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.

Sources, fate, and transport of nitrogen and phosphorus in the Chesapeake Bay watershed-An empirical model

Science.gov (United States)

Ator, Scott W.; Brakebill, John W.; Blomquist, Joel D.

2011-01-01

Spatially Referenced Regression on Watershed Attributes (SPARROW) was used to provide empirical estimates of the sources, fate, and transport of total nitrogen (TN) and total phosphorus (TP) in the Chesapeake Bay watershed, and the mean annual TN and TP flux to the bay and in each of 80,579 nontidal tributary stream reaches. Restoration efforts in recent decades have been insufficient to meet established standards for water quality and ecological conditions in Chesapeake Bay. The bay watershed includes 166,000 square kilometers of mixed land uses, multiple nutrient sources, and variable hydrogeologic, soil, and weather conditions, and bay restoration is complicated by the multitude of nutrient sources and complex interacting factors affecting the occurrence, fate, and transport of nitrogen and phosphorus from source areas to streams and the estuary. Effective and efficient nutrient management at the regional scale in support of Chesapeake Bay restoration requires a comprehensive understanding of the sources, fate, and transport of nitrogen and phosphorus in the watershed, which is only available through regional models. The current models, Chesapeake Bay nutrient SPARROW models, version 4 (CBTN_v4 and CBTP_v4), were constructed at a finer spatial resolution than previous SPARROW models for the Chesapeake Bay watershed (versions 1, 2, and 3), and include an updated timeframe and modified sources and other explantory terms.

An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans.

Science.gov (United States)

Zhang, Kui; Wiener, Howard; Beasley, Mark; George, Varghese; Amos, Christopher I; Allison, David B

2006-08-01

Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.

Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach.

Science.gov (United States)

Spencer, Amy V; Cox, Angela; Lin, Wei-Yu; Easton, Douglas F; Michailidou, Kyriaki; Walters, Kevin

2016-04-01

There is a large amount of functional genetic data available, which can be used to inform fine-mapping association studies (in diseases with well-characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP-level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine-mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP-specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can lead to improved causal SNPs ranks compared to ranking with identical prior probabilities of association. We demonstrate the use of our methods by applying the methods to the fine mapping of the CASP8 region of chromosome 2 using genotype data from the Collaborative Oncological Gene-Environment Study (COGS) Consortium. The data we analysed included approximately 46,000 breast cancer case and 43,000 healthy control samples. © 2016 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

An Empirical Bayes before-after evaluation of road safety effects of a new motorway in Norway.

Science.gov (United States)

Elvik, Rune; Ulstein, Heidi; Wifstad, Kristina; Syrstad, Ragnhild S; Seeberg, Aase R; Gulbrandsen, Magnus U; Welde, Morten

2017-11-01

This paper presents an Empirical Bayes before-after evaluation of the road safety effects of a new motorway (freeway) in Østfold county, Norway. The before-period was 1996-2002. The after-period was 2009-2015. The road was rebuilt from an undivided two-lane road into a divided four-lane road. The number of killed or seriously injured road users was reduced by 75 percent, controlling for (downward) long-term trends and regression-to-the-mean (statistically significant at the 5 percent level; recorded numbers 71 before, 11 after). There were small changes in the number of injury accidents (185 before, 123 after; net effect -3%) and the number of slightly injured road users (403 before 279 after; net effect +5%). Motorways appear to mainly reduce injury severity, not the number of accidents. The paper discusses challenges in implementing the Empirical Bayes design when less than ideal data are available. Copyright © 2017 Elsevier Ltd. All rights reserved.

Non-parametric production analysis of pesticides use in the Netherlands

NARCIS (Netherlands)

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

2004-01-01

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

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.

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.

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...

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.

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.

Nonparametric statistical inference

CERN Document Server

Gibbons, Jean Dickinson

2010-01-01

Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente

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.

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.)

Generalized least squares and empirical Bayes estimation in regional partial duration series index-flood modeling

DEFF Research Database (Denmark)

Madsen, Henrik; Rosbjerg, Dan

1997-01-01

parameters is inferred from regional data using generalized least squares (GLS) regression. Two different Bayesian T-year event estimators are introduced: a linear estimator that requires only some moments of the prior distributions to be specified and a parametric estimator that is based on specified......A regional estimation procedure that combines the index-flood concept with an empirical Bayes method for inferring regional information is introduced. The model is based on the partial duration series approach with generalized Pareto (GP) distributed exceedances. The prior information of the model...

Analyses of reliability characteristics of emergency diesel generator population using empirical Bayes methods

International Nuclear Information System (INIS)

Vesely, W.E.; Uryas'ev, S.P.; Samanta, P.K.

1993-01-01

Emergency Diesel Generators (EDGs) provide backup power to nuclear power plants in case of failure of AC buses. The reliability of EDGs is important to assure response to loss-of-offsite power accident scenarios, a dominant contributor to the plant risk. The reliable performance of EDGs has been of concern both for regulators and plant operators. In this paper the authors present an approach and results from the analysis of failure data from a large population of EDGs. They used empirical Bayes approach to obtain both the population distribution and the individual failure probabilities from EDGs failure to start and load-run data over 4 years for 194 EDGs at 63 plant units

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.

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.

Empirical Bayes conditional independence graphs for regulatory network recovery

Science.gov (United States)

Mahdi, Rami; Madduri, Abishek S.; Wang, Guoqing; Strulovici-Barel, Yael; Salit, Jacqueline; Hackett, Neil R.; Crystal, Ronald G.; Mezey, Jason G.

2012-01-01

Motivation: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods. Methods: We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures. Results: Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO. We also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion. Availability and implementation: Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. Contact: ramimahdi@yahoo.com or jgm45@cornell.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22685074

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.

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…

Generalized empirical likelihood methods for analyzing longitudinal data

KAUST Repository

Wang, S.; Qian, L.; Carroll, R. J.

2010-01-01

Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks

Writing Indigenous women's lives in the Bay of Bengal: cultures of empire in the Andaman Islands, 1789-1906.

Science.gov (United States)

Anderson, Clare

2011-01-01

This article explores the lives of two Andamanese women, both of whom the British called “Tospy.” The first part of the article takes an indigenous and gendered perspective on early British colonization of the Andamans in the 1860s, and through the experiences of a woman called Topsy stresses the sexual violence that underpinned colonial settlement as well as the British reliance on women as cultural interlocutors. Second, the article discusses colonial naming practices, and the employment of Andamanese women and men as nursemaids and household servants during the 1890s–1910s. Using an extraordinary murder case in which a woman known as Topsy-ayah was a central witness, it argues that both reveal something of the enduring associations and legacies of slavery, as well as the cultural influence of the Atlantic in the Bay of Bengal. In sum, these women's lives present a kaleidoscope view of colonization, gender, networks of Empire, labor, and domesticity in the Bay of Bengal.

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.

Nonparametric correlation models for portfolio allocation

DEFF Research Database (Denmark)

Aslanidis, Nektarios; Casas, Isabel

2013-01-01

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

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

Nonparametric statistics for social and behavioral sciences

CERN Document Server

Kraska-MIller, M

2013-01-01

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

A contingency table approach to nonparametric testing

CERN Document Server

Rayner, JCW

2000-01-01

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

Nonparametric factor analysis of time series

OpenAIRE

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

1998-01-01

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

Nonparametric tests for censored data

CERN Document Server

Bagdonavicus, Vilijandas; Nikulin, Mikhail

2013-01-01

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

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...

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Directory of Open Access Journals (Sweden)

Yajie Zou

2017-01-01

Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

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...

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.

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...

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.

EbayesThresh: R Programs for Empirical Bayes Thresholding

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Iain Johnstone

2005-04-01

Full Text Available Suppose that a sequence of unknown parameters is observed sub ject to independent Gaussian noise. The EbayesThresh package in the S language implements a class of Empirical Bayes thresholding methods that can take advantage of possible sparsity in the sequence, to improve the quality of estimation. The prior for each parameter in the sequence is a mixture of an atom of probability at zero and a heavy-tailed density. Within the package, this can be either a Laplace (double exponential density or else a mixture of normal distributions with tail behavior similar to the Cauchy distribution. The mixing weight, or sparsity parameter, is chosen automatically by marginal maximum likelihood. If estimation is carried out using the posterior median, this is a random thresholding procedure; the estimation can also be carried out using other thresholding rules with the same threshold, and the package provides the posterior mean, and hard and soft thresholding, as additional options. This paper reviews the method, and gives details (far beyond those previously published of the calculations needed for implementing the procedures. It explains and motivates both the general methodology, and the use of the EbayesThresh package, through simulated and real data examples. When estimating the wavelet transform of an unknown function, it is appropriate to apply the method level by level to the transform of the observed data. The package can carry out these calculations for wavelet transforms obtained using various packages in R and S-PLUS. Details, including a motivating example, are presented, and the application of the method to image estimation is also explored. The final topic considered is the estimation of a single sequence that may become progressively sparser along the sequence. An iterated least squares isotone regression method allows for the choice of a threshold that depends monotonically on the order in which the observations are made. An alternative

Flexible Modeling of Epidemics with an Empirical Bayes Framework

Science.gov (United States)

Brooks, Logan C.; Farrow, David C.; Hyun, Sangwon; Tibshirani, Ryan J.; Rosenfeld, Roni

2015-01-01

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic’s behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the “Predict the Influenza Season Challenge”, with the task of predicting key epidemiological measures for the 2013–2014 U.S. influenza season with the help of digital surveillance data. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts made for the 2013–2014 U.S. influenza season, and compare the framework’s cross-validated prediction error on historical data to

Bayesian nonparametric data analysis

CERN Document Server

Müller, Peter; Jara, Alejandro; Hanson, Tim

2015-01-01

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

Nonparametric tests for equality of psychometric functions.

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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 functional mapping of quantitative trait loci.

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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.

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

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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.

Introduction to nonparametric statistics for the biological sciences using R

CERN Document Server

MacFarland, Thomas W

2016-01-01

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

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

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Alejandro Jara

2011-04-01

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

An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies

DEFF Research Database (Denmark)

Thompson, Wesley K.; Wang, Yunpeng; Schork, Andrew J.

2015-01-01

-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via...... analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn’s disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While...... minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local...

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.

A Bayes linear Bayes method for estimation of correlated event rates.

Science.gov (United States)

Quigley, John; Wilson, Kevin J; Walls, Lesley; Bedford, Tim

2013-12-01

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates. © 2013 Society for Risk Analysis.

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)

Extensive Chaetoceros curvisetus bloom in relation to water quality in Port Blair Bay, Andaman Islands.

Science.gov (United States)

Begum, Mehmuna; Sahu, Biraja Kumar; Das, Apurba Kumar; Vinithkumar, Nambali Valsalan; Kirubagaran, Ramalingam

2015-05-01

Blooming of diatom species Chaetoceros curvisetus (Cleve, 1889) was observed in Junglighat Bay and Haddo Harbour of Port Blair Bay of Andaman and Nicobar Islands during June 2010. Physico-chemical parameters, nutrient concentrations and phytoplankton composition data collected from five stations during 2010 were classified as bloom area (BA) and non-bloom area (NBA) and compared. Elevated values of dissolved oxygen were recorded in the BA, and it significantly varied (p NBA. Among the nutrient parameters studied, nitrate concentration indicated significant variation in BA and NBA (p NBA, indicating its utilization. In Junglighat Bay, the C. curvisetus species constituted 93.4 and 69.2% composition of total phytoplankton population during day 1 and day 2, respectively. The bloom forming stations separated out from the non-bloom forming station in non-parametric multidimensional scaling (nMDS) ordinations; cluster analysis powered by SIMPROF test also grouped the stations as BA and NBA.

A nonparametric mixture model for cure rate estimation.

Science.gov (United States)

Peng, Y; Dear, K B

2000-03-01

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

A Structural Labor Supply Model with Nonparametric Preferences

NARCIS (Netherlands)

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

2000-01-01

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

Nonparametric Bayesian inference for multidimensional compound Poisson processes

NARCIS (Netherlands)

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

2015-01-01

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

Urban Noise Modelling in Boka Kotorska Bay

Directory of Open Access Journals (Sweden)

Aleksandar Nikolić

2014-04-01

Full Text Available Traffic is the most significant noise source in urban areas. The village of Kamenari in Boka Kotorska Bay is a site where, in a relatively small area, road traffic and sea (ferry traffic take place at the same time. Due to the specificity of the location, i.e. very rare synergy of sound effects of road and sea traffic in the urban area, as well as the expressed need for assessment of noise level in a simple and quick way, a research was conducted, using empirical methods and statistical analysis methods, which led to the creation of acoustic model for the assessment of equivalent noise level (Leq. The developed model for noise assessment in the Village of Kamenari in Boka Kotorska Bay quite realistically provides data on possible noise levels at the observed site, with very little deviations in relation to empirically obtained values.

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...

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.

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

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

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 HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING

OpenAIRE

Temel, Tugrul T.

2001-01-01

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

Nonparametric identification of copula structures

KAUST Repository

Li, Bo; Genton, Marc G.

2013-01-01

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

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

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.

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

Bacterial biomass and heterotrophic potential in the waters of the Chesapeake Bay plume and contiguous continental shelf

Science.gov (United States)

Kator, H. I.; Zubkoff, P. L.

1981-01-01

Seasonal baseline data on bacterial biomass and heterotrophic uptake in the Chesapeake Bay plume and contiguous Atlantic Ocean shelf waters are discussed. Viable count bacterial numbers in surface water samples collected during June 1980 ranged from a maximum of 190,000 MPN (most probable number)/ml at the Bay mouth to a minimum of 7900 MPN/ml offshore. Similarly, direct count densities ranged from 1,800,000 BU (bacterial units)/ml to 24,000 BU/ml. Heterotrophic potential (V max) was largest at the Bay mouth and lowest offshore. Biomass and V max values usually decreased with depth although subsurface maxima were occasionally observed at inshore stations. Correlation of biomass and heterotrophic potential data with selected hydrographic variables was determind with a nonparametric statistic. Results indicate viable counts are positively and significantly correlated with total chlorophyll, temperature, direct count and V max during June 1980; significant negative correlations are obtained with salinity and depth. Calculations of bacterial standing crop are discussed.

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.

Block Empirical Likelihood for Longitudinal Single-Index Varying-Coefficient Model

Directory of Open Access Journals (Sweden)

Yunquan Song

2013-01-01

Full Text Available In this paper, we consider a single-index varying-coefficient model with application to longitudinal data. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to longitudinal single-index varying-coefficient model, and prove a nonparametric version of Wilks’ theorem which can be used to construct the block empirical likelihood confidence region with asymptotically correct coverage probability for the parametric component. In comparison with normal approximations, the proposed method does not require a consistent estimator for the asymptotic covariance matrix, making it easier to conduct inference for the model's parametric component. Simulations demonstrate how the proposed method works.

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...

Nonparametric analysis of blocked ordered categories data: some examples revisited

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O. Thas

2006-08-01

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

Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality

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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.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

Directory of Open Access Journals (Sweden)

Jo Nishino

2018-04-01

Full Text Available Genome-wide association studies (GWAS suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1. For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.

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.

Convex Optimization in R

Directory of Open Access Journals (Sweden)

Roger Koenker

2014-09-01

Full Text Available Convex optimization now plays an essential role in many facets of statistics. We briefly survey some recent developments and describe some implementations of these methods in R . Applications of linear and quadratic programming are introduced including quantile regression, the Huber M-estimator and various penalized regression methods. Applications to additively separable convex problems subject to linear equality and inequality constraints such as nonparametric density estimation and maximum likelihood estimation of general nonparametric mixture models are described, as are several cone programming problems. We focus throughout primarily on implementations in the R environment that rely on solution methods linked to R, like MOSEK by the package Rmosek. Code is provided in R to illustrate several of these problems. Other applications are available in the R package REBayes, dealing with empirical Bayes estimation of nonparametric mixture models.

Nonparametric Inference for Periodic Sequences

KAUST Repository

Sun, Ying

2012-02-01

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

An 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.

Generalized empirical likelihood methods for analyzing longitudinal data

KAUST Repository

Wang, S.

2010-02-16

Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to investigate the finite sample properties of the proposed methods and compare them with the block empirical likelihood method by You et al. (2006) and the normal approximation with a correctly estimated variance-covariance. The results suggest that the proposed methods are generally more efficient than existing methods which ignore the correlation structure, and better in coverage compared to the normal approximation with correctly specified within-subject correlation. An application illustrating our methods and supporting the simulation study results is also presented.

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage.

Science.gov (United States)

Mejia, Amanda F; Nebel, Mary Beth; Barber, Anita D; Choe, Ann S; Pekar, James J; Caffo, Brian S; Lindquist, Martin A

2018-05-15

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICC MSE ) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully

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

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.

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.

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.

An update on the "empirical turn" in bioethics: analysis of empirical research in nine bioethics journals.

Science.gov (United States)

Wangmo, Tenzin; Hauri, Sirin; Gennet, Eloise; Anane-Sarpong, Evelyn; Provoost, Veerle; Elger, Bernice S

2018-02-07

A review of literature published a decade ago noted a significant increase in empirical papers across nine bioethics journals. This study provides an update on the presence of empirical papers in the same nine journals. It first evaluates whether the empirical trend is continuing as noted in the previous study, and second, how it is changing, that is, what are the characteristics of the empirical works published in these nine bioethics journals. A review of the same nine journals (Bioethics; Journal of Medical Ethics; Journal of Clinical Ethics; Nursing Ethics; Cambridge Quarterly of Healthcare Ethics; Hastings Center Report; Theoretical Medicine and Bioethics; Christian Bioethics; and Kennedy Institute of Ethics Journal) was conducted for a 12-year period from 2004 to 2015. Data obtained was analysed descriptively and using a non-parametric Chi-square test. Of the total number of original papers (N = 5567) published in the nine bioethics journals, 18.1% (n = 1007) collected and analysed empirical data. Journal of Medical Ethics and Nursing Ethics led the empirical publications, accounting for 89.4% of all empirical papers. The former published significantly more quantitative papers than qualitative, whereas the latter published more qualitative papers. Our analysis reveals no significant difference (χ2 = 2.857; p = 0.091) between the proportion of empirical papers published in 2004-2009 and 2010-2015. However, the increasing empirical trend has continued in these journals with the proportion of empirical papers increasing from 14.9% in 2004 to 17.8% in 2015. This study presents the current state of affairs regarding empirical research published nine bioethics journals. In the quarter century of data that is available about the nine bioethics journals studied in two reviews, the proportion of empirical publications continues to increase, signifying a trend towards empirical research in bioethics. The growing volume is mainly attributable to two

A chi-square goodness-of-fit test for non-identically distributed random variables: with application to empirical Bayes

International Nuclear Information System (INIS)

Conover, W.J.; Cox, D.D.; Martz, H.F.

1997-12-01

When using parametric empirical Bayes estimation methods for estimating the binomial or Poisson parameter, the validity of the assumed beta or gamma conjugate prior distribution is an important diagnostic consideration. Chi-square goodness-of-fit tests of the beta or gamma prior hypothesis are developed for use when the binomial sample sizes or Poisson exposure times vary. Nine examples illustrate the application of the methods, using real data from such diverse applications as the loss of feedwater flow rates in nuclear power plants, the probability of failure to run on demand and the failure rates of the high pressure coolant injection systems at US commercial boiling water reactors, the probability of failure to run on demand of emergency diesel generators in US commercial nuclear power plants, the rate of failure of aircraft air conditioners, baseball batting averages, the probability of testing positive for toxoplasmosis, and the probability of tumors in rats. The tests are easily applied in practice by means of corresponding Mathematica reg-sign computer programs which are provided

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

Science.gov (United States)

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

2017-06-30

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

Prediction of maximum earthquake intensities for the San Francisco Bay region

Science.gov (United States)

Borcherdt, Roger D.; Gibbs, James F.

1975-01-01

The intensity data for the California earthquake of April 18, 1906, are strongly dependent on distance from the zone of surface faulting and the geological character of the ground. Considering only those sites (approximately one square city block in size) for which there is good evidence for the degree of ascribed intensity, the empirical relation derived between 1906 intensities and distance perpendicular to the fault for 917 sites underlain by rocks of the Franciscan Formation is: Intensity = 2.69 - 1.90 log (Distance) (km). For sites on other geologic units intensity increments, derived with respect to this empirical relation, correlate strongly with the Average Horizontal Spectral Amplifications (AHSA) determined from 99 three-component recordings of ground motion generated by nuclear explosions in Nevada. The resulting empirical relation is: Intensity Increment = 0.27 +2.70 log (AHSA), and average intensity increments for the various geologic units are -0.29 for granite, 0.19 for Franciscan Formation, 0.64 for the Great Valley Sequence, 0.82 for Santa Clara Formation, 1.34 for alluvium, 2.43 for bay mud. The maximum intensity map predicted from these empirical relations delineates areas in the San Francisco Bay region of potentially high intensity from future earthquakes on either the San Andreas fault or the Hazard fault.

Prediction of maximum earthquake intensities for the San Francisco Bay region

Energy Technology Data Exchange (ETDEWEB)

Borcherdt, R.D.; Gibbs, J.F.

1975-01-01

The intensity data for the California earthquake of Apr 18, 1906, are strongly dependent on distance from the zone of surface faulting and the geological character of the ground. Considering only those sites (approximately one square city block in size) for which there is good evidence for the degree of ascribed intensity, the empirical relation derived between 1906 intensities and distance perpendicular to the fault for 917 sites underlain by rocks of the Franciscan formation is intensity = 2.69 - 1.90 log (distance) (km). For sites on other geologic units, intensity increments, derived with respect to this empirical relation, correlate strongly with the average horizontal spectral amplifications (AHSA) determined from 99 three-component recordings of ground motion generated by nuclear explosions in Nevada. The resulting empirical relation is intensity increment = 0.27 + 2.70 log (AHSA), and average intensity increments for the various geologic units are -0.29 for granite, 0.19 for Franciscan formation, 0.64 for the Great Valley sequence, 0.82 for Santa Clara formation, 1.34 for alluvium, and 2.43 for bay mud. The maximum intensity map predicted from these empirical relations delineates areas in the San Francisco Bay region of potentially high intensity from future earthquakes on either the San Andreas fault or the Hayward fault.

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.

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...

Empirical Methods for Detecting Regional Trends and Other Spatial Expressions in Antrim Shale Gas Productivity, with Implications for Improving Resource Projections Using Local Nonparametric Estimation Techniques

Science.gov (United States)

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

2012-01-01

The primary objectives of this research were to (1) investigate empirical methods for establishing regional trends in unconventional gas resources as exhibited by historical production data and (2) determine whether or not incorporating additional knowledge of a regional trend in a suite of previously established local nonparametric resource prediction algorithms influences assessment results. Three different trend detection methods were applied to publicly available production data (well EUR aggregated to 80-acre cells) from the Devonian Antrim Shale gas play in the Michigan Basin. This effort led to the identification of a southeast-northwest trend in cell EUR values across the play that, in a very general sense, conforms to the primary fracture and structural orientations of the province. However, including this trend in the resource prediction algorithms did not lead to improved results. Further analysis indicated the existence of clustering among cell EUR values that likely dampens the contribution of the regional trend. The reason for the clustering, a somewhat unexpected result, is not completely understood, although the geological literature provides some possible explanations. With appropriate data, a better understanding of this clustering phenomenon may lead to important information about the factors and their interactions that control Antrim Shale gas production, which may, in turn, help establish a more general protocol for better estimating resources in this and other shale gas plays. ?? 2011 International Association for Mathematical Geology (outside the USA).

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 statistical inference

CERN Document Server

Gibbons, Jean Dickinson

2014-01-01

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

Non-Parametric Estimation of Correlation Functions

DEFF Research Database (Denmark)

Brincker, Rune; Rytter, Anders; Krenk, Steen

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

Application of nonparametric statistic method for DNBR limit calculation

International Nuclear Information System (INIS)

Dong Bo; Kuang Bo; Zhu Xuenong

2013-01-01

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

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Directory of Open Access Journals (Sweden)

Saerom Park

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

An empirical Bayes safety evaluation of tram/streetcar signal and lane priority measures in Melbourne.

Science.gov (United States)

Naznin, Farhana; Currie, Graham; Sarvi, Majid; Logan, David

2016-01-01

Streetcars/tram systems are growing worldwide, and many are given priority to increase speed and reliability performance in mixed traffic conditions. Research related to the road safety impact of tram priority is limited. This study explores the road safety impacts of tram priority measures including lane and intersection/signal priority measures. A before-after crash study was conducted using the empirical Bayes (EB) method to provide more accurate crash impact estimates by accounting for wider crash trends and regression to the mean effects. Before-after crash data for 29 intersections with tram signal priority and 23 arterials with tram lane priority in Melbourne, Australia, were analyzed to evaluate the road safety impact of tram priority. The EB before-after analysis results indicated a statistically significant adjusted crash reduction rate of 16.4% after implementation of tram priority measures. Signal priority measures were found to reduce crashes by 13.9% and lane priority by 19.4%. A disaggregate level simple before-after analysis indicated reductions in total and serious crashes as well as vehicle-, pedestrian-, and motorcycle-involved crashes. In addition, reductions in on-path crashes, pedestrian-involved crashes, and collisions among vehicles moving in the same and opposite directions and all other specific crash types were found after tram priority implementation. Results suggest that streetcar/tram priority measures result in safety benefits for all road users, including vehicles, pedestrians, and cyclists. Policy implications and areas for future research are discussed.

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 ...

Bio-optical water quality dynamics observed from MERIS in Pensacola Bay, Florida

Science.gov (United States)

Observed bio-optical water quality data collected from 2009 to 2011 in Pensacola Bay, Florida were used to develop empirical remote sensing retrieval algorithms for chlorophyll a (Chla), colored dissolved organic matter (CDOM), and suspended particulate matter (SPM). Time-series ...

Nonparametric regression using the concept of minimum energy

International Nuclear Information System (INIS)

Williams, Mike

2011-01-01

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

Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

Science.gov (United States)

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

2013-04-01

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

Effects of local geology on ground motion in the San Francisco Bay region, California—A continued study

Science.gov (United States)

Gibbs, James F.; Borcherdt, Roger D.

1974-01-01

Measurements of ground motion generated by nuclear explosions in Nevada have been completed for 99 locations in the San Francisco Bay region, California. The seismograms, Fourier amplitude spectra, spectral amplification curves for the signal, and the Fourier amplitude spectra of the seismic noise are presented for 60 locations. Analog amplifications, based on the maximum signal amplitude, are computed for an additional 39 locations. The recordings of the nuclear explosions show marked amplitude variations which are consistently related to the local geologic conditions of the recording site. The average spectral amplifications observed for vertical and horizontal ground motions are, respectively: (1, 1) for granite, (1.5, 1.6) for the Franciscan Formation, (2.3, 2.3), for other pre-Tertiary and Tertiary rocks, (3.0, 2.7) for the Santa Clara Formation, (3.3, 4.4) for older bay sediments, and (3.7, 11.3) for younger bay mud. Spectral amplification curves define predominant ground frequencies for younger bay mud sites and for some older bay sediment sites. The predominant frequencies for most sites were not clearly defined by the amplitude spectra computed from the seismic background noise. The intensities ascribed to various sites in the San Francisco Bay region for the California earthquake of April 18, 1906, are strongly dependent on distance from the zone of surface faulting and the geological character of the ground. Considering only those sites (approximately one square city block in size) for which there is good evidence for the degree of ascribed intensity, the intensities for 917 sites on Franciscan rocks generally decrease with the logarithm of distance as Intensity = 2.69 - 1.90 log (Distance Km). For sites on other geologic units, intensity increments, derived from this empirical rela.tion, correlate strongly with the Average Horizontal Spectral Amplifications (MISA) according to the empirical relation Intensity Increment= 0.27 + 2.70 log(AHSA). Average

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.

Empirical Bayesian inference and model uncertainty

International Nuclear Information System (INIS)

Poern, K.

1994-01-01

This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability

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...

Experimental study of water desorption isotherms and thin-layer convective drying kinetics of bay laurel leaves

Science.gov (United States)

Ghnimi, Thouraya; Hassini, Lamine; Bagane, Mohamed

2016-12-01

The aim of this work is to determine the desorption isotherms and the drying kinetics of bay laurel leaves ( Laurus Nobilis L.). The desorption isotherms were performed at three temperature levels: 50, 60 and 70 °C and at water activity ranging from 0.057 to 0.88 using the statistic gravimetric method. Five sorption models were used to fit desorption experimental isotherm data. It was found that Kuhn model offers the best fitting of experimental moisture isotherms in the mentioned investigated ranges of temperature and water activity. The Net isosteric heat of water desorption was evaluated using The Clausius-Clapeyron equation and was then best correlated to equilibrium moisture content by the empirical Tsami's equation. Thin layer convective drying curves of bay laurel leaves were obtained for temperatures of 45, 50, 60 and 70 °C, relative humidity of 5, 15, 30 and 45 % and air velocities of 1, 1.5 and 2 m/s. A non linear regression procedure of Levenberg-Marquardt was used to fit drying curves with five semi empirical mathematical models available in the literature, The R2 and χ2 were used to evaluate the goodness of fit of models to data. Based on the experimental drying curves the drying characteristic curve (DCC) has been established and fitted with a third degree polynomial function. It was found that the Midilli Kucuk model was the best semi-empirical model describing thin layer drying kinetics of bay laurel leaves. The bay laurel leaves effective moisture diffusivity and activation energy were also identified.

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

Nonparametric conditional predictive regions for time series

NARCIS (Netherlands)

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

2000-01-01

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

Nonparametric e-Mixture Estimation.

Science.gov (United States)

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

2016-12-01

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

Bayes procedures for adaptive inference in inverse problems for the white noise model

NARCIS (Netherlands)

Knapik, B.T.; Szabó, B.T.; van der Vaart, A.W.; van Zanten, J.H.

2016-01-01

We study empirical and hierarchical Bayes approaches to the problem of estimating an infinite-dimensional parameter in mildly ill-posed inverse problems. We consider a class of prior distributions indexed by a hyperparameter that quantifies regularity. We prove that both methods we consider succeed

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

THE RESPONSE OF MONTEREY BAY TO THE 2010 CHILEAN EARTHQUAKE

Directory of Open Access Journals (Sweden)

Laurence C. Breaker

2011-01-01

Full Text Available The primary frequencies contained in the arrival sequence produced by the tsunami from the Chilean earthquake of 2010 in Monterey Bay were extracted to determine the seiche modes that were produced. Singular Spectrum Analysis (SSA and Ensemble Empirical Mode Decomposition (EEMD were employed to extract the primary frequencies of interest. The wave train from the Chilean tsunami lasted for at least four days due to multipath arrivals that may not have included reflections from outside the bay but most likely did include secondary undulations, and energy trapping in the form of edge waves, inside the bay. The SSA decomposition resolved oscillations with periods of 52-57, 34-35, 26-27, and 21-22 minutes, all frequencies that have been predicted and/or observed in previous studies. The EEMD decomposition detected oscillations with periods of 50-55 and 21-22 minutes. Periods in the range of 50-57 minutes varied due to measurement uncertainties but almost certainly correspond to the first longitudinal mode of oscillation for Monterey Bay, periods of 34-35 minutes correspond to the first transverse mode of oscillation that assumes a nodal line across the entrance of the bay, a period of 26- 27 minutes, although previously observed, may not represent a fundamental oscillation, and a period of 21-22 minutes has been predicted and observed previously. A period of ~37 minutes, close to the period of 34-35 minutes, was generated by the Great Alaskan Earthquake of 1964 in Monterey Bay and most likely represents the same mode of oscillation. The tsunamis associated with the Great Alaskan Earthquake and the Chilean Earthquake both entered Monterey Bay but initially arrived outside the bay from opposite directions. Unlike the Great Alaskan Earthquake, however, which excited only one resonant mode inside the bay, the Chilean Earthquake excited several modes suggesting that the asymmetric shape of the entrance to Monterey Bay was an important factor and that the

Nonparametric estimation in models for unobservable heterogeneity

OpenAIRE

Hohmann, Daniel

2014-01-01

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

Nonparametric estimation of location and scale parameters

KAUST Repository

Potgieter, C.J.; Lombard, F.

2012-01-01

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

A Bayesian Nonparametric Approach to Factor Analysis

DEFF Research Database (Denmark)

Piatek, Rémi; Papaspiliopoulos, Omiros

2018-01-01

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

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

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

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.

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.

Empirical water depth predictions in Dublin Bay based on satellite EO multispectral imagery and multibeam data using spatially weighted geographical analysis

Science.gov (United States)

Monteys, Xavier; Harris, Paul; Caloca, Silvia

2014-05-01

The coastal shallow water zone can be a challenging and expensive environment within which to acquire bathymetry and other oceanographic data using traditional survey methods. Dangers and limited swath coverage make some of these areas unfeasible to survey using ship borne systems, and turbidity can preclude marine LIDAR. As a result, an extensive part of the coastline worldwide remains completely unmapped. Satellite EO multispectral data, after processing, allows timely, cost efficient and quality controlled information to be used for planning, monitoring, and regulating coastal environments. It has the potential to deliver repetitive derivation of medium resolution bathymetry, coastal water properties and seafloor characteristics in shallow waters. Over the last 30 years satellite passive imaging methods for bathymetry extraction, implementing analytical or empirical methods, have had a limited success predicting water depths. Different wavelengths of the solar light penetrate the water column to varying depths. They can provide acceptable results up to 20 m but become less accurate in deeper waters. The study area is located in the inner part of Dublin Bay, on the East coast of Ireland. The region investigated is a C-shaped inlet covering an area of 10 km long and 5 km wide with water depths ranging from 0 to 10 m. The methodology employed on this research uses a ratio of reflectance from SPOT 5 satellite bands, differing to standard linear transform algorithms. High accuracy water depths were derived using multibeam data. The final empirical model uses spatially weighted geographical tools to retrieve predicted depths. The results of this paper confirm that SPOT satellite scenes are suitable to predict depths using empirical models in very shallow embayments. Spatial regression models show better adjustments in the predictions over non-spatial models. The spatial regression equation used provides realistic results down to 6 m below the water surface, with

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...

An empirical test of the 'shark nursery area concept' in Texas bays using a long-term fisheries-independent data set

Science.gov (United States)

Froeschke, John T.; Stunz, Gregory W.; Sterba-Boatwright, Blair; Wildhaber, Mark L.

2010-01-01

Using a long-term fisheries-independent data set, we tested the 'shark nursery area concept' proposed by Heupel et al. (2007) with the suggested working assumptions that a shark nursery habitat would: (1) have an abundance of immature sharks greater than the mean abundance across all habitats where they occur; (2) be used by sharks repeatedly through time (years); and (3) see immature sharks remaining within the habitat for extended periods of time. We tested this concept using young-of-the-year (age 0) and juvenile (age 1+ yr) bull sharks Carcharhinus leucas from gill-net surveys conducted in Texas bays from 1976 to 2006 to estimate the potential nursery function of 9 coastal bays. Of the 9 bay systems considered as potential nursery habitat, only Matagorda Bay satisfied all 3 criteria for young-of-the-year bull sharks. Both Matagorda and San Antonio Bays met the criteria for juvenile bull sharks. Through these analyses we examined the utility of this approach for characterizing nursery areas and we also describe some practical considerations, such as the influence of the temporal or spatial scales considered when applying the nursery role concept to shark populations.

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

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

33 CFR 100.124 - Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York. 100.124 Section 100.124 Navigation and Navigable... NAVIGABLE WATERS § 100.124 Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York...

Multivariate nonparametric regression and visualization with R and applications to finance

CERN Document Server

Klemelä, Jussi

2014-01-01

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

Using non-parametric methods in econometric production analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

2012-01-01

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

Multilevel eEmpirical Bayes modeling for improved estimation of toxicant formulations tosuppress parasitic sea lamprey in the Upper Great Lakes

Science.gov (United States)

Hatfield, Laura A.; Gutreuter, Steve; Boogaard, Michael A.; Carlin, Bradley P.

2011-01-01

Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data.

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.

PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment.

Science.gov (United States)

Lartillot, Nicolas; Rodrigue, Nicolas; Stubbs, Daniel; Richer, Jacques

2013-07-01

Modeling across site variation of the substitution process is increasingly recognized as important for obtaining more accurate phylogenetic reconstructions. Both finite and infinite mixture models have been proposed and have been shown to significantly improve on classical single-matrix models. Compared with their finite counterparts, infinite mixtures have a greater expressivity. However, they are computationally more challenging. This has resulted in practical compromises in the design of infinite mixture models. In particular, a fast but simplified version of a Dirichlet process model over equilibrium frequency profiles implemented in PhyloBayes has often been used in recent phylogenomics studies, while more refined model structures, more realistic and empirically more fit, have been practically out of reach. We introduce a message passing interface version of PhyloBayes, implementing the Dirichlet process mixture models as well as more classical empirical matrices and finite mixtures. The parallelization is made efficient thanks to the combination of two algorithmic strategies: a partial Gibbs sampling update of the tree topology and the use of a truncated stick-breaking representation for the Dirichlet process prior. The implementation shows close to linear gains in computational speed for up to 64 cores, thus allowing faster phylogenetic reconstruction under complex mixture models. PhyloBayes MPI is freely available from our website www.phylobayes.org.

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

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.

Sustainable development in the Hudson Bay/James Bay bioregion

International Nuclear Information System (INIS)

Anon.

1991-01-01

An overview is presented of projects planned for the James Bay/Hudson Bay region, and the expected environmental impacts of these projects. The watershed of James Bay and Hudson Bay covers well over one third of Canada, from southern Alberta to central Ontario to Baffin Island, as well as parts of north Dakota and Minnesota in the U.S.A. Hydroelectric power developments that change the timing and rate of flow of fresh water may cause changes in the nature and duration of ice cover, habitats of marine mammals, fish and migratory birds, currents into and out of Hudson Bay/James Bay, seasonal and annual loads of sediments and nutrients to marine ecosystems, and anadromous fish populations. Hydroelectric projects are proposed for the region by Quebec, Ontario and Manitoba. In January 1992, the Canadian Arctic Resources Committee (CARC), the Environmental Committee of Sanikuluaq, and the Rawson Academy of Arctic Science will launch the Hudson Bay/James Bay Bioregion Program, an independent initiative to apply an ecosystem approach to the region. Two main objectives are to provide a comprehensive assessment of the cumulative impacts of human activities on the marine and freshwater ecosystems of the Hudson Bay/James Bay bioregion, and to foster sustainable development by examining and proposing cooperative processes for decision making among governments, developers, aboriginal peoples and other stakeholders. 1 fig

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

Distribution and behavior of major and trace elements in Tokyo Bay, Mutsu Bay and Funka Bay marine sediments

International Nuclear Information System (INIS)

Honda, Teruyuki; Kimura, Ken-ichiro

2003-01-01

Fourteen major and trace elements in marine sediment core samples collected from the coasts along eastern Japan, i.e. Tokyo Bay (II) (the recess), Tokyo Bay (IV) (the mouth), Mutsu Bay and Funka Bay and the Northwest Pacific basin as a comparative subject were determined by the instrumental neutron activation analysis (INAA). The sedimentation rates and sedimentary ages were calculated for the coastal sediment cores by the 210 Pb method. The results obtained in this study are summarized as follows: (1) Lanthanoid abundance patterns suggested that the major origin of the sediments was terrigenous material. La*/Lu* and Ce*/La* ratios revealed that the sediments from Tokyo Bay (II) and Mutsu Bay more directly reflected the contribution from river than those of other regions. In addition, the Th/Sc ratio indicated that the coastal sediments mainly originated in the materials from the volcanic island-arcs, Japanese islands, whereas those from the Northwest Pacific mainly from the continent. (2) The correlation between the Ce/U and Th/U ratios with high correlation coefficients of 0.920 to 0.991 indicated that all the sediments from Tokyo Bay (II) and Funka Bay were in reducing conditions while at least the upper sediments from Tokyo Bay (IV) and Mutsu Bay were in oxidizing conditions. (3) It became quite obvious that the sedimentation mechanism and the sedimentation environment at Tokyo Bay (II) was different from those at Tokyo Bay (IV), since the sedimentation rate at Tokyo Bay (II) was approximately twice as large as that at Tokyo Bay (IV). The sedimentary age of the 5th layer (8∼10 cm in depth) from Funka Bay was calculated at approximately 1940∼50, which agreed with the time, 1943∼45 when Showa-shinzan was formed by the eruption of the Usu volcano. (author)

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

Resilience of coastal wetlands to extreme hydrologicevents in Apalachicola Bay

Science.gov (United States)

Medeiros, S. C.; Singh, A.; Tahsin, S.

2017-12-01

Extreme hydrologic events such as hurricanes and droughts continuously threaten wetlands which provide key ecosystem services in coastal areas. The recovery time for vegetation after impact fromthese extreme events can be highly variable depending on the hazard type and intensity. Apalachicola Bay in Florida is home to a rich variety of saltwater and freshwater wetlands and is subject to a wide rangeof hydrologic hazards. Using spatiotemporal changes in Landsat-based empirical vegetation indices, we investigate the impact of hurricane and drought on both freshwater and saltwater wetlands from year 2000to 2015 in Apalachicola Bay. Our results indicate that saltwater wetlands are more resilient than freshwater wetlands and suggest that in response to hurricanes, the coastal wetlands took almost a year to recover,while recovery following a drought period was observed after only a month.

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.

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...

Empirical Bayes Credibility Models for Economic Catastrophic Losses by Regions

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Jindrová Pavla

2017-01-01

Full Text Available Catastrophic events affect various regions of the world with increasing frequency and intensity. The number of catastrophic events and the amount of economic losses is varying in different world regions. Part of these losses is covered by insurance. Catastrophe events in last years are associated with increases in premiums for some lines of business. The article focus on estimating the amount of net premiums that would be needed to cover the total or insured catastrophic losses in different world regions using Bühlmann and Bühlmann-Straub empirical credibility models based on data from Sigma Swiss Re 2010-2016. The empirical credibility models have been developed to estimate insurance premiums for short term insurance contracts using two ingredients: past data from the risk itself and collateral data from other sources considered to be relevant. In this article we deal with application of these models based on the real data about number of catastrophic events and about the total economic and insured catastrophe losses in seven regions of the world in time period 2009-2015. Estimated credible premiums by world regions provide information how much money in the monitored regions will be need to cover total and insured catastrophic losses in next year.

Remotely Sensing Pollution: Detection and Monitoring of PCBs in the San Francisco Bay

Science.gov (United States)

Hilton, A.; Kudela, R. M.; Bausell, J.

2016-12-01

While the EPA banned polychlorinated biphenyls (PCBs) in 1977, they continue to persist in San Francisco Bay (SF Bay), often at dangerously high concentrations due to their long half-life. However, in spite of their associated health and environmental risks, PCB monitoring within SF Bay is extremely limited, due in large part to the high costs, both in terms of labor and capital that are associated with it. In this study, a cost effective alternative to in-situ PCB sampling is presented by demonstrating the feasibility of PCB detection via remote sensing. This was done by first establishing relationships between in-situ measurements of sum of 40 PCB concentrations and total suspended sediment concentration (SSC) collected from 1998-2006 at 37 stations distributed throughout SF Bay. A correlation was discovered for all stations at (R2 =0.32), which improved markedly upon partitioning stations into north bay, (R2 =0.64), central bay (R2 =0.80) and south bay (R2 =0.52) regions. SSC was then compared from three USGS monitoring stations with temporally consistent Landsat 8 imagery. The resulting correlation between Landsat 8 (Rrs 654) and SSC measured at USGS stations (R2 =0.50) was validated using an Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) image. The end product is a two-step empirical algorithm that can derive PCB from Landsat 8 imagery within SF Bay. This algorithm can generate spatial PCB concentration maps for SF Bay, which can in turn be utilized to increase ability to forecast PCB concentration. The observation that correlation between AVIRIS (Rrs 657) and SSC was stronger than that of Landsat 8 suggests that the accuracy of this algorithm could be enhanced with improved atmospheric correction.

Humic Substances from Manila Bay and Bolinao Bay Sediments

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Elma Llaguno

1997-12-01

Full Text Available The C,H,N composition of sedimentary humic acids (HA extracted from three sites in Manila Bay and six sites in Bolinao Bay yielded H/C atomic ratios of 1.1-1.4 and N/C atomic ratios of 0.09 - 0.16. The Manila Bay HA's had lower H/C and N/C ratios compared to those from Bolinao Bay. The IR spectra showed prominent aliphatic C-H and amide I and II bands. Manila Bay HA's also had less diverse molecular composition based on the GC-MS analysis of the CuO and alkaline permanganate oxidation products of the humic acids.

Multilevel Empirical Bayes Modeling for Improved Estimation of Toxicant Formulations to Suppress Parasitic Sea Lamprey in the Upper Great Lakes

Science.gov (United States)

Hatfield, L.A.; Gutreuter, S.; Boogaard, M.A.; Carlin, B.P.

2011-01-01

Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data. ?? 2011, The International Biometric Society.

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

Directory of Open Access Journals (Sweden)

John W. Lau

2012-01-01

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

Single versus mixture Weibull distributions for nonparametric satellite reliability

International Nuclear Information System (INIS)

Castet, Jean-Francois; Saleh, Joseph H.

2010-01-01

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

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

CERN Document Server

Taskinen, Sara

2015-01-01

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

Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs

NARCIS (Netherlands)

Kuosmanen, T.K.

2005-01-01

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

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.

An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies.

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Wesley K Thompson

2015-12-01

Full Text Available Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn's disease (CD and the other for schizophrenia (SZ. A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the

An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies.

Science.gov (United States)

Thompson, Wesley K; Wang, Yunpeng; Schork, Andrew J; Witoelar, Aree; Zuber, Verena; Xu, Shujing; Werge, Thomas; Holland, Dominic; Andreassen, Ole A; Dale, Anders M

2015-12-01

Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn's disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of

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.

Non-Parametric Analysis of Rating Transition and Default Data

DEFF Research Database (Denmark)

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

2004-01-01

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

Adaptive nonparametric Bayesian inference using location-scale mixture priors

NARCIS (Netherlands)

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

2010-01-01

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

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

African Journals Online (AJOL)

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

A Nonparametric Bayesian Approach For Emission Tomography Reconstruction

International Nuclear Information System (INIS)

Barat, Eric; Dautremer, Thomas

2007-01-01

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

International Conference on Robust Rank-Based and Nonparametric Methods

CERN Document Server

McKean, Joseph

2016-01-01

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

Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.

Science.gov (United States)

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

2015-01-01

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

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

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Xin Tian

2017-06-01

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

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 modeling of dynamic functional connectivity in fmri data

DEFF Research Database (Denmark)

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

2015-01-01

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

Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support

Czech Academy of Sciences Publication Activity Database

Kalina, Jan; Zvárová, Jana

2017-01-01

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

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

Science.gov (United States)

Zhao, Zhibiao

2011-06-01

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

Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.

Science.gov (United States)

Jaber, Abobaker M; Ismail, Mohd Tahir; Altaher, Alsaidi M

2014-01-01

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

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.

Determining individual variation in growth and its implication for life-history and population processes using the empirical Bayes method.

Directory of Open Access Journals (Sweden)

Simone Vincenzi

2014-09-01

Full Text Available The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics. Modern statistical and computational methods allow the investigation of individual and shared (among homogeneous groups determinants of the observed variation in growth. We use an Empirical Bayes approach to estimate individual and shared variation in somatic growth using a von Bertalanffy growth model with random effects. To illustrate the power and generality of the method, we consider two populations of marble trout Salmo marmoratus living in Slovenian streams, where individually tagged fish have been sampled for more than 15 years. We use year-of-birth cohort, population density during the first year of life, and individual random effects as potential predictors of the von Bertalanffy growth function's parameters k (rate of growth and L∞ (asymptotic size. Our results showed that size ranks were largely maintained throughout marble trout lifetime in both populations. According to the Akaike Information Criterion (AIC, the best models showed different growth patterns for year-of-birth cohorts as well as the existence of substantial individual variation in growth trajectories after accounting for the cohort effect. For both populations, models including density during the first year of life showed that growth tended to decrease with increasing population density early in life. Model validation showed that predictions of individual growth trajectories using the random-effects model were more accurate than predictions based on mean size-at-age of fish.

Determining individual variation in growth and its implication for life-history and population processes using the empirical Bayes method.

Science.gov (United States)

Vincenzi, Simone; Mangel, Marc; Crivelli, Alain J; Munch, Stephan; Skaug, Hans J

2014-09-01

The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics. Modern statistical and computational methods allow the investigation of individual and shared (among homogeneous groups) determinants of the observed variation in growth. We use an Empirical Bayes approach to estimate individual and shared variation in somatic growth using a von Bertalanffy growth model with random effects. To illustrate the power and generality of the method, we consider two populations of marble trout Salmo marmoratus living in Slovenian streams, where individually tagged fish have been sampled for more than 15 years. We use year-of-birth cohort, population density during the first year of life, and individual random effects as potential predictors of the von Bertalanffy growth function's parameters k (rate of growth) and L∞ (asymptotic size). Our results showed that size ranks were largely maintained throughout marble trout lifetime in both populations. According to the Akaike Information Criterion (AIC), the best models showed different growth patterns for year-of-birth cohorts as well as the existence of substantial individual variation in growth trajectories after accounting for the cohort effect. For both populations, models including density during the first year of life showed that growth tended to decrease with increasing population density early in life. Model validation showed that predictions of individual growth trajectories using the random-effects model were more accurate than predictions based on mean size-at-age of fish.

An empirical study to determine the critical success factors of export industry

Directory of Open Access Journals (Sweden)

Masoud Babakhani

2011-01-01

Full Text Available Exporting goods and services play an important role on economy of developing countries. There are many countries in the world whose economy is solely based on exporting raw materials such as oil and gas. Many believe that countries cannot develop their economy as long as they rely on exporting one single group of raw materials. Therefore, there is a need to help other sectors of industries build good infrastructure for exporting diversified products. In this paper, we perform an empirical analysis to determine the critical success factors on exporting different goods. The results are analyzed using some statistical non-parametric methods and some useful guidelines are also suggested.

33 CFR 100.919 - International Bay City River Roar, Bay City, MI.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false International Bay City River Roar, Bay City, MI. 100.919 Section 100.919 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF... Bay City River Roar, Bay City, MI. (a) Regulated Area. A regulated area is established to include all...

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

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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...

An empirical examination of restructured electricity prices

International Nuclear Information System (INIS)

Knittel, C.R.; Roberts, M.R.

2005-01-01

We present an empirical analysis of restructured electricity prices. We study the distributional and temporal properties of the price process in a non-parametric framework, after which we parametrically model the price process using several common asset price specifications from the asset-pricing literature, as well as several less conventional models motivated by the peculiarities of electricity prices. The findings reveal several characteristics unique to electricity prices including several deterministic components of the price series at different frequencies. An 'inverse leverage effect' is also found, where positive shocks to the price series result in larger increases in volatility than negative shocks. We find that forecasting performance in dramatically improved when we incorporate features of electricity prices not commonly modelled in other asset prices. Our findings have implications for how empiricists model electricity prices, as well as how theorists specify models of energy pricing. (author)

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.

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.

Nonparametric combinatorial sequence models.

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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.

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

77 FR 2972 - Thunder Bay Power Company, Thunder Bay Power, LLC, et al.

Science.gov (United States)

2012-01-20

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission Thunder Bay Power Company, Thunder Bay Power, LLC, et al.; Notice of Application for Transfer of Licenses, and Soliciting Comments and Motions To Intervene Thunder Bay Power Company Project No. 2404-095 Thunder Bay Power, LLC Midwest Hydro, Inc...

Genomic breeding value estimation using nonparametric additive regression models

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Solberg Trygve

2009-01-01

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

Sensitivity of Technical Efficiency Estimates to Estimation Methods: An Empirical Comparison of Parametric and Non-Parametric Approaches

OpenAIRE

de-Graft Acquah, Henry

2014-01-01

This paper highlights the sensitivity of technical efficiency estimates to estimation approaches using empirical data. Firm specific technical efficiency and mean technical efficiency are estimated using the non parametric Data Envelope Analysis (DEA) and the parametric Corrected Ordinary Least Squares (COLS) and Stochastic Frontier Analysis (SFA) approaches. Mean technical efficiency is found to be sensitive to the choice of estimation technique. Analysis of variance and Tukey’s test sugge...

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.

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

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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

A Nonparametric Test for Seasonal Unit Roots

OpenAIRE

Kunst, Robert M.

2009-01-01

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

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

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Rabia Ece OMAY

2013-06-01

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

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

OpenAIRE

Froelich, Markus; Puhani, Patrick

2004-01-01

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

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.

33 CFR 162.125 - Sturgeon Bay and the Sturgeon Bay Ship Canal, Wisc.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Sturgeon Bay and the Sturgeon Bay Ship Canal, Wisc. 162.125 Section 162.125 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY INLAND WATERWAYS NAVIGATION REGULATIONS § 162.125 Sturgeon Bay and the Sturgeon Bay Ship...

Bird surveys at McKinley Bay and Hutchison Bay, Northwest Territories, in 1991

Energy Technology Data Exchange (ETDEWEB)

Cornish, B J; Dickson, D L; Dickson, H L

1992-03-01

McKinley Bay is a shallow protected bay along the eastern Beaufort Sea coast which provides an important habitat for diving ducks. Since 1979, the bay has been the site of a winter harbor and support base for oil and gas exploraton in the Beaufort Sea. Aerial surveys for bird abundance and distribution were conducted in August 1991 as a continuation of long-term monitoring of birds in McKinley Bay and Hutchison Bay, a nearby area used as a control. The main objectives of the 1991 surveys were to expand the set of baseline data on natural annual fluctuations in diving duck numbers, and to determine if numbers of diving ducks had changed since the initial 1981-85 surveys. On the day with the best survey conditions, the population of diving ducks at McKinley bay was estimated at ca 32,000, significantly more than 1981-85. At Hutchison Bay, there were an estimated 11,000 ducks. As in previous years, large numbers of diving ducks were observed off Atkinson Point at the northwest corner of McKinley Bay, at the south end of the bay, and in the northeast corner near a long spit. Most divers in Hutchison Bay were at the west side. Diving ducks, primarily Oldsquaw and scoter, were the most abundant bird group in the study area. Observed distribution patterns of birds are discussed with reference to habitat preferences. 16 refs., 7 figs., 30 tabs.

Pb’s high sedimentation inside the bay mouth of Jiaozhou Bay

Science.gov (United States)

Yang, Dongfang; Miao, Zhenqing; Huang, Xinmin; Wei, Linzhen; Feng, Ming

2017-12-01

Sedimentation is one of the key environmental behaviors of pollutants in the ocean. This paper analyzed the seasonal and temporal variations of Pb’s sedimentation process in Jiaozhou Bay in 1987. Results showed that Pb contents in bottom waters in Jiaozhou Bay in May, July and November 1987 were 1.87-2.60 μg L-1, 15.11-19.68 μg L-1 and 11.08-15.18 μg L-1, and the pollution levels of Pb in May, July and November 1987 were slight, heavy and heavy, respectively. In May 1987, there was low sedimentation process in waters in the outside of the bay mouth, yet were high sedimentation process in waters in the middle and inside of the bay mouth. In July and November 1987, there was low sedimentation process in waters in the outside of the bay mouth, yet were high sedimentation process in waters in the inside of the bay mouth. The seasonal-temporal variation of sedimentation processes of Pb were determined by the variations of sources input and the vertical water’s effect.

Non-parametric estimation of the individual's utility map

OpenAIRE

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

2013-01-01

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

Nonparametric Bayesian models through probit stick-breaking processes.

Science.gov (United States)

Rodríguez, Abel; Dunson, David B

2011-03-01

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

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

NARCIS (Netherlands)

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

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

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

Science.gov (United States)

Fronczyk, Kassandra; Kottas, Athanasios

2014-03-01

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

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

Science.gov (United States)

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

2011-05-01

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

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

CSIR Research Space (South Africa)

Van der Walt, CM

2013-12-01

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

A general approach to posterior contraction in nonparametric inverse problems

NARCIS (Netherlands)

Knapik, Bartek; Salomond, Jean Bernard

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

Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data

Directory of Open Access Journals (Sweden)

Ishan Joshi

2015-09-01

Full Text Available Coastal bays, such as Barataria Bay, are important transition zones between the terrigenous and marine environments that are also optically complex due to elevated amounts of particulate and dissolved constituents. Monthly field data collected over a period of 15 months in 2010 and 2011 in Barataria Bay were used to develop an empirical band ratio algorithm for the Landsat-5 TM that showed a good correlation with the Colored Dissolved Organic Matter (CDOM absorption coefficient at 355 nm (ag355 (R2 = 0.74. Landsat-derived CDOM maps generally captured the major details of CDOM distribution and seasonal influences, suggesting the potential use of Landsat imagery to monitor biogeochemistry in coastal water environments. An investigation of the seasonal variation in ag355 conducted using Landsat-derived ag355 as well as field data suggested the strong influence of seasonality in the different regions of the bay with the marine end members (lower bay experiencing generally low but highly variable ag355 and the freshwater end members (upper bay experiencing high ag355 with low variability. Barataria Bay experienced a significant increase in ag355 during the freshwater release at the Davis Pond Freshwater Diversion (DPFD following the Deep Water Horizon oil spill in 2010 and following the Mississippi River (MR flood conditions in 2011, resulting in a weak linkage to salinity in comparison to the other seasons. Tree based statistical analysis showed the influence of high river flow conditions, high- and low-pressure systems that appeared to control ag355 by ~28%, 29% and 43% of the time duration over the study period at the marine end member just outside the bay. An analysis of CDOM variability in 2010 revealed the strong influence of the MR in controlling CDOM abundance in the lower bay during the high flow conditions, while strong winds associated with cold fronts significantly increase CDOM abundance in the upper bay, thus revealing the important

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)

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

Science.gov (United States)

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

Exact nonparametric inference for detection of nonlinear determinism

OpenAIRE

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

2005-01-01

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

Transitioning a Chesapeake Bay Ecological Prediction System to Operations

Science.gov (United States)

Brown, C.; Green, D. S.; Eco Forecasters

2011-12-01

Ecological prediction of the impacts of physical, chemical, biological, and human-induced change on ecosystems and their components, encompass a wide range of space and time scales, and subject matter. They vary from predicting the occurrence and/or transport of certain species, such harmful algal blooms, or biogeochemical constituents, such as dissolved oxygen concentrations, to large-scale ecosystem responses and higher trophic levels. The timescales of ecological prediction, including guidance and forecasts, range from nowcasts and short-term forecasts (days), to intraseasonal and interannual outlooks (weeks to months), to decadal and century projections in climate change scenarios. The spatial scales range from small coastal inlets to basin and global scale biogeochemical and ecological forecasts. The types of models that have been used include conceptual, empirical, mechanistic, and hybrid approaches. This presentation will identify the challenges and progress toward transitioning experimental model-based ecological prediction into operational guidance and forecasting. Recent efforts are targeting integration of regional ocean, hydrodynamic and hydrological models and leveraging weather and water service infrastructure to enable the prototyping of an operational ecological forecast capability for the Chesapeake Bay and its tidal tributaries. A path finder demonstration predicts the probability of encountering sea nettles (Chrysaora quinquecirrha), a stinging jellyfish. These jellyfish can negatively impact safety and economic activities in the bay and an impact-based forecast that predicts where and when this biotic nuisance occurs may help management effects. The issuance of bay-wide nowcasts and three-day forecasts of sea nettle probability are generated daily by forcing an empirical habitat model (that predicts the probability of sea nettles) with real-time and 3-day forecasts of sea-surface temperature (SST) and salinity (SSS). In the first demonstration

The onset of deglaciation of Cumberland Bay and Stromness Bay, South Georgia

NARCIS (Netherlands)

Van Der Putten, N.; Verbruggen, C.

Carbon dating of basal peat deposits in Cumberland Bay and Stromness Bay and sediments from a lake in Stromness Bay, South Georgia indicates deglaciation at the very beginning of the Holocene before c. 9500 14C yr BP. This post-dates the deglaciation of one local lake which has been ice-free since

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.

Effects of local geological conditions in the San Francisco Bay region on ground motions and the intensities of the 1906 earthquake

International Nuclear Information System (INIS)

Borcherdt, R.D.; Gibbs, J.F.

1976-01-01

Measurements of ground motion generated by nuclear explosions in Nevada have been completed for 99 locations in the San Francisco Bay region, California. The recordings show marked amplitude variations in the frequency band 0.25 to 3.0 Hz that are consistently related to the local geological conditions of the recording site. The average spectral amplifications observed for vertical and horizontal ground motions are, respectively: (1,1) for granite, (1.5, 1.6) for the Franciscan Formation, (3.0, 2.7) for the Santa Clara Formation, (3.3, 4.4) for alluvium, and (3.7, 11.3) for bay mud. Spectral amplification curves define predominant ground frequencies in the band 0.25 to 3.0 H for bay mud sites and for some alluvial sites. Amplitude spectra computed from recordings of seismic background noise at 50 sites do not generally define predominant ground frequencies. The intensities ascribed to various sites in the San Francisco Bay region for the California earthquake of April 18, 1906, are strongly dependent on distance from the zone of surface faulting and the geological character of the ground. Considering only those sites (approximately one square city block in size) for which there is good evidence for the degree of ascribed intensity, the intensities for 917 sites on Franciscan rocks generally decrease with the logarithm of distance as Intensity = 2.69 -- 1.90 log (Distance in kilometers). For sites on other geological units, intensity increments, derived from this empirical relation, correlate strongly with the Average Horizontal Spectral Amplifications (AHSA) according to the empirical relation Intensity Increment = 0.27 + 2.70 log (AHSA). Average intensity increments predicted for the various geological units are --0.3 for granite, 0.2 for the Franciscan Formation, 0.6 for the Great Valley sequence, 0.8 for the Santa Clara Formation, 1.3 for alluvium, and 2.4 for bay mud

Effects of local geological conditions in the San Francisco Bay region on ground motions and the intensities of the 1906 earthquake

Energy Technology Data Exchange (ETDEWEB)

Borcherdt, R.D.; Gibbs, J.F.

1976-04-01

Measurements of ground motion generated by nuclear explosions in Nevada have been completed for 99 locations in the San Francisco Bay region, California. The recordings show marked amplitude variations in the frequency band 0.25 to 3.0 Hz that are consistently related to the local geological conditions of the recording site. The average spectral amplifications observed for vertical and horizontal ground motions are, respectively: (1,1) for granite, (1.5, 1.6) for the Franciscan Formation, (3.0, 2.7) for the Santa Clara Formation, (3.3, 4.4) for alluvium, and (3.7, 11.3) for bay mud. Spectral amplification curves define predominant ground frequencies in the band 0.25 to 3.0 H for bay mud sites and for some alluvial sites. Amplitude spectra computed from recordings of seismic background noise at 50 sites do not generally define predominant ground frequencies. The intensities ascribed to various sites in the San Francisco Bay region for the California earthquake of April 18, 1906, are strongly dependent on distance from the zone of surface faulting and the geological character of the ground. Considering only those sites (approximately one square city block in size) for which there is good evidence for the degree of ascribed intensity, the intensities for 917 sites on Franciscan rocks generally decrease with the logarithm of distance as Intensity = 2.69 -- 1.90 log (Distance in kilometers). For sites on other geological units, intensity increments, derived from this empirical relation, correlate strongly with the Average Horizontal Spectral Amplifications (AHSA) according to the empirical relation Intensity Increment = 0.27 + 2.70 log (AHSA). Average intensity increments predicted for the various geological units are --0.3 for granite, 0.2 for the Franciscan Formation, 0.6 for the Great Valley sequence, 0.8 for the Santa Clara Formation, 1.3 for alluvium, and 2.4 for bay mud.

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

Bayesian methods for data analysis

CERN Document Server

Carlin, Bradley P.

2009-01-01

Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors

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

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

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

International Nuclear Information System (INIS)

Janurová, Kateřina; Briš, Radim

2014-01-01

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

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

DEFF Research Database (Denmark)

Georgiadis, Stylianos; Limnios, Nikolaos

2016-01-01

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

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

NARCIS (Netherlands)

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

2010-01-01

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

Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression

NARCIS (Netherlands)

Yoo, W.W.; Ghosal, S

2016-01-01

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

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

NARCIS (Netherlands)

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

2012-01-01

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

75 FR 8297 - Tongass National Forest, Thorne Bay Ranger District, Thorne Bay, AK

Science.gov (United States)

2010-02-24

..., Thorne Bay, AK AGENCY: Forest Service, USDA. ACTION: Cancellation of Notice of intent to prepare an... Roberts, Zone Planner, Thorne Bay Ranger District, Tongass National Forest, P.O. Box 19001, Thorne Bay, AK 99919, telephone: 907-828-3250. SUPPLEMENTARY INFORMATION: The 47,007-acre Kosciusko Project Area is...

77 FR 44140 - Drawbridge Operation Regulation; Sturgeon Bay Ship Canal, Sturgeon Bay, WI

Science.gov (United States)

2012-07-27

... Maple-Oregon Bridges so vehicular traffic congestion would not develop on downtown Sturgeon Bay streets... movement of vehicular traffic in Sturgeon Bay. The Sturgeon Bay Ship Canal is approximately 8.6 miles long... significant increase in vehicular and vessel traffic during the peak tourist and navigation season between...

Constructive Verification, Empirical Induction, and Falibilist Deduction: A Threefold Contrast

Directory of Open Access Journals (Sweden)

Julio Michael Stern

2011-10-01

Full Text Available This article explores some open questions related to the problem of verification of theories in the context of empirical sciences by contrasting three epistemological frameworks. Each of these epistemological frameworks is based on a corresponding central metaphor, namely: (a Neo-empiricism and the gambling metaphor; (b Popperian falsificationism and the scientific tribunal metaphor; (c Cognitive constructivism and the object as eigen-solution metaphor. Each of one of these epistemological frameworks has also historically co-evolved with a certain statistical theory and method for testing scientific hypotheses, respectively: (a Decision theoretic Bayesian statistics and Bayes factors; (b Frequentist statistics and p-values; (c Constructive Bayesian statistics and e-values. This article examines with special care the Zero Probability Paradox (ZPP, related to the verification of sharp or precise hypotheses. Finally, this article makes some remarks on Lakatos’ view of mathematics as a quasi-empirical science.

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.

Using non-parametric methods in econometric production analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

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

Estimation of Stochastic Volatility Models by Nonparametric Filtering

DEFF Research Database (Denmark)

Kanaya, Shin; Kristensen, Dennis

2016-01-01

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

Physical processes in a coupled bay-estuary coastal system: Whitsand Bay and Plymouth Sound

Science.gov (United States)

Uncles, R. J.; Stephens, J. A.; Harris, C.

2015-09-01

Whitsand Bay and Plymouth Sound are located in the southwest of England. The Bay and Sound are separated by the ∼2-3 km-wide Rame Peninsula and connected by ∼10-20 m-deep English Channel waters. Results are presented from measurements of waves and currents, drogue tracking, surveys of salinity, temperature and turbidity during stratified and unstratified conditions, and bed sediment surveys. 2D and 3D hydrodynamic models are used to explore the generation of tidally- and wind-driven residual currents, flow separation and the formation of the Rame eddy, and the coupling between the Bay and the Sound. Tidal currents flow around the Rame Peninsula from the Sound to the Bay between approximately 3 h before to 2 h after low water and form a transport path between them that conveys lower salinity, higher turbidity waters from the Sound to the Bay. These waters are then transported into the Bay as part of the Bay-mouth limb of the Rame eddy and subsequently conveyed to the near-shore, east-going limb and re-circulated back towards Rame Head. The Simpson-Hunter stratification parameter indicates that much of the Sound and Bay are likely to stratify thermally during summer months. Temperature stratification in both is pronounced during summer and is largely determined by coastal, deeper-water stratification offshore. Small tidal stresses in the Bay are unable to move bed sediment of the observed sizes. However, the Bay and Sound are subjected to large waves that are capable of driving a substantial bed-load sediment transport. Measurements show relatively low levels of turbidity, but these respond rapidly to, and have a strong correlation with, wave height.

Nonparametric Bayesian inference in biostatistics

CERN Document Server

Müller, Peter

2015-01-01

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

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

CERN Document Server

Kulchitskii, Yu A

2000-01-01

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

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

Science.gov (United States)

Bornkamp, Björn; Ickstadt, Katja

2009-03-01

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

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

CSIR Research Space (South Africa)

Rademeyer, E

2016-12-01

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

Neural network modelling of planform geometry of headland-bay beaches

Science.gov (United States)

Iglesias, G.; López, I.; Castro, A.; Carballo, R.

2009-02-01

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms - 50 different options combining network architecture and training algorithm - were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.

78 FR 46813 - Safety Zone; Evening on the Bay Fireworks; Sturgeon Bay, WI

Science.gov (United States)

2013-08-02

...-AA00 Safety Zone; Evening on the Bay Fireworks; Sturgeon Bay, WI AGENCY: Coast Guard, DHS. ACTION.... This temporary safety zone will restrict vessels from a portion of Sturgeon Bay due to a fireworks... hazards associated with the fireworks display. DATES: This rule is effective from 8 p.m. until 10 p.m. on...

[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.

77 FR 38488 - Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence River, Alexandria Bay, NY

Science.gov (United States)

2012-06-28

... 1625-AA00 Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence River, Alexandria Bay, NY... restrict vessels from a portion of the St. Lawrence River during the Alexandria Bay Chamber of Commerce... of proposed rulemaking (NPRM) entitled Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence...

Statistical detection of EEG synchrony using empirical bayesian inference.

Directory of Open Access Journals (Sweden)

Archana K Singh

Full Text Available There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001 for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

Statistical detection of EEG synchrony using empirical bayesian inference.

Science.gov (United States)

Singh, Archana K; Asoh, Hideki; Takeda, Yuji; Phillips, Steven

2015-01-01

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

Dividend yield strategies: Dogs of the Dow and Hounds of the Bay

OpenAIRE

Kapur, Ratul; Suryavanshi, Saurabh

2006-01-01

Over the years ‘Dogs of The Dow’ strategy has become an increasingly popular and intensely argued subject for both practitioners and academicians. This thesis examines the multifarious aspects of the ‘Dogs of The Dow’ (DoD) strategy and highlights both the euphemism of the believers and reservations of the skeptics. Further on, we empirically test the DoD strategy over a 16-year period from 1990 to 2005. A parallel study, Hounds of The Bay (HoB) is also carried out for the Canadian markets, o...

Evaluation of Nonparametric Probabilistic Forecasts of Wind Power

DEFF Research Database (Denmark)

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

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

Estimation of 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

Assessment of texture stationarity using the asymptotic behavior of the empirical mean and variance.

Science.gov (United States)

Blanc, Rémy; Da Costa, Jean-Pierre; Stitou, Youssef; Baylou, Pierre; Germain, Christian

2008-09-01

Given textured images considered as realizations of 2-D stochastic processes, a framework is proposed to evaluate the stationarity of their mean and variance. Existing strategies focus on the asymptotic behavior of the empirical mean and variance (respectively EM and EV), known for some types of nondeterministic processes. In this paper, the theoretical asymptotic behaviors of the EM and EV are studied for large classes of second-order stationary ergodic processes, in the sense of the Wold decomposition scheme, including harmonic and evanescent processes. Minimal rates of convergence for the EM and the EV are derived for these processes; they are used as criteria for assessing the stationarity of textures. The experimental estimation of the rate of convergence is achieved using a nonparametric block sub-sampling method. Our framework is evaluated on synthetic processes with stationary or nonstationary mean and variance and on real textures. It is shown that anomalies in the asymptotic behavior of the empirical estimators allow detecting nonstationarities of the mean and variance of the processes in an objective way.

Semiparametric Bernstein–von Mises for the error standard deviation

NARCIS (Netherlands)

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

2013-01-01

We study Bayes procedures for nonparametric regression problems with Gaussian errors, giving conditions under which a Bernstein–von Mises result holds for the marginal posterior distribution of the error standard deviation. We apply our general results to show that a single Bayes procedure using a

Semiparametric Bernstein-von Mises for the error standard deviation

NARCIS (Netherlands)

de Jonge, R.; van Zanten, H.

2013-01-01

We study Bayes procedures for nonparametric regression problems with Gaussian errors, giving conditions under which a Bernstein-von Mises result holds for the marginal posterior distribution of the error standard deviation. We apply our general results to show that a single Bayes procedure using a

Retrospective Review of Watershed Characteristics and a Framework for Future Research in the Sarasota Bay Watershed, Florida

Science.gov (United States)

Kish, George R.; Harrison, Arnell S.; Alderson, Mark

2008-01-01

The U.S. Geological Survey, in cooperation with the Sarasota Bay Estuary Program conducted a retrospective review of characteristics of the Sarasota Bay watershed in west-central Florida. This report describes watershed characteristics, surface- and ground-water processes, and the environmental setting of the Sarasota Bay watershed. Population growth during the last 50 years is transforming the Sarasota Bay watershed from rural and agriculture to urban and suburban. The transition has resulted in land-use changes that influence surface- and ground-water processes in the watershed. Increased impervious cover decreases recharge to ground water and increases overland runoff and the pollutants carried in the runoff. Soil compaction resulting from agriculture, construction, and recreation activities also decreases recharge to ground water. Conventional approaches to stormwater runoff have involved conveyances and large storage areas. Low-impact development approaches, designed to provide recharge near the precipitation point-of-contact, are being used increasingly in the watershed. Simple pollutant loading models applied to the Sarasota Bay watershed have focused on large-scale processes and pollutant loads determined from empirical values and mean event concentrations. Complex watershed models and more intensive data-collection programs can provide the level of information needed to quantify (1) the effects of lot-scale land practices on runoff, storage, and ground-water recharge, (2) dry and wet season flux of nutrients through atmospheric deposition, (3) changes in partitioning of water and contaminants as urbanization alters predevelopment rainfall-runoff relations, and (4) linkages between watershed models and lot-scale models to evaluate the effect of small-scale changes over the entire Sarasota Bay watershed. As urbanization in the Sarasota Bay watershed continues, focused research on water-resources issues can provide information needed by water

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.

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.

Gradient Analysis and Classification of Carolina Bay Vegetation: A Framework for Bay Wetlands Conservation and Restoration

Energy Technology Data Exchange (ETDEWEB)

Diane De Steven,Ph.D.; Maureen Tone,PhD.

1997-10-01

This report address four project objectives: (1) Gradient model of Carolina bay vegetation on the SRS--The authors use ordination analyses to identify environmental and landscape factors that are correlated with vegetation composition. Significant factors can provide a framework for site-based conservation of existing diversity, and they may also be useful site predictors for potential vegetation in bay restorations. (2) Regional analysis of Carolina bay vegetation diversity--They expand the ordination analyses to assess the degree to which SRS bays encompass the range of vegetation diversity found in the regional landscape of South Carolina's western Upper Coastal Plain. Such comparisons can indicate floristic status relative to regional potentials and identify missing species or community elements that might be re-introduced or restored. (3) Classification of vegetation communities in Upper Coastal Plain bays--They use cluster analysis to identify plant community-types at the regional scale, and explore how this classification may be functional with respect to significant environmental and landscape factors. An environmentally-based classification at the whole-bay level can provide a system of templates for managing bays as individual units and for restoring bays to desired plant communities. (4) Qualitative model for bay vegetation dynamics--They analyze present-day vegetation in relation to historic land uses and disturbances. The distinctive history of SRS bays provides the possibility of assessing pathways of post-disturbance succession. They attempt to develop a coarse-scale model of vegetation shifts in response to changing site factors; such qualitative models can provide a basis for suggesting management interventions that may be needed to maintain desired vegetation in protected or restored bays.

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.

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

Science.gov (United States)

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

2018-03-01

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

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

Directory of Open Access Journals (Sweden)

Jinchao Feng

2018-03-01

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

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

NARCIS (Netherlands)

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

2006-01-01

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

Digital spectral analysis parametric, non-parametric and advanced methods

CERN Document Server

Castanié, Francis

2013-01-01

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

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

KAUST Repository

Dai, Wenlin; Tong, Tiejun; Zhu, Lixing

2017-01-01

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

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

KAUST Repository

Dai, Wenlin

2017-09-01

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

Discharge, water-quality characteristics, and nutrient loads from McKay Bay, Delaney Creek, and East Bay, Tampa, Florida, 1991-1993

Science.gov (United States)

Stoker, Y.E.; Levesque, V.A.; Fritz, E.M.

1996-01-01

Nutrient enrichment in Tampa Bay has caused a decline in water quality in the estuary. Efforts to reduce the nutrient loading to Tampa Bay have resulted in improvement in water quality from 1981 to 1991. However, Tampa Bay still is onsidered enriched with nutrients. Water quality in East Bay (located at the northeastern part of Hillsborough Bay, which is an embayment in Tampa Bay) is not improving at the same rate as the rest of the bay. East Bay is the center of shipping activity in Tampa Bay and the seventh largest port in the United States. One of the primary cargoes is phosphate ore and related products such as fertilizer. The potential for nutrient loading to East Bay from shipping activities is high and has not previously been measured. Nitrogen and phosphorus loads from East Bay to Hillsborough Bay were measured during selected time periods during June 1992 through May 1993; these data were used to estimate seasonal and annual loads. These loads were evaluated to determine whether the loss of fertilizer products from shipping activities resulted in increased nutrient loading to Hillsborough Bay. Discharge was measured, and water-quality samples were collected at the head of East Bay (exiting McKay Bay), and at the mouth of East Bay. Discharge and nitrogen and phosphorus concentrations for the period June 1992 through May 1993 were used to compute loads. Discharges from McKay Bay, Delaney Creek, and East Bay are highly variable because of the effect of tide. Flow patterns during discharge measurements generally were unidirectional in McKay Bay and Delaney Creek, but more complex, bidirectional patterns were observed at the mouth of East Bay. Tidally affected discharge data were digitally filtered with the Godin filter to remove the effects of tide so that residual, or net, discharge could be determined. Daily mean discharge from McKay Bay ranged from -1,900 to 2,420 cubic feet per second; from Delaney Creek, -3.8 to 162 cubic feet per second; and from East

46 CFR 7.20 - Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and...

Science.gov (United States)

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and easterly entrance to Long Island Sound, NY. 7.20 Section 7.20... Atlantic Coast § 7.20 Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island...

33 CFR 165.1182 - Safety/Security Zone: San Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Safety/Security Zone: San... Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY... Areas Eleventh Coast Guard District § 165.1182 Safety/Security Zone: San Francisco Bay, San Pablo Bay...

Modeling Trace Element Concentrations in the San Francisco Bay Estuary from Remote Measurement of Suspended Solids

Science.gov (United States)

Press, J.; Broughton, J.; Kudela, R. M.

2014-12-01

Suspended and dissolved trace elements are key determinants of water quality in estuarine and coastal waters. High concentrations of trace element pollutants in the San Francisco Bay estuary necessitate consistent and thorough monitoring to mitigate adverse effects on biological systems and the contamination of water and food resources. Although existing monitoring programs collect annual in situ samples from fixed locations, models proposed by Benoit, Kudela, & Flegal (2010) enable calculation of the water column total concentration (WCT) and the water column dissolved concentration (WCD) of 14 trace elements in the San Francisco Bay from a more frequently sampled metric—suspended solids concentration (SSC). This study tests the application of these models with SSC calculated from remote sensing data, with the aim of validating a tool for continuous synoptic monitoring of trace elements in the San Francisco Bay. Using HICO imagery, semi-analytical and empirical SSC algorithms were tested against a USGS dataset. A single-band method with statistically significant linear fit (p Arsenic, Iron, and Lead in the southern region of the Bay were found to exceed EPA water quality criteria for human health and aquatic life. The results of this study demonstrate the potential of monitoring programs using remote observation of trace element concentrations, and provide the foundation for investigation of pollutant sources and pathways over time.

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.

78 FR 62293 - Safety Zone, Oyster Festival 30th Anniversary Fireworks Display, Oyster Bay; Oyster Bay, NY

Science.gov (United States)

2013-10-15

... Safety Zone, Oyster Festival 30th Anniversary Fireworks Display, Oyster Bay; Oyster Bay, NY AGENCY: Coast... zone on the navigable waters of Oyster Bay near Oyster Bay, NY for the Oyster Festival 30th Anniversary... Oyster Festival 30th Anniversary Fireworks Display is scheduled for October 19, 2013 and is one of...

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.

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

eBay.com

DEFF Research Database (Denmark)

Engholm, Ida

2014-01-01

Celebrated as one of the leading and most valuable brands in the world, eBay has acquired iconic status on par with century-old brands such as Coca-Cola and Disney. The eBay logo is now synonymous with the world’s leading online auction website, and its design is associated with the company...

Discharge between San Antonio Bay and Aransas Bay, southern Gulf Coast, Texas, May-September 1999

Science.gov (United States)

East, Jeffery W.

2001-01-01

Along the Gulf Coast of Texas, many estuaries and bays are important habitat and nurseries for aquatic life. San Antonio Bay and Aransas Bay, located about 50 and 30 miles northeast, respectively, of Corpus Christi, are two important estuarine nurseries on the southern Gulf Coast of Texas (fig. 1). According to the Texas Parks and Wildlife Department, “Almost 80 percent of the seagrasses [along the Texas Gulf Coast] are located in the Laguna Madre, an estuary that begins just south of Corpus Christi Bay and runs southward 140 miles to South Padre Island. Most of the remaining seagrasses, about 45,000 acres, are located in the heavily traveled San Antonio, Aransas and Corpus Christi Bay areas” (Shook, 2000).Population growth has led to greater demands on water supplies in Texas. The Texas Water Development Board, the Texas Parks and Wildlife Department, and the Texas Natural Resource Conservation Commission have the cooperative task of determining inflows required to maintain the ecological health of the State’s streams, rivers, bays, and estuaries. To determine these inflow requirements, the three agencies collect data and conduct studies on the need for instream flows and freshwater/ saline water inflows to Texas estuaries.To assist in the determination of freshwater inflow requirements, the U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board, conducted a hydrographic survey of discharge (flow) between San Antonio Bay and Aransas Bay during the period May–September 1999. Automated instrumentation and acoustic technology were used to maximize the amount and quality of data that were collected, while minimizing personnel requirements. This report documents the discharge measured at two sites between the bays during May–September 1999 and describes the influences of meteorologic (wind and tidal) and hydrologic (freshwater inflow) conditions on discharge between the two bays. The movement of water between the bays is

Classification using Hierarchical Naive Bayes models

DEFF Research Database (Denmark)

Langseth, Helge; Dyhre Nielsen, Thomas

2006-01-01

Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe......, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models...

75 FR 11837 - Chesapeake Bay Watershed Initiative

Science.gov (United States)

2010-03-12

... DEPARTMENT OF AGRICULTURE Commodity Credit Corporation Chesapeake Bay Watershed Initiative AGENCY...: Notice of availability of program funds for the Chesapeake Bay Watershed Initiative. SUMMARY: The... through the Chesapeake Bay Watershed Initiative for agricultural producers in the Chesapeake Bay watershed...

Urban Greening Bay Area

Science.gov (United States)

Information about the San Francisco Bay Water Quality Project (SFBWQP) Urban Greening Bay Area, a large-scale effort to re-envision urban landscapes to include green infrastructure (GI) making communities more livable and reducing stormwater runoff.

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.

USGS Tampa Bay Pilot Study

Science.gov (United States)

Yates, K.K.; Cronin, T. M.; Crane, M.; Hansen, M.; Nayeghandi, A.; Swarzenski, P.; Edgar, T.; Brooks, G.R.; Suthard, B.; Hine, A.; Locker, S.; Willard, D.A.; Hastings, D.; Flower, B.; Hollander, D.; Larson, R.A.; Smith, K.

2007-01-01

Many of the nation's estuaries have been environmentally stressed since the turn of the 20th century and will continue to be impacted in the future. Tampa Bay, one the Gulf of Mexico's largest estuaries, exemplifies the threats that our estuaries face (EPA Report 2001, Tampa Bay Estuary Program-Comprehensive Conservation and Management Plan (TBEP-CCMP)). More than 2 million people live in the Tampa Bay watershed, and the population constitutes to grow. Demand for freshwater resources, conversion of undeveloped areas to resident and industrial uses, increases in storm-water runoff, and increased air pollution from urban and industrial sources are some of the known human activities that impact Tampa Bay. Beginning on 2001, additional anthropogenic modifications began in Tampa Bat including construction of an underwater gas pipeline and a desalinization plant, expansion of existing ports, and increased freshwater withdrawal from three major tributaries to the bay. In January of 2001, the Tampa Bay Estuary Program (TBEP) and its partners identifies a critical need for participation from the U.S. Geological Survey (USGS) in providing multidisciplinary expertise and a regional-scale, integrated science approach to address complex scientific research issue and critical scientific information gaps that are necessary for continued restoration and preservation of Tampa Bay. Tampa Bay stakeholders identified several critical science gaps for which USGS expertise was needed (Yates et al. 2001). These critical science gaps fall under four topical categories (or system components): 1) water and sediment quality, 2) hydrodynamics, 3) geology and geomorphology, and 4) ecosystem structure and function. Scientists and resource managers participating in Tampa Bay studies recognize that it is no longer sufficient to simply examine each of these estuarine system components individually, Rather, the interrelation among system components must be understood to develop conceptual and

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.

Concentration of PSP (Paralytic Shellfish Poisoning) Toxin On Shellfish From Inner Ambon Bay and Kao Bay North Halmahera

Science.gov (United States)

Pello, F. S.; Haumahu, S.; Huliselan, N. V.; Tuapattinaja, M. A.

2017-10-01

The Inner Ambon Bay and Kao Bay have potential on fisheries resources which one of them is molluscs. Molluscs especially for class bivalve have economical values and are consumed by coastal community. The research had been done to analyze saxitoxin (STX) concentration on bivalves from Kao Bay and Inner Ambon Bay. The Saxitoxin Elisa Test Kit Protocol was used to determine saxitoxin concentration. The measurement showed that the highest concentration of saxitoxin (392.42 µg STXeq/100g shellfish meat) was Gafrarium tumidum from Ambon Bay, whereas concentration of saxitoxin (321.83 µg STXeq/100g shellfish meat) was Mactra mera from Kao Bay

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%.

Marine littoral diatoms from the Gordon’s bay region of False Bay, Cape Province, South Africa

CSIR Research Space (South Africa)

Giffen, MH

1971-01-01

Full Text Available and Comic/i for Scientific and Industrial Research, Pretoria (Received: 5.2. 1970) The Gordon?s Bay region occupies the north western corner of False Bay, a large rectangular bay, bounded on the west by the Cape Peninsula ending at Cape Point...

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

Description of gravity cores from San Pablo Bay and Carquinez Strait, San Francisco Bay, California

Science.gov (United States)

Woodrow, Donald L.; John L. Chin,; Wong, Florence L.; Fregoso, Theresa A.; Jaffe, Bruce E.

2017-06-27

Seventy-two gravity cores were collected by the U.S. Geological Survey in 1990, 1991, and 2000 from San Pablo Bay and Carquinez Strait, California. The gravity cores collected within San Pablo Bay contain bioturbated laminated silts and sandy clays, whole and broken bivalve shells (mostly mussels), fossil tube structures, and fine-grained plant or wood fragments. Gravity cores from the channel wall of Carquinez Strait east of San Pablo Bay consist of sand and clay layers, whole and broken bivalve shells (less than in San Pablo Bay), trace fossil tubes, and minute fragments of plant material.

STATCAT, Statistical Analysis of Parametric and Non-Parametric Data

International Nuclear Information System (INIS)

David, Hugh

1990-01-01

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

Non-parametric system identification from non-linear stochastic response

DEFF Research Database (Denmark)

Rüdinger, Finn; Krenk, Steen

2001-01-01

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

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

DEFF Research Database (Denmark)

Gørgens, Tue; Wurtz, Allan

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

75 FR 15343 - Regulated Navigation Area: Narragansett Bay, RI and Mount Hope Bay, RI and MA, Including the...

Science.gov (United States)

2010-03-29

...: Narragansett Bay, RI and Mount Hope Bay, RI and MA, Including the Providence River and Taunton River AGENCY... River and Mount Hope Bay in the vicinity of the two Brightman Street bridges have not been adopted and... Island and Mt. Hope Bay, MA.'' The notice was prompted primarily by two events: (1) The U.S. Army Corps...

A nonparametric test for industrial specialization

OpenAIRE

Billings, Stephen B.; Johnson, Erik B.

2010-01-01

Urban economists hypothesize that industrial diversity matters for urban growth and development, but metrics for empirically testing this relationship are limited to simple concentration metrics (e.g. location quotient) or summary diversity indices (e.g. Gini, Herfindahl). As shown by recent advances in how we measure localization and specialization, these measures of industrial diversity may be subject to bias under small samples or the Modifiable Areal Unit Problem. Furthermore, empirically...

Integration of least angle regression with empirical Bayes for multi-locus genome-wide association studies

Science.gov (United States)

Multi-locus genome-wide association studies has become the state-of-the-art procedure to identify quantitative trait loci (QTL) associated with traits simultaneously. However, implementation of multi-locus model is still difficult. In this study, we integrated least angle regression with empirical B...

Bay breeze climatology at two sites along the Chesapeake bay from 1986-2010: Implications for surface ozone.

Science.gov (United States)

Stauffer, Ryan M; Thompson, Anne M

Hourly surface meteorological measurements were coupled with surface ozone (O 3 ) mixing ratio measurements at Hampton, Virginia and Baltimore, Maryland, two sites along the Chesapeake Bay in the Mid-Atlantic United States, to examine the behavior of surface O 3 during bay breeze events and quantify the impact of the bay breeze on local O 3 pollution. Analyses were performed for the months of May through September for the years 1986 to 2010. The years were split into three groups to account for increasingly stringent environmental regulations that reduced regional emissions of nitrogen oxides (NO x ): 1986-1994, 1995-2002, and 2003-2010. Each day in the 25-year record was marked either as a bay breeze day, a non-bay breeze day, or a rainy/cloudy day based on the meteorological data. Mean eight hour (8-h) averaged surface O 3 values during bay breeze events were 3 to 5 parts per billion by volume (ppbv) higher at Hampton and Baltimore than on non-bay breeze days in all year periods. Anomalies from mean surface O 3 were highest in the afternoon at both sites during bay breeze days in the 2003-2010 study period. In conjunction with an overall lowering of baseline O 3 after the 1995-2002 period, the percentage of total exceedances of the Environmental Protection Agency (EPA) 75 ppbv 8-h O 3 standard that occurred on bay breeze days increased at Hampton for 2003-2010, while remaining steady at Baltimore. These results suggest that bay breeze circulations are becoming more important to causing exceedance events at particular sites in the region, and support the hypothesis of Martins et al. (2012) that highly localized meteorology increasingly drives air quality events at Hampton.

Monitoring of bird abundance and distribution at McKinley Bay and Hutchison Bay, Northwest Territories, 1981 to 1993

Energy Technology Data Exchange (ETDEWEB)

Cornish, B J; Dickson, D L

1994-04-01

McKinley Bay has been identified as a preferred site for a harbor to support oil and gas production in the Beaufort Sea. As the bay is a molting area for several species of diving duck, a study was initiated to monitor the effect of harbor development on birds using the bay. Baseline information on the natural annual fluctuations in the number of birds were collected for nine years at McKinley Bay and eight years at neighboring Hutchinson Bay, an area chosen as the control. The final report of the predevelopment phase of the monitoring study is presented, including results of the 1993 surveys and a summary of results of all years of surveys. There were significantly more diving ducks in McKinley Bay in early August 1990 to 1993, on average, than from 1981 to 1985. No statistically significant change in total diving ducks was noted at Hutchinson Bay. Numbers of species of divers varied substantially between years at the two bays but not to the same degree. Significantly more Pacific loons, red-throated loons, and northern pintails were recorded in the 1990-1993 surveys at McKinley Bay than in earlier surveys. Potential explanations for the large between-year fluctuations in diving duck numbers are discussed. The variations may be due to bird responses to changes in the physical environment or related to the limitations of the aerial survey techniques used. Because of the large natural fluctuations in numbers of molting diving ducks using these bays in early August, it will be difficult to detect future impacts of industrial disturbance, even when sources of survey bias are minimized. It is concluded that aerial surveys of molting diving ducks in the two bays are unsuitable for monitoring the effects of industrial development. 41 refs., 7 figs., 23 tabs.

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

OpenAIRE

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

2008-01-01

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

Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

OpenAIRE

Wang, Wenshuo; Xi, Junqiang; Zhao, Ding

2017-01-01

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

Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality

OpenAIRE

Li, Zhanchao; Gu, Chongshi; Wu, Zhongru

2013-01-01

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

Default Bayes factors for ANOVA designs

NARCIS (Netherlands)

Rouder, Jeffrey N.; Morey, Richard D.; Speckman, Paul L.; Province, Jordan M.

2012-01-01

Bayes factors have been advocated as superior to p-values for assessing statistical evidence in data. Despite the advantages of Bayes factors and the drawbacks of p-values, inference by p-values is still nearly ubiquitous. One impediment to the adoption of Bayes factors is a lack of practical

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.

77 FR 21890 - Drawbridge Operation Regulation; Sturgeon Bay Ship Canal, Sturgeon Bay, WI

Science.gov (United States)

2012-04-12

... Street and Maple-Oregon Bridges so vehicular traffic congestion would not develop on downtown Sturgeon... the efficient movement of vehicular traffic in Sturgeon Bay. The Sturgeon Bay Ship Canal is... experiences a significant increase in vehicular and vessel traffic during the peak tourist and navigation...

Study on origin and sedimentary environment of marine sediments from Kii Channel, Hiroshima Bay and Tosa Bay

International Nuclear Information System (INIS)

Suzuki, Misaki

2008-01-01

The trace amounts of elements in the sediments of sea bottom in Kii Channel, Hiroshima Bay and Tosa Bay were determined quantitatively by the neutron activation analysis. The following facts were illustrated particularly from the quantitative analysis of scandium, rare earths, thorium and uranium: 1) It was known from Ce/La ratio that the geological feature in the west part of Japan is reflected in Kii Channel, Hiroshima Bay and Tosa Bay; 2) The rare-earth element pattern and La/Lu ratio suggest the fact that Kii Channel, Hiroshima Bay and Tosa Bay are essentially composed of the materials of which origin is land; 3) From the fact that Ce/La ratio in these sites are slightly under 1.0, these sites are considered to be affected mainly by the materials of which origin is land; 4) The sedimentary environment in the marine bottom of the Japanese coasts has been found to be mostly under a reductive state. (M.H.)

76 FR 28309 - Drawbridge Operation Regulation; Sturgeon Bay Ship Canal, Sturgeon Bay, WI

Science.gov (United States)

2011-05-17

... vehicular traffic congestion would not develop on downtown Sturgeon Bay streets due to unscheduled bridge... schedules during the peak tourist and navigation seasons to provide for the efficient movement of vehicular... between Lake Michigan and Green Bay. The area experiences a significant increase in vehicular and vessel...

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

Science.gov (United States)

Hof, Stefanie

2014-01-01

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

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

CERN Document Server

Siebert, Carl F

2017-01-01

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

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

International Nuclear Information System (INIS)

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

2017-01-01

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

76 FR 22809 - Safety Zone; Bay Ferry II Maritime Security Exercise; San Francisco Bay, San Francisco, CA

Science.gov (United States)

2011-04-25

... DEPARTMENT OF HOMELAND SECURITY Coast Guard 33 CFR Part 165 [Docket No. USCG-2011-0196] RIN 1625-AA00 Safety Zone; Bay Ferry II Maritime Security Exercise; San Francisco Bay, San Francisco, CA AGENCY... Security Exercise; San Francisco Bay, San Francisco, CA. (a) Location. The limits of this safety zone...

Responses of upland herpetofauna to the restoration of Carolina Bays and thinning of forested Bay Margins.

Energy Technology Data Exchange (ETDEWEB)

Ledvina, Joseph A.

2008-05-01

Research on the effects of wetland restoration on reptiles and amphibians is becoming more common, but almost all of these studies have observed the colonization of recently disturbed habitats that were completely dry at the time of restoration. In a similar manner, investigations herpetofaunal responses to forest management have focused on clearcuts, and less intensive stand manipulations are not as well studied. To evaluate community and population responses of reptiles and amphibians to hydrology restoration and canopy removal in the interior of previously degraded Carolina bays, I monitored herpetofauna in the uplands adjacent to six historically degraded Carolina bays at the Savannah River Site (SRS) in South Carolina for four years after restoration. To evaluate the effects of forest thinning on upland herpetofauna, forests were thinned in the margins of three of these bays. I used repeated measures ANOVA to compare species richness and diversity and the abundance of selected species and guilds between these bays and with those at three reference bays that were not historically drained and three control bays that remained degraded. I also used Non-metric Multidimensional Scaling (NMDS) to look for community-level patterns based treatments.

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

Science.gov (United States)

Siciliani, Luigi

2006-01-01

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

Categorical and nonparametric data analysis choosing the best statistical technique

CERN Document Server

Nussbaum, E Michael

2014-01-01

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

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

OpenAIRE

Kappus, Johanna

2013-01-01

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

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

International Nuclear Information System (INIS)

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

2010-01-01

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

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

International Nuclear Information System (INIS)

Veglia, A.

1981-08-01

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

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

Science.gov (United States)

Mittag, Kathleen Cage

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

Florida Bay: A history of recent ecological changes

Science.gov (United States)

Fourqurean, J.W.; Robblee, M.B.

1999-01-01

Florida Bay is a unique subtropical estuary at the southern tip of the Florida peninsula. Recent ecological changes (seagrass die-off, algal blooms, increased turbidity) to the Florida Bay ecosystem have focused the attention of the public, commercial interests, scientists, and resource managers on the factors influencing the structure and function of Florida Bay. Restoring Florida Bay to some historic condition is the goal of resource managers, but what is not clear is what an anthropogenically-unaltered Florida Bay would look like. While there is general consensus that human activities have contributed to the changes occurring in the Florida Bay ecosystem, a high degree of natural system variability has made elucidation of the links between human activity and Florida Bay dynamics difficult. Paleoecological analyses, examination of long-term datasets, and directed measurements of aspects of the ecology of Florida Bay all contribute to our understanding of the behavior of the bay, and allow quantification of the magnitude of the recent ecological changes with respect to historical variability of the system.

Bird surveys at McKinley Bay and Hutchinson Bay, Northwest Territories, in 1990

Energy Technology Data Exchange (ETDEWEB)

Cornish, B J; Dickson, D L; Dickson, H L

1991-03-01

Monitoring surveys of bird abundance and distribution were conducted in 1990 at McKinley Bay in the Northwest Territories, the site of a winter harbour for drillships and the proposed location for a major year-round support base for oil and gas exploration. Primary objectives of the survey were to determine whether diving duck numbers had changed since the initial phase of the study from 1981-1985, and to provide additional baseline data on natural annual fluctuations in diving duck numbers. Three aerial surveys at each bay were carried out using techniques identical to those in previous years. On 5 August 1990, when survey conditions were considered best of the three surveys, more than twice as many diving ducks were found in McKinley Bay and Hutchinson Bay than on average during the five years of 1981-1985. Old squaw and scooters comprised ca 90% of the diving ducks observed, and both species showed significant increases in numbers. The increase in abundance of diving ducks was likely unrelated to industrial activity in the area since a similar increase occurred in the control area, Hutchinson Bay. Many factors, including both environmental factors such as those affecting nesting success and timing of the moult, and factors related to the survey methods, could be involved in causing the large fluctuations observed. 9 refs., 8 figs., 10 tabs.

Bird surveys at McKinley Bay and Hutchinson Bay, Northwest Territories, in 1990

International Nuclear Information System (INIS)

Cornish, B.J.; Dickson, D.L.; Dickson, H.L.

1991-01-01

Monitoring surveys of bird abundance and distribution were conducted in 1990 at McKinley Bay in the Northwest Territories, the site of a winter harbour for drillships and the proposed location for a major year-round support base for oil and gas exploration. Primary objectives of the survey were to determine whether diving duck numbers had changed since the initial phase of the study from 1981-1985, and to provide additional baseline data on natural annual fluctuations in diving duck numbers. Three aerial surveys at each bay were carried out using techniques identical to those in previous years. On 5 August 1990, when survey conditions were considered best of the three surveys, more than twice as many diving ducks were found in McKinley Bay and Hutchinson Bay than on average during the five years of 1981-1985. Old squaw and scooters comprised ca 90% of the diving ducks observed, and both species showed significant increases in numbers. The increase in abundance of diving ducks was likely unrelated to industrial activity in the area since a similar increase occurred in the control area, Hutchinson Bay. Many factors, including both environmental factors such as those affecting nesting success and timing of the moult, and factors related to the survey methods, could be involved in causing the large fluctuations observed. 9 refs., 8 figs., 10 tabs

78 FR 27126 - East Bay, St. Andrews Bay and the Gulf of Mexico at Tyndall Air Force Base, Florida; Restricted...

Science.gov (United States)

2013-05-09

... DEPARTMENT OF DEFENSE Department of the Army, Corps of Engineers 33 CFR Part 334 East Bay, St. Andrews Bay and the Gulf of Mexico at Tyndall Air Force Base, Florida; Restricted Areas AGENCY: U.S. Army... read as follows: Sec. 334.665 East Bay, St. Andrews Bay and the Gulf of Mexico, Restricted Areas...

Spill management strategy for the Chesapeake Bay

International Nuclear Information System (INIS)

Butler, H.L.; Chapman, R.S.; Johnson, B.H.

1990-01-01

The Chesapeake Bay Program is a unique cooperative effort between state and Federal agencies to restore the health and productivity of America's largest estuary. To assist in addressing specific management issues, a comprehensive three-dimensional, time-varying hydrodynamic and water quality model has ben developed. The Bay modeling strategy will serve as an excellent framework for including submodules to predict the movement, dispersion, and weathering of accidental spills, such as for petroleum products or other chemicals. This paper presents sample results from the Bay application to illustrate the success of the model system in simulating Bay processes. Also, a review of model requirements for successful spill modeling in Chesapeake Bay is presented. Recommendations are given for implementing appropriate spill modules with the Bay model framework and establishing a strategy for model use in addressing management issues

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

DEFF Research Database (Denmark)

Tan, Qihua; Thomassen, Mads; Burton, Mark

2017-01-01

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

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

International Nuclear Information System (INIS)

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

2013-01-01

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

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

KAUST Repository

Ryu, Duchwan

2010-09-28

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

Panel data nonparametric estimation of production risk and risk preferences

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

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

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.

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.

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.

Chesapeake Bay plume dynamics from LANDSAT

Science.gov (United States)

Munday, J. C., Jr.; Fedosh, M. S.

1981-01-01

LANDSAT images with enhancement and density slicing show that the Chesapeake Bay plume usually frequents the Virginia coast south of the Bay mouth. Southwestern (compared to northern) winds spread the plume easterly over a large area. Ebb tide images (compared to flood tide images) show a more dispersed plume. Flooding waters produce high turbidity levels over the shallow northern portion of the Bay mouth.

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.

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.

Mesozooplankton production, grazing and respiration in the Bay of Bengal: Implications for net heterotrophy

Science.gov (United States)

Fernandes, Veronica; Ramaiah, N.

2016-03-01

Mesozooplankton samples were collected from the mixed layer along a central (along 88°E) and a western transect in the Bay of Bengal during four seasons covered between 2001 and 2006 in order to investigate spatio-temporal variability in their biomass. At these stations, grazing and respiration rates were measured from live zooplankton hauled in from the surface during December 2005. Akin to the mesozooplankton "paradox" in the central and eastern Arabian Sea, biomass in the mixed layer was more or less invariant in the central and western Bay of Bengal, even as the chl a showed marginal temporal variation. By empirical equation, the mesozooplankton production rate calculated to be 70-246 mg C m- 2 d- 1 is on par with the Arabian Sea. Contrary to the conventional belief, mesozooplankton grazing impact was up to 83% on primary production (PP). Low PP coupled with very high zooplankton production (70% of PP) along with abundant bacterial production (50% of the PP; Ramaiah et al., 2009) is likely to render the Bay of Bengal net heterotrophic, especially during the spring intermonsoon. Greater estimates of fecal pellet-carbon egestion by mesozooplankton compared to the average particulate organic carbon flux in sediment traps, implies that much of the matter is recycled by heterotrophic communities in the mixed layer facilitating nutrient regeneration for phytoplankton growth. We also calculated that over a third of the primary production is channelized for basin-wide zooplankton respiration that accounts for 52 Mt C annually. In the current scenario of global warming, if low (primary) productive warm pools like the Bay of Bengal continue to be net heterotrophic, negative implications like enhanced emission of CO2 to the atmosphere, increased particulate flux to the deeper waters and greater utilization of dissolved oxygen resulting in expansion of the existing oxygen minimum zone are imminent.

Management case study: Tampa Bay, Florida

Science.gov (United States)

Morrison, Gerold; Greening, Holly; Yates, Kimberly K.; Wolanski, Eric; McLusky, Donald S.

2011-01-01

Tampa Bay, Florida, USA, is a shallow, subtropical estuary that experienced severe cultural eutrophication between the 1940s and 1980s, a period when the human population of its watershed quadrupled. In response, citizen action led to the formation of a public- and private-sector partnership (the Tampa Bay Estuary Program), which adopted a number of management objectives to support the restoration and protection of the bay’s living resources. These included numeric chlorophyll a and water-clarity targets, as well as long-term goals addressing the spatial extent of seagrasses and other selected habitat types, to support estuarine-dependent faunal guilds. Over the past three decades, nitrogen controls involving sources such as wastewater treatment plants, stormwater conveyance systems, fertilizer manufacturing and shipping operations, and power plants have been undertaken to meet these and other management objectives. Cumulatively, these controls have resulted in a 60% reduction in annual total nitrogen (TN) loads relative to earlier worse-case (latter 1970s) conditions. As a result, annual water-clarity and chlorophyll a targets are currently met in most years, and seagrass cover measured in 2008 was the highest recorded since 1950. Factors that have contributed to the observed improvements in Tampa Bay over the past several decades include the following: (1) Development of numeric, science-based water-quality targets to meet a long-term goal of restoring seagrass acreage to 1950s levels. Empirical and mechanistic models found that annual average chlorophyll a concentrations were a primary manageable factor affecting light attenuation. The models also quantified relationships between TN loads, chlorophyll a concentrations, light attenuation, and fluctuations in seagrass cover. The availability of long-term monitoring data, and a systematic process for using the data to evaluate the effectiveness of management actions, has allowed managers to track progress and

Short Term Sediment Exchange Between Marshes and Bays Using Beryllium-7 as a Tracer, Fourleague Bay, Louisiana.

Science.gov (United States)

Restreppo, G. A.; Bentley, S. J.; Xu, K.; Wang, J.

2016-12-01

Modern delta models focus on the availability and exchange of coarse sediment as one of the major factors of deltaic growth or decay. Fine-grained sediment exchange within a river's delta is relatively poorly understood, as is the impact that this exchange has on land building and land loss. To better understand the dynamics of fine grain sediment exchange between river mouth, adjacent bays, and marshland, sediment cores from Fourleague Bay, LA, were collected and analyzed for 7Be, a naturally occurring radioisotope that serves as a marker for recently deposited sediment. Time-series push cores were collected every two months at ten sites, five located across a longitudinal transect in the middle bay and five located along adjacent marshes, from May 2015 to May 2016. All sites fall within 11 to 28 km of the Atchafalaya Delta, along a gradient extending towards the open ocean. Cores were extruded in 2 cm intervals, dried, ground, and analyzed via gamma spectrometry for the presence of 7Be. Inventories of 7Be were then calculated and used to determine bimonthly sedimentation rates over the course twelve months. Sediment deposition on the bay floor and marsh surface were then compared to Atchafalaya River discharge, wind speed and direction, and wave action. Preliminary results indicate patterns of initial fluvial sediment transfer from river to bay floor, then bay floor to marsh surface, with decreasing fluvial influence towards the open ocean. Sediment transport from bay to marsh appears to be coupled with meteorological forcing that induces bay-floor sediment resuspension and the flooding of marsh surfaces. This indirect mechanism of fluvial sediment supply to wetland surfaces may extend the region of influence for sediment delivery from man-made river-sediment diversions.

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.

BOOK REVIEW OF "CHESAPEAKE BAY BLUES: SCIENCE, POLITICS, AND THE STRUGGLE TO SAVE THE BAY"

Science.gov (United States)

This is a book review of "Chesapeake Bay Blues: Science, Politics, and the Struggle to Save the Bay". This book is very well written and provides an easily understandable description of the political challenges faced by those proposing new or more stringent environmental regulat...

Latest results from Daya Bay

Science.gov (United States)

Vorobel, Vit; Daya Bay Collaboration

2017-07-01

The Daya Bay Reactor Neutrino Experiment was designed to measure θ 13, the smallest mixing angle in the three-neutrino mixing framework, with unprecedented precision. The experiment consists of eight functionally identical detectors placed underground at different baselines from three pairs of nuclear reactors in South China. Since Dec. 2011, the experiment has been running stably for more than 4 years, and has collected the largest reactor anti-neutrino sample to date. Daya Bay is able to greatly improve the precision on θ 13 and to make an independent measurement of the effective mass splitting in the electron antineutrino disappearance channel. Daya Bay can also perform a number of other precise measurements, such as a high-statistics determination of the absolute reactor antineutrino flux and spectrum, as well as a search for sterile neutrino mixing, among others. The most recent results from Daya Bay are discussed in this paper, as well as the current status and future prospects of the experiment.

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),

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

Towards a sustainable future in Hudson Bay

International Nuclear Information System (INIS)

Okrainetz, G.

1991-01-01

To date, ca $40-50 billion has been invested in or committed to hydroelectric development on the rivers feeding Hudson Bay. In addition, billions more have been invested in land uses such as forestry and mining within the Hudson Bay drainage basin. However, there has never been a study of the possible impacts on Hudson Bay resulting from this activity. Neither has there been any federal environmental assessment on any of the economic developments that affect Hudson Bay. To fill this gap in knowledge, the Hudson Bay Program was established. The program will not conduct scientific field research but will rather scan the published literature and consult with leading experts in an effort to identify biophysical factors that are likely to be significantly affected by the cumulative influence of hydroelectric and other developments within and outside the region. An annotated bibliography on Hudson Bay has been completed and used to prepare a science overview paper, which will be circulated for comment, revised, and used as the basis for a workshop on cumulative effects in Hudson Bay. Papers will then be commissioned for a second workshop to be held in fall 1993. A unique feature of the program is its integration of traditional ecological knowledge among the Inuit and Cree communities around Hudson Bay with the scientific approach to cumulative impact assessment. One goal of the program is to help these communities bring forward their knowledge in such a way that it can be integrated into the cumulative effects assessment

POTENTIAL HAZARDS OF SEDIMENT IN KENDARI BAY, SOUTHEAST SULAWESI

Directory of Open Access Journals (Sweden)

Nur Adi Kristanto

2017-07-01

Full Text Available Kendari bay is located in front of Kendari city. There are two harbors in the inner part of bay which very important to support economic activities such as shipping and passenger transportation. The result of coastal characteristic mapping and physical oceanography survey show various coastal morphology, vegetation, weathering processes, sedimentation, currents, and water depth and sea floor morphology. Kendari bay is an enclosed bay; the area is wide in the inner part and narrow in mouth of bay (outlet, the morphology look like a bottle’s neck. Numerous mouth rivers are concentrate around the bay. The rivers load material from land since erosion on land is intensive enough. There is indication that sediment supplies from land trough river mouth not equivalent with outlet capacity. Sediment load is trapped in the inner bay caused the outlet morphology. So high sediment rate play an important role in the process of shallow of water depth in Kendari bay. This condition make the Kendari bay is a prone area of sediment hazard due to height rate of sedimentary process. Therefore, to anticipate the hazards, precaution should be taken related to the Kendari bay as the center of activities in southeast of Sulawesi. The further survey is needed such as marine geotechnique and on land environmental to collect data, which can be used as database for development planning. Key words: Potential hazard, sediment, Kendari Bay Teluk

MODELING THE 1958 LITUYA BAY MEGA-TSUNAMI, II

Directory of Open Access Journals (Sweden)

Charles L. Mader

2002-01-01

Full Text Available Lituya Bay, Alaska is a T-Shaped bay, 7 miles long and up to 2 miles wide. The two arms at the head of the bay, Gilbert and Crillon Inlets, are part of a trench along the Fairweather Fault. On July 8, 1958, an 7.5 Magnitude earthquake occurred along the Fairweather fault with an epicenter near Lituya Bay.A mega-tsunami wave was generated that washed out trees to a maximum altitude of 520 meters at the entrance of Gilbert Inlet. Much of the rest of the shoreline of the Bay was denuded by the tsunami from 30 to 200 meters altitude.In the previous study it was determined that if the 520 meter high run-up was 50 to 100 meters thick, the observed inundation in the rest of Lituya Bay could be numerically reproduced. It was also concluded that further studies would require full Navier-Stokes modeling similar to those required for asteroid generated tsunami waves.During the Summer of 2000, Hermann Fritz conducted experiments that reproduced the Lituya Bay 1958 event. The laboratory experiments indicated that the 1958 Lituya Bay 524 meter run-up on the spur ridge of Gilbert Inlet could be caused by a landslide impact.The Lituya Bay impact landslide generated tsunami was modeled with the full Navier- Stokes AMR Eulerian compressible hydrodynamic code called SAGE with includes the effect of gravity.

Radionuclides in sediments from Port Phillip Bay, Australia

International Nuclear Information System (INIS)

Smith, J.D.; Tinker, R.A.; Towler, P.H.

1998-01-01

Full text: Sediment cores were collected from two sites in Port Phillip Bay, Australia, in 1994 and 1995. The concentration of 210 Pb and parameters including water content were measured. The sites chosen were near the centre of the bay where fine sediment accumulates, and towards the northern end of the bay closer to the mouth of the Yarra River. The mid-bay sediment had a high water content (about 1.8 g water per g dry sediment) and a supported 210 Pb activity of about 22 mBq per g of dry sediment. The sediments from further north in the bay were more consolidated, with a lower water content (about 0.6 g water per g dry sediment), and had a supported 210 Pb activity of about 6 mBq per g of dry sediment. Unsupported 210 Pb occurred to depths of about 10 cm in the mid-bay sediment and about 20 cm in sediment from further north in the bay. Models incorporating the water and 210 Pb contents of the sediments were used to calculate possible rates of sediment accumulation and mixing. The distribution of other radionuclides was used as an aid in understanding the sediment behaviour in Port Phillip Bay

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

Directory of Open Access Journals (Sweden)

Richard C. Zink

2012-07-01

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

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

CERN Document Server

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

2002-01-01

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

Developing a wintering waterfowl community baseline for environmental monitoring of Narragansett Bay, Rhode Island [version 3; referees: 1 approved, 2 approved with reservations

Directory of Open Access Journals (Sweden)

Betty J. Kreakie

2015-12-01

Full Text Available In 2004, the Atlantic Ecology Division of the US Environmental Protection Agency’s Office of Research and Development began an annual winter waterfowl survey of Rhode Island’s Narragansett Bay. Herein, we explore the survey data gathered from 2004 to 2011 in order to establish a benchmark understanding of our waterfowl communities and to establish a statistical framework for future environmental monitoring. The abundance and diversity of wintering waterfowl were relatively stable during the initial years of this survey, except in 2010 when there was a large spike in abundance and a reciprocal fall in diversity. There was no significant change in ranked abundance of most waterfowl species, with only Bufflehead (Bucephala albeola and Hooded Merganser (Lophodytes cucllatus showing a slight yet significant upward trend during the course of our survey period. Nonmetric multidimensional scaling (NMDS was used to examine the community structure of wintering waterfowl. The results of the NMDS indicate that there is a spatial structure to the waterfowl communities of Narragansett Bay and this structure has remained relatively stable since the survey began. Our NMDS analysis helps to solidify what is known anecdotally about the bay’s waterfowl ecology, and provides a formalized benchmark for long-term monitoring of Narragansett Bay’s waterfowl communities. Birds, including waterfowl, are preferred bioindicators and we propose using our multivariate approach to monitor the future health of the bay. While this research focuses on a specific area of New England, these methods can be easily applied to novel areas of concern and provide a straightforward nonparametric approach to community-level monitoring. The methods provide a statistic test to examine potential drivers of community turnover and well-suited visualization tools.

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.

Environmental Conditions Associated with Elevated Vibrio parahaemolyticus Concentrations in Great Bay Estuary, New Hampshire.

Directory of Open Access Journals (Sweden)

Erin A Urquhart

Full Text Available Reports from state health departments and the Centers for Disease Control and Prevention indicate that the annual number of reported human vibriosis cases in New England has increased in the past decade. Concurrently, there has been a shift in both the spatial distribution and seasonal detection of Vibrio spp. throughout the region based on limited monitoring data. To determine environmental factors that may underlie these emerging conditions, this study focuses on a long-term database of Vibrio parahaemolyticus concentrations in oyster samples generated from data collected from the Great Bay Estuary, New Hampshire over a period of seven consecutive years. Oyster samples from two distinct sites were analyzed for V. parahaemolyticus abundance, noting significant relationships with various biotic and abiotic factors measured during the same period of study. We developed a predictive modeling tool capable of estimating the likelihood of V. parahaemolyticus presence in coastal New Hampshire oysters. Results show that the inclusion of chlorophyll a concentration to an empirical model otherwise employing only temperature and salinity variables, offers improved predictive capability for modeling the likelihood of V. parahaemolyticus in the Great Bay Estuary.

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

OpenAIRE

von Hirschhausen, Christian R.; Cullmann, Astrid

2005-01-01

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

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

OpenAIRE

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

2004-01-01

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

Daya bay reactor neutrino experiment

International Nuclear Information System (INIS)

Cao Jun

2010-01-01

Daya Bay Reactor Neutrino Experiment is a large international collaboration experiment under construction. The experiment aims to precisely determine the neutrino mixing angle θ 13 by detecting the neutrinos produced by the Daya Bay Nuclear Power Plant. θ 13 is one of two unknown fundamental parameters in neutrino mixing. Its magnitude is a roadmap of the future neutrino physics, and very likely related to the puzzle of missing antimatter in our universe. The precise measurement has very important physics significance. The detectors of Daya Bay is under construction now. The full operation is expected in 2011. Three years' data taking will reach the designed the precision, to determine sin 2 2θ 13 to better than 0.01. Daya Bay neutrino detector is an underground large nuclear detector of low background, low energy, and high precision. In this paper, the layout of the experiment, the design and fabrication progress of the detectors, and some highlighted nuclear detecting techniques developed in the detector R and D are introduced. (author)

226Ra and 228Ra in the mixing zones of the Pee Dee River-Winyah Bay, Yangtze River and Delaware Bay Estuaries

International Nuclear Information System (INIS)

Elsinger, R.J.; Moore, W.S.

1984-01-01

226 Ra and 228 Ra have non-conservative excess concentrations in the mixing zones of the Pee Dee River-Winyah Bay estuary, the Yangtze River estuary, and the Delaware Bay estuary. Laboratory experiments, using Pee Dee River sediment, indicate desorption of 226 Ra to increase with increasing salinities up to 20 per mille. In Winyah Bay desorption from river-borne sediments could contribute almost all of the increases for both isotopes. Desorption adds only a portion of the excess 228 Ra measured in the Yangtze River and adjacent Shelf waters and Delaware Bay. In the Yangtze River the mixing zone extends over a considerable portion of the Continental Shelf where 228 Ra is added to the water column by diffusion from bottom sediments, while 226 Ra concentrations decrease from dilution. Diffusion of 228 Ra from bottom sediments in Delaware Bay primarily occurs in the upper part of the bay ( 228 Ra of 0.33 dpm cm -2 year was determined for Delaware Bay. (author)

The Neoglacial landscape and human history of Glacier Bay, Glacier Bay National Park and Preserve, southeast Alaska, USA

Science.gov (United States)

Connor, C.; Streveler, G.; Post, A.; Monteith, D.; Howell, W.

2009-01-01

The Neoglacial landscape of the Huna Tlingit homeland in Glacier Bay is recreated through new interpretations of the lower Bay's fjordal geomorphology, late Quaternary geology and its ethnographic landscape. Geological interpretation is enhanced by 38 radiocarbon dates compiled from published and unpublished sources, as well as 15 newly dated samples. Neoglacial changes in ice positions, outwash and lake extents are reconstructed for c. 5500?????"200 cal. yr ago, and portrayed as a set of three landscapes at 1600?????"1000, 500?????"300 and 300?????"200 cal. yr ago. This history reveals episodic ice advance towards the Bay mouth, transforming it from a fjordal seascape into a terrestrial environment dominated by glacier outwash sediments and ice-marginal lake features. This extensive outwash plain was building in lower Glacier Bay by at least 1600 cal. yr ago, and had filled the lower bay by 500 cal. yr ago. The geologic landscape evokes the human-described landscape found in the ethnographic literature. Neoglacial climate and landscape dynamism created difficult but endurable environmental conditions for the Huna Tlingit people living there. Choosing to cope with environmental hardship was perhaps preferable to the more severely deteriorating conditions outside of the Bay as well as conflicts with competing groups. The central portion of the outwash plain persisted until it was overridden by ice moving into Icy Strait between AD 1724?????"1794. This final ice advance was very abrupt after a prolonged still-stand, evicting the Huna Tlingit from their Glacier Bay homeland. ?? 2009 SAGE Publications.

Organic Matter Remineralization Predominates Phosphorus Cycling in the Mid-Bay Sediments in the Chesapeake Bay

Energy Technology Data Exchange (ETDEWEB)

Sunendra, Joshi R.; Kukkadapu, Ravi K.; Burdige, David J.; Bowden, Mark E.; Sparks, Donald L.; Jaisi, Deb P.

2015-05-19

The Chesapeake Bay, the largest and most productive estuary in the US, suffers from varying degrees of water quality issues fueled by both point and non–point source nutrient sources. Restoration of the bay is complicated by the multitude of nutrient sources, their variable inputs and hydrological conditions, and complex interacting factors including climate forcing. These complexities not only restrict formulation of effective restoration plans but also open up debates on accountability issues with nutrient loading. A detailed understanding of sediment phosphorus (P) dynamics enables one to identify the exchange of dissolved constituents across the sediment- water interface and aid to better constrain mechanisms and processes controlling the coupling between the sediments and the overlying waters. Here we used phosphate oxygen isotope ratios (δ18Op) in concert with sediment chemistry, XRD, and Mössbauer spectroscopy on the sediment retrieved from an organic rich, sulfidic site in the meso-haline portion of the mid-bay to identify sources and pathway of sedimentary P cycling and to infer potential feedback effect on bottom water hypoxia and surface water eutrophication. Isotope data indicate that the regeneration of inorganic P from organic matter degradation (remineralization) is the predominant, if not sole, pathway for authigenic P precipitation in the mid-bay sediments. We interpret that the excess inorganic P generated by remineralization should have overwhelmed any bottom-water and/or pore-water P derived from other sources or biogeochemical processes and exceeded saturation with respect to authigenic P precipitation. It is the first research that identifies the predominance of remineralization pathway against remobilization (coupled Fe-P cycling) pathway in the Chesapeake Bay. Therefore, these results are expected to have significant implications for the current understanding of P cycling and benthic-pelagic coupling in the bay, particularly on the

Algae Reefs in Shark Bay, Western Australia, Australia

Science.gov (United States)

1990-01-01

Numerous algae reefs are seen in Shark Bay, Western Australia, Australia (26.0S, 113.5E) especially in the southern portions of the bay. The south end is more saline because tidal flow in and out of the bay is restricted by sediment deposited at the north and central end of the bay opposite the mouth of the Wooramel River. This extremely arid region produces little sediment runoff so that the waters are very clear, saline and rich in algae.

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.

Unique thermal record in False Bay

CSIR Research Space (South Africa)

Grundlingh, ML

1993-10-01

Full Text Available Over the past decade False Bay has assumed a prime position in terms of research in to large South African bays. This is manifested by investigations that cover flow conditions modelling, thermal structure, management, biology and nutrients, geology...

Predicting potentially toxigenic Pseudo-nitzschia blooms in the Chesapeake Bay

Science.gov (United States)

Anderson, Clarissa R.; Sapiano, Mathew R. P.; Prasad, M. Bala Krishna; Long, Wen; Tango, Peter J.; Brown, Christopher W.; Murtugudde, Raghu

2010-11-01

Harmful algal blooms are now recognized as a significant threat to the Chesapeake Bay as they can severely compromise the economic viability of important recreational and commercial fisheries in the largest estuary of the United States. This study describes the development of empirical models for the potentially domoic acid-producing Pseudo-nitzschia species complex present in the Bay, developed from a 22-year time series of cell abundance and concurrent measurements of hydrographic and chemical properties. Using a logistic Generalized Linear Model (GLM) approach, model parameters and performance were compared over a range of Pseudo-nitzschia bloom thresholds relevant to toxin production by different species. Small-threshold blooms (≥10 cells mL -1) are explained by time of year, location, and variability in surface values of phosphate, temperature, nitrate plus nitrite, and freshwater discharge. Medium- (100 cells mL -1) to large- threshold (1000 cells mL -1) blooms are further explained by salinity, silicic acid, dissolved organic carbon, and light attenuation (Secchi) depth. These predictors are similar to other models for Pseudo-nitzschia blooms on the west coast, suggesting commonalities across ecosystems. Hindcasts of bloom probabilities at a 19% bloom prediction point yield a Heidke Skill Score of ~53%, a Probability of Detection ˜ 75%, a False Alarm Ratio of ˜ 52%, and a Probability of False Detection ˜9%. The implication of possible future changes in Baywide nutrient stoichiometry on Pseudo-nitzschia blooms is discussed.

NUMERICAL MODELS AS TOOLS TO UNDERSTAND THE DYNAMICS IN BAYS: CASE OF STUDY CHETUMAL BAY, QUINTANA ROO

Directory of Open Access Journals (Sweden)

David Avalos-Cueva

2017-07-01

Full Text Available In this study performed the simulation of currents generated by the wind on the Bay of Chetumal, Quintana Roo through the use of a stationary shallow-water model. A homogeneous climatic wind was used for the entire Bay, with a velocity of 3m·s-1 , and directions North, South, Northeast, Northwest, East, Southeast, Southwest and West. The results showed a rather complex dynamics in Chetumal Bay, in which important turns were observed in deep areas, with speeds reaching up to 13 cm·s-1 .

76 FR 31851 - Safety Zone; Put-in-Bay Fireworks, Fox's the Dock Pier; South Bass Island, Put-in-Bay, OH

Science.gov (United States)

2011-06-02

... DEPARTMENT OF HOMELAND SECURITY Coast Guard 33 CFR Part 165 [Docket No. USCG-2011-0417] RIN 1625-AA00 Safety Zone; Put-in-Bay Fireworks, Fox's the Dock Pier; South Bass Island, Put-in-Bay, OH AGENCY.... Add Sec. 165.T09-0417 as follows: Sec. 165.T09-0417 Safety Zone; Put-In-Bay Fireworks, Fox's the Dock...

Chesapeake Bay under stress

Science.gov (United States)

According to extensive data obtained over its 13,000 km of shoreline, the Chesapeake Bay has been suffering a major, indeed unprecedented, reduction in submerged vegetation. Chesapeake Bay is alone in experiencing decline in submerged vegetation. Other estuary systems on the east coast of the United States are not so affected. These alarming results were obtained by the synthesis of the findings of numerous individual groups in addition to large consortium projects on the Chesapeake done over the past decade. R. J. Orth and R. A. Moore of the Virginia Institute of Marine Science pointed to the problem of the severe decline of submerged grasses on the Bay and along its tributaries. In a recent report, Orth and Moore note: “The decline, which began in the 1960's and accelerated in the 1970's, has affected all species in all areas. Many major river systems are now totally devoid of any rooted vegetation” (Science, 222, 51-53, 1983).

Chondrichthyan occurrence and abundance trends in False Bay ...

African Journals Online (AJOL)

Commercial fishing in False Bay, South Africa, began in the 1600s. Today chondrichthyans are regularly taken in fisheries throughout the bay. Using a combination of catch, survey and life history data, the occurrence and long-term changes in populations of chondrichthyans in False Bay are described. Analyses of time ...

Toxic phytoplankton in San Francisco Bay

Science.gov (United States)

Rodgers, Kristine M.; Garrison, David L.; Cloern, James E.

1996-01-01

The Regional Monitoring Program (RMP) was conceived and designed to document the changing distribution and effects of trace substances in San Francisco Bay, with focus on toxic contaminants that have become enriched by human inputs. However, coastal ecosystems like San Francisco Bay also have potential sources of naturally-produced toxic substances that can disrupt food webs and, under extreme circumstances, become threats to public health. The most prevalent source of natural toxins is from blooms of algal species that can synthesize metabolites that are toxic to invertebrates or vertebrates. Although San Francisco Bay is nutrient-rich, it has so far apparently been immune from the epidemic of harmful algal blooms in the world’s nutrient-enriched coastal waters. This absence of acute harmful blooms does not imply that San Francisco Bay has unique features that preclude toxic blooms. No sampling program has been implemented to document the occurrence of toxin-producing algae in San Francisco Bay, so it is difficult to judge the likelihood of such events in the future. This issue is directly relevant to the goals of RMP because harmful species of phytoplankton have the potential to disrupt ecosystem processes that support animal populations, cause severe illness or death in humans, and confound the outcomes of toxicity bioassays such as those included in the RMP. Our purpose here is to utilize existing data on the phytoplankton community of San Francisco Bay to provide a provisional statement about the occurrence, distribution, and potential threats of harmful algae in this Estuary.

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.

Heavy metals in superficial sediment of Algiers Bay

International Nuclear Information System (INIS)

Benamar, M.A.; Toumert, C.L.; Chaouch, L.; Bacha, L.; Tobbeche, S.

1996-01-01

Sediment samples were collected in 33 stations from the bay of Algiers for the potential sources of pollution. the analyses were made x-ray fluorescence (XRF) and atomic absorption spectrometry (AAS) the results give information about level of concentrations morphology of the bay (funnel form of bay). only Co,Mn,Fe, and Cd present a particular repartition (unrelated to the sedimentary facies). the level pollution bu heavy metals of the bottom sediments in algiers bay have been compared with those of Surkouk considered as a region with low anthropogenic activities

Hierarchical mixtures of naive Bayes classifiers

NARCIS (Netherlands)

Wiering, M.A.

2002-01-01

Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical

Studies of movement of sediments in Santos bay

International Nuclear Information System (INIS)

Bandeira, J.V.; Aun, P.E.; Bomtempo, V.L.; Salim, L.H.; Minardi, P.S.P.; Santos, J.A.

1990-01-01

In the years of 1973, 74, 80, 81 and 85 several studies were performed at Santos bay, using radioactive tracers, with the following main objectives: to evaluate the behaviour (on the bottom and in suspension) of the mixture of silt and clay which is dredged from the estuary and from its access channel and dumped at pre-determined sites, in the bay and surrounding regions, with the objective of optimizing dredging disposal operations; to quantify the movement of sandy sediments on the bottom, in 3 areas of the bay, in summer and winter conditions, to obtain pertinent information related to the siltation of the access channel. As results of these studies, it was found that: the ancient dumping site, near Itaipu Point, in the western limit of the bay, was inadequate, since the material could return to the bay and to the estuary. The dumping site was moved to a region at the south of Moela Island, located eastwards relative to the bay, which brought substantial economies in dredging works; the bottom sediment transport was quantified, following clouds of tagged materials for about 8 months, thus obtaining important conclusions about transport rates in different regions of the bay. An analysis of the intervening hydrodynamic agents is also presented. (author) (L.J.C.)

Integrating science and resource management in Tampa Bay, Florida

Science.gov (United States)

Yates, Kimberly K.; Greening, Holly; Morrison, Gerold

2011-01-01

Tampa Bay is recognized internationally for its remarkable progress towards recovery since it was pronounced "dead" in the late 1970s. Due to significant efforts by local governments, industries and private citizens throughout the watershed, water clarity in Tampa Bay is now equal to what it was in 1950, when population in the watershed was less than one-quarter of what it is today. Seagrass extent has increased by more than 8,000 acres since the mid-1980s, and fish and wildlife populations are increasing. Central to this successful turn-around has been the Tampa Bay resource management community's long-term commitment to development and implementation of strong science-based management strategies. Research institutions and agencies, including Eckerd College, the Florida Wildlife Commission Fish and Wildlife Research Institute, Mote Marine Laboratory, National Oceanic and Atmospheric Administration, the Southwest Florida Water Management District, University of South Florida, U.S. Environmental Protection Agency, U.S. Geological Survey, local and State governments, and private companies contribute significantly to the scientific basis of our understanding of Tampa Bay's structure and ecological function. Resource management agencies, including the Tampa Bay Regional Planning Council's Agency on Bay Management, the Southwest Florida Water Management District's Surface Water Improvement and Management Program, and the Tampa Bay Estuary Program, depend upon this scientific basis to develop and implement regional adaptive management programs. The importance of integrating science with management has become fully recognized by scientists and managers throughout the region, State and Nation. Scientific studies conducted in Tampa Bay over the past 10–15 years are increasingly diverse and complex, and resource management programs reflect our increased knowledge of geology, hydrology and hydrodynamics, ecology and restoration techniques. However, a synthesis of this

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...

Whose Bay Street? Competing Narratives of Nassau's City Centre

Directory of Open Access Journals (Sweden)

Nona Patara Martin

2009-05-01

Full Text Available Bay Street has always been at the centre of commercial, cultural and political life in the Bahama Islands. It also acts as a gateway for millions of tourists who come to Nassau, the Bahamian capital, via cruise ships every year. Not surprisingly, Bahamians and non-Bahamians have widely divergent impressions of Bay Street. The need to accommodate the tourists who are critical to the Bahamian economy has meant that Bay Street, despite its deep social significance for Bahamians, has increasingly become a tourist space. With reference to the ‘sense of place’ and place attachment literature, this paper traces the transformation of Bay Street and attempts to tease out the most obvious tensions between the Bay Street that Bahamians experience and Bay Street as a port of call.

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.

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.

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

Fecal indicator bacteria at Havana Bay

International Nuclear Information System (INIS)

Lopez Perez, Lisse; Gomez D'Angelo, Yamiris; Beltran Gonzalez, Jesus; Alvarez Valiente, Reinaldo

2013-01-01

Aims: Fecal indicator bacteria concentrations were evaluated in Havana Bay. Methods: Concentrations of traditional fecal indicator bacteria were calculated between April 2010 and February 2011, by MPN methods. Concentrations of thermo tolerant coliform (CTT), Escherichia coli, fecal streptococci (EF), intestinal enterococci (ENT) in seawater, and Clostridium perfringens in sediment surface, were determined. Results: CTT and E. coli levels were far above Cuban water quality standard for indirect contact with water, showing the negative influence of sewage and rivers on the bay. The EF and ENT were measured during sewage spills at the discharge site and they were suitable indicators of fecal contamination, but these indicators didn't show the same behavior in other selected sites. This result comes from its well-known inactivation by solar light in tropical zones and the presumable presence of humid acids in the waters of the bay. Conclusion: Fecal indicator bacteria and its statistical relationships reflect recent and chronic fecal contamination at the bay and near shores.

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

Science.gov (United States)

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.

Science.gov (United States)

Macmillan, N A; Creelman, C D

1996-06-01

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

Estimating the empirical probability of submarine landslide occurrence

Science.gov (United States)

Geist, Eric L.; Parsons, Thomas E.; Mosher, David C.; Shipp, Craig; Moscardelli, Lorena; Chaytor, Jason D.; Baxter, Christopher D. P.; Lee, Homa J.; Urgeles, Roger

2010-01-01

The empirical probability for the occurrence of submarine landslides at a given location can be estimated from age dates of past landslides. In this study, tools developed to estimate earthquake probability from paleoseismic horizons are adapted to estimate submarine landslide probability. In both types of estimates, one has to account for the uncertainty associated with age-dating individual events as well as the open time intervals before and after the observed sequence of landslides. For observed sequences of submarine landslides, we typically only have the age date of the youngest event and possibly of a seismic horizon that lies below the oldest event in a landslide sequence. We use an empirical Bayes analysis based on the Poisson-Gamma conjugate prior model specifically applied to the landslide probability problem. This model assumes that landslide events as imaged in geophysical data are independent and occur in time according to a Poisson distribution characterized by a rate parameter λ. With this method, we are able to estimate the most likely value of λ and, importantly, the range of uncertainty in this estimate. Examples considered include landslide sequences observed in the Santa Barbara Channel, California, and in Port Valdez, Alaska. We confirm that given the uncertainties of age dating that landslide complexes can be treated as single events by performing statistical test of age dates representing the main failure episode of the Holocene Storegga landslide complex.

Heme oxygenase-1 mediates BAY 11-7085 induced ferroptosis.

Science.gov (United States)

Chang, Ling-Chu; Chiang, Shih-Kai; Chen, Shuen-Ei; Yu, Yung-Luen; Chou, Ruey-Hwang; Chang, Wei-Chao

2018-03-01

Ferroptosis is a form of oxidative cell death and has become a chemotherapeutic target for cancer treatment. BAY 11-7085 (BAY), which is a well-known IκBα inhibitor, suppressed viability in cancer cells via induction of ferroptotic death in an NF-κB-independent manner. Reactive oxygen species scavenging, relief of lipid peroxidation, replenishment of glutathione and thiol-containing agents, as well as iron chelation, rescued BAY-induced cell death. BAY upregulated a variety of Nrf2 target genes related to redox regulation, particularly heme oxygenase-1 (HO-1). Studies with specific inhibitors and shRNA interventions suggested that the hierarchy of induction is Nrf2-SLC7A11-HO-1. SLC7A11 inhibition by erastin, sulfasalazine, or shRNA interference sensitizes BAY-induced cell death. Overexperession of SLC7A11 attenuated BAY-inhibited cell viability. The ferroptotic process induced by hHO-1 overexpression further indicated that HO-1 is a key mediator of BAY-induced ferroptosis that operates through cellular redox regulation and iron accumulation. BAY causes compartmentalization of HO-1 into the nucleus and mitochondrion, and followed mitochondrial dysfunctions, leading to lysosome targeting for mitophagy. In this study, we first discovered that BAY induced ferroptosis via Nrf2-SLC7A11-HO-1 pathway and HO-1 is a key mediator by responding to the cellular redox status. Copyright © 2017 Elsevier B.V. All rights reserved.

Changing Salinity Patterns in Biscayne Bay, Florida

Science.gov (United States)

,

2004-01-01

Biscayne Bay, Fla., is a 428-square-mile (1,109-square-kilometer) subtropical estuarine ecosystem that includes Biscayne National Park, the largest marine park in the U.S. national park system (fig. 1). The bay began forming between 5,000 and 3,000 years ago as sea level rose and southern Florida was flooded. Throughout most of its history, the pristine waters of the bay supported abundant and diverse fauna and flora, and the bay was a nursery for the adjacent coral-reef and marine ecosystems. In the 20th century, urbanization of the Miami-Dade County area profoundly affected the environment of the bay. Construction of powerplants, water-treatment plants, and solid-waste sites and large-scale development along the shoreline stressed the ecosystem. Biscayne National Monument was established in 1968 to ?preserve and protect for the education, inspiration, recreation and enjoyment of present and future generations a rare combination of terrestrial, marine, and amphibious life in a tropical setting of great natural beauty? (Public Law 90?606). The monument was enlarged in 1980 and designated a national park.

Incident wave run-up into narrow sloping bays and estuaries

Science.gov (United States)

Sinan Özeren, M.; Postacioglu, Nazmi; Canlı, Umut

2015-04-01

The problem is investigated using Carrier Greenspan hodograph transformations.We perform a quasi-one-dimensional solution well into the bay, far enough of the mouth of the bay. The linearized boundary conditions at the mouth of the bay lead to an integral equation for 2-D geometry. A semi analytical optimization method has been developed to solve this integral equation. When the wavelength of the incident wave is much larger than the width of the bay, the conformalmapping of the bay and the semi infinite sea onto upper complex plane provides a solution of the integral equation in closed form. Particular emphasis is placed on the case where the frequency of the incident waves matches the real-part of the natural frequency of the oscillation of the bay. These natural frequencies are complex because of the radiation conditions imposed at the mouth of the bay. It is found that the complex part of these natural frequencies decreases with decreasing width of the bay. Thus the trapping of the waves in narrower bays leads to a strong resonance phenomenon when the frequency of the incident wave is equal to the real part of the natural frequency.

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...

Elemental analysis of Uranouchi bay seabed sludge using PIXE

International Nuclear Information System (INIS)

Kabir, M. Hasnat; Narusawa, Tadashi; Nishiyama, Fumitaka; Sumi, Katsuhiro

2006-01-01

Elemental analyses were carried out for the seabed sludge collected from Uranouchi bay (Kochi, Japan) using Particle Induced X-ray Emission (PIXE). Seabed-sludge contamination with heavy metals as well as toxic elements becomes one of the most serious environmental problems. The aim of the present study is to investigate the polluted areas in the bay by heavy and toxic elements. As a results of analyses of samples collected from eleven different places in the bay, seventeen elements including toxic ones were detected. The results suggest that the center region of the bay is seriously contaminated by heavy and toxic elements in comparison with the other areas in the bay. (author)

South Bay Salt Pond Mercury Studies Project

Science.gov (United States)

Information about the SFBWQP South Bay Salt Pond Mercury Studies Project, part of an EPA competitive grant program to improve SF Bay water quality focused on restoring impaired waters and enhancing aquatic resources.

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

Energy Technology Data Exchange (ETDEWEB)

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

2009-07-01

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

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

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.

DEVELOP Chesapeake Bay Watershed Hydrology - UAV Sensor Web

Science.gov (United States)

Holley, S. D.; Baruah, A.

2008-12-01

The Chesapeake Bay is the largest estuary in the United States, with a watershed extending through six states and the nation's capital. Urbanization and agriculture practices have led to an excess runoff of nutrients and sediment into the bay. Nutrients and sediment loading stimulate the growth of algal blooms associated with various problems including localized dissolved oxygen deficiencies, toxic algal blooms and death of marine life. The Chesapeake Bay Program, among other stakeholder organizations, contributes greatly to the restoration efforts of the Chesapeake Bay. These stakeholders contribute in many ways such as monitoring the water quality, leading clean-up projects, and actively restoring native habitats. The first stage of the DEVELOP Chesapeake Bay Coastal Management project, relating to water quality, contributed to the restoration efforts by introducing NASA satellite-based water quality data products to the stakeholders as a complement to their current monitoring methods. The second stage, to be initiated in the fall 2008 internship term, will focus on the impacts of land cover variability within the Chesapeake Bay Watershed. Multiple student led discussions with members of the Land Cover team at the Chesapeake Bay Program Office in the DEVELOP GSFC 2008 summer term uncovered the need for remote sensing data for hydrological mapping in the watershed. The Chesapeake Bay Program expressed in repeated discussions on Land Cover mapping that significant portions of upper river areas, streams, and the land directly interfacing those waters are not accurately depicted in the watershed model. Without such hydrological mapping correlated with land cover data the model will not be useful in depicting source areas of nutrient loading which has an ecological and economic impact in and around the Chesapeake Bay. The fall 2008 DEVELOP team will examine the use of UAV flown sensors in connection with in-situ and Earth Observation satellite data. To maximize the

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.

Humboldt Bay, California Benthic Habitats 2009 Geoform

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Humboldt Bay is the largest estuary in California north of San Francisco Bay and represents a significant resource for the north coast region. Beginning in 2007 the...

Humboldt Bay, California Benthic Habitats 2009 Substrate

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Humboldt Bay is the largest estuary in California north of San Francisco Bay and represents a significant resource for the north coast region. Beginning in 2007 the...

Humboldt Bay Benthic Habitats 2009 Aquatic Setting

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Humboldt Bay is the largest estuary in California north of San Francisco Bay and represents a significant resource for the north coast region. Beginning in 2007 the...

Humboldt Bay, California Benthic Habitats 2009 Geodatabase

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Humboldt Bay is the largest estuary in California north of San Francisco Bay and represents a significant resource for the north coast region. Beginning in 2007 the...

Contaminant transport in Massachusetts Bay

Science.gov (United States)

Butman, Bradford

Construction of a new treatment plant and outfall to clean up Boston Harbor is currently one of the world's largest public works projects, costing about $4 billion. There is concern about the long-term impact of contaminants on Massachusetts Bay and adjacent Gulf of Maine because these areas are used extensively for transportation, recreation, fishing, and tourism, as well as waste disposal. Public concern also focuses on Stellwagen Bank, located on the eastern side of Massachusetts Bay, which is an important habitat for endangered whales. Contaminants reach Massachusetts Bay not only from Boston Harbor, but from other coastal communities on the Gulf of Maine, as well as from the atmosphere. Knowledge of the pathways, mechanisms, and rates at which pollutants are transported throughout these coastal environments is needed to address a wide range of management questions.

Bayes linear statistics, theory & methods

CERN Document Server

Goldstein, Michael

2007-01-01

Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field. The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples. The book covers:The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification...

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.

Mex Bay

African Journals Online (AJOL)

user

2015-02-23

Feb 23, 2015 ... surveys to assess the vulnerability of the most important physical and eutrophication parameters along. El- Mex Bay coast. As a result of increasing population and industrial development, poorly untreated industrial waste, domestic sewage, shipping industry and agricultural runoff are being released to the.

San Francisco Bay Water Quality Improvement Fund

Science.gov (United States)

EPAs grant program to protect and restore San Francisco Bay. The San Francisco Bay Water Quality Improvement Fund (SFBWQIF) has invested in 58 projects along with 70 partners contributing to restore wetlands, water quality, and reduce polluted runoff.,

PEMANFATAN TEOREMA BAYES DALAM PENENTUAN PENYAKIT THT

Directory of Open Access Journals (Sweden)

Sri Winiarti

2008-07-01

Full Text Available Dalam konsep pelacakan dalam mencari solusi dengan pendekatan artificial inteligent, ada berbagai metode yang dapat diterapkan untuk mengatasi masalah ketidakpastian saat proses pelacakan terjadi. Salah satunya adalah teorema bayes. Adanya ketidakpastian pada proses pelacakan dapat terjadi karena adanya perubahan pengetahuan yang ada di dalam sistem. Untuk itu diperlukan adanya suatu metode untuk mengatasi permasalahan tersebut. Dalam penelitian ini telah diterapkan suatu metode untuk mengatasi ketidakpastian dengan teorema Bayes pada kasus pelacakan untuk mendiagnosa penyakit pada THT (Telinga,Hidung dan Tenggorokan. Subjek pada penelitian ini adalah proses pelacakan untuk menentukan penyakit THT dengan model penalaran forward chaining dan metode kepastiannya menggunakan teorema bayes dengan cara menghitung nilai probabilitas suatu penyakit dan membandingkan probabilitas setiap gejalanya. Model pengembangan perangkat lunak yang digunakan dalam penelitian ini adalah Waterfall. Metode Waterfall diawali dengan analisis data, perancangan sistem, pengkodean menggunakan Visual Basic 6.0, pengujian sistem dengan black box test dan alfa test. Dari penelitian yang dilakukan menghasilkan sebuah perangkat lunak yaitu yang mampu menentukan penyakit pada THT dengan menerapkan metode bayes untuk mengatasi ketidakpastian. Hasil uji coba sistem menujukkan bahwa aplikasi ini layak dan dapat digunakan. Kata kunci : Penyakit, THT, Teorema Bayes.

Geochemistry of sediments in the Back Bay and Yellowknife Bay of the Great Slave Lake

International Nuclear Information System (INIS)

Mudroch, A.; Joshi, S.R.; Sutherland, D.; Mudroch, P.; Dickson, K.M.

1989-01-01

Gold mining activities have generated wastes with high concentrations of arsenic and zinc in the vicinity of Yellowknife, Northwest Territories, Canada. Some of the waste material has been discharged into Yellowknife Bay of Great Slave Lake. Concentrations of arsenic and zinc were determined in sediment cores collected at the depositional areas of Yellowknife Bay. Sedimentation rates were estimated using two different radiometric approaches: the depth profiles of cesium 137 and lead 210. Geochemical analysis of the sediment cores indicated input of similar material into sampling areas over the past 50 yr. Age profiles of the sediment constructed from the radionuclide measurements were used to determine historical trends of arsenic and zinc inputs into Yellowknife Bay. The historical record was in good agreement with implemented remedial actions and the usage patterns of both elements. 16 refs., 6 figs., 3 tabs

Deep Borehole Instrumentation Along San Francisco Bay Bridges: 1996 - 2003 and Strong Ground Motion Systhesis Along the San Francisco/Oakland Bay Bridge

Energy Technology Data Exchange (ETDEWEB)

Hutchings, L; Foxall, W; Kasameyer, P; larsen, S; Hayek, C; Tyler-Turpin, C; Aquilino, J; Long, L

2005-04-22

As a result of collaboration between the Berkeley Seismographic Station, Lawrence Livermore National Laboratory, and Caltrans, instrument packages have been placed in bedrock in six boreholes and two surface sites along the San Francisco/Oakland Bay Bridge. Since 1996 over 200 local earthquakes have been recorded. Prior to this study few seismic recording instruments existed in bed-rock in San Francisco Bay. We utilized the data to perform analysis of ground motion variability, wave passage, site response, and up-and down-hole wave propagation along the Bay Bridge. We also synthesized strong ground motion at nine locations along the Bay Bridge. Key to these studies is LLNL's effort to exploit the information available in weak ground motions (generally from earthquakes < M=4.0) to enhance predictions of seismic hazards. We found that Yerba Island has no apparent site response at the surface relative to a borehole site. The horizontal to vertical spectral ratio method best revealed no site response, while the complex signal spectral ratio method had the lowest variance for spectral ratios and best predicted surface recordings when the borehole recording was used as input. Both methods identified resonances at about the same frequencies. Regional attenuation results in a significant loss of high frequencies in both surface and borehole recordings. Records are band limited at near 3 Hz. Therefore a traditional rock outcrop site response, flat to high frequency in displacement, is not available. We applied a methodology to predict and synthesize strong ground motion along the San Francisco/Oakland Bay Bridge from a M=7.25 earthquake along the Hayward fault, about12 km distant. We synthesized for three-components and broad-band (0.0-25.0 Hz) ground motion accelerations, velocities, and displacements. We examined two different possible rupture scenarios, a ''mean'' and ''one standard deviation'' model. We combined the high

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

Directory of Open Access Journals (Sweden)

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.

Semiparametric Bernstein–von Mises for the error standard deviation

OpenAIRE

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

2013-01-01

We study Bayes procedures for nonparametric regression problems with Gaussian errors, giving conditions under which a Bernstein–von Mises result holds for the marginal posterior distribution of the error standard deviation. We apply our general results to show that a single Bayes procedure using a hierarchical spline-based prior on the regression function and an independent prior on the error variance, can simultaneously achieve adaptive, rate-optimal estimation of a smooth, multivariate regr...

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

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....

Optimasi Naive Bayes Dengan Pemilihan Fitur Dan Pembobotan Gain Ratio

Directory of Open Access Journals (Sweden)

I Guna Adi Socrates

2016-03-01

Full Text Available Naïve Bayes merupakan salah satu metode data mining yang umum digunakan dalam klasifikasi dokumen berbasis text. Kelebihan dari metode ini adalah algoritma yang sederhana dengan  kompleksitas  perhitungan  yang  rendah.  Akan  tetapi,  pada  metode  Naïve  Bayes terdapat kelemahan dimana sifat independensi dari fitur Naïve Bayes tidak dapat selalu diterapkan sehingga akan berpengaruh pada tingkat akurasi perhitungan. Maka dari itu, metode Naïve Bayes perlu dioptimasi dengan cara pemberian bobot mengunakan Gain Ratio. Namun, pemberian bobot pada Naïve Bayes menimbulkan permasalahan pada penghitungan probabilitas setiap    dokumen, dimana fitur  yang tidak  merepresentasikan kelas  yang diuji banyak muncul sehingga terjadi kesalahan klasifikasi. Oleh karena itu, pembobotan Naïve Bayes   masih   belum   optimal.   Paper   ini mengusulkan  optimasi  metode   Naïve   Bayes mengunakan pembobotan Gain Ratio yang ditambahkan dengan metode pemilihan fitur pada kasus klasifikasi teks. Hasil penelitian ini menunjukkan bahwa optimasi metode Naïve Bayes menggunakan pemilihan fitur dan pembobotan menghasilkan akurasi sebesar 94%.

Recent results from Daya Bay

Directory of Open Access Journals (Sweden)

Chua Ming-chung

2016-01-01

Full Text Available Utilizing powerful nuclear reactors as antineutrino sources, high mountains to provide ample shielding from cosmic rays in the vicinity, and functionally identical detectors with large target volume for near-far relative measurement, the Daya Bay Reactor Neutrino Experiment has achieved unprecedented precision in measuring the neutrino mixing angle θ13 and the neutrino mass squared difference |Δm2ee|. I will report the latest Daya Bay results on neutrino oscillations and light sterile neutrino search.

77 FR 30443 - Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence River, Alexandria Bay, NY

Science.gov (United States)

2012-05-23

...The Coast Guard proposes to establish a temporary safety zone on the St. Lawrence River, Alexandria Bay, NY. This proposed rule is intended to restrict vessels from a portion of the St. Lawrence River during the Alexandria Bay Chamber of Commerce fireworks display. The safety zone established by this proposed rule is necessary to protect spectators and vessels from the hazards associated with a fireworks display.

Holocene evolution of Apalachicola Bay, Florida

Science.gov (United States)

Osterman, L.E.; Twichell, D.C.; Poore, R.Z.

2009-01-01

A program of geophysical mapping and vibracoring was conducted to better understand the geologic evolution of Apalachicola Bay. Analyses of the geophysical data and sediment cores along with age control provided by 34 AMS 14C dates on marine shells and wood reveal the following history. As sea level rose in the early Holocene, fluvial deposits filled the Apalachicola River paleochannel, which extended southward under the central part of the bay and seaward across the continental shelf. Sediments to either side of the paleochannel contain abundant wood fragments, with dates documenting that those areas were forested at 8,000 14C years b.p. As sea level continued to rise, spits formed of headland prodelta deposits. Between ???6,400 and ???2,500 14C years b.p., an Apalachicola prodelta prograded and receded several times across the inner shelf that underlies the western part of the bay. An eastern deltaic lobe was active for a shorter time, between ???5,800 and 5,100 14C years b.p. Estuarine benthic foraminiferal assemblages occurred in the western bay as early as 6,400 14C years b.p., and indicate that there was some physical barrier to open-ocean circulation and shelf species established by that time. It is considered that shoals formed in the region of the present barrier islands as the rising sea flooded an interstream divide. Estuarine conditions were established very early in the post-glacial flooding of the bay. ?? 2009 US Government.

A Coastal Bay Summer Breeze Study, Part 1: Results of the Quiberon 2006 Experimental Campaign

Science.gov (United States)

Mestayer, Patrice G.; Calmet, Isabelle; Herlédant, Olivier; Barré, Sophie; Piquet, Thibaud; Rosant, Jean-Michel

2018-04-01

The Quiberon 2006 experiment was launched to document the onset and development of land and sea breezes over a semi-circular coastal bay propitious to inshore sailing competitions. The measurements were taken during the 2 weeks of 16-28 June 2006. Micrometeorological variables were recorded at three shore sites around the bay using turbulence sensors on 10-30-m high masts, on four instrumented catamarans at selected sites within the bay, and at a fourth shore site with a Sodar. Synoptic data and local measurements are analyzed here from the point of view of both micrometeorologists and competition skippers, testing in particular the empirical rules of breeze veering and backing according to the wind direction with respect to the coastline orientation at the mesoscale (the quadrant theory). Our analysis focuses on the patterns of lower-altitude wind direction and speed around the bay and over the water basin, and the temporal variations during the periods of the breeze onset, establishment and thermal reinforcement. In offshore synoptic-flow conditions (quadrants 1 and 2), the clockwise rotation of the surface flow had a very large amplitude, reaching up to 360°. The breeze strength was negatively correlated to that of the synoptic wind speed. In conditions of onshore synoptic flow from the west (quadrant 3) at an angle to the mainland coast but perpendicular to the Quiberon peninsula, the rotation of the flow was backwards in the early morning and clockwise during the day with a moderate amplitude (40°-50°) around the synoptic wind direction. As the surface wind speed was much larger than the synoptic wind speed, such a case we have designated as a "synoptic breeze". The breeze onset was shown to fail several times under the influence of weak non-thermal events, e.g., the passage of an occluded front or clouds or an excess of convection. Finally, several local-scale influences of the complex coastal shape appeared in our measurements, e.g., wind fanning in the

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

Science.gov (United States)

Kottas, Athanasios; Behseta, Sam

2010-03-01

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

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

Science.gov (United States)

Steinhardt, Charles L.; Jermyn, Adam S.

2018-02-01

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

Long-term morphologic evolution of the Hangzhou Bay, China

Science.gov (United States)

Wen, W.; Zhijun, D.; Hualiang, X.

2013-12-01

Estuaries are the most productive ecosystems of coastal zones in the world, which are significant to mankind as places of navigation, recreation and commerce as well as extensive and diverse habitats for wildlife. However, most estuary environments in the world had occurred greatly changes in recent decades. These estuaries have suffered from impacts of forcing factors including wave climate, mean sea level change and storm surge, especial to the intensive human activities such as training wall construction, channel dredging, sand mining and dam constructions. Thus, there have been increasing concerns about estuary environment changes under effects of different factors. Riverine loads into the Changjiang Estuary have declined dramatically with the construction of Three Gorges Dam (TGD) in 2003. The morphological evolution of the Hangzhou bay that located the southern proximity of the Yangtze estuary starts to attract increasing attentions due to most material of the Hangzhou bay received from Yangtze estuary. In this paper, historical bathymetric charts were digitized and analyzed within a GIS to provide quantitative estimate of changes in volumes in different regions below 0 m elevation. The results show that Hangzhou bay has experienced a major loss in estuarine volume of about 15% with annual mean sediment deposition rate of 80 million m3/a during the last 75 years. However, there is a large-scale spatial adjustment in Hangzhou bay: Bathymetric changes of the Hangzhou bay can be rapidly shifted within the range of 8-10 classes. Volume of the Jinshanzui upstream of the Hangzhou bay has obviously decreased in the last 75 years, especially during 2003-2008. However, Volume of the southern Hangzhou bay has experienced slowly decrease with minor deposition. The northern Hangzhou bay had largely volume changes with rapidly decrease during 1931-1981, and drastically increase since 2003. Further analysis of the bathymetric data relating to possible factors indicates

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

Science.gov (United States)

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

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

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

Directory of Open Access Journals (Sweden)

Urbi Garay

2016-03-01

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

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

NARCIS (Netherlands)

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

2003-01-01

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

Bayesian nonparametric adaptive control using Gaussian processes.

Science.gov (United States)

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

2015-03-01

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

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

Science.gov (United States)

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

2018-03-01

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

Topobathymetric model of Mobile Bay, Alabama

Science.gov (United States)

Danielson, Jeffrey J.; Brock, John C.; Howard, Daniel M.; Gesch, Dean B.; Bonisteel-Cormier, Jamie M.; Travers, Laurinda J.

2013-01-01

Topobathymetric Digital Elevation Models (DEMs) are a merged rendering of both topography (land elevation) and bathymetry (water depth) that provides a seamless elevation product useful for inundation mapping, as well as for other earth science applications, such as the development of sediment-transport, sea-level rise, and storm-surge models. This 1/9-arc-second (approximately 3 meters) resolution model of Mobile Bay, Alabama was developed using multiple topographic and bathymetric datasets, collected on different dates. The topographic data were obtained primarily from the U.S. Geological Survey (USGS) National Elevation Dataset (NED) (http://ned.usgs.gov/) at 1/9-arc-second resolution; USGS Experimental Advanced Airborne Research Lidar (EAARL) data (2 meters) (http://pubs.usgs.gov/ds/400/); and topographic lidar data (2 meters) and Compact Hydrographic Airborne Rapid Total Survey (CHARTS) lidar data (2 meters) from the U.S. Army Corps of Engineers (USACE) (http://www.csc.noaa.gov/digitalcoast/data/coastallidar/). Bathymetry was derived from digital soundings obtained from the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (NGDC) (http://www.ngdc.noaa.gov/mgg/geodas/geodas.html) and from water-penetrating lidar sources, such as EAARL and CHARTS. Mobile Bay is ecologically important as it is the fourth largest estuary in the United States. The Mobile and Tensaw Rivers drain into the bay at the northern end with the bay emptying into the Gulf of Mexico at the southern end. Dauphin Island (a barrier island) and the Fort Morgan Peninsula form the mouth of Mobile Bay. Mobile Bay is 31 miles (50 kilometers) long by a maximum width of 24 miles (39 kilometers) with a total area of 413 square miles (1,070 square kilometers). The vertical datum of the Mobile Bay topobathymetric model is the North American Vertical Datum of 1988 (NAVD 88). All the topographic datasets were originally referenced to NAVD 88 and no transformations

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

Parameter Identification by Bayes Decision and Neural Networks

DEFF Research Database (Denmark)

Kulczycki, P.; Schiøler, Henrik

1994-01-01

The problem of parameter identification by Bayes point estimation using neural networks is investigated.......The problem of parameter identification by Bayes point estimation using neural networks is investigated....

Chesapeake Bay baseline data acquisition, toxics in the Chesapeake Bay. Final preliminary report, 1946-78

International Nuclear Information System (INIS)

1978-07-01

This report identifies researchers, research activities, and data files applicable to the Chesapeake Bay estuarine system. The identified data were generated after 1973 on the following: submerged aquatic vegetation, shellfish bed closures, eutrophication, toxics accumulation in the food chain, dredging and spoil disposal, hydrologic modifications, modification of fisheries, shoreline erosion, wetlands alterations, and the effects of boating and shipping on water quality. Major past and current program monitoring in the Bay and its tributaries are summarized according to frequency

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.

Wind-Driven Waves in Tampa Bay, Florida

Science.gov (United States)

Gilbert, S. A.; Meyers, S. D.; Luther, M. E.

2002-12-01

Turbidity and nutrient flux due to sediment resuspension by waves and currents are important factors controlling water quality in Tampa Bay. During December 2001 and January 2002, four Sea Bird Electronics SeaGauge wave and tide recorders were deployed in Tampa Bay in each major bay segment. Since May 2002, a SeaGauge has been continuously deployed at a site in middle Tampa Bay as a component of the Bay Regional Atmospheric Chemistry Experiment (BRACE). Initial results for the summer 2002 data indicate that significant wave height is linearly dependent on wind speed and direction over a range of 1 to 12 m/s. The data were divided into four groups according to wind direction. Wave height dependence on wind speed was examined for each group. Both northeasterly and southwesterly winds force significant wave heights that are about 30% larger than those for northwesterly and southeasterly winds. This difference is explained by variations in fetch due to basin shape. Comparisons are made between these observations and the results of a SWAN-based model of Tampa Bay. The SWAN wave model is coupled to a three-dimensional circulation model and computes wave spectra at each model grid cell under observed wind conditions and modeled water velocity. When SWAN is run without dissipation, the model results are generally similar in wave period but about 25%-50% higher in significant wave height than the observations. The impact of various dissipation mechanisms such as bottom drag and whitecapping on the wave state is being investigated. Preliminary analyses on winter data give similar results.

Searching for Logistics and Regulatory Determinants Affecting Overseas Direct Purchase: An Empirical Cross-National Study

Directory of Open Access Journals (Sweden)

Hyuksoo Cho

2017-03-01

Full Text Available Cross-border e-commerce has been very successful in the last decade. Merchants and consumers have been encouraged to participate in e-commerce, including B2C or B2B. B2C is not limited to a domestic market anymore. Consumers across countries are interested in overseas direct purchases. They are willing to purchase products from major online shopping sites such as Amazon and eBay. We aim at gaining a better understanding of overseas direct purchases. Determinants of overseas direct purchases based on cross-national data are identified. Accordingly, we investigate logistics and regulatory determinants. Furthermore, external environments such as regulatory institutions and globalization are discussed in terms of overseas direct purchases. This study incorporates theoretical foundations into empirical findings. Specifically, the institutional theory and the resource-based view are applied to explain the internal and external determinants to increase overseas direct purchases. We conduct an empirical test using panel data for each country to identify the various determinants associated with overseas direct purchases.

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

Directory of Open Access Journals (Sweden)

Liang Xu

2016-08-01

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

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

Phytoplankton growth, dissipation, and succession in estuarine environments. [Chesapeake Bay

Energy Technology Data Exchange (ETDEWEB)

Seliger, H H

1976-01-01

Two major advances in a study of phytoplankton ecology in the Chesapeake Bay are reported. The annual subsurface transport of a dinoflagellate species (Prorocentrum mariae labouriae) from the mouth of the bay a distance northward of 120 nautical miles to the region of the Bay Bridge was followed. Prorocentrum is a major seasonal dinoflagellate in the Chespeake Bay and annually has been reported to form mahogany tides, dense reddish-brown patches, in the northern bay beginning in late spring and continuing through the summer. Subsequent to this annual appearance the Prorocentrum spread southward and into the western tributary estuaries. The physiological behavioral characteristics of the Prorocentrum were correlated with the physical water movements in the bay. A phytoplankton cage technique for the measurement in situ of the growth rates of natural mixed populations is described. (CH)

Mobile Bay turbidity study

Science.gov (United States)

Crozier, G. F.; Schroeder, W. W.

1978-01-01

The termination of studies carried on for almost three years in the Mobile Bay area and adjacent continental shelf are reported. The initial results concentrating on the shelf and lower bay were presented in the interim report. The continued scope of work was designed to attempt a refinement of the mathematical model, assess the effectiveness of optical measurement of suspended particulate material and disseminate the acquired information. The optical characteristics of particulate solutions are affected by density gradients within the medium, density of the suspended particles, particle size, particle shape, particle quality, albedo, and the angle of refracted light. Several of these are discussed in detail.

Humboldt Bay Orthoimages

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of 0.5-meter pixel resolution, four band orthoimages covering the Humboldt Bay area. An orthoimage is remotely sensed image data in which...

With Prudhoe Bay in decline

International Nuclear Information System (INIS)

Davis, J.M.; Pollock, J.R.

1992-01-01

Almost every day, it seems, someone is mentioning Prudhoe Bay---its development activities, the direction of its oil production, and more recently its decline rate. Almost as frequently, someone is mentioning the number of companies abandoning exploration in Alaska. The state faces a double-edged dilemma: decline of its most important oil field and a diminished effort to find a replacement for the lost production. ARCO has seen the Prudhoe Bay decline coming for some time and has been planning for it. We have reduced staff, and ARCO and BP Exploration are finding cost-effective ways to work more closely together through such vehicles as shared services. At the same time, ARCO is continuing its high level of Alaskan exploration. This article will assess the future of Prudhoe Bay from a technical perspective, review ARCO's exploration plans for Alaska, and suggest what the state can do to encourage other companies to invest in this crucial producing region and exploratory frontier

The Holocene History of Placentia Bay, Newfoundland

DEFF Research Database (Denmark)

Sheldon, Christina; Seidenkrantz, Marit-Solveig; Reynisson, Njall

2013-01-01

Marine sediments analyzed from cores taken in Placentia Bay, Newfoundland, located in the Labrador Sea, captured oceanographic and climatic changes from the end of the Younger Dryas through the Holocene. Placentia Bay is an ideal site to capture changes in both the south-flowing Labrador Current ...

Contribution of Online Trading of Used Goods to Resource Efficiency: An Empirical Study of eBay Users

Directory of Open Access Journals (Sweden)

Jens Clausen

2010-06-01

Full Text Available This paper discusses the sustainability impact (contribution to sustainability, reduction of adverse environmental impacts of online second-hand trading. A survey of eBay users shows that a relationship between the trading of used goods and the protection of natural resources is hardly realized. Secondly, the environmental motivation and the willingness to act in a sustainable manner differ widely between groups of consumers. Given these results from a user perspective, the paper tries to find some objective hints of online second-hand trading’s environmental impact. The greenhouse gas emissions resulting from the energy used for the trading transactions seem to be considerably lower than the emissions due to the (avoided production of new goods. The paper concludes with a set of recommendations for second-hand trade and consumer policy. Information about the sustainability benefits of purchasing second-hand goods should be included in general consumer information, and arguments for changes in behavior should be targeted to different groups of consumers.

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

Benthic harpacticoid copepods of Jiaozhou Bay, Qingdao

Science.gov (United States)

Ma, Lin; Li, Xinzheng

2017-09-01

The species richness of benthic harpacticoid copepod fauna in Jiaozhou Bay, Qingdao, on the southern coast of Shandong Peninsula, has not been comprehensively studied. We present a preliminary inventory of species for this region based on material found in nine sediment samples collected from 2011 to 2012. Our list includes 15 species belonging to 15 genera in 9 families, the most speciose family was the Miraciidae Dana, 1846 (seven species); all other families were represented by single species only. Sediment characteristics and depth are determined to be important environmental determinants of harpacticoid distribution in this region. We briefly detail the known distributions of species and provide a key to facilitate their identification. Both harpacticoid species richness and the species/genus ratio in Jiaozhou Bay are lower than in Bohai Gulf and Gwangyang Bay. The poor knowledge of the distribution of benthic harpacticoids, in addition to low sampling effort in Jiaozhou Bay, likely contribute to low species richness.

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

Science.gov (United States)

Lu, Tao

2016-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2008-03-07

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

Concentrations of PAHs (Polycyclicaromatic Hydrocarbons Pollutant in Sediment of The Banten Bay

Directory of Open Access Journals (Sweden)

Khozanah Munawir

2018-02-01

Full Text Available Banten Bay is end of stream for a few rivers from Banten mainland where many manufactures and petrochemical industries are built. This may give environmental pressure of water quality of the bay due to pollutant input, such as Polycyclic Aromatic Hydrocarbons (PAHs. This study is to identify those pollutants and determine their total concentration and distribution in sediments. Surface sediment samples were collected in four zones: inner coastline within the bay, middle bay, coastline off the bay and outer of the Bay in April 2016. PAH components were extracted and measured using a gas chromatography-mass spectrometry. Levels of total PAHs in sediments in inner coastline within the bay ranged between 0.381-2.654 ppm with an average of 1.288 ppm, middle of the bay ranged between 0.747-1.762 ppm with an average of 1.198 ppm, outer of the bay ranged between 0.192-1.394 ppm with an average of 0.921 ppm, and east coast of the bay ranged between 0.191-1.394 ppm and an average of 0.778 ppm. The levels of total PAH contamination is apparently lower than those of PAH threshold in sediments (i.e. 4.5 ppm. Keywords: PAHs (Polycyclic Aromatic Hydrocarbons, Banten Bay

Rapid Crustal Uplift at Birch Bay, Washington

Science.gov (United States)

Sherrod, B. L.; Kelsey, H. M.; Blakely, R. J.

2010-12-01

Geomorphology and coastal marsh stratigraphy suggest late Holocene uplift of the shoreline at Birch Bay, located northwest of Bellingham, Washington, during an earthquake on a shallow fault. LiDAR images show a raised, late Holocene shoreline along Birch Bay, with ~1 m of elevation difference between the modern shoreline and the inferred paleoshoreline. Commercial seismic reflection images reveal an anticline in Tertiary and possibly Quaternary deposits underlying Birch Bay. NW-trending magnetic anomalies are likely associated with the Birch Bay anticline and other nearby structures. Taken together, the geophysical data and lidar images suggest uplift of young deposits along a NW-trending blind reverse fault. Stratigraphy from Terrell Creek marsh, located just south of Birch Bay, shows freshwater peat buried by lower intertidal muds, indicating local submergence ~1300 yr BP. Stratigraphy of a 70-cm sediment core from Birch Bay marsh, sitting astride the anticline imaged with seismic reflection data, shows mud buried by detrital peat. One radiocarbon age from the core places the abrupt change from mud to peat prior to 1520-1700 yr BP. We divide fossil diatom assemblages straddling the mud-peat contact at Birch Bay into three zones. The oldest zone consists primarily of intertidal and marine diatoms, dominated by Paralia sulcata, Scoleoneis tumida, Grammataphora oceanica, and Gyrosigma balticum. An intermediate zone, beginning at the sharp contact between mud and overlying peat, consists of a mixture of brackish marsh and freshwater species, dominated by Diploneis interrupta, with lesser amounts of Aulacoseira sp., Pinnularia viridis, Eunotia pectinalis, and Paralia sulcata. A third and youngest zone lies in the upper half of the peat and is dominated by poorly preserved freshwater diatoms, mostly Aulacoseira cf. crassapuntata, Pinnularia viridis, P. maior, Eunotia pectinalis, and E. praerupta. Paleoecological inferences, based on distributions of modern diatoms

Defining a data management strategy for USGS Chesapeake Bay studies

Science.gov (United States)

Ladino, Cassandra

2013-01-01

The mission of U.S. Geological Survey’s (USGS) Chesapeake Bay studies is to provide integrated science for improved understanding and management of the Chesapeake Bay ecosystem. Collective USGS efforts in the Chesapeake Bay watershed began in the 1980s, and by the mid-1990s the USGS adopted the watershed as one of its national place-based study areas. Great focus and effort by the USGS have been directed toward Chesapeake Bay studies for almost three decades. The USGS plays a key role in using “ecosystem-based adaptive management, which will provide science to improve the efficiency and accountability of Chesapeake Bay Program activities” (Phillips, 2011). Each year USGS Chesapeake Bay studies produce published research, monitoring data, and models addressing aspects of bay restoration such as, but not limited to, fish health, water quality, land-cover change, and habitat loss. The USGS is responsible for collaborating and sharing this information with other Federal agencies and partners as described under the President’s Executive Order 13508—Strategy for Protecting and Restoring the Chesapeake Bay Watershed signed by President Obama in 2009. Historically, the USGS Chesapeake Bay studies have relied on national USGS databases to store only major nationally available sources of data such as streamflow and water-quality data collected through local monitoring programs and projects, leaving a multitude of other important project data out of the data management process. This practice has led to inefficient methods of finding Chesapeake Bay studies data and underutilization of data resources. Data management by definition is “the business functions that develop and execute plans, policies, practices and projects that acquire, control, protect, deliver and enhance the value of data and information.” (Mosley, 2008a). In other words, data management is a way to preserve, integrate, and share data to address the needs of the Chesapeake Bay studies to better

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 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.

Numerical Analysis of Storm Surge and Seiche at Tokyo Bay caused by the 2 Similar Typhoons, Typhoon Phanphon and Vongfong in 2014

Science.gov (United States)

Iwamoto, T.; Takagawa, T.

2017-12-01

A long period damped oscillation, or seiche, sometimes happens inside a harbor after passing typhoon. For some cases, a maximum sea level is observed due to the superposition of astronomical tide and seiche rather than a peak of storm surge. Hence to clarify seiche factors for reducing disaster potential is important, a long-period seiche with a fundamental period of 5.46 hours in Tokyo Bay (Konishi, 2008) was investigated through numerical simulations and analyses. We examined the case of Typhoon Phanphon and Vongfong in 2014 (Hereafter Case P and V). The intensity and moving velocity were similar and the best-tracks were an arc-shaped, typical one approaching to Tokyo Bay. The track of Case V was about 1.5 degree higher latitude than that of Case P, only Typhoon Phanphon caused significant seiche.Firstly, numerical simulations for the 2 storm surges at Tokyo Bay were conducted by Regional Ocean Modeling System (ROMS) and Meso-Scale Model Grid Point Values (MSM-GPV). MSM-GPV gave the 10m wind speed and Sea Level Pressure (SLP), especially the Mean Error (ME) and Root Mean Squire Error (RMSE) of SLP were low compared to the 12 JMA observation points data (Case P: ME -0.303hPa, RMSE 1.87hPa, Case V: ME -0.285hPa, RMSE 0.74hPa). The computational results showed that the maximum of storm surge was underestimated but the difference was less than 20cm at 5 observation points in Tokyo Bay(Fig.1, 2).Then, power spectrals, coherences and phase differences of storm surges at the 5 observation points were obtained by spectral analysis of observed and simulated waveforms. For Case P, the phase-difference between the bay mouth and innermost part of Tokyo Bay was little, and coherence was almost 1(Fig.3, 4). However, for Case V, coherence was small around the fundamental period of 5.46 hours. Furthermore, Empirical Orthogonal Function (EOF) analysis of storm surge, SLP and sea surface stress were conducted. The contributions of EOF1 were above 90% for the all variables, the

Influence of net freshwater supply on salinity in Florida Bay

Science.gov (United States)

Nuttle, William K.; Fourqurean, James W.; Cosby, Bernard J.; Zieman, Joseph C.; Robblee, Michael B.

2000-01-01

An annual water budget for Florida Bay, the large, seasonally hypersaline estuary in the Everglades National Park, was constructed using physically based models and long‐term (31 years) data on salinity, hydrology, and climate. Effects of seasonal and interannual variations of the net freshwater supply (runoff plus rainfall minus evaporation) on salinity variation within the bay were also examined. Particular attention was paid to the effects of runoff, which are the focus of ambitious plans to restore and conserve the Florida Bay ecosystem. From 1965 to 1995 the annual runoff from the Everglades into the bay was less than one tenth of the annual direct rainfall onto the bay, while estimated annual evaporation slightly exceeded annual rainfall. The average net freshwater supply to the bay over a year was thus approximately zero, and interannual variations in salinity appeared to be affected primarily by interannual fluctuations in rainfall. At the annual scale, runoff apparently had little effect on the bay as a whole during this period. On a seasonal basis, variations in rainfall, evaporation, and runoff were not in phase, and the net freshwater supply to the bay varied between positive and negative values, contributing to a strong seasonal pattern in salinity, especially in regions of the bay relatively isolated from exchanges with the Gulf of Mexico and Atlantic Ocean. Changes in runoff could have a greater effect on salinity in the bay if the seasonal patterns of rainfall and evaporation and the timing of the runoff are considered. One model was also used to simulate spatial and temporal patterns of salinity responses expected to result from changes in net freshwater supply. Simulations in which runoff was increased by a factor of 2 (but with no change in spatial pattern) indicated that increased runoff will lower salinity values in eastern Florida Bay, increase the variability of salinity in the South Region, but have little effect on salinity in the Central

Safety culture development at Daya Bay NPP

International Nuclear Information System (INIS)

Zhang Shanming

2001-01-01

From view on Organization Behavior theory, the concept, development and affecting factors of safety culture are introduced. The focuses are on the establishment, development and management practice for safety culture at Daya Bay NPP. A strong safety culture, also demonstrated, has contributed greatly to improving performance at Daya Bay

Lost lake - restoration of a Carolina bay

Energy Technology Data Exchange (ETDEWEB)

Hanlin, H.G.; McLendon, J.P. [Univ. of South Carolina, Aiken, SC (United States). Dept. of Biology and Geology; Wike, L.D. [Univ. of South Carolina, Aiken, SC (United States). Dept. of Biology and Geology]|[Westinghouse Savannah River Co., Aiken, SC (United States). Savannah River Technology Center; Dietsch, B.M. [Univ. of South Carolina, Aiken, SC (United States). Dept. of Biology and Geology]|[Univ. of Georgia, Aiken, SC (United States)

1994-09-01

Carolina bays are shallow wetland depressions found only on the Atlantic Coastal Plain. Although these isolated interstream wetlands support many types of communities, they share the common features of having a sandy margin, a fluctuating water level, an elliptical shape, and a northwest to southeast orientation. Lost Lake, an 11.3 hectare Carolina bay, was ditched and drained for agricultural production before establishment of the Savannah River Site in 1950. Later it received overflow from a seepage basin containing a variety of chemicals, primarily solvents and some heavy metals. In 1990 a plan was developed for the restoration of Lost Lake, and restoration activities were complete by mid-1991. Lost Lake is the first known project designed for the restoration and recovery of a Carolina bay. The bay was divided into eight soil treatment zones, allowing four treatments in duplicate. Each of the eight zones was planted with eight species of native wetland plants. Recolonization of the bay by amphibians and reptiles is being evaluated by using drift fences with pitfall traps and coverboard arrays in each of the treatment zones. Additional drift fences in five upland habitats were also established. Hoop turtle traps, funnel minnow traps, and dip nets were utilized for aquatic sampling. The presence of 43 species common to the region has been documented at Lost Lake. More than one-third of these species show evidence of breeding populations being established. Three species found prior to the restoration activity and a number of species common to undisturbed Carolina bays were not encountered. Colonization by additional species is anticipated as the wetland undergoes further succession.

[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

76 FR 70480 - Otay River Estuary Restoration Project, South San Diego Bay Unit of the San Diego Bay National...

Science.gov (United States)

2011-11-14

... River Estuary Restoration Project, South San Diego Bay Unit of the San Diego Bay National Wildlife...), intend to prepare an environmental impact statement (EIS) for the proposed Otay River Estuary Restoration... any one of the following methods. Email: [email protected] . Please include ``Otay Estuary NOI'' in the...

PENERAPAN ALGORITMA NAIVE BAYES UNTUK MENGKLASIFIKASI DATA NASABAH ASURANSI

Directory of Open Access Journals (Sweden)

Bustami Bustami

2014-01-01

Full Text Available Data mining adalah teknik yang memanfaatkan data dalam jumlah yang besar untuk memperoleh informasi berharga yang sebelumnya tidak diketahui dan dapat dimanfaatkan untuk pengambilan keputusan penting. Pada penelitian ini, penulis berusaha menambang data (data mining nasabah sebuah perusahaan asuransi untuk mengetahui lancar, kurang lancar atau tidak lancarnya nasabah tersebut. Data yang ada dianalisis menggunakan algoritma Naive Bayes. Naive Bayes merupakan salah satu meode pada probabilistic reasoning. Algoritma Naive Bayes bertujuan untuk melakukan klasifikasi data pada kelas tertentu, kemudian pola tersebut dapat digunakan untuk memperkirakan nasabah yang bergabung, sehingga perusahaan bisa mengambil keputusan menerima atau menolak calon nasabah tersebut. Kata Kunci : data mining, asuransi, klasifikasi, algoritma Naive Bayes

Automation in tube finishing bay

International Nuclear Information System (INIS)

Bhatnagar, Prateek; Satyadev, B.; Raghuraman, S.; Syama Sundara Rao, B.

1997-01-01

Automation concept in tube finishing bay, introduced after the final pass annealing of PHWR tubes resulted in integration of number of sub-systems in synchronisation with each other to produce final cut fuel tubes of specified length, tube finish etc. The tube finishing bay which was physically segregated into four distinct areas: 1. tube spreader and stacking area, 2. I.D. sand blasting area, 3. end conditioning, wad blowing, end capping and O.D. wet grinding area, 4. tube inspection, tube cutting and stacking area has been studied

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...

Empirical data and moral theory. A plea for integrated empirical ethics.

Science.gov (United States)

Molewijk, Bert; Stiggelbout, Anne M; Otten, Wilma; Dupuis, Heleen M; Kievit, Job

2004-01-01

Ethicists differ considerably in their reasons for using empirical data. This paper presents a brief overview of four traditional approaches to the use of empirical data: "the prescriptive applied ethicists," "the theorists," "the critical applied ethicists," and "the particularists." The main aim of this paper is to introduce a fifth approach of more recent date (i.e. "integrated empirical ethics") and to offer some methodological directives for research in integrated empirical ethics. All five approaches are presented in a table for heuristic purposes. The table consists of eight columns: "view on distinction descriptive-prescriptive sciences," "location of moral authority," "central goal(s)," "types of normativity," "use of empirical data," "method," "interaction empirical data and moral theory," and "cooperation with descriptive sciences." Ethicists can use the table in order to identify their own approach. Reflection on these issues prior to starting research in empirical ethics should lead to harmonization of the different scientific disciplines and effective planning of the final research design. Integrated empirical ethics (IEE) refers to studies in which ethicists and descriptive scientists cooperate together continuously and intensively. Both disciplines try to integrate moral theory and empirical data in order to reach a normative conclusion with respect to a specific social practice. IEE is not wholly prescriptive or wholly descriptive since IEE assumes an interdepence between facts and values and between the empirical and the normative. The paper ends with three suggestions for consideration on some of the future challenges of integrated empirical ethics.

46 CFR 7.110 - Mamala Bay, HI.

Science.gov (United States)

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Mamala Bay, HI. 7.110 Section 7.110 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Hawaii § 7.110 Mamala Bay, HI. A line drawn from Barbers Point Light to Diamond Head Light. Pacific Coast ...

33 CFR 165.1187 - Security Zones; Golden Gate Bridge and the San Francisco-Oakland Bay Bridge, San Francisco Bay...

Science.gov (United States)

2010-07-01

... Limited Access Areas Eleventh Coast Guard District § 165.1187 Security Zones; Golden Gate Bridge and the... Golden Gate Bridge and the San Francisco-Oakland Bay Bridge, in San Francisco Bay, California. (b... siren, radio, flashing light, or other means, the operator of a vessel shall proceed as directed. [COTP...

Tectonic framework of the Hanoe Bay area, southern Baltic Sea

International Nuclear Information System (INIS)

Wannaes, K.O.; Floden, T.

1994-06-01

The tectonic framework and the general geologic development of the Hanoe Bay, from the Scanian coast in the west to south of Oeland in the east, has been investigated by means of reflection seismic methods. The Hanoe Bay is in this paper subdivided into four areas of different geologic settings. These are: 1) The Hanoe Bay slope, which forms a southward dipping continuation of the rigid Blekinge coastal plain. 2) The eastward dipping Kalmarsund Slope, which southwards from Oeland forms the western part of the Paleozoic Baltic Syneclise. 3) The Mesozoic Hanoe Bay Halfgraben, which forms the central and southern parts of the Hanoe Bay. The ongoing subsidence of the Halfgraben is estimated to be in the order of 20-60 m during the Quaternary. 4) The Yoldia Structural Element, which forms a deformed, tilted and possibly rotated block of Paleozoic bedrock located east of the Hanoe Bay Halfgraben. Two tectonic phases dominate the post-Paleozoic development of the Hanoe Bay, these are: 1) The Early Kimmerian phase, which initiated subsidence and reactivated older faults. 2) The Late Cretaceous phase, which is the main subsidence phase of the Hanoe Bay Halfgraben. The tectonic fault pattern of the Hanoe Bay is dominated by three directions, i.e. NW-SE, NE-SW and WNW-ESE. The two main tectonic elements of the area are the Kullen-Christiansoe Ridge System (NW-SE) and the Bornholm Gat Tectonic Zone (NE-SW). Sinistral strike-slip movements in order of 2-3 km are interpreted to have occurred along the Bornholm Gat Tectonic Zone during the late Cretaceous. 20 refs, 19 figs

Measuring Macrobenthos Biodiversity at Oyster Aquaculture Sites in the Delaware Inland Bays

Science.gov (United States)

Fuoco, M. J.; Ozbay, G.

2016-12-01

The Delaware Inland Bays consists of three shallow coastal bays located in the southern portion of Delaware. Anthropogenic activities have led to the degradation of water quality, because the bays are surrounded by highly developed areas and have low flushing rates. This results in loss of biodiversity and abundance of organisms. Ongoing degradation of the bays has led to a dramatic decline in local oyster populations since the late 1800s. Oysters are keystone species, which provide habitats for organisms and help to improve water quality. This study aims to find if the introduction of oyster aquaculture improves local biodiversity and abundance of macrobenthos. The study was conducted in Rehoboth Bay, Indian River Bay and Little Assawoman Bay. Aquaculture gear was placed at one location in each of the bays and 24 sediment core samples were taken once a month. From these core samples all worms were fixed and stained in a 10% Formalin Rose Bengal solution and preserved in 70% Ethanol for later identification. Stable carbon and nitrogen isotope analysis of oyster tissue will also be performed to assess the health of the bay. The goals of this research are to better understand the role of oyster aquaculture in restoring the viability and health of the Delaware Inland Bays.

Use of Geographic Information Systems to examine cumulative impacts of development on Mobile Bay, AL and Galveston Bay, TX

International Nuclear Information System (INIS)

Rosigno, P.F.; McNiff, M.E.; Watzin, M.C.; Ji, W.

1993-01-01

Databases from Mobile Bay, Alabama and Galveston Bay, Texas were compiled using ARC/INFO Geographic Information Systems (GIS) to examine the cumulative impacts from urbanization and industrialization on these two Gulf of Mexico estuaries. The databases included information on wetland habitats, pollution sources, metal contamination, bird-nesting sites, and oyster reefs, among others. A series of maps were used to represent the impacts within and between each ecosystem. These two estuaries share many similarities in the types of developmental pressures that each experience. However, difference in the magnitude of industrial activity, pollution loading, and urban growth coupled with distinct hydrodynamic and geochemical differences in sediment mineralogy, freshwater inflows and salinity regimens results in differing responses. With growing human population and extensive oil and gas development, the demands on Galveston Bay are quite different than those placed on Mobile Bay which has lower growth and less extensive oil and gas infrastructure. Mobile Bay tends to retain whatever contamination enters into the system because of the high levels of clay and organic carbon found in its sediment. Some of these chemicals bioaccumulate, posing an extra risk to natural resources. Geographic Information Systems provide natural resource managers with the technology to manage complex databases. The analytical and mapping capabilities of GIS can be used to consider cumulative effects in a regional context and to develop plans to protect ecologically sensitive areas

Relationships between precipitation and surface water chemistry in three Carolina bays

International Nuclear Information System (INIS)

Monegue, R.L.; Jagoe, C.H.

1995-01-01

Carolina Bays are shallow freshwater wetlands, the only naturally occurring lentic systems on the southeastern coastal plain. Bays are breeding sites for many amphibian species, but data on precipitation/surface water relationships and long-term chemical trends are lacking. Such data are essential to interpret major fluctuations in amphibian populations. Surface water and bulk precipitation were sampled bi-weekly for over two years at three bays along a 25 km transect on the Savannah River Site in South Carolina. Precipitation chemistry was similar at all sites; average pH was 4.56, and the major ions were H + (30.8 % of total), and SO 4 (50.3% of total). H + was positively correlated with SO 4 , suggesting the importance of anthropogenic acids to precipitation chemistry. All three bays, Rainbow Bay (RB), Thunder Bay (TB), and Ellenton Bay (EB), contained soft (specific conductivity 5--90 microS/cm), acidic water (pH 4.0--5.9) with DOM from 4--40 mg/L. The major cation for RB, TB, and EB, respectively, was: Mg (30.8 % of total); Na (27% of total); and Ca (34.2% of total). DOM was the major anion for all bays, and SO 4 represented 13 to 28 % of total anions. H + was not correlated to DOM or SO, in RB; H + was positively correlated to DOM and SO 4 in TB, and negatively correlated to DOM and SO 4 in EB. Different biogeochemical processes probably control pH and other chemical variables in each bay. While surface water H + was not directly correlated with precipitation H + , NO 3 , or SO 4 , precipitation and shallow groundwater are dominant water sources for these bays. Atmospheric inputs of anthropogenic acids and other chemicals are important factors influencing bay chemistry

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.

Empirical validation of directed functional connectivity.

Science.gov (United States)

Mill, Ravi D; Bagic, Anto; Bostan, Andreea; Schneider, Walter; Cole, Michael W

2017-02-01

Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via "ground truth" connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established "sensory reactivation" effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI ("raw" and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing. Copyright © 2016 Elsevier Inc. All rights reserved.

Hydrodynamics and water quality models applied to Sepetiba Bay

Science.gov (United States)

Cunha, Cynara de L. da N.; Rosman, Paulo C. C.; Ferreira, Aldo Pacheco; Carlos do Nascimento Monteiro, Teófilo

2006-10-01

A coupled hydrodynamic and water quality model is used to simulate the pollution in Sepetiba Bay due to sewage effluent. Sepetiba Bay has a complicated geometry and bottom topography, and is located on the Brazilian coast near Rio de Janeiro. In the simulation, the dissolved oxygen (DO) concentration and biochemical oxygen demand (BOD) are used as indicators for the presence of organic matter in the body of water, and as parameters for evaluating the environmental pollution of the eastern part of Sepetiba Bay. Effluent sources in the model are taken from DO and BOD field measurements. The simulation results are consistent with field observations and demonstrate that the model has been correctly calibrated. The model is suitable for evaluating the environmental impact of sewage effluent on Sepetiba Bay from river inflows, assessing the feasibility of different treatment schemes, and developing specific monitoring activities. This approach has general applicability for environmental assessment of complicated coastal bays.

Inputs and spatial distribution patterns of Cr in Jiaozhou Bay

Science.gov (United States)

Yang, Dongfang; Miao, Zhenqing; Huang, Xinmin; Wei, Linzhen; Feng, Ming

2018-03-01

Cr pollution in marine bays has been one of the critical environmental issues, and understanding the input and spatial distribution patterns is essential to pollution control. In according to the source strengths of the major pollution sources, the input patterns of pollutants to marine bay include slight, moderate and heavy, and the spatial distribution are corresponding to three block models respectively. This paper analyzed input patterns and distributions of Cr in Jiaozhou Bay, eastern China based on investigation on Cr in surface waters during 1979-1983. Results showed that the input strengths of Cr in Jiaozhou Bay could be classified as moderate input and slight input, and the input strengths were 32.32-112.30 μg L-1 and 4.17-19.76 μg L-1, respectively. The input patterns of Cr included two patterns of moderate input and slight input, and the horizontal distributions could be defined by means of Block Model 2 and Block Model 3, respectively. In case of moderate input pattern via overland runoff, Cr contents were decreasing from the estuaries to the bay mouth, and the distribution pattern was parallel. In case of moderate input pattern via marine current, Cr contents were decreasing from the bay mouth to the bay, and the distribution pattern was parallel to circular. The Block Models were able to reveal the transferring process of various pollutants, and were helpful to understand the distributions of pollutants in marine bay.

Bayes and Networks

NARCIS (Netherlands)

Gao, F.

2017-01-01

The dissertation consists of research in three subjects in two themes—Bayes and networks: The first studies the posterior contraction rates for the Dirichlet-Laplace mixtures in a deconvolution setting (Chapter 1). The second subject regards the statistical inference in preferential attachment

Trends in Accretion Rates of Riverine Sediments in a Distal Bay and Wetlands Using 7-Beryllium as a Tracer: Fourleague Bay, Louisiana.

Science.gov (United States)

Restreppo, G. A.; Bentley, S. J.; Wang, J.; Xu, K.

2017-12-01

To combat land loss along the Mississippi River Delta, Louisiana has launched a historic campaign to sustain and regrow coastal lands using, in part, sediment diversions. Previous research has focused primarily on sand sized sediment load, which is usually deposited proximal to a river's delta or a diversion's outlet. Fine sediments constitute the majority of sediment load in the Mississippi, but are under-studied with respect to dispersal processes, particularly in terms of sediment supply to distal deltaic bays and wetlands. The Atchafalaya River and associated wetlands serve as prime study areas for this purpose. Bimonthly time-series push cores were collected from May 2015 to May 2016 along ten sites within Fourleague Bay, Louisiana. Fourleague Bay has remained stable against the deteriorative effects of relative sea level rise, standing out along Louisiana's declining coastline. Of the ten field sites, five are located across a longitudinal transect in the middle bay, while the other five are located in adjacent marshes. All sites fall within 10 to 30 km of the Atchafalaya Delta, extending south towards the Gulf of Mexico. Cores were extruded in 2 cm intervals, dried, ground, and analyzed via gamma spectrometry for the presence of 7Be. Inventories of 7Be were then calculated and used to determine daily mass accretion rate (MAR) over twelve months. Average MAR values for the bay and the marshes are compared with Atchafalaya River discharge, wind data, and atmospheric pressure through the year of sampling. Peak marsh MAR, 0.88 ± 0.20 kg m-2 d-1, occurs just after historically high river discharge. Peak bay MAR, 1.2 ± 0.67 kg m-2 d-1, occurs during seasonal low river discharge and calm winds. Average bay and marsh MARs have a moderate to strong, negative correlation when compared. Results indicate sediment bypass of the bay floor during periods of moderate to high river discharge, entering the marshes directly when inundation occurs and enhanced by the passage

Theories of transporting processes of Cu in Jiaozhou Bay

Science.gov (United States)

Yang, Dongfang; Su, Chunhua; Zhu, Sixi; Wu, Yunjie; Zhou, Wei

2018-02-01

Many marine bays have been polluted along with the rapid development of industry and population size, and understanding the transporting progresses of pollutants is essential to pollution control. In order to better understanding the transporting progresses of pollutants in marine, this paper carried on a comprehensive research of the theories of transporting processes of Cu in Jiaozhou Bay. Results showed that the transporting processes of Cu in this bay could be summarized into seven key theories including homogeneous theory, environmental dynamic theory, horizontal loss theory, source to waters transporting theory, sedimentation transporting theory, migration trend theory and vertical transporting theory, respectively. These theories helpful to better understand the migration progress of pollutants in marine bay.

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

Hammond Bay Biological Station

Data.gov (United States)

Federal Laboratory Consortium — Hammond Bay Biological Station (HBBS), located near Millersburg, Michigan, is a field station of the USGS Great Lakes Science Center (GLSC). HBBS was established by...

Pärnu Bay Golf Club = Pärnu Bay Golf Club / Arhitekt11

Index Scriptorium Estoniae

2016-01-01

Pärnu Bay Golf Club, arhitektid Jürgen Lepper, Anto Savi, Margus Soonets, Janar Toomesso (Arhitekt11), sisearhitektid Liina Vaino, Kaari Metslang, Hannelore Kääramees (Arhitekt11). Kultuurkapitali Arhitektuuri sihtkapitali aastapreemia nominent 2016

Microbial biogeography of San Francisco Bay sediments

Science.gov (United States)

Lee, J. A.; Francis, C. A.

2014-12-01

The largest estuary on the west coast of North America, San Francisco Bay is an ecosystem of enormous biodiversity, and also enormous human impact. The benthos has experienced dredging, occupation by invasive species, and over a century of sediment input as a result of hydraulic mining. Although the Bay's great cultural and ecological importance has inspired numerous surveys of the benthic macrofauna, to date there has been almost no investigation of the microbial communities on the Bay floor. An understanding of those microbial communities would contribute significantly to our understanding of both the biogeochemical processes (which are driven by the microbiota) and the physical processes (which contribute to microbial distributions) in the Bay. Here, we present the first broad survey of bacterial and archaeal taxa in the sediments of the San Francisco Bay. We conducted 16S rRNA community sequencing of bacteria and archaea in sediment samples taken bimonthly for one year, from five sites spanning the salinity gradient between Suisun and Central Bay, in order to capture the effect of both spatial and temporal environmental variation on microbial diversity. From the same samples we also conducted deep sequencing of a nitrogen-cycling functional gene, nirS, allowing an assessment of evolutionary diversity at a much finer taxonomic scale within an important and widespread functional group of bacteria. We paired these sequencing projects with extensive geochemical metadata as well as information about macrofaunal distribution. Our data reveal a diversity of distinct biogeographical patterns among different taxa: clades ubiquitous across sites; clades that respond to measurable environmental drivers; and clades that show geographical site-specificity. These community datasets allow us to test the hypothesis that salinity is a major driver of both overall microbial community structure and community structure of the denitrifying bacteria specifically; and to assess

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.

Social and environmental impacts of the James Bay hydroelectric project

International Nuclear Information System (INIS)

Hornig, J.F.

1999-01-01

The book, which is an analysis and not an advocacy, examines the anatomy of the controversy that has swirled around the James Bay project - the La Grande and Great Whale projects combined - from the 1970s to the 1990s, and seeks, in the process, to determine whether there are lessons that can be learned from such an analysis that are applicable to other cases as well as to James Bay itself. The contributors are interested, at one and the same time, in finding ways to integrate the knowledge of natural scientists and social scientists to deepen the understanding of human/environment relations and to link science and policy to encourage a productive dialogue between practitioners and scholars in this increasingly important area of inquiry. The contributor's papers include the following: introduction to the issues; hydroelectric power development at James Bay: establishing a frame of reference; James Bay: environmental considerations for building large hydroelectric dams and reservoirs in Quebec; elevated mercury in fish as a result of the James Bay hydroelectric power development: perception and reality; the Cree people of James Bay: assessing the social impacts of hydroelectric dams and reservoirs; culture, social change, and Cree opposition to the James Bay hydroelectric development; and the impact of James Bay hydroelectric development on the art and craft of the James Bay Cree. The authors of the volume have attempted to stand back and examine just a few of these issues from the perspective of a variety of disciplines, and their purpose is to inform and stimulate thoughtful consideration by providing an overall perspective that might might serve to broaden the context in which specific issues can be debated. refs., 3 tabs., 5 figs

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 ...