Margerin, Ludovic

2009-07-01

In this paper, we present a nonparametric method to estimate the heterogeneity of a random medium from the angular distribution of intensity of waves transmitted through a slab of random material. Our approach is based on the modeling of forward multiple scattering using compound Poisson processes on compact Lie groups. The estimation technique is validated through numerical simulations based on radiative transfer theory.

An improved nonparametric lower bound of species richness via a modified good-turing frequency formula.

Science.gov (United States)

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.

Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation

Science.gov (United States)

Lee, Kit-Hang; Fu, Denny K.C.; Leong, Martin C.W.; Chow, Marco; Fu, Hing-Choi; Althoefer, Kaspar; Sze, Kam Yim; Yeung, Chung-Kwong

2017-01-01

Abstract Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments. PMID:29251567

Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment

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...

Nonparametric inference in nonlinear principal components analysis : exploration and beyond

NARCIS (Netherlands)

Linting, Mariëlle

2007-01-01

In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA),

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

Directory of Open Access Journals (Sweden)

César Merino Soto

2009-05-01

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

Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a Savanna landscape

CSIR Research Space (South Africa)

Cho, Moses A

2012-11-01

Full Text Available ) and Random Forest (RF)). The spectral data used consisted of 8 WorldView-2 multispectral bands simulated from 72 VNIR bands image acquired over the study areas using the Carnegie Airborne Observatory (CAO) system. With the exception of SAM, the nonparametric...

Parametric Methods for Order Tracking Analysis

DEFF Research Database (Denmark)

Nielsen, Jesper Kjær; Jensen, Tobias Lindstrøm

2017-01-01

Order tracking analysis is often used to find the critical speeds at which structural resonances are excited by a rotating machine. Typically, order tracking analysis is performed via non-parametric methods. In this report, however, we demonstrate some of the advantages of using a parametric method...

Efficient nonparametric n -body force fields from machine learning

Science.gov (United States)

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.

Multiple predictor smoothing methods for sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Helton, Jon Craig; Storlie, Curtis B.

2006-08-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.

Multiple predictor smoothing methods for sensitivity analysis

International Nuclear Information System (INIS)

Helton, Jon Craig; Storlie, Curtis B.

2006-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Nonparametric Second-Order Theory of Error Propagation on Motion Groups.

Science.gov (United States)

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.

Energy-saving and emission-abatement potential of Chinese coal-fired power enterprise: A non-parametric analysis

International Nuclear Information System (INIS)

Wei, Chu; Löschel, Andreas; Liu, Bing

2015-01-01

In the context of soaring demand for electricity, mitigating and controlling greenhouse gas emissions is a great challenge for China's power sector. Increasing attention has been placed on the evaluation of energy efficiency and CO 2 abatement potential in the power sector. However, studies at the micro-level are relatively rare due to serious data limitations. This study uses the 2004 and 2008 Census data of Zhejiang province to construct a non-parametric frontier in order to assess the abatement space of energy and associated CO 2 emission from China's coal-fired power enterprises. A Weighted Russell Directional Distance Function (WRDDF) is applied to construct an energy-saving potential index and a CO 2 emission-abatement potential index. Both indicators depict the inefficiency level in terms of energy utilization and CO 2 emissions of electric power plants. Our results show a substantial variation of energy-saving potential and CO 2 abatement potential among enterprises. We find that large power enterprises are less efficient in 2004, but become more efficient than smaller enterprises in 2008. State-owned enterprises (SOE) are not significantly different in 2008 from 2004, but perform better than their non-SOE counterparts in 2008. This change in performance for large enterprises and SOE might be driven by the “top-1000 Enterprise Energy Conservation Action” that was implemented in 2006. - Highlights: • Energy-saving potential and CO 2 abatement-potential for Chinese power enterprise are evaluated. • The potential to curb energy and emission shows great variation and dynamic changes. • Large enterprise is less efficient than small enterprise in 2004, but more efficient in 2008. • The state-owned enterprise performs better than non-state-owned enterprise in 2008

The geometry of distributional preferences and a non-parametric identification approach: The Equality Equivalence Test.

Science.gov (United States)

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.

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.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

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.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

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.

Application of a nonparametric approach to analyze delta-pCO2 data from the Southern Ocean

CSIR Research Space (South Africa)

Pretorius, WB

2011-11-01

Full Text Available NONPARAMETRIC APPROACH TO ANALYZE ?pCO2 DATA FROM THE SOUTHERN OCEAN Wesley B. Pretorius*1, Sonali Das2 and Paul J. Mostert1 *1 Cell: 0722897595, Department of Statistics and Actuarial Science, University of Stellenbosch, Private Bag X1, Matieland 7602...(9-10):1601-1622. Tans, P.P., Fung, I. Y. & Takahashi, T. 1990 Observational constraints on the global atmospheric CO2-budget. Science, 247:1431-1438. Telszewski, M., Chazottes, A., Schuster, U., Watson, A.J., Moulin, C., Bakker, D.C.E., Gonzalez-Davila, M...

Assessing systematic risk in the S&P500 index between 2000 and 2011: A Bayesian nonparametric approach

OpenAIRE

Rodríguez, Abel; Wang, Ziwei; Kottas, Athanasios

2017-01-01

We develop a Bayesian nonparametric model to assess the effect of systematic risks on multiple financial markets, and apply it to understand the behavior of the S&P500 sector indexes between January 1, 2000 and December 31, 2011. More than prediction, our main goal is to understand the evolution of systematic and idiosyncratic risks in the U.S. economy over this particular time period, leading to novel sector-specific risk indexes. To accomplish this goal, we model the appearance of extreme l...

The Infinitesimal Jackknife with Exploratory Factor Analysis

Science.gov (United States)

Zhang, Guangjian; Preacher, Kristopher J.; Jennrich, Robert I.

2012-01-01

The infinitesimal jackknife, a nonparametric method for estimating standard errors, has been used to obtain standard error estimates in covariance structure analysis. In this article, we adapt it for obtaining standard errors for rotated factor loadings and factor correlations in exploratory factor analysis with sample correlation matrices. Both…

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.

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

Science.gov (United States)

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 Estimation of Targeted Agent Effects on Biomarker Change to Predict Clinical Outcome

Science.gov (United States)

Graziani, Rebecca; Guindani, Michele; Thall, Peter F.

2015-01-01

Summary The effect of a targeted agent on a cancer patient's clinical outcome putatively is mediated through the agent's effect on one or more early biological events. This is motivated by pre-clinical experiments with cells or animals that identify such events, represented by binary or quantitative biomarkers. When evaluating targeted agents in humans, central questions are whether the distribution of a targeted biomarker changes following treatment, the nature and magnitude of this change, and whether it is associated with clinical outcome. Major difficulties in estimating these effects are that a biomarker's distribution may be complex, vary substantially between patients, and have complicated relationships with clinical outcomes. We present a probabilistically coherent framework for modeling and estimation in this setting, including a hierarchical Bayesian nonparametric mixture model for biomarkers that we use to define a functional profile of pre-versus-post treatment biomarker distribution change. The functional is similar to the receiver operating characteristic used in diagnostic testing. The hierarchical model yields clusters of individual patient biomarker profile functionals, and we use the profile as a covariate in a regression model for clinical outcome. The methodology is illustrated by analysis of a dataset from a clinical trial in prostate cancer using imatinib to target platelet-derived growth factor, with the clinical aim to improve progression-free survival time. PMID:25319212

A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test.

Science.gov (United States)

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 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...

Mokken scale analysis of mental health and well-being questionnaire item responses: a non-parametric IRT method in empirical research for applied health researchers.

Science.gov (United States)

Stochl, Jan; Jones, Peter B; Croudace, Tim J

2012-06-11

Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Scalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12)--when binary scored--were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech's "well-being" and "distress" clinical scales). An illustration of ordinal item analysis confirmed that all 14 positively worded items of the Warwick-Edinburgh Mental

Mokken scale analysis of mental health and well-being questionnaire item responses: a non-parametric IRT method in empirical research for applied health researchers

Directory of Open Access Journals (Sweden)

Stochl Jan

2012-06-01

Full Text Available Abstract Background Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Methods Scalability of data from 1 a cross-sectional health survey (the Scottish Health Education Population Survey and 2 a general population birth cohort study (the National Child Development Study illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. Results and conclusions After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items we show that all items from the 12-item General Health Questionnaire (GHQ-12 – when binary scored – were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech’s “well-being” and “distress” clinical scales. An illustration of ordinal item analysis

Nonparametric Bounds and Sensitivity Analysis of Treatment Effects

Science.gov (United States)

Richardson, Amy; Hudgens, Michael G.; Gilbert, Peter B.; Fine, Jason P.

2015-01-01

This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed. PMID:25663743

Strong consistency of nonparametric Bayes density estimation on compact metric spaces with applications to specific manifolds.

Science.gov (United States)

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.

Dependence between fusion temperatures and chemical components of a certain type of coal using classical, non-parametric and bootstrap techniques

Energy Technology Data Exchange (ETDEWEB)

Gonzalez-Manteiga, W.; Prada-Sanchez, J.M.; Fiestras-Janeiro, M.G.; Garcia-Jurado, I. (Universidad de Santiago de Compostela, Santiago de Compostela (Spain). Dept. de Estadistica e Investigacion Operativa)

1990-11-01

A statistical study of the dependence between various critical fusion temperatures of a certain kind of coal and its chemical components is carried out. As well as using classical dependence techniques (multiple, stepwise and PLS regression, principal components, canonical correlation, etc.) together with the corresponding inference on the parameters of interest, non-parametric regression and bootstrap inference are also performed. 11 refs., 3 figs., 8 tabs.

Investigating the complex relationship between in situ Southern Ocean pCO2 and its ocean physics and biogeochemical drivers using a nonparametric regression approach

CSIR Research Space (South Africa)

Pretorius, W

2014-01-01

Full Text Available the relationship more accurately in terms of MSE, RMSE and MAE, than a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict p...

Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position

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.

A framework for Bayesian nonparametric inference for causal effects of mediation.

Science.gov (United States)

Kim, Chanmin; Daniels, Michael J; Marcus, Bess H; Roy, Jason A

2017-06-01

We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part 1 is a flexible model (using BNP) for the observed data distribution. Part 2 is a set of uncheckable assumptions with sensitivity parameters that in conjunction with Part 1 allows identification and estimation of the causal parameters and allows for uncertainty about these assumptions via priors on the sensitivity parameters. For Part 1, we specify a Dirichlet process mixture of multivariate normals as a prior on the joint distribution of the outcome, mediator, and covariates. This approach allows us to obtain a (simple) closed form of each marginal distribution. For Part 2, we consider two sets of assumptions: (a) the standard sequential ignorability (Imai et al., 2010) and (b) weakened set of the conditional independence type assumptions introduced in Daniels et al. (2012) and propose sensitivity analyses for both. We use this approach to assess mediation in a physical activity promotion trial. © 2016, The International Biometric Society.

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

Science.gov (United States)

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

2015-01-01

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

Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution

Directory of Open Access Journals (Sweden)

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.

Using discriminant analysis as a nucleation event classification method

Directory of Open Access Journals (Sweden)

S. Mikkonen

2006-01-01

Full Text Available More than three years of measurements of aerosol size-distribution and different gas and meteorological parameters made in Po Valley, Italy were analysed for this study to examine which of the meteorological and trace gas variables effect on the emergence of nucleation events. As the analysis method, we used discriminant analysis with non-parametric Epanechnikov kernel, included in non-parametric density estimation method. The best classification result in our data was reached with the combination of relative humidity, ozone concentration and a third degree polynomial of radiation. RH appeared to have a preventing effect on the new particle formation whereas the effects of O3 and radiation were more conductive. The concentration of SO2 and NO2 also appeared to have significant effect on the emergence of nucleation events but because of the great amount of missing observations, we had to exclude them from the final analysis.

Semiparametric mixed-effects analysis of PK/PD models using differential equations.

Science.gov (United States)

Wang, Yi; Eskridge, Kent M; Zhang, Shunpu

2008-08-01

Motivated by the use of semiparametric nonlinear mixed-effects modeling on longitudinal data, we develop a new semiparametric modeling approach to address potential structural model misspecification for population pharmacokinetic/pharmacodynamic (PK/PD) analysis. Specifically, we use a set of ordinary differential equations (ODEs) with form dx/dt = A(t)x + B(t) where B(t) is a nonparametric function that is estimated using penalized splines. The inclusion of a nonparametric function in the ODEs makes identification of structural model misspecification feasible by quantifying the model uncertainty and provides flexibility for accommodating possible structural model deficiencies. The resulting model will be implemented in a nonlinear mixed-effects modeling setup for population analysis. We illustrate the method with an application to cefamandole data and evaluate its performance through simulations.

Statistical Analysis of Environmental Tritium around Wolsong Site

Energy Technology Data Exchange (ETDEWEB)

Kim, Ju Youl [FNC Technology Co., Yongin (Korea, Republic of)

2010-04-15

To find the relationship among airborne tritium, tritium in rainwater, TFWT (Tissue Free Water Tritium) and TBT (Tissue Bound Tritium), statistical analysis is conducted based on tritium data measured at KHNP employees' house around Wolsong nuclear power plants during 10 years from 1999 to 2008. The results show that tritium in such media exhibits a strong seasonal and annual periodicity. Tritium concentration in rainwater is observed to be highly correlated with TFWT and directly transmitted to TFWT without delay. The response of environmental radioactivity of tritium around Wolsong site is analyzed using time-series technique and non-parametric trend analysis. Tritium in the atmosphere and rainwater is strongly auto-correlated by seasonal and annual periodicity. TFWT concentration in pine needle is proven to be more sensitive to rainfall phenomenon than other weather variables. Non-parametric trend analysis of TFWT concentration within pine needle shows a increasing slope in terms of confidence level of 95%. This study demonstrates a usefulness of time-series and trend analysis for the interpretation of environmental radioactivity relationship with various environmental media.

Multiple predictor smoothing methods for sensitivity analysis: Description of techniques

International Nuclear Information System (INIS)

Storlie, Curtis B.; Helton, Jon C.

2008-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Effects of dating errors on nonparametric trend analyses of speleothem time series

Directory of Open Access Journals (Sweden)

M. Mudelsee

2012-10-01

Full Text Available A fundamental problem in paleoclimatology is to take fully into account the various error sources when examining proxy records with quantitative methods of statistical time series analysis. Records from dated climate archives such as speleothems add extra uncertainty from the age determination to the other sources that consist in measurement and proxy errors. This paper examines three stalagmite time series of oxygen isotopic composition (δ¹⁸O from two caves in western Germany, the series AH-1 from the Atta Cave and the series Bu1 and Bu4 from the Bunker Cave. These records carry regional information about past changes in winter precipitation and temperature. U/Th and radiocarbon dating reveals that they cover the later part of the Holocene, the past 8.6 thousand years (ka. We analyse centennial- to millennial-scale climate trends by means of nonparametric Gasser–Müller kernel regression. Error bands around fitted trend curves are determined by combining (1 block bootstrap resampling to preserve noise properties (shape, autocorrelation of the δ¹⁸O residuals and (2 timescale simulations (models StalAge and iscam. The timescale error influences on centennial- to millennial-scale trend estimation are not excessively large. We find a "mid-Holocene climate double-swing", from warm to cold to warm winter conditions (6.5 ka to 6.0 ka to 5.1 ka, with warm–cold amplitudes of around 0.5‰ δ¹⁸O; this finding is documented by all three records with high confidence. We also quantify the Medieval Warm Period (MWP, the Little Ice Age (LIA and the current warmth. Our analyses cannot unequivocally support the conclusion that current regional winter climate is warmer than that during the MWP.

Multiple predictor smoothing methods for sensitivity analysis: Example results

International Nuclear Information System (INIS)

Storlie, Curtis B.; Helton, Jon C.

2008-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

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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

Data analysis and approximate models model choice, location-scale, analysis of variance, nonparametric regression and image analysis

CERN Document Server

Davies, Patrick Laurie

2014-01-01

Introduction IntroductionApproximate Models Notation Two Modes of Statistical AnalysisTowards One Mode of Analysis Approximation, Randomness, Chaos, Determinism ApproximationA Concept of Approximation Approximation Approximating a Data Set by a Model Approximation Regions Functionals and EquivarianceRegularization and Optimality Metrics and DiscrepanciesStrong and Weak Topologies On Being (almost) Honest Simulations and Tables Degree of Approximation and p-values ScalesStability of Analysis The Choice of En(α, P) Independence Procedures, Approximation and VaguenessDiscrete Models The Empirical Density Metrics and Discrepancies The Total Variation Metric The Kullback-Leibler and Chi-Squared Discrepancies The Po(λ) ModelThe b(k, p) and nb(k, p) Models The Flying Bomb Data The Student Study Times Data OutliersOutliers, Data Analysis and Models Breakdown Points and Equivariance Identifying Outliers and Breakdown Outliers in Multivariate Data Outliers in Linear Regression Outliers in Structured Data The Location...

Nonparametric estimates of drift and diffusion profiles via Fokker-Planck algebra.

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Lund, Steven P; Hubbard, Joseph B; Halter, Michael

2014-11-06

Diffusion processes superimposed upon deterministic motion play a key role in understanding and controlling the transport of matter, energy, momentum, and even information in physics, chemistry, material science, biology, and communications technology. Given functions defining these random and deterministic components, the Fokker-Planck (FP) equation is often used to model these diffusive systems. Many methods exist for estimating the drift and diffusion profiles from one or more identifiable diffusive trajectories; however, when many identical entities diffuse simultaneously, it may not be possible to identify individual trajectories. Here we present a method capable of simultaneously providing nonparametric estimates for both drift and diffusion profiles from evolving density profiles, requiring only the validity of Langevin/FP dynamics. This algebraic FP manipulation provides a flexible and robust framework for estimating stationary drift and diffusion coefficient profiles, is not based on fluctuation theory or solved diffusion equations, and may facilitate predictions for many experimental systems. We illustrate this approach on experimental data obtained from a model lipid bilayer system exhibiting free diffusion and electric field induced drift. The wide range over which this approach provides accurate estimates for drift and diffusion profiles is demonstrated through simulation.

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

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

2017-12-01

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

Nonparametric Information Geometry: From Divergence Function to Referential-Representational Biduality on Statistical Manifolds

Directory of Open Access Journals (Sweden)

Jun Zhang

2013-12-01

Full Text Available Divergence functions are the non-symmetric “distance” on the manifold, Μθ, of parametric probability density functions over a measure space, (Χ,μ. Classical information geometry prescribes, on Μθ: (i a Riemannian metric given by the Fisher information; (ii a pair of dual connections (giving rise to the family of α-connections that preserve the metric under parallel transport by their joint actions; and (iii a family of divergence functions ( α-divergence defined on Μθ x Μθ, which induce the metric and the dual connections. Here, we construct an extension of this differential geometric structure from Μθ (that of parametric probability density functions to the manifold, Μ, of non-parametric functions on X, removing the positivity and normalization constraints. The generalized Fisher information and α-connections on M are induced by an α-parameterized family of divergence functions, reflecting the fundamental convex inequality associated with any smooth and strictly convex function. The infinite-dimensional manifold, M, has zero curvature for all these α-connections; hence, the generally non-zero curvature of M can be interpreted as arising from an embedding of Μθ into Μ. Furthermore, when a parametric model (after a monotonic scaling forms an affine submanifold, its natural and expectation parameters form biorthogonal coordinates, and such a submanifold is dually flat for α = ± 1, generalizing the results of Amari’s α-embedding. The present analysis illuminates two different types of duality in information geometry, one concerning the referential status of a point (measurable function expressed in the divergence function (“referential duality” and the other concerning its representation under an arbitrary monotone scaling (“representational duality”.

The 12-item World Health Organization Disability Assessment Schedule II (WHO-DAS II: a nonparametric item response analysis

Directory of Open Access Journals (Sweden)

Fernandez Ana

2010-05-01

Full Text Available Abstract Background Previous studies have analyzed the psychometric properties of the World Health Organization Disability Assessment Schedule II (WHO-DAS II using classical omnibus measures of scale quality. These analyses are sample dependent and do not model item responses as a function of the underlying trait level. The main objective of this study was to examine the effectiveness of the WHO-DAS II items and their options in discriminating between changes in the underlying disability level by means of item response analyses. We also explored differential item functioning (DIF in men and women. Methods The participants were 3615 adult general practice patients from 17 regions of Spain, with a first diagnosed major depressive episode. The 12-item WHO-DAS II was administered by the general practitioners during the consultation. We used a non-parametric item response method (Kernel-Smoothing implemented with the TestGraf software to examine the effectiveness of each item (item characteristic curves and their options (option characteristic curves in discriminating between changes in the underliying disability level. We examined composite DIF to know whether women had a higher probability than men of endorsing each item. Results Item response analyses indicated that the twelve items forming the WHO-DAS II perform very well. All items were determined to provide good discrimination across varying standardized levels of the trait. The items also had option characteristic curves that showed good discrimination, given that each increasing option became more likely than the previous as a function of increasing trait level. No gender-related DIF was found on any of the items. Conclusions All WHO-DAS II items were very good at assessing overall disability. Our results supported the appropriateness of the weights assigned to response option categories and showed an absence of gender differences in item functioning.

Subpixel Snow Cover Mapping from MODIS Data by Nonparametric Regression Splines

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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

Trends in three decades of HIV/AIDS epidemic in Thailand by nonparametric backcalculation method.

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Punyacharoensin, Narat; Viwatwongkasem, Chukiat

2009-06-01

To reconstruct the past HIV incidence and prevalence in Thailand from 1980 to 2008 and predict the country's AIDS incidence from 2009 to 2011. Nonparametric backcalculation was adopted utilizing 100 quarterly observed new AIDS counts excluding pediatric cases. The accuracy of data was enhanced through a series of data adjustments using the weight method to account for several surveillance reporting issues. The mixture of time-dependent distributions allowed the effects of age at seroconversion and antiretroviral therapy to be incorporated simultaneously. Sensitivity analyses were conducted to assess model variations that were subject to major uncertainties. Future AIDS incidence was projected for various predetermined HIV incidence patterns. HIV incidence in Thailand reached its peak in 1992 with approximately 115,000 cases. A steep decline thereafter discontinued in 1997 and was followed by another strike of 42,000 cases in 1999. The second surge, which happened concurrently with the major economic crisis, brought on 60,000 new infections. As of December 2008, more than 1 million individuals had been infected and around 430,000 adults were living with HIV corresponding to a prevalence rate of 1.2%. The incidence rate had become less than 0.1% since 2002. The backcalculated estimates were dominated by postulated median AIDS progression time and adjustments to surveillance data. Our analysis indicated that, thus far, the 1990s was the most severe era of HIV/AIDS epidemic in Thailand with two HIV incidence peaks. A drop in new infections led to a decrease in recent AIDS incidence, and this tendency is likely to remain unchanged until 2011, if not further.

Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity.

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Ferrarini, Luca; Veer, Ilya M; van Lew, Baldur; Oei, Nicole Y L; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, J

2011-06-01

In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover, functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however, was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r(2) statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches, based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity. Copyright © 2010 Elsevier Inc. All rights reserved.

A Non-Parametric Item Response Theory Evaluation of the CAGE Instrument Among Older Adults.

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Abdin, Edimansyah; Sagayadevan, Vathsala; Vaingankar, Janhavi Ajit; Picco, Louisa; Chong, Siow Ann; Subramaniam, Mythily

2018-02-23

The validity of the CAGE using item response theory (IRT) has not yet been examined in older adult population. This study aims to investigate the psychometric properties of the CAGE using both non-parametric and parametric IRT models, assess whether there is any differential item functioning (DIF) by age, gender and ethnicity and examine the measurement precision at the cut-off scores. We used data from the Well-being of the Singapore Elderly study to conduct Mokken scaling analysis (MSA), dichotomous Rasch and 2-parameter logistic IRT models. The measurement precision at the cut-off scores were evaluated using classification accuracy (CA) and classification consistency (CC). The MSA showed the overall scalability H index was 0.459, indicating a medium performing instrument. All items were found to be homogenous, measuring the same construct and able to discriminate well between respondents with high levels of the construct and the ones with lower levels. The item discrimination ranged from 1.07 to 6.73 while the item difficulty ranged from 0.33 to 2.80. Significant DIF was found for 2-item across ethnic group. More than 90% (CC and CA ranged from 92.5% to 94.3%) of the respondents were consistently and accurately classified by the CAGE cut-off scores of 2 and 3. The current study provides new evidence on the validity of the CAGE from the IRT perspective. This study provides valuable information of each item in the assessment of the overall severity of alcohol problem and the precision of the cut-off scores in older adult population.

Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites.

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Donnelly, Aoife; Misstear, Bruce; Broderick, Brian

2011-02-15

Background concentrations of nitrogen dioxide (NO(2)) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO(2) concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO(2) concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO(2) in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO(2) at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling

Theory and Application of DNA Histogram Analysis.

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Bagwell, Charles Bruce

The underlying principles and assumptions associated with DNA histograms are discussed along with the characteristics of fluorescent probes. Information theory was described and used to calculate the information content of a DNA histogram. Two major types of DNA histogram analyses are proposed: parametric and nonparametric analysis. Three levels…

Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error

KAUST Repository

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.

Nonparametric predictive inference for combining diagnostic tests with parametric copula

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

2017-09-01

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

A robust nonparametric method for quantifying undetected extinctions.

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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.

Bayesian dynamic mediation analysis.

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Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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.

Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

The Photoplethismographic Signal Processed with Nonlinear Time Series Analysis Tools

International Nuclear Information System (INIS)

Hernandez Caceres, Jose Luis; Hong, Rolando; Garcia Lanz, Abel; Garcia Dominguez, Luis; Cabannas, Karelia

2001-01-01

Finger photoplethismography (PPG) signals were submitted to nonlinear time series analysis. The applied analytical techniques were: (i) High degree polynomial fitting for baseline estimation; (ii) FFT analysis for estimating power spectra; (iii) fractal dimension estimation via the Higuchi's time-domain method, and (iv) kernel nonparametric estimation for reconstructing noise free-attractors and also for estimating signal's stochastic components

The relationship between multilevel models and non-parametric multilevel mixture models: Discrete approximation of intraclass correlation, random coefficient distributions, and residual heteroscedasticity.

Science.gov (United States)

Rights, Jason D; Sterba, Sonya K

2016-11-01

Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non-parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non-parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non-standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed. © 2016 The British Psychological Society.

Exploring Incomplete Rating Designs with Mokken Scale Analysis

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Wind, Stefanie A.; Patil, Yogendra J.

2018-01-01

Recent research has explored the use of models adapted from Mokken scale analysis as a nonparametric approach to evaluating rating quality in educational performance assessments. A potential limiting factor to the widespread use of these techniques is the requirement for complete data, as practical constraints in operational assessment systems…

Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach

International Nuclear Information System (INIS)

Mekaroonreung, Maethee; Johnson, Andrew L.

2012-01-01

Weak disposability between outputs and pollutants, defined as a simultaneous proportional reduction of both outputs and pollutants, assumes that pollutants are byproducts of the output generation process and that a firm can “freely dispose” of both by scaling down production levels, leaving some inputs idle. Based on the production axioms of monotonicity, convexity and weak disposability, we formulate a convex nonparametric least squares (CNLS) quadratic optimization problem to estimate a frontier production function assuming either a deterministic disturbance term consisting only of inefficiency, or a composite disturbance term composed of both inefficiency and noise. The suggested methodology extends the stochastic semi-nonparametric envelopment of data (StoNED) described in Kuosmanen and Kortelainen (2011). Applying the method to estimate the shadow prices of SO 2 and NO x generated by U.S. coal power plants, we conclude that the weak disposability StoNED method provides more consistent estimates of market prices. - Highlights: ► Develops methodology to estimate shadow prices for SO 2 and NO x in the U.S. coal power plants. ► Extends CNLS and StoNED methods to include the weak disposability assumption. ► Estimates the range of SO 2 and NO x shadow prices as 201–343 $/ton and 409–1352 $/ton. ► StoNED method provides more accurate estimates of shadow prices than deterministic frontier.

Analysis of Parasite and Other Skewed Counts

Science.gov (United States)

Alexander, Neal

2012-01-01

Objective To review methods for the statistical analysis of parasite and other skewed count data. Methods Statistical methods for skewed count data are described and compared, with reference to those used over a ten year period of Tropical Medicine and International Health. Two parasitological datasets are used for illustration. Results Ninety papers were identified, 89 with descriptive and 60 with inferential analysis. A lack of clarity is noted in identifying measures of location, in particular the Williams and geometric mean. The different measures are compared, emphasizing the legitimacy of the arithmetic mean for skewed data. In the published papers, the t test and related methods were often used on untransformed data, which is likely to be invalid. Several approaches to inferential analysis are described, emphasizing 1) non-parametric methods, while noting that they are not simply comparisons of medians, and 2) generalized linear modelling, in particular with the negative binomial distribution. Additional methods, such as the bootstrap, with potential for greater use are described. Conclusions Clarity is recommended when describing transformations and measures of location. It is suggested that non-parametric methods and generalized linear models are likely to be sufficient for most analyses. PMID:22943299

Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

Science.gov (United States)

Del Brio, Esther B.; Mora-Valencia, Andrés; Perote, Javier

2014-05-01

The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram-Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s t distributions.

Assessing the copula selection for bivariate frequency analysis ...

Indian Academy of Sciences (India)

58

Copulas are applied to overcome the restriction of traditional bivariate frequency ... frequency analysis methods cannot describe the random variable properties that ... In order to overcome the limitation of multivariate distributions, a copula is a ..... The Mann-Kendall (M-K) test is a non-parametric statistical test which is used ...

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

Science.gov (United States)

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

Science.gov (United States)

Talathi, Sachin S.; Hwang, Dong-Uk; Spano, Mark L.; Simonotto, Jennifer; Furman, Michael D.; Myers, Stephen M.; Winters, Jason T.; Ditto, William L.; Carney, Paul R.

2008-03-01

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

Statistical trend analysis methods for temporal phenomena

Energy Technology Data Exchange (ETDEWEB)

Lehtinen, E.; Pulkkinen, U. [VTT Automation, (Finland); Poern, K. [Poern Consulting, Nykoeping (Sweden)

1997-04-01

We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods. 14 refs, 10 figs.

Statistical trend analysis methods for temporal phenomena

International Nuclear Information System (INIS)

Lehtinen, E.; Pulkkinen, U.; Poern, K.

1997-04-01

We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods

Modelling lecturer performance index of private university in Tulungagung by using survival analysis with multivariate adaptive regression spline

Science.gov (United States)

Hasyim, M.; Prastyo, D. D.

2018-03-01

Survival analysis performs relationship between independent variables and survival time as dependent variable. In fact, not all survival data can be recorded completely by any reasons. In such situation, the data is called censored data. Moreover, several model for survival analysis requires assumptions. One of the approaches in survival analysis is nonparametric that gives more relax assumption. In this research, the nonparametric approach that is employed is Multivariate Regression Adaptive Spline (MARS). This study is aimed to measure the performance of private university’s lecturer. The survival time in this study is duration needed by lecturer to obtain their professional certificate. The results show that research activities is a significant factor along with developing courses material, good publication in international or national journal, and activities in research collaboration.

On Rigorous Drought Assessment Using Daily Time Scale: Non-Stationary Frequency Analyses, Revisited Concepts, and a New Method to Yield Non-Parametric Indices

Directory of Open Access Journals (Sweden)

Charles Onyutha

2017-10-01

Full Text Available Some of the problems in drought assessments are that: analyses tend to focus on coarse temporal scales, many of the methods yield skewed indices, a few terminologies are ambiguously used, and analyses comprise an implicit assumption that the observations come from a stationary process. To solve these problems, this paper introduces non-stationary frequency analyses of quantiles. How to use non-parametric rescaling to obtain robust indices that are not (or minimally skewed is also introduced. To avoid ambiguity, some concepts on, e.g., incidence, extremity, etc., were revisited through shift from monthly to daily time scale. Demonstrations on the introduced methods were made using daily flow and precipitation insufficiency (precipitation minus potential evapotranspiration from the Blue Nile basin in Africa. Results show that, when a significant trend exists in extreme events, stationarity-based quantiles can be far different from those when non-stationarity is considered. The introduced non-parametric indices were found to closely agree with the well-known standardized precipitation evapotranspiration indices in many aspects but skewness. Apart from revisiting some concepts, the advantages of the use of fine instead of coarse time scales in drought assessment were given. The links for obtaining freely downloadable tools on how to implement the introduced methods were provided.

A non-parametric conditional bivariate reference region with an application to height/weight measurements on normal girls

DEFF Research Database (Denmark)

Petersen, Jørgen Holm

2009-01-01

A conceptually simple two-dimensional conditional reference curve is described. The curve gives a decision basis for determining whether a bivariate response from an individual is "normal" or "abnormal" when taking into account that a third (conditioning) variable may influence the bivariate...... response. The reference curve is not only characterized analytically but also by geometric properties that are easily communicated to medical doctors - the users of such curves. The reference curve estimator is completely non-parametric, so no distributional assumptions are needed about the two......-dimensional response. An example that will serve to motivate and illustrate the reference is the study of the height/weight distribution of 7-8-year-old Danish school girls born in 1930, 1950, or 1970....

Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

Science.gov (United States)

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.

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

Science.gov (United States)

Pentaris, Fragkiskos P.; Fouskitakis, George N.

2014-05-01

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

A NON-PARAMETRIC APPROACH TO CONSTRAIN THE TRANSFER FUNCTION IN REVERBERATION MAPPING

Energy Technology Data Exchange (ETDEWEB)

Li, Yan-Rong; Wang, Jian-Min [Key Laboratory for Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences, 19B Yuquan Road, Beijing 100049 (China); Bai, Jin-Ming, E-mail: liyanrong@mail.ihep.ac.cn [Yunnan Observatories, Chinese Academy of Sciences, Kunming 650011 (China)

2016-11-10

Broad emission lines of active galactic nuclei stem from a spatially extended region (broad-line region, BLR) that is composed of discrete clouds and photoionized by the central ionizing continuum. The temporal behaviors of these emission lines are blurred echoes of continuum variations (i.e., reverberation mapping, RM) and directly reflect the structures and kinematic information of BLRs through the so-called transfer function (also known as the velocity-delay map). Based on the previous works of Rybicki and Press and Zu et al., we develop an extended, non-parametric approach to determine the transfer function for RM data, in which the transfer function is expressed as a sum of a family of relatively displaced Gaussian response functions. Therefore, arbitrary shapes of transfer functions associated with complicated BLR geometry can be seamlessly included, enabling us to relax the presumption of a specified transfer function frequently adopted in previous studies and to let it be determined by observation data. We formulate our approach in a previously well-established framework that incorporates the statistical modeling of continuum variations as a damped random walk process and takes into account long-term secular variations which are irrelevant to RM signals. The application to RM data shows the fidelity of our approach.

A NON-PARAMETRIC APPROACH TO CONSTRAIN THE TRANSFER FUNCTION IN REVERBERATION MAPPING

International Nuclear Information System (INIS)

Li, Yan-Rong; Wang, Jian-Min; Bai, Jin-Ming

2016-01-01

Broad emission lines of active galactic nuclei stem from a spatially extended region (broad-line region, BLR) that is composed of discrete clouds and photoionized by the central ionizing continuum. The temporal behaviors of these emission lines are blurred echoes of continuum variations (i.e., reverberation mapping, RM) and directly reflect the structures and kinematic information of BLRs through the so-called transfer function (also known as the velocity-delay map). Based on the previous works of Rybicki and Press and Zu et al., we develop an extended, non-parametric approach to determine the transfer function for RM data, in which the transfer function is expressed as a sum of a family of relatively displaced Gaussian response functions. Therefore, arbitrary shapes of transfer functions associated with complicated BLR geometry can be seamlessly included, enabling us to relax the presumption of a specified transfer function frequently adopted in previous studies and to let it be determined by observation data. We formulate our approach in a previously well-established framework that incorporates the statistical modeling of continuum variations as a damped random walk process and takes into account long-term secular variations which are irrelevant to RM signals. The application to RM data shows the fidelity of our approach.

Meta-analysis of genome-wide linkage studies in BMI and obesity

NARCIS (Netherlands)

Saunders, Catherine L.; Chiodini, Benedetta D.; Sham, Pak; Lewis, Cathryn M.; Abkevich, Victor; Adeyemo, Adebowale A.; de Andrade, Mariza; Arya, Rector; Berenson, Gerald S.; Blangero, John; Boehnke, Michael; Borecki, Ingrid B.; Chagnon, Yvon C.; Chen, Wei; Comuzzie, Anthony G.; Deng, Hong-Wen; Duggirala, Ravindranath; Feitosa, Mary F.; Froguel, Philippe; Hanson, Robert L.; Hebebrand, Johannes; Huezo-Dias, Patricia; Kissebah, Ahmed H.; Li, Weidong; Luke, Amy; Martin, Lisa J.; Nash, Matthew; Ohman, Muena; Palmer, Lyle J.; Peltonen, Leena; Perola, Markus; Price, R. Arlen; Redline, Susan; Srinivasan, Sathanur R.; Stern, Michael P.; Stone, Steven; Stringham, Heather; Turner, Stephen; Wijmenga, Cisca; Collier, David A.

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000

Exploring Rating Quality in Rater-Mediated Assessments Using Mokken Scale Analysis

Science.gov (United States)

Wind, Stefanie A.; Engelhard, George, Jr.

2016-01-01

Mokken scale analysis is a probabilistic nonparametric approach that offers statistical and graphical tools for evaluating the quality of social science measurement without placing potentially inappropriate restrictions on the structure of a data set. In particular, Mokken scaling provides a useful method for evaluating important measurement…

Nonparametric modeling of longitudinal covariance structure in functional mapping of quantitative trait loci.

Science.gov (United States)

Yap, John Stephen; Fan, Jianqing; Wu, Rongling

2009-12-01

Estimation of the covariance structure of longitudinal processes is a fundamental prerequisite for the practical deployment of functional mapping designed to study the genetic regulation and network of quantitative variation in dynamic complex traits. We present a nonparametric approach for estimating the covariance structure of a quantitative trait measured repeatedly at a series of time points. Specifically, we adopt Huang et al.'s (2006, Biometrika 93, 85-98) approach of invoking the modified Cholesky decomposition and converting the problem into modeling a sequence of regressions of responses. A regularized covariance estimator is obtained using a normal penalized likelihood with an L(2) penalty. This approach, embedded within a mixture likelihood framework, leads to enhanced accuracy, precision, and flexibility of functional mapping while preserving its biological relevance. Simulation studies are performed to reveal the statistical properties and advantages of the proposed method. A real example from a mouse genome project is analyzed to illustrate the utilization of the methodology. The new method will provide a useful tool for genome-wide scanning for the existence and distribution of quantitative trait loci underlying a dynamic trait important to agriculture, biology, and health sciences.

A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images

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.

msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures

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.

[Linkage analysis of susceptibility loci in 2 target chromosomes in pedigrees with paranoid schizophrenia and undifferentiated schizophrenia].

Science.gov (United States)

Zeng, Li-ping; Hu, Zheng-mao; Mu, Li-li; Mei, Gui-sen; Lu, Xiu-ling; Zheng, Yong-jun; Li, Pei-jian; Zhang, Ying-xue; Pan, Qian; Long, Zhi-gao; Dai, He-ping; Zhang, Zhuo-hua; Xia, Jia-hui; Zhao, Jing-ping; Xia, Kun

2011-06-01

To investigate the relationship of susceptibility loci in chromosomes 1q21-25 and 6p21-25 and schizophrenia subtypes in Chinese population. A genomic scan and parametric and non-parametric analyses were performed on 242 individuals from 36 schizophrenia pedigrees, including 19 paranoid schizophrenia and 17 undifferentiated schizophrenia pedigrees, from Henan province of China using 5 microsatellite markers in the chromosome region 1q21-25 and 8 microsatellite markers in the chromosome region 6p21-25, which were the candidates of previous studies. All affected subjects were diagnosed and typed according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revised (DSM-IV-TR; American Psychiatric Association, 2000). All subjects signed informed consent. In chromosome 1, parametric analysis under the dominant inheritance mode of all 36 pedigrees showed that the maximum multi-point heterogeneity Log of odds score method (HLOD) score was 1.33 (α = 0.38). The non-parametric analysis and the single point and multi-point nonparametric linkage (NPL) scores suggested linkage at D1S484, D1S2878, and D1S196. In the 19 paranoid schizophrenias pedigrees, linkage was not observed for any of the 5 markers. In the 17 undifferentiated schizophrenia pedigrees, the multi-point NPL score was 1.60 (P= 0.0367) at D1S484. The single point NPL score was 1.95(P= 0.0145) and the multi-point NPL score was 2.39 (P= 0.0041) at D1S2878. Additionally, the multi-point NPL score was 1.74 (P= 0.0255) at D1S196. These same three loci showed suggestive linkage during the integrative analysis of all 36 pedigrees. In chromosome 6, parametric linkage analysis under the dominant and recessive inheritance and the non-parametric linkage analysis of all 36 pedigrees and the 17 undifferentiated schizophrenia pedigrees, linkage was not observed for any of the 8 markers. In the 19 paranoid schizophrenias pedigrees, parametric analysis showed that under recessive

Evaluation of world's largest social welfare scheme: An assessment using non-parametric approach.

Science.gov (United States)

Singh, Sanjeet

2016-08-01

Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is the world's largest social welfare scheme in India for the poverty alleviation through rural employment generation. This paper aims to evaluate and rank the performance of the states in India under MGNREGA scheme. A non-parametric approach, Data Envelopment Analysis (DEA) is used to calculate the overall technical, pure technical, and scale efficiencies of states in India. The sample data is drawn from the annual official reports published by the Ministry of Rural Development, Government of India. Based on three selected input parameters (expenditure indicators) and five output parameters (employment generation indicators), I apply both input and output oriented DEA models to estimate how well the states utilize their resources and generate outputs during the financial year 2013-14. The relative performance evaluation has been made under the assumption of constant returns and also under variable returns to scale to assess the impact of scale on performance. The results indicate that the main source of inefficiency is both technical and managerial practices adopted. 11 states are overall technically efficient and operate at the optimum scale whereas 18 states are pure technical or managerially efficient. It has been found that for some states it necessary to alter scheme size to perform at par with the best performing states. For inefficient states optimal input and output targets along with the resource savings and output gains are calculated. Analysis shows that if all inefficient states operate at optimal input and output levels, on an average 17.89% of total expenditure and a total amount of $780million could have been saved in a single year. Most of the inefficient states perform poorly when it comes to the participation of women and disadvantaged sections (SC&ST) in the scheme. In order to catch up with the performance of best performing states, inefficient states on an average need to enhance

Essays on parametric and nonparametric modeling and estimation with applications to energy economics

Science.gov (United States)

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

A Non-Parametric Surrogate-based Test of Significance for T-Wave Alternans Detection

Science.gov (United States)

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

Nonparametric study of the evolution of the cosmological equation of state with SNeIa, BAO, and high-redshift GRBs

Energy Technology Data Exchange (ETDEWEB)

Postnikov, S. [Nuclear Theory Center, Indiana University, Bloomington, IN (United States); Dainotti, M. G. [Physics Department, Stanford University, Via Pueblo Mall 382, Stanford, CA (United States); Hernandez, X. [Instituto de Astronomía, Universidad Nacional Autónoma de México, México D.F. 04510 (Mexico); Capozziello, S., E-mail: spostnik@indiana.edu, E-mail: mdainott@stanford.edu, E-mail: dainotti@oa.uj.edu.pl, E-mail: xavier@astros.unam.mx, E-mail: capozziello@na.infn.it [Dipartimento di Fisica, Universitá di Napoli " Federico II," Compl. Univ. di Monte S. Angelo, Edificio G, Via Cinthia, I-80126 Napoli (Italy)

2014-03-10

We study the dark energy equation of state as a function of redshift in a nonparametric way, without imposing any a priori w(z) (ratio of pressure over energy density) functional form. As a check of the method, we test our scheme through the use of synthetic data sets produced from different input cosmological models that have the same relative errors and redshift distribution as the real data. Using the luminosity-time L{sub X} -T{sub a} correlation for gamma-ray burst (GRB) X-ray afterglows (the Dainotti et al. correlation), we are able to utilize GRB samples from the Swift satellite as probes of the expansion history of the universe out to z ≈ 10. Within the assumption of a flat Friedmann-Lemaître-Robertson-Walker universe and combining supernovae type Ia (SNeIa) data with baryonic acoustic oscillation constraints, the resulting maximum likelihood solutions are close to a constant w = –1. If one imposes the restriction of a constant w, we obtain w = –0.99 ± 0.06 (consistent with a cosmological constant) with the present-day Hubble constant as H {sub 0} = 70.0 ± 0.6km s{sup –1} Mpc{sup –1} and density parameter as Ω{sub Λ0} = 0.723 ± 0.025, while nonparametric w(z) solutions give us a probability map that is centered at H {sub 0} = 70.04 ± 1km s{sup –1} Mpc{sup –1} and Ω{sub Λ0} = 0.724 ± 0.03. Our chosen GRB data sample with a full correlation matrix allows us to estimate the amount, as well as quality (errors), of data needed to constrain w(z) in the redshift range extending an order of magnitude beyond the farthest SNeIa measured.

A Comparison of Distribution Free and Non-Distribution Free Factor Analysis Methods

Science.gov (United States)

Ritter, Nicola L.

2012-01-01

Many researchers recognize that factor analysis can be conducted on both correlation matrices and variance-covariance matrices. Although most researchers extract factors from non-distribution free or parametric methods, researchers can also extract factors from distribution free or non-parametric methods. The nature of the data dictates the method…

VGC analyzer: a software for statistical analysis of fully crossed multiple-reader multiple-case visual grading characteristics studies

International Nuclear Information System (INIS)

Baath, Magnus; Hansson, Jonny

2016-01-01

Visual grading characteristics (VGC) analysis is a non-parametric rank-invariant method for analysis of visual grading data. In VGC analysis, image quality ratings for two different conditions are compared by producing a VGC curve, similar to how the ratings for normal and abnormal cases in receiver operating characteristic (ROC) analysis are used to create an ROC curve. The use of established ROC software for the analysis of VGC data has therefore previously been proposed. However, the ROC analysis is based on the assumption of independence between normal and abnormal cases. In VGC analysis, this independence cannot always be assumed, e.g. if the ratings are based on the same patients imaged under both conditions. A dedicated software intended for analysis of VGC studies, which takes possible dependencies between ratings into account in the statistical analysis of a VGC study, has therefore been developed. The software-VGC Analyzer-determines the area under the VGC curve and its uncertainty using non-parametric re-sampling techniques. This article gives an introduction to VGC Analyzer, describes the types of analyses that can be performed and instructs the user about the input and output data. (authors)

rSeqNP: a non-parametric approach for detecting differential expression and splicing from RNA-Seq data.

Science.gov (United States)

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.

Examining the Feasibility and Utility of Estimating Partial Expected Value of Perfect Information (via a Nonparametric Approach) as Part of the Reimbursement Decision-Making Process in Ireland: Application to Drugs for Cancer.

Science.gov (United States)

McCullagh, Laura; Schmitz, Susanne; Barry, Michael; Walsh, Cathal

2017-11-01

In Ireland, all new drugs for which reimbursement by the healthcare payer is sought undergo a health technology assessment by the National Centre for Pharmacoeconomics. The National Centre for Pharmacoeconomics estimate expected value of perfect information but not partial expected value of perfect information (owing to computational expense associated with typical methodologies). The objective of this study was to examine the feasibility and utility of estimating partial expected value of perfect information via a computationally efficient, non-parametric regression approach. This was a retrospective analysis of evaluations on drugs for cancer that had been submitted to the National Centre for Pharmacoeconomics (January 2010 to December 2014 inclusive). Drugs were excluded if cost effective at the submitted price. Drugs were excluded if concerns existed regarding the validity of the applicants' submission or if cost-effectiveness model functionality did not allow required modifications to be made. For each included drug (n = 14), value of information was estimated at the final reimbursement price, at a threshold equivalent to the incremental cost-effectiveness ratio at that price. The expected value of perfect information was estimated from probabilistic analysis. Partial expected value of perfect information was estimated via a non-parametric approach. Input parameters with a population value at least €1 million were identified as potential targets for research. All partial estimates were determined within minutes. Thirty parameters (across nine models) each had a value of at least €1 million. These were categorised. Collectively, survival analysis parameters were valued at €19.32 million, health state utility parameters at €15.81 million and parameters associated with the cost of treating adverse effects at €6.64 million. Those associated with drug acquisition costs and with the cost of care were valued at €6.51 million and €5.71�

Marginal Bayesian nonparametric model for time to disease arrival of threatened amphibian populations.

Science.gov (United States)

Zhou, Haiming; Hanson, Timothy; Knapp, Roland

2015-12-01

The global emergence of Batrachochytrium dendrobatidis (Bd) has caused the extinction of hundreds of amphibian species worldwide. It has become increasingly important to be able to precisely predict time to Bd arrival in a population. The data analyzed herein present a unique challenge in terms of modeling because there is a strong spatial component to Bd arrival time and the traditional proportional hazards assumption is grossly violated. To address these concerns, we develop a novel marginal Bayesian nonparametric survival model for spatially correlated right-censored data. This class of models assumes that the logarithm of survival times marginally follow a mixture of normal densities with a linear-dependent Dirichlet process prior as the random mixing measure, and their joint distribution is induced by a Gaussian copula model with a spatial correlation structure. To invert high-dimensional spatial correlation matrices, we adopt a full-scale approximation that can capture both large- and small-scale spatial dependence. An efficient Markov chain Monte Carlo algorithm with delayed rejection is proposed for posterior computation, and an R package spBayesSurv is provided to fit the model. This approach is first evaluated through simulations, then applied to threatened frog populations in Sequoia-Kings Canyon National Park. © 2015, The International Biometric Society.

Nonparametric Forecasting for Biochar Utilization in Poyang Lake Eco-Economic Zone in China

Directory of Open Access Journals (Sweden)

Meng-Shiuh Chang

2014-01-01

Full Text Available Agriculture is the least profitable industry in China. However, even with large financial subsidies from the government, farmers’ living standards have had no significant impact so far due to the historical, geographical, climatic factors. The study examines and quantifies the net economic and environmental benefits by utilizing biochar as a soil amendment in eleven counties in the Poyang Lake Eco-Economic Zone. A nonparametric kernel regression model is employed to estimate the relation between the scaled environmental and economic factors, which are determined as regression variables. In addition, the partial linear and single index regression models are used for comparison. In terms of evaluations of mean squared errors, the kernel estimator, exceeding the other estimators, is employed to forecast benefits of using biochar under various scenarios. The results indicate that biochar utilization can potentially increase farmers’ income if rice is planted and the net economic benefits can be achieved up to ¥114,900. The net economic benefits are higher when the pyrolysis plant is built in the south of Poyang Lake Eco-Economic Zone than when it is built in the north as the southern land is relatively barren, and biochar can save more costs on irrigation and fertilizer use.

REGRES: A FORTRAN-77 program to calculate nonparametric and ``structural'' parametric solutions to bivariate regression equations

Science.gov (United States)

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.

An Evaluation of Parametric and Nonparametric Models of Fish Population Response.

Energy Technology Data Exchange (ETDEWEB)

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.

An artificial neural network architecture for non-parametric visual odometry in wireless capsule endoscopy

Science.gov (United States)

Dimas, George; Iakovidis, Dimitris K.; Karargyris, Alexandros; Ciuti, Gastone; Koulaouzidis, Anastasios

2017-09-01

Wireless capsule endoscopy is a non-invasive screening procedure of the gastrointestinal (GI) tract performed with an ingestible capsule endoscope (CE) of the size of a large vitamin pill. Such endoscopes are equipped with a usually low-frame-rate color camera which enables the visualization of the GI lumen and the detection of pathologies. The localization of the commercially available CEs is performed in the 3D abdominal space using radio-frequency (RF) triangulation from external sensor arrays, in combination with transit time estimation. State-of-the-art approaches, such as magnetic localization, which have been experimentally proved more accurate than the RF approach, are still at an early stage. Recently, we have demonstrated that CE localization is feasible using solely visual cues and geometric models. However, such approaches depend on camera parameters, many of which are unknown. In this paper the authors propose a novel non-parametric visual odometry (VO) approach to CE localization based on a feed-forward neural network architecture. The effectiveness of this approach in comparison to state-of-the-art geometric VO approaches is validated using a robotic-assisted in vitro experimental setup.

An artificial neural network architecture for non-parametric visual odometry in wireless capsule endoscopy

International Nuclear Information System (INIS)

Dimas, George; Iakovidis, Dimitris K; Karargyris, Alexandros; Ciuti, Gastone; Koulaouzidis, Anastasios

2017-01-01

Wireless capsule endoscopy is a non-invasive screening procedure of the gastrointestinal (GI) tract performed with an ingestible capsule endoscope (CE) of the size of a large vitamin pill. Such endoscopes are equipped with a usually low-frame-rate color camera which enables the visualization of the GI lumen and the detection of pathologies. The localization of the commercially available CEs is performed in the 3D abdominal space using radio-frequency (RF) triangulation from external sensor arrays, in combination with transit time estimation. State-of-the-art approaches, such as magnetic localization, which have been experimentally proved more accurate than the RF approach, are still at an early stage. Recently, we have demonstrated that CE localization is feasible using solely visual cues and geometric models. However, such approaches depend on camera parameters, many of which are unknown. In this paper the authors propose a novel non-parametric visual odometry (VO) approach to CE localization based on a feed-forward neural network architecture. The effectiveness of this approach in comparison to state-of-the-art geometric VO approaches is validated using a robotic-assisted in vitro experimental setup. (paper)

Survival analysis using S analysis of time-to-event data

CERN Document Server

Tableman, Mara

2003-01-01

Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter ...

A SAS(®) macro implementation of a multiple comparison post hoc test for a Kruskal-Wallis analysis.

Science.gov (United States)

Elliott, Alan C; Hynan, Linda S

2011-04-01

The Kruskal-Wallis (KW) nonparametric analysis of variance is often used instead of a standard one-way ANOVA when data are from a suspected non-normal population. The KW omnibus procedure tests for some differences between groups, but provides no specific post hoc pair wise comparisons. This paper provides a SAS(®) macro implementation of a multiple comparison test based on significant Kruskal-Wallis results from the SAS NPAR1WAY procedure. The implementation is designed for up to 20 groups at a user-specified alpha significance level. A Monte-Carlo simulation compared this nonparametric procedure to commonly used parametric multiple comparison tests. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

[Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].

Science.gov (United States)

Schmidt, K; Witte, H

1999-11-01

Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.

Predicting long-term risk for relationship dissolution using nonparametric conditional survival trees.

Science.gov (United States)

Kliem, Sören; Weusthoff, Sarah; Hahlweg, Kurt; Baucom, Katherine J W; Baucom, Brian R

2015-12-01

Identifying risk factors for divorce or separation is an important step in the prevention of negative individual outcomes and societal costs associated with relationship dissolution. Programs that aim to prevent relationship distress and dissolution typically focus on changing processes that occur during couple conflict, although the predictive ability of conflict-specific variables has not been examined in the context of other factors related to relationship dissolution. The authors examine whether emotional responding and communication during couple conflict predict relationship dissolution after controlling for overall relationship quality and individual well-being. Using nonparametric conditional survival trees, the study at hand simultaneously examined the predictive abilities of physiological (systolic and diastolic blood pressure, heart rate, cortisol) and behavioral (fundamental frequency; f0) indices of emotional responding, as well as observationally coded positive and negative communication behavior, on long-term relationship stability after controlling for relationship satisfaction and symptoms of depression. One hundred thirty-six spouses were assessed after participating in a randomized clinical trial of a relationship distress prevention program as well as 11 years thereafter; 32.5% of the couples' relationships had dissolved by follow up. For men, the only significant predictor of relationship dissolution was cortisol change score (p = .012). For women, only f0 range was a significant predictor of relationship dissolution (p = .034). These findings highlight the importance of emotional responding during couple conflict for long-term relationship stability. (c) 2015 APA, all rights reserved).

A non-parametric mixture model for genome-enabled prediction of genetic value for a quantitative trait.

Science.gov (United States)

Gianola, Daniel; Wu, Xiao-Lin; Manfredi, Eduardo; Simianer, Henner

2010-10-01

A Bayesian nonparametric form of regression based on Dirichlet process priors is adapted to the analysis of quantitative traits possibly affected by cryptic forms of gene action, and to the context of SNP-assisted genomic selection, where the main objective is to predict a genomic signal on phenotype. The procedure clusters unknown genotypes into groups with distinct genetic values, but in a setting in which the number of clusters is unknown a priori, so that standard methods for finite mixture analysis do not work. The central assumption is that genetic effects follow an unknown distribution with some "baseline" family, which is a normal process in the cases considered here. A Bayesian analysis based on the Gibbs sampler produces estimates of the number of clusters, posterior means of genetic effects, a measure of credibility in the baseline distribution, as well as estimates of parameters of the latter. The procedure is illustrated with a simulation representing two populations. In the first one, there are 3 unknown QTL, with additive, dominance and epistatic effects; in the second, there are 10 QTL with additive, dominance and additive × additive epistatic effects. In the two populations, baseline parameters are inferred correctly. The Dirichlet process model infers the number of unique genetic values correctly in the first population, but it produces an understatement in the second one; here, the true number of clusters is over 900, and the model gives a posterior mean estimate of about 140, probably because more replication of genotypes is needed for correct inference. The impact on inferences of the prior distribution of a key parameter (M), and of the extent of replication, was examined via an analysis of mean body weight in 192 paternal half-sib families of broiler chickens, where each sire was genotyped for nearly 7,000 SNPs. In this small sample, it was found that inference about the number of clusters was affected by the prior distribution of M. For a

Wavelets in functional data analysis

CERN Document Server

Morettin, Pedro A; Vidakovic, Brani

2017-01-01

Wavelet-based procedures are key in many areas of statistics, applied mathematics, engineering, and science. This book presents wavelets in functional data analysis, offering a glimpse of problems in which they can be applied, including tumor analysis, functional magnetic resonance and meteorological data. Starting with the Haar wavelet, the authors explore myriad families of wavelets and how they can be used. High-dimensional data visualization (using Andrews' plots), wavelet shrinkage (a simple, yet powerful, procedure for nonparametric models) and a selection of estimation and testing techniques (including a discussion on Stein’s Paradox) make this a highly valuable resource for graduate students and experienced researchers alike.

Design Automation Using Script Languages. High-Level CAD Templates in Non-Parametric Programs

Science.gov (United States)

Moreno, R.; Bazán, A. M.

2017-10-01

The main purpose of this work is to study the advantages offered by the application of traditional techniques of technical drawing in processes for automation of the design, with non-parametric CAD programs, provided with scripting languages. Given that an example drawing can be solved with traditional step-by-step detailed procedures, is possible to do the same with CAD applications and to generalize it later, incorporating references. In today’s modern CAD applications, there are striking absences of solutions for building engineering: oblique projections (military and cavalier), 3D modelling of complex stairs, roofs, furniture, and so on. The use of geometric references (using variables in script languages) and their incorporation into high-level CAD templates allows the automation of processes. Instead of repeatedly creating similar designs or modifying their data, users should be able to use these templates to generate future variations of the same design. This paper presents the automation process of several complex drawing examples based on CAD script files aided with parametric geometry calculation tools. The proposed method allows us to solve complex geometry designs not currently incorporated in the current CAD applications and to subsequently create other new derivatives without user intervention. Automation in the generation of complex designs not only saves time but also increases the quality of the presentations and reduces the possibility of human errors.

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

Estimation of Esfarayen Farmers Risk Aversion Coefficient and Its Influencing Factors (Nonparametric Approach

Directory of Open Access Journals (Sweden)

Z. Nematollahi

2016-03-01

absolute risk aversion of 0.00003, which is lower than for the subsample existing of farmers in the 'non-wealthy' group. This assumption that the absolute risk aversion is a decreasing function of wealth is in accordance with Arrow (1970 expectation. The method used was to calculate the proportional risk premium (PRP representing the proportion of the expected payoff of a risky prospect that the farmers would be willing to pay to trade away all the risk for a certain thing, proposed by Hardaker (2000. Our finding showed that the higher risk averse the farmer was, the higher will the PRP would be. Farmers risk premium was 303113 IRR. It should be mentioned that the 'non-wealthy' group had a larger PRP than the 'wealthy' group. Following Freund (1956, if the net revenue for each activity is normally distributed and assuming a negative exponential utility function, we can utilize the absolute risk aversion coefficient to obtain relative risk aversion coefficient (Rr. Based on this study, Rr vary from 0.31 to 8.49 and the relative coefficient of risk aversion in our sample was 4.79. Our results showed that the majority of farmers in the study area are highly risk averse (Anderson and Dillon, 1992. The relationships between the relative risk aversion coefficients of farmers and their socio-economic characteristics were also evaluated in this study. Results showed that the age had a positive impact, level of wealth and diversity had negative impacts on farmers' risk aversion coefficient. Conclusion: Due to existence of the risk and uncertainty in agriculture, the present study was designed to determine the risk aversion coefficient for Esfarayen farmers. A new non-parametric method and the QP method were used to calculate the coefficient of risk aversion. The model used in this analysis found the optimal farm plan given a planning horizon of 1 year. Thus, the historical mean GM vector and variance-covariance matrix were assumed to represent farmers beliefs. Our results showed

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

Science.gov (United States)

Granato, Gregory E.

2006-01-01

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and

Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models

International Nuclear Information System (INIS)

Storlie, Curtis B.; Swiler, Laura P.; Helton, Jon C.; Sallaberry, Cedric J.

2009-01-01

The analysis of many physical and engineering problems involves running complex computational models (simulation models, computer codes). With problems of this type, it is important to understand the relationships between the input variables (whose values are often imprecisely known) and the output. The goal of sensitivity analysis (SA) is to study this relationship and identify the most significant factors or variables affecting the results of the model. In this presentation, an improvement on existing methods for SA of complex computer models is described for use when the model is too computationally expensive for a standard Monte-Carlo analysis. In these situations, a meta-model or surrogate model can be used to estimate the necessary sensitivity index for each input. A sensitivity index is a measure of the variance in the response that is due to the uncertainty in an input. Most existing approaches to this problem either do not work well with a large number of input variables and/or they ignore the error involved in estimating a sensitivity index. Here, a new approach to sensitivity index estimation using meta-models and bootstrap confidence intervals is described that provides solutions to these drawbacks. Further, an efficient yet effective approach to incorporate this methodology into an actual SA is presented. Several simulated and real examples illustrate the utility of this approach. This framework can be extended to uncertainty analysis as well.

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

Science.gov (United States)

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

2017-06-01

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

Measuring the efficiency of dental departments in medical centers: a nonparametric analysis approach.

Science.gov (United States)

Wang, Su-Chen; Tsai, Chi-Cheng; Huang, Shun-Te; Hong, Yu-Jue

2002-12-01

Data envelopment analysis (DEA), a cross-sectional study design based on secondary data analysis, was used to evaluate the relative operational efficiency of 16 dental departments in medical centers in Taiwan in 1999. The results indicated that 68.7% of all dental departments in medical centers had poor performance in terms of overall efficiency and scale efficiency. All relatively efficient dental departments were in private medical centers. Half of these dental departments were unable to fully utilize available medical resources. 75.0% of public medical centers did not take full advantage of medical resources at their disposal. In the returns to scale, 56.3% of dental departments in medical centers exhibited increasing returns to scale, due to the insufficient scale influencing overall hospital operational efficiency. Public medical centers accounted for 77.8% of the institutions affected. The scale of dental departments in private medical centers was more appropriate than those in public medical centers. In the sensitivity analysis, the numbers of residents, interns, and published papers were used to assess teaching and research. Greater emphasis on teaching and research in medical centers has a large effect on the relative inefficiency of hospital operation. Dental departments in private medical centers had a higher mean overall efficiency score than those in public medical centers, and the overall efficiency of dental departments in non-university hospitals was greater than those in university hospitals. There was no information to evaluate the long-term efficiency of each dental department in all hospitals. A different combination of input and output variables, using common multipliers for efficiency value measurements in DEA, may help establish different pioneering dental departments in hospitals.

The use of the bootstrap in the analysis of case-control studies with missing data

DEFF Research Database (Denmark)

Siersma, Volkert Dirk; Johansen, Christoffer

2004-01-01

nonparametric bootstrap, bootstrap confidence intervals, missing values, multiple imputation, matched case-control study......nonparametric bootstrap, bootstrap confidence intervals, missing values, multiple imputation, matched case-control study...

Use of NON-PARAMETRIC Item Response Theory to develop a shortened version of the Positive and Negative Syndrome Scale (PANSS)

Science.gov (United States)

2011-01-01

Background Nonparametric item response theory (IRT) was used to examine (a) the performance of the 30 Positive and Negative Syndrome Scale (PANSS) items and their options ((levels of severity), (b) the effectiveness of various subscales to discriminate among differences in symptom severity, and (c) the development of an abbreviated PANSS (Mini-PANSS) based on IRT and a method to link scores to the original PANSS. Methods Baseline PANSS scores from 7,187 patients with Schizophrenia or Schizoaffective disorder who were enrolled between 1995 and 2005 in psychopharmacology trials were obtained. Option characteristic curves (OCCs) and Item Characteristic Curves (ICCs) were constructed to examine the probability of rating each of seven options within each of 30 PANSS items as a function of subscale severity, and summed-score linking was applied to items selected for the Mini-PANSS. Results The majority of items forming the Positive and Negative subscales (i.e. 19 items) performed very well and discriminate better along symptom severity compared to the General Psychopathology subscale. Six of the seven Positive Symptom items, six of the seven Negative Symptom items, and seven out of the 16 General Psychopathology items were retained for inclusion in the Mini-PANSS. Summed score linking and linear interpolation was able to produce a translation table for comparing total subscale scores of the Mini-PANSS to total subscale scores on the original PANSS. Results show scores on the subscales of the Mini-PANSS can be linked to scores on the original PANSS subscales, with very little bias. Conclusions The study demonstrated the utility of non-parametric IRT in examining the item properties of the PANSS and to allow selection of items for an abbreviated PANSS scale. The comparisons between the 30-item PANSS and the Mini-PANSS revealed that the shorter version is comparable to the 30-item PANSS, but when applying IRT, the Mini-PANSS is also a good indicator of illness severity

Use of non-parametric item response theory to develop a shortened version of the Positive and Negative Syndrome Scale (PANSS).

Science.gov (United States)

Khan, Anzalee; Lewis, Charles; Lindenmayer, Jean-Pierre

2011-11-16

Nonparametric item response theory (IRT) was used to examine (a) the performance of the 30 Positive and Negative Syndrome Scale (PANSS) items and their options ((levels of severity), (b) the effectiveness of various subscales to discriminate among differences in symptom severity, and (c) the development of an abbreviated PANSS (Mini-PANSS) based on IRT and a method to link scores to the original PANSS. Baseline PANSS scores from 7,187 patients with Schizophrenia or Schizoaffective disorder who were enrolled between 1995 and 2005 in psychopharmacology trials were obtained. Option characteristic curves (OCCs) and Item Characteristic Curves (ICCs) were constructed to examine the probability of rating each of seven options within each of 30 PANSS items as a function of subscale severity, and summed-score linking was applied to items selected for the Mini-PANSS. The majority of items forming the Positive and Negative subscales (i.e. 19 items) performed very well and discriminate better along symptom severity compared to the General Psychopathology subscale. Six of the seven Positive Symptom items, six of the seven Negative Symptom items, and seven out of the 16 General Psychopathology items were retained for inclusion in the Mini-PANSS. Summed score linking and linear interpolation was able to produce a translation table for comparing total subscale scores of the Mini-PANSS to total subscale scores on the original PANSS. Results show scores on the subscales of the Mini-PANSS can be linked to scores on the original PANSS subscales, with very little bias. The study demonstrated the utility of non-parametric IRT in examining the item properties of the PANSS and to allow selection of items for an abbreviated PANSS scale. The comparisons between the 30-item PANSS and the Mini-PANSS revealed that the shorter version is comparable to the 30-item PANSS, but when applying IRT, the Mini-PANSS is also a good indicator of illness severity.

Theoretical remarks on the statistics of three discriminants in Piety's automated signature analysis of PSD [Power Spectral Density] data

International Nuclear Information System (INIS)

Behringer, K.; Spiekerman, G.

1984-01-01

Piety (1977) proposed an automated signature analysis of power spectral density data. Eight statistical decision discriminants are introduced. For nearly all the discriminants, improved confidence statements can be made. The statistical characteristics of the last three discriminants, which are applications of non-parametric tests, are considered. (author)

A critique of non-parametric efficiency analysis in energy economics studies

International Nuclear Information System (INIS)

Chen, Chien-Ming

2013-01-01

The paper reexamines non-additive environmental efficiency models with weakly-disposable undesirable outputs appeared in the literature of energy economics. These efficiency models are used in numerous studies published in this journal and other energy-related outlets. Recent studies, however, have found key limitations of the weak-disposability assumption in its application to environmental efficiency analysis. It is found that efficiency scores obtained from non-additive efficiency models can be non-monotonic in pollution quantities under the weak-disposability assumption — which is against common intuition and the principle of environmental economics. In this paper, I present taxonomy of efficiency models found in the energy economics literature and illustrate the above limitations and discuss implications of monotonicity from a practical viewpoint. Finally, I review the formulations for a variable returns-to-scale technology with weakly-disposable undesirable outputs, which has been misused in a number of papers in the energy economics literature. An application to evaluating the energy efficiencies of 23 European Union states is presented to illustrate the problem. - Highlights: • Review different environmental efficiency model used in energy economics studies • Highlight limitations of these environmental efficiency models • These limitations have not been recognized in the existing energy economics literature. • Data from 23 European Union states are used to illustrate the methodological consequences

Multiscale Signal Analysis and Modeling

CERN Document Server

Zayed, Ahmed

2013-01-01

Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory. This book also: Discusses recently developed signal modeling techniques, such as the multiscale method for complex time series modeling, multiscale positive density estimations, Bayesian Shrinkage Strategies, and algorithms for data adaptive statistics Introduces new sampling algorithms for multidimensional signal processing Provides comprehensive coverage of wavelets with presentations on waveform design and modeling, wavelet analysis of ECG signals and wavelet filters Reviews features extraction and classification algorithms for multiscale signal and image proce...

PERFORMANCE PERSISTENCE OF TURKISH A AND B TYPE MUTUAL FUNDS: THE PARAMETRIC AND NONPARAMETRIC APPROACH

Directory of Open Access Journals (Sweden)

VELİ AKEL

2013-06-01

Full Text Available In this study, single index models are applied to a free survivorship bias database of 51 A and 51 B Types Turkish mutual funds using monthly returns over 5 years from 2000 to 2004. Then, it has been investigated whether mutual fund managers have market timing ability. Turkish Institutional Investment Managers’ Association A and B Type Fund Indexes are firstly used as benchmark portfolios. The challenging question is whether Turkish mutual funds have performance persistency over the short and long term or not. This study uses both parametric and non-parametric techniques to examine performance persistence. The overall conclusion is that Type A mutual funds managers do not have stock selection and market timing ability. However, Type B mutual funds managers do have stock selection ability. Type A mutual funds show evidence of relative and absolute persistence in the short term while Type B mutual funds show significant results of relative and absolute persistence in both of the terms. Although there are various results in performance persistence of mutual funds, the repeat winner phenomenon is stronger over shorter periods of evaluation. Consequently, it seems that Turkish mutual funds have performance persistency at least for the short term.

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

NARCIS (Netherlands)

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

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

Quantitative Phylogenomics of Within-Species Mitogenome Variation: Monte Carlo and Non-Parametric Analysis of Phylogeographic Structure among Discrete Transatlantic Breeding Areas of Harp Seals (Pagophilus groenlandicus.

Directory of Open Access Journals (Sweden)

Steven M Carr

-stepping-stone biogeographic models, but not a simple 1-step trans-Atlantic model. Plots of the cumulative pairwise sequence difference curves among seals in each of the four populations provide continuous proxies for phylogenetic diversification within each. Non-parametric Kolmogorov-Smirnov (K-S tests of maximum pairwise differences between these curves indicates that the Greenland Sea population has a markedly younger phylogenetic structure than either the White Sea population or the two Northwest Atlantic populations, which are of intermediate age and homogeneous structure. The Monte Carlo and K-S assessments provide sensitive quantitative tests of within-species mitogenomic phylogeography. This is the first study to indicate that the White Sea and Greenland Sea populations have different population genetic histories. The analysis supports the hypothesis that Harp Seals comprises three genetically distinguishable breeding populations, in the White Sea, Greenland Sea, and Northwest Atlantic. Implications for an ice-dependent species during ongoing climate change are discussed.

Survey of sampling-based methods for uncertainty and sensitivity analysis

International Nuclear Information System (INIS)

Helton, J.C.; Johnson, J.D.; Sallaberry, C.J.; Storlie, C.B.

2006-01-01

Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) definition of probability distributions to characterize epistemic uncertainty in analysis inputs (ii) generation of samples from uncertain analysis inputs (iii) propagation of sampled inputs through an analysis (iv) presentation of uncertainty analysis results, and (v) determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two-dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Johnson, Jay Dean; Helton, Jon Craig; Sallaberry, Cedric J. PhD. (.; .); Storlie, Curt B. (Colorado State University, Fort Collins, CO)

2006-06-01

Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.

Analysis of uncertainties of thermal hydraulic calculations

International Nuclear Information System (INIS)

Macek, J.; Vavrin, J.

2002-12-01

In 1993-1997 it was proposed, within OECD projects, that a common program should be set up for uncertainty analysis by a probabilistic method based on a non-parametric statistical approach for system computer codes such as RELAP, ATHLET and CATHARE and that a method should be developed for statistical analysis of experimental databases for the preparation of the input deck and statistical analysis of the output calculation results. Software for such statistical analyses would then have to be processed as individual tools independent of the computer codes used for the thermal hydraulic analysis and programs for uncertainty analysis. In this context, a method for estimation of a thermal hydraulic calculation is outlined and selected methods of statistical analysis of uncertainties are described, including methods for prediction accuracy assessment based on the discrete Fourier transformation principle. (author)

Frequency Analysis Using Bootstrap Method and SIR Algorithm for Prevention of Natural Disasters

Science.gov (United States)

Kim, T.; Kim, Y. S.

2017-12-01

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results show that probabilistic daily snowfall depth by frequency analysis is decreased at most stations, and most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics. Acknowledgment.This research was supported by a grant(MPSS-NH-2015-79) from Disaster Prediction and Mitigation Technology Development Program funded by Korean Ministry of Public Safety and Security(MPSS).

The analysis of professional competencies of a lecturer in adult education.

Science.gov (United States)

Žeravíková, Iveta; Tirpáková, Anna; Markechová, Dagmar

2015-01-01

In this article, we present the andragogical research project and evaluation of its results using nonparametric statistical methods and the semantic differential method. The presented research was realized in the years 2012-2013 in the dissertation of I. Žeravíková: Analysis of professional competencies of lecturer and creating his competence profile (Žeravíková 2013), and its purpose was based on the analysis of work activities of a lecturer to identify his most important professional competencies and to create a suggestion of competence profile of a lecturer in adult education.

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

NARCIS (Netherlands)

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

2011-01-01

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

Statistical analysis applied to safety culture self-assessment

International Nuclear Information System (INIS)

Macedo Soares, P.P.

2002-01-01

Interviews and opinion surveys are instruments used to assess the safety culture in an organization as part of the Safety Culture Enhancement Programme. Specific statistical tools are used to analyse the survey results. This paper presents an example of an opinion survey with the corresponding application of the statistical analysis and the conclusions obtained. Survey validation, Frequency statistics, Kolmogorov-Smirnov non-parametric test, Student (T-test) and ANOVA means comparison tests and LSD post-hoc multiple comparison test, are discussed. (author)

Longitudinal data analysis a handbook of modern statistical methods

CERN Document Server

Fitzmaurice, Garrett; Verbeke, Geert; Molenberghs, Geert

2008-01-01

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint

Discriminant analysis of plasma fusion data

International Nuclear Information System (INIS)

Kardaun, O.J.W.F.; Kardaun, J.W.P.F.; Itoh, S.; Itoh, K.

1992-06-01

Several discriminant analysis methods has been applied and compared to predict the type of ELM's in H-mode discharges: (a) quadratic discriminant analysis (linear discriminant analysis being a special case), (b) discrimination by non-parametric (kernel-) density estimates, and (c) discrimination by a product multinomial model on a discretised scale. Practical evaluation was performed using SAS in the first two cases, and INDEP, a standard FORTRAN program, initially developed for medical applications, in the last case. We give here a flavour of the approach and its results. In summary, discriminant analysis can be used as a useful descriptive method of specifying regions where particular types of plasma discharges can be produced. Parametric methods have the advantage of a rather compact mathematical formulation . Pertinent graphical representations are useful to make the theory and the results more palatable to the experimental physicists. (J.P.N.)

Non-parametric analysis of technical efficiency: factors affecting efficiency of West Java rice farms

Czech Academy of Sciences Publication Activity Database

Brázdik, František

-, č. 286 (2006), s. 1-45 ISSN 1211-3298 R&D Projects: GA MŠk LC542 Institutional research plan: CEZ:AV0Z70850503 Keywords : rice farms * data envelopment analysis Subject RIV: AH - Economics http://www.cerge-ei.cz/pdf/wp/Wp286.pdf

Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

Energy Technology Data Exchange (ETDEWEB)

Stevens, Andrew J.; Pu, Yunchen; Sun, Yannan; Spell, Gregory; Carin, Lawrence

2017-04-20

We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.

Statistical analysis of the electric energy production from photovoltaic conversion using mobile and fixed constructions

Science.gov (United States)

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.

Statistical Analysis of Data for Timber Strengths

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

2003-01-01

Statistical analyses are performed for material strength parameters from a large number of specimens of structural timber. Non-parametric statistical analysis and fits have been investigated for the following distribution types: Normal, Lognormal, 2 parameter Weibull and 3-parameter Weibull...... fits to the data available, especially if tail fits are used whereas the Log Normal distribution generally gives a poor fit and larger coefficients of variation, especially if tail fits are used. The implications on the reliability level of typical structural elements and on partial safety factors...... for timber are investigated....

Ambient Response Analysis of the Heritage Court Tower Building Structure

DEFF Research Database (Denmark)

Brincker, Rune; Andersen, Palle

2000-01-01

In this paper an ambient response analysis of the Heritage Court Building structure is presented. The work is a part of a blind test organized by Professor Carlos Ventura, University of British Columbia. The response data were analyses using two different techniques: a non-parametric based on Fre...... modes, natural frequencies identified by the two techniques shoved good agreement, mode shape estimates were nearly identical, and damping ratios reasonably close and around 1-2% in the most cases....

Ambient Response Analysis of the Heritage Court Tower Building Structure

DEFF Research Database (Denmark)

Brincker, Rune; Andersen, Palle

2000-01-01

In this paper an ambient response analysis of the Heritage Court Building structure is presented. The work is a part of a blind test organized by Professor Carlos Ventura, University of British Columbia. The response data were analyses using two different techniques: a non-parametric based...... modes, natural frequencies identified by the two techniques shoved good agreement, mode shape estimates were nearly identical, and damping ratios reasonably close and around 1-2% in the most cases....

Modeling the World Health Organization Disability Assessment Schedule II using non-parametric item response models.

Science.gov (United States)

Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana

2015-03-01

The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology. Copyright © 2014 John Wiley & Sons, Ltd.

The Stellar Initial Mass Function in Early-type Galaxies from Absorption Line Spectroscopy. IV. A Super-Salpeter IMF in the Center of NGC 1407 from Non-parametric Models

Energy Technology Data Exchange (ETDEWEB)

Conroy, Charlie [Department of Astronomy, Harvard University, Cambridge, MA, 02138 (United States); Van Dokkum, Pieter G. [Department of Astronomy, Yale University, New Haven, CT, 06511 (United States); Villaume, Alexa [Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064 (United States)

2017-03-10

It is now well-established that the stellar initial mass function (IMF) can be determined from the absorption line spectra of old stellar systems, and this has been used to measure the IMF and its variation across the early-type galaxy population. Previous work focused on measuring the slope of the IMF over one or more stellar mass intervals, implicitly assuming that this is a good description of the IMF and that the IMF has a universal low-mass cutoff. In this work we consider more flexible IMFs, including two-component power laws with a variable low-mass cutoff and a general non-parametric model. We demonstrate with mock spectra that the detailed shape of the IMF can be accurately recovered as long as the data quality is high (S/N ≳ 300 Å{sup −1}) and cover a wide wavelength range (0.4–1.0 μ m). We apply these flexible IMF models to a high S/N spectrum of the center of the massive elliptical galaxy NGC 1407. Fitting the spectrum with non-parametric IMFs, we find that the IMF in the center shows a continuous rise extending toward the hydrogen-burning limit, with a behavior that is well-approximated by a power law with an index of −2.7. These results provide strong evidence for the existence of extreme (super-Salpeter) IMFs in the cores of massive galaxies.

Basic statistical tools in research and data analysis

Directory of Open Access Journals (Sweden)

Zulfiqar Ali

2016-01-01

Full Text Available Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

Stepwise data envelopment analysis (DEA); choosing variables for measuring technical efficiency in Norwegian electricity distribution

International Nuclear Information System (INIS)

Kittelsen, S.A.C.

1993-04-01

Electric power distribution is an activity that in principle delivers a separate product to each customer. A specification of products for a utility as a whole leads potentially to a large number of product aspects including topographic and climatic conditions, and the level of disaggregation of factors and products may give the production and cost functions a high dimensionality. Some aggregation is therefore necessary. Non-parametric methods like Data Envelopment Analysis (DEA) have the advantage that they may give meaningful results when parametric methods would not have enough degrees of freedom, but will have related problems if the variables are collinear or are irrelevant. Although aggregate efficiency measures will not be much affected, rates of transformation will be corrupted and observations with extreme values may be measured as efficient by default. Little work has been done so far on the statistical properties of the non-parametric efficiency measure. This paper utilizes a suggestion by Rajiv Banker to measure the significance of the change in results when disaggregating or introducing an extra variable, and shows how one can let the data participate in deciding which variables should be included in the analysis. 32 refs., 7 figs., 4 tabs

Frontier Analysis

DEFF Research Database (Denmark)

Assaf, A. George; Josiassen, Alexander

2016-01-01

This article presents a comprehensive review of frontier studies in the tourism literature. We discuss the main advantages and disadvantages of the various frontier approaches, in particular, the nonparametric and parametric frontier approaches. The study further differentiates between micro...

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

Science.gov (United States)

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

Beyond reliability, multi-state failure analysis of satellite subsystems: A statistical approach

International Nuclear Information System (INIS)

Castet, Jean-Francois; Saleh, Joseph H.

2010-01-01

Reliability is widely recognized as a critical design attribute for space systems. In recent articles, we conducted nonparametric analyses and Weibull fits of satellite and satellite subsystems reliability for 1584 Earth-orbiting satellites launched between January 1990 and October 2008. In this paper, we extend our investigation of failures of satellites and satellite subsystems beyond the binary concept of reliability to the analysis of their anomalies and multi-state failures. In reliability analysis, the system or subsystem under study is considered to be either in an operational or failed state; multi-state failure analysis introduces 'degraded states' or partial failures, and thus provides more insights through finer resolution into the degradation behavior of an item and its progression towards complete failure. The database used for the statistical analysis in the present work identifies five states for each satellite subsystem: three degraded states, one fully operational state, and one failed state (complete failure). Because our dataset is right-censored, we calculate the nonparametric probability of transitioning between states for each satellite subsystem with the Kaplan-Meier estimator, and we derive confidence intervals for each probability of transitioning between states. We then conduct parametric Weibull fits of these probabilities using the Maximum Likelihood Estimation (MLE) approach. After validating the results, we compare the reliability versus multi-state failure analyses of three satellite subsystems: the thruster/fuel; the telemetry, tracking, and control (TTC); and the gyro/sensor/reaction wheel subsystems. The results are particularly revealing of the insights that can be gleaned from multi-state failure analysis and the deficiencies, or blind spots, of the traditional reliability analysis. In addition to the specific results provided here, which should prove particularly useful to the space industry, this work highlights the importance

Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis

International Nuclear Information System (INIS)

Wu, F.; Fan, L.W.; Zhou, P.; Zhou, D.Q.

2012-01-01

Global awareness on energy security and climate change has created much interest in assessing economy-wide energy efficiency performance. A number of previous studies have contributed to evaluate energy efficiency performance using different analytical techniques among which data envelopment analysis (DEA) has recently received increasing attention. Most of DEA-related energy efficiency studies do not consider undesirable outputs such as CO 2 emissions in their modeling framework, which may lead to biased energy efficiency values. Within a joint production framework of desirable and undesirable outputs, in this paper we construct both static and dynamic energy efficiency performance indexes for measuring industrial energy efficiency performance by using several environmental DEA models with CO 2 emissions. The dynamic energy efficiency performance indexes have further been decomposed into two contributing components. We finally apply the indexes proposed to assess the industrial energy efficiency performance of different provinces in China over time. Our empirical study shows that the energy efficiency improvement in China's industrial sector was mainly driven by technological improvement. - Highlights: ► China's industrial energy efficiency is evaluated by DEA models with CO 2 emissions. ► China's industrial energy efficiency improved by 5.6% annually since 1997. ► Industrial energy efficiency improvement in China was mainly driven by technological improvement.

A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie; Hansen, Lars Kai

2011-01-01

Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions......: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we show that variance inflation is also present in kernel principal component analysis (kPCA) and we provide a non-parametric renormalization scheme which can quite efficiently restore generalizability in kPCA....... As for PCA our analysis also suggests a simplified approximate expression. © 2011 Trine J. Abrahamsen and Lars K. Hansen....

An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq.

Science.gov (United States)

Xu, Maoqi; Chen, Liang

2018-01-01

The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean-variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq). As a distribution-free nonparametric model, ELTSeq handles individual heterogeneity by estimating an empirical probability for each observation without making any assumption about read-count distribution. It also incorporates a constraint for the read-count overdispersion, which is widely observed in RNA-seq data. ELTSeq demonstrates a significant improvement over existing methods such as edgeR, DESeq, t-tests, Wilcoxon tests and the classic empirical likelihood-ratio test when handling heterogeneous groups. It will significantly advance the transcriptomics studies of cancers and other complex disease. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Linkage analysis: Inadequate for detecting susceptibility loci in complex disorders?

Energy Technology Data Exchange (ETDEWEB)

Field, L.L.; Nagatomi, J. [Univ. of Calgary, Alberta (Canada)

1994-09-01

Insulin-dependent diabetes mellitus (IDDM) may provide valuable clues about approaches to detecting susceptibility loci in other oligogenic disorders. Numerous studies have demonstrated significant association between IDDM and a VNTR in the 5{prime} flanking region of the insulin (INS) gene. Paradoxically, all attempts to demonstrate linkage of IDDM to this VNTR have failed. Lack of linkage has been attributed to insufficient marker locus information, genetic heterogeneity, or high frequency of the IDDM-predisposing allele in the general population. Tyrosine hydroxylase (TH) is located 2.7 kb from INS on the 5` side of the VNTR and shows linkage disequilibrium with INS region loci. We typed a highly polymorphic microsatellite within TH in 176 multiplex families, and performed parametric (lod score) linkage analysis using various intermediate reduced penetrance models for IDDM (including rare and common disease allele frequencies), as well as non-parametric (affected sib pair) linkage analysis. The scores significantly reject linkage for recombination values of .05 or less, excluding the entire 19 kb region containing TH, the 5{prime} VNTR, the INS gene, and IGF2 on the 3{prime} side of INS. Non-parametric linkage analysis also provided no significant evidence for linkage (mean TH allele sharing 52.5%, P=.12). These results have important implications for efforts to locate genes predisposing to complex disorders, strongly suggesting that regions which are significantly excluded by linkage methods may nevertheless contain predisposing genes readily detectable by association methods. We advocate that investigators routinely perform association analyses in addition to linkage analyses.

Multisegmental Foot and Ankle Motion Analysis After Hallux Valgus Surgery

Science.gov (United States)

Canseco, Karl; Long, Jason; Smedberg, Thomas; Tarima, Sergey; Marks, Richard M.; Harris, Gerald F.

2015-01-01

Background Gait changes in patients with hallux valgus, including altered kinematic and temporal-spatial parameters, have been documented in the literature. Although operative treatment can yield favorable clinical and radiographic results, restoration of normal gait in this population remains unclear. Segmental kinematic changes within the foot and ankle during ambulation after operative correction of hallux valgus have not been reported. The aim of this study was to analyze changes in multisegmental foot and ankle kinematics in patients who underwent operative correction of hallux valgus. Methods A 15-camera Vicon Motion Analysis System was used to evaluate 24 feet in 19 patients with hallux valgus preoperatively and postoperatively. The Milwaukee Foot Model was used to characterize segmental kinematics and temporal-spatial parameters (TSPs). Preoperative and postoperative kinematics and TSPs were compared using paired nonparametric methods; comparisons with normative data were performed using unpaired nonparametric methods. Outcomes were evaluated using the SF-36 assessment tool. Results Preoperatively, patients with hallux valgus showed significantly altered temporal-spatial and kinematic parameters. Postoperatively, kinematic analysis demonstrated restoration of hallux position to normal. Hallux valgus angles and intermetatarsal angles were significantly improved, and outcomes showed a significant increase in performance of physical activities. Temporal-spatial parameters and kinematics in the more proximal segments were not significantly changed postoperatively. Conclusion Postoperative results demonstrated significant improvement in foot geometry and hallux kinematics in the coronal and transverse planes. However, the analysis did not identify restoration of proximal kinematics. Clinical Relevance Further investigation is necessary to explore possible causes/clinical relevance and appropriate treatment interventions for the persistently altered kinematics

A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany).

Science.gov (United States)

Böhning, Dankmar; Karasek, Sarah; Terschüren, Claudia; Annuß, Rolf; Fehr, Rainer

2013-03-09

Life expectancy is of increasing prime interest for a variety of reasons. In many countries, life expectancy is growing linearly, without any indication of reaching a limit. The state of North Rhine-Westphalia (NRW) in Germany with its 54 districts is considered here where the above mentioned growth in life expectancy is occurring as well. However, there is also empirical evidence that life expectancy is not growing linearly at the same level for different regions. To explore this situation further a likelihood-based cluster analysis is suggested and performed. The modelling uses a nonparametric mixture approach for the latent random effect. Maximum likelihood estimates are determined by means of the EM algorithm and the number of components in the mixture model are found on the basis of the Bayesian Information Criterion. Regions are classified into the mixture components (clusters) using the maximum posterior allocation rule. For the data analyzed here, 7 components are found with a spatial concentration of lower life expectancy levels in a centre of NRW, formerly an enormous conglomerate of heavy industry, still the most densely populated area with Gelsenkirchen having the lowest level of life expectancy growth for both genders. The paper offers some explanations for this fact including demographic and socio-economic sources. This case study shows that life expectancy growth is widely linear, but it might occur on different levels.

Discriminant analysis of normal and malignant breast tissue based upon INAA investigation of elemental concentration

International Nuclear Information System (INIS)

Kwanhoong Ng; Senghuat Ong; Bradley, D.A.; Laimeng Looi

1997-01-01

Discriminant analysis of six trace element concentrations measured by instrumental neutron activation analysis (INAA) in 26 paired-samples of malignant and histologically normal human breast tissues shows the technique to be a potentially valuable clinical tool for making malignant-normal classification. Nonparametric discriminant analysis is performed for the data obtained. Linear and quadratic discriminant analyses are also carried out for comparison. For this data set a formal analysis shows that the elements which may be useful in distinguishing between malignant and normal tissues are Ca, Rb and Br, providing correct classification for 24 out of 26 normal samples and 22 out of 26 malignant samples. (Author)