Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid
2016-09-01
In this study, 60-year monthly rainfall data of Bangladesh were analysed to detect trends. Modified Mann-Kendall, Spearman's rho tests and Sen's slope estimators were applied to find the long-term annual, dry season and monthly trends. Sequential Mann-Kendall analysis was applied to detect the potential trend turning points. Spatial variations of the trends were examined using inverse distance weighting (IDW) interpolation. AutoRegressive integrated moving average (ARIMA) model was used for the country mean rainfall and for other two stations data which depicted the highest and the lowest trend in the Mann-Kendall and Spearman's rho tests. Results showed that there is no significant trend in annual rainfall pattern except increasing trends for Cox's Bazar, Khulna, Satkhira and decreasing trend for Srimagal areas. For the dry season, only Bogra area represented significant decreasing trend. Long-term monthly trends demonstrated a mixed pattern; both negative and positive changes were found from February to September. Comilla area showed a significant decreasing trend for consecutive 3 months while Rangpur and Khulna stations confirmed the significant rising trends for three different months in month-wise trends analysis. Rangpur station data gave a maximum increasing trend in April whereas a maximum decreasing trend was found in August for Comilla station. ARIMA models predict +3.26, +8.6 and -2.30 mm rainfall per year for the country, Cox's Bazar and Srimangal areas, respectively. However, all the test results and predictions revealed a good agreement among them in the study.
Directory of Open Access Journals (Sweden)
Gabriel Constantino Blain
2013-05-01
Full Text Available Pre-whitening approaches have been widely used to remove the influence of serial correlations on the Mann-Kendall trend test (MK_prew. However, previous studies indicate that this procedure may lead to a false reduction of the significance of a trend. An alternative approach (MK_interact has been proposed to improve the assessment of the significance of a trend in auto-correlated data. Therefore, the present study compared the performance of the MK_prew and MK_interact for detecting trends in auto-correlated series. Sets of Monte Carlo experiments were carried out to evaluate the occurrence of type I and II errors obtained from both approaches. The analyses were also based on 10-day values of the difference between precipitation and potential evapotranspiration (P-EP obtained from the location of Campinas, State of São Paulo, Brazil. The results found in this study allow us to conclude that the MK_interac outperformed the MK_prew in correctly identifying the significance of trends and that, concerning agricultural interests, the decreasing trend described by the MK_interac during the beginning of the crop growing seasons may reveal an unfavorable temporal distribution of the P-EP values.
A Review on Climate Change in Weather Stations of Guilan Province Using Mann-Kendal Methodand GIS
Behzadi, Jalal
2016-07-01
Climate has always been changing during the life time of the earth, and has appeared in the form of ice age, hurricanes, severe and sudden temperature changes, precipitation and other climatic elements, and has dramatically influenced the environment, and in some cases has caused severe changes and even destructions. Some of the most important aspects of climate changes can be found in precipitation types of different regions in the world and especially Guilan, which is influenced by drastic land conversions and greenhouse gases. Also, agriculture division, industrial activities and unnecessary land conversions are thought to have a huge influence on climate change. Climate change is a result of abnormalcies of metorologyl parameters. Generally, the element of precipitation is somehow included in most theories about climate change. The present study aims to reveal precipitation abnormalcies in Guilan which lead to climate change, and possible deviations of precipitation parameter based on annual, seasonal and monthly series have been evaluated. The Mann-Kendal test has been used to reveal likely deviations leading to climate change. The trend of precipitation changes in long-term has been identifiedusing this method. Also, the beginning and end of these changes have been studied in five stations as representatives of all the thirteen weather stations. Then,the areas which have experienced climate change have been identified using the GIS software along with the severity of the changes with an emphasis on drought. These results can be used in planning and identifying the effects of these changes on the environment. Keywords: Climate Change, Guilan, Mann-Kendal, GIS
Spatio-statistical analysis of temperature fluctuation using Mann-Kendall and Sen's slope approach
Atta-ur-Rahman; Dawood, Muhammad
2016-04-01
This article deals with the spatio-statistical analysis of temperature trend using Mann-Kendall trend model (MKTM) and Sen's slope estimator (SSE) in the eastern Hindu Kush, north Pakistan. The climate change has a strong relationship with the trend in temperature and resultant changes in rainfall pattern and river discharge. In the present study, temperature is selected as a meteorological parameter for trend analysis and slope magnitude. In order to achieve objectives of the study, temperature data was collected from Pakistan Meteorological Department for all the seven meteorological stations that falls in the eastern Hindu Kush region. The temperature data were analysed and simulated using MKTM, whereas for the determination of temperature trend and slope magnitude SSE method have been applied to exhibit the type of fluctuations. The analysis reveals that a positive (increasing) trend in mean maximum temperature has been detected for Chitral, Dir and Saidu Sharif met stations, whereas, negative (decreasing) trend in mean minimum temperature has been recorded for met station Saidu Sharif and Timergara. The analysis further reveals that the concern variation in temperature trend and slope magnitude is attributed to climate change phenomenon in the region.
Nonparametric tests for censored data
Bagdonavicus, Vilijandas; Nikulin, Mikhail
2013-01-01
This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
Trend Analysis of Golestan's Rivers Discharges Using Parametric and Non-parametric Methods
Mosaedi, Abolfazl; Kouhestani, Nasrin
2010-05-01
One of the major problems in human life is climate changes and its problems. Climate changes will cause changes in rivers discharges. The aim of this research is to investigate the trend analysis of seasonal and yearly rivers discharges of Golestan province (Iran). In this research four trend analysis method including, conjunction point, linear regression, Wald-Wolfowitz and Mann-Kendall, for analyzing of river discharges in seasonal and annual periods in significant level of 95% and 99% were applied. First, daily discharge data of 12 hydrometrics stations with a length of 42 years (1965-2007) were selected, after some common statistical tests such as, homogeneity test (by applying G-B and M-W tests), the four mentioned trends analysis tests were applied. Results show that in all stations, for summer data time series, there are decreasing trends with a significant level of 99% according to Mann-Kendall (M-K) test. For autumn time series data, all four methods have similar results. For other periods, the results of these four tests were more or less similar together. While, for some stations the results of tests were different. Keywords: Trend Analysis, Discharge, Non-parametric methods, Wald-Wolfowitz, The Mann-Kendall test, Golestan Province.
A contingency table approach to nonparametric testing
Rayner, JCW
2000-01-01
Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more comp
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
Nonparametric tests for pathwise properties of semimartingales
Cont, Rama; 10.3150/10-BEJ293
2011-01-01
We propose two nonparametric tests for investigating the pathwise properties of a signal modeled as the sum of a L\\'{e}vy process and a Brownian semimartingale. Using a nonparametric threshold estimator for the continuous component of the quadratic variation, we design a test for the presence of a continuous martingale component in the process and a test for establishing whether the jumps have finite or infinite variation, based on observations on a discrete-time grid. We evaluate the performance of our tests using simulations of various stochastic models and use the tests to investigate the fine structure of the DM/USD exchange rate fluctuations and SPX futures prices. In both cases, our tests reveal the presence of a non-zero Brownian component and a finite variation jump component.
Influence of test and person characteristics on nonparametric appropriateness measurement
Meijer, Rob R.; Molenaar, Ivo W.; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Influence of Test and Person Characteristics on Nonparametric Appropriateness Measurement
Meijer, Rob R; Molenaar, Ivo W; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Nonparametric multivariate rank tests and their unbiasedness
Jurečková, Jana; 10.3150/10-BEJ326
2012-01-01
Although unbiasedness is a basic property of a good test, many tests on vector parameters or scalar parameters against two-sided alternatives are not finite-sample unbiased. This was already noticed by Sugiura [Ann. Inst. Statist. Math. 17 (1965) 261--263]; he found an alternative against which the Wilcoxon test is not unbiased. The problem is even more serious in multivariate models. When testing the hypothesis against an alternative which fits well with the experiment, it should be verified whether the power of the test under this alternative cannot be smaller than the significance level. Surprisingly, this serious problem is not frequently considered in the literature. The present paper considers the two-sample multivariate testing problem. We construct several rank tests which are finite-sample unbiased against a broad class of location/scale alternatives and are finite-sample distribution-free under the hypothesis and alternatives. Each of them is locally most powerful against a specific alternative of t...
Nonparametric test for detecting change in distribution with panel data
Pommeret, Denys; Ghattas, Badih
2011-01-01
This paper considers the problem of comparing two processes with panel data. A nonparametric test is proposed for detecting a monotone change in the link between the two process distributions. The test statistic is of CUSUM type, based on the empirical distribution functions. The asymptotic distribution of the proposed statistic is derived and its finite sample property is examined by bootstrap procedures through Monte Carlo simulations.
Testing for a constant coefficient of variation in nonparametric regression
Dette, Holger; Marchlewski, Mareen; Wagener, Jens
2010-01-01
In the common nonparametric regression model Y_i=m(X_i)+sigma(X_i)epsilon_i we consider the problem of testing the hypothesis that the coefficient of the scale and location function is constant. The test is based on a comparison of the observations Y_i=\\hat{sigma}(X_i) with their mean by a smoothed empirical process, where \\hat{sigma} denotes the local linear estimate of the scale function. We show weak convergence of a centered version of this process to a Gaussian process under the null ...
A Non-Parametric Spatial Independence Test Using Symbolic Entropy
Directory of Open Access Journals (Sweden)
López Hernández, Fernando
2008-01-01
Full Text Available In the present paper, we construct a new, simple, consistent and powerful test forspatial independence, called the SG test, by using symbolic dynamics and symbolic entropyas a measure of spatial dependence. We also give a standard asymptotic distribution of anaffine transformation of the symbolic entropy under the null hypothesis of independencein the spatial process. The test statistic and its standard limit distribution, with theproposed symbolization, are invariant to any monotonuous transformation of the data.The test applies to discrete or continuous distributions. Given that the test is based onentropy measures, it avoids smoothed nonparametric estimation. We include a MonteCarlo study of our test, together with the well-known Moran’s I, the SBDS (de Graaffet al, 2001 and (Brett and Pinkse, 1997 non parametric test, in order to illustrate ourapproach.
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...
Curve registration by nonparametric goodness-of-fit testing
Dalalyan, Arnak
2011-01-01
The problem of curve registration appears in many different areas of applications ranging from neuroscience to road traffic modeling. In the present work, we propose a nonparametric testing framework in which we develop a generalized likelihood ratio test to perform curve registration. We first prove that, under the null hypothesis, the resulting test statistic is asymptotically distributed as a chi-squared random variable. This result, often referred to as Wilks' phenomenon, provides a natural threshold for the test of a prescribed asymptotic significance level and a natural measure of lack-of-fit in terms of the p-value of the chi squared test. We also prove that the proposed test is consistent, i.e., its power is asymptotically equal to 1. Some numerical experiments on synthetic datasets are reported as well.
Nonparametric reconstruction of the Om diagnostic to test LCDM
Escamilla-Rivera, Celia
2015-01-01
Cosmic acceleration is usually related with the unknown dark energy, which equation of state, w(z), is constrained and numerically confronted with independent astrophysical data. In order to make a diagnostic of w(z), the introduction of a null test of dark energy can be done using a diagnostic function of redshift, Om. In this work we present a nonparametric reconstruction of this diagnostic using the so-called Loess-Simex factory to test the concordance model with the advantage that this approach offers an alternative way to relax the use of priors and find a possible 'w' that reliably describe the data with no previous knowledge of a cosmological model. Our results demonstrate that the method applied to the dynamical Om diagnostic finds a preference for a dark energy model with equation of state w =-2/3, which correspond to a static domain wall network.
Nonparametric predictive inference for combining diagnostic tests with parametric copula
Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.
2017-09-01
Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of
Non-parametric combination and related permutation tests for neuroimaging.
Winkler, Anderson M; Webster, Matthew A; Brooks, Jonathan C; Tracey, Irene; Smith, Stephen M; Nichols, Thomas E
2016-04-01
In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.
Non-parametric tests of productive efficiency with errors-in-variables
Kuosmanen, T.K.; Post, T.; Scholtes, S.
2007-01-01
We develop a non-parametric test of productive efficiency that accounts for errors-in-variables, following the approach of Varian. [1985. Nonparametric analysis of optimizing behavior with measurement error. Journal of Econometrics 30(1/2), 445-458]. The test is based on the general Pareto-Koopmans
Nonparametric statistical tests for the continuous data: the basic concept and the practical use.
Nahm, Francis Sahngun
2016-02-01
Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of sample means is normally distributed. However, parametric test can be misleading when this assumption is not satisfied. In this circumstance, nonparametric tests are the alternative methods available, because they do not required the normality assumption. Nonparametric tests are the statistical methods based on signs and ranks. In this article, we will discuss about the basic concepts and practical use of nonparametric tests for the guide to the proper use.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of consump
t-tests, non-parametric tests, and large studies—a paradox of statistical practice?
Directory of Open Access Journals (Sweden)
Fagerland Morten W
2012-06-01
Full Text Available Abstract Background During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences. Methods A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whitney (WMW test and the two-sample t-test for increasing sample size. Samples are drawn from skewed distributions with equal means and medians but with a small difference in spread. A hypothetical case study is used for illustration and motivation. Results The WMW test produces, on average, smaller p-values than the t-test. This discrepancy increases with increasing sample size, skewness, and difference in spread. For heavily skewed data, the proportion of p Conclusions Non-parametric tests are most useful for small studies. Using non-parametric tests in large studies may provide answers to the wrong question, thus confusing readers. For studies with a large sample size, t-tests and their corresponding confidence intervals can and should be used even for heavily skewed data.
A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests
Boswijk, H. Peter; Lucas, Andre; Taylor, Nick
1999-01-01
This paper provides an extensive Monte-Carlo comparison of severalcontemporary cointegration tests. Apart from the familiar Gaussian basedtests of Johansen, we also consider tests based on non-Gaussianquasi-likelihoods. Moreover, we compare the performance of these parametrictests with tests that es
A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests
Boswijk, H. Peter; Lucas, Andre; Taylor, Nick
1999-01-01
This paper provides an extensive Monte-Carlo comparison of severalcontemporary cointegration tests. Apart from the familiar Gaussian basedtests of Johansen, we also consider tests based on non-Gaussianquasi-likelihoods. Moreover, we compare the performance of these parametrictests with tests that
A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests
Boswijk, H. Peter; Lucas, Andre; Taylor, Nick
1999-01-01
This paper provides an extensive Monte-Carlo comparison of severalcontemporary cointegration tests. Apart from the familiar Gaussian basedtests of Johansen, we also consider tests based on non-Gaussianquasi-likelihoods. Moreover, we compare the performance of these parametrictests with tests that es
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.
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.
Wei, Jiawei; Carroll, Raymond J; Maity, Arnab
2011-07-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.
A Unified Approach to Constructing Nonparametric Rank Tests.
1986-07-06
82177 - , % 2- W -T- . ,o.. .o I" Va - , . - ,.-- tic, the Kolmogorov-Smirnov statistic, and the Wald - Wolfowitz statistic for the two-sample...linear, such as the Kolmogorov-Smirnov test, the Wald - Wolfowitz (1940) "runs test", the so-called "tests based on exceeding observations" (H;jek 32...metric U induces the Kolmogorov-Smirnov test statistic, for equal sample sizes. The following result shows that the Wald - Wolfowitz test statistic is also
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...
The Probability of Exceedance as a Nonparametric Person-Fit Statistic for Tests of Moderate Length
Tendeiro, Jorge N.; Meijer, Rob R.
2013-01-01
To classify an item score pattern as not fitting a nonparametric item response theory (NIRT) model, the probability of exceedance (PE) of an observed response vector x can be determined as the sum of the probabilities of all response vectors that are, at most, as likely as x, conditional on the test
Non-parametric Tuning of PID Controllers A Modified Relay-Feedback-Test Approach
Boiko, Igor
2013-01-01
The relay feedback test (RFT) has become a popular and efficient tool used in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text. Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) meth...
Nonparametric Tests of Collectively Rational Consumption Behavior : An Integer Programming Procedure
Cherchye, L.J.H.; de Rock, B.; Sabbe, J.; Vermeulen, F.M.P.
2008-01-01
We present an IP-based nonparametric (revealed preference) testing proce- dure for rational consumption behavior in terms of general collective models, which include consumption externalities and public consumption. An empiri- cal application to data drawn from the Russia Longitudinal Monitoring
Applications of Assignment Algorithms to Nonparametric Tests for Homogeneity
2009-09-01
and Siegmund MST Minimum spanning tree MD Mahalanobis distance MD-R Mahalanobis distance, robust NAP Non-Bipartite Accumulated Pairs NNVE...competitor, the maximum likelihood ratio test of James, James, and Siegmund (JJS), even when the parametric assumptions for that test are met. When those...competitor, the maximum likelihood ratio test of James, James, and Siegmund (1992), for various cases including different underlying distributions
Testing the Non-Parametric Conditional CAPM in the Brazilian Stock Market
Directory of Open Access Journals (Sweden)
Daniel Reed Bergmann
2014-04-01
Full Text Available This paper seeks to analyze if the variations of returns and systematic risks from Brazilian portfolios could be explained by the nonparametric conditional Capital Asset Pricing Model (CAPM by Wang (2002. There are four informational variables available to the investors: (i the Brazilian industrial production level; (ii the broad money supply M4; (iii the inflation represented by the Índice de Preços ao Consumidor Amplo (IPCA; and (iv the real-dollar exchange rate, obtained by PTAX dollar quotation.This study comprised the shares listed in the BOVESPA throughout January 2002 to December 2009. The test methodology developed by Wang (2002 and retorted to the Mexican context by Castillo-Spíndola (2006 was used. The observed results indicate that the nonparametric conditional model is relevant in explaining the portfolios’ returns of the sample considered for two among the four tested variables, M4 and PTAX dollar at 5% level of significance.
Non-Parametric Tests of Structure for High Angular Resolution Diffusion Imaging in Q-Space
Olhede, Sofia C
2010-01-01
High angular resolution diffusion imaging data is the observed characteristic function for the local diffusion of water molecules in tissue. This data is used to infer structural information in brain imaging. Non-parametric scalar measures are proposed to summarize such data, and to locally characterize spatial features of the diffusion probability density function (PDF), relying on the geometry of the characteristic function. Summary statistics are defined so that their distributions are, to first order, both independent of nuisance parameters and also analytically tractable. The dominant direction of the diffusion at a spatial location (voxel) is determined, and a new set of axes are introduced in Fourier space. Variation quantified in these axes determines the local spatial properties of the diffusion density. Non-parametric hypothesis tests for determining whether the diffusion is unimodal, isotropic or multi-modal are proposed. More subtle characteristics of white-matter microstructure, such as the degre...
A NONPARAMETRIC PROCEDURE OF THE SAMPLE SIZE DETERMINATION FOR SURVIVAL RATE TEST
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Objective This paper proposes a nonparametric procedure of the sample size determination for survival rate test. Methods Using the classical asymptotic normal procedure yields the required homogenetic effective sample size and using the inverse operation with the prespecified value of the survival function of censoring times yields the required sample size. Results It is matched with the rate test for censored data, does not involve survival distributions, and reduces to its classical counterpart when there is no censoring. The observed power of the test coincides with the prescribed power under usual clinical conditions. Conclusion It can be used for planning survival studies of chronic diseases.
Applications of Parametric and Nonparametric Tests for Event Studies on ISE
Handan YOLSAL
2011-01-01
In this study, we conducted a research as to whether splits in shares on the ISE-ON Index at the Istanbul Stock Exchange have had an impact on returns generated from shares between 2005 and 2011 or not using event study method. This study is based on parametric tests, as well as on nonparametric tests developed as an alternative to them. It has been observed that, when cross-sectional variance adjustment is applied to data set, such null hypothesis as “there is no average abnormal return at d...
Two new non-parametric tests to the distance duality relation with galaxy clusters
Costa, S S; Holanda, R F L
2015-01-01
The cosmic distance duality relation is a milestone of cosmology involving the luminosity and angular diameter distances. Any departure of the relation points to new physics or systematic errors in the observations, therefore tests of the relation are extremely important to build a consistent cosmological framework. Here, two new tests are proposed based on galaxy clusters observations (angular diameter distance and gas mass fraction) and $H(z)$ measurements. By applying Gaussian Processes, a non-parametric method, we are able to derive constraints on departures of the relation where no evidence of deviation is found in both methods, reinforcing the cosmological and astrophysical hypotheses adopted so far.
Poage, J. L.
1975-01-01
A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.
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.
Robust non-parametric one-sample tests for the analysis of recurrent events.
Rebora, Paola; Galimberti, Stefania; Valsecchi, Maria Grazia
2010-12-30
One-sample non-parametric tests are proposed here for inference on recurring events. The focus is on the marginal mean function of events and the basis for inference is the standardized distance between the observed and the expected number of events under a specified reference rate. Different weights are considered in order to account for various types of alternative hypotheses on the mean function of the recurrent events process. A robust version and a stratified version of the test are also proposed. The performance of these tests was investigated through simulation studies under various underlying event generation processes, such as homogeneous and nonhomogeneous Poisson processes, autoregressive and renewal processes, with and without frailty effects. The robust versions of the test have been shown to be suitable in a wide variety of event generating processes. The motivating context is a study on gene therapy in a very rare immunodeficiency in children, where a major end-point is the recurrence of severe infections. Robust non-parametric one-sample tests for recurrent events can be useful to assess efficacy and especially safety in non-randomized studies or in epidemiological studies for comparison with a standard population.
Hassani, Hossein; Huang, Xu; Gupta, Rangan; Ghodsi, Mansi
2016-10-01
In a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures based on monthly data covering the period 1880:1-2013:9. The authors find that standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers do not cause global temperatures for both full and sub-samples, namely 1880:1-1936:2, 1936:3-1986:11 and 1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain causality test detects predictability for the full-sample at short (2-2.6 months) cycle lengths, but not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks, Gupta et al., (2015) conclude that the evidence of causality running from sunspot numbers to global temperatures is weak and inconclusive. Given the importance of the issue of global warming, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot numbers have predictive ability for global temperatures for the three sub-samples, over and above the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests and highlighted that sunspot numbers have always been important in predicting global temperatures.
Carroll, Raymond J.
2011-03-01
In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.
Nonparametric test of consistency between cosmological models and multiband CMB measurements
Aghamousa, Amir
2015-01-01
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 $\\Lambda$CDM model at $95\\% (\\sim 2\\sigma)$ confidence distance from the center of the nonparametri...
COLOR IMAGE RETRIEVAL BASED ON NON-PARAMETRIC STATISTICAL TESTS OF HYPOTHESIS
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R. Shekhar
2016-09-01
Full Text Available A novel method for color image retrieval, based on statistical non-parametric tests such as twosample Wald Test for equality of variance and Man-Whitney U test, is proposed in this paper. The proposed method tests the deviation, i.e. distance in terms of variance between the query and target images; if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. distance between the mean values of the two images; otherwise, the test is dropped. If the query and target images pass the tests then it is inferred that the two images belong to the same class, i.e. both the images are same; otherwise, it is assumed that the images belong to different classes, i.e. both images are different. The proposed method is robust for scaling and rotation, since it adjusts itself and treats either the query image or the target image is the sample of other.
Lange, C; Lyon, H; DeMeo, D; Raby, B; Silverman, EK; Weiss, ST
2003-01-01
We introduce a new powerful nonparametric testing strategy for family-based association studies in which multiple quantitative traits are recorded and the phenotype with the strongest genetic component is not known prior to the analysis. In the first stage, using a population-based test based on the
A Nonparametric Approach for Assessing Goodness-of-Fit of IRT Models in a Mixed Format Test
Liang, Tie; Wells, Craig S.
2015-01-01
Investigating the fit of a parametric model plays a vital role in validating an item response theory (IRT) model. An area that has received little attention is the assessment of multiple IRT models used in a mixed-format test. The present study extends the nonparametric approach, proposed by Douglas and Cohen (2001), to assess model fit of three…
Non-parametric three-way mixed ANOVA with aligned rank tests.
Oliver-Rodríguez, Juan C; Wang, X T
2015-02-01
Research problems that require a non-parametric analysis of multifactor designs with repeated measures arise in the behavioural sciences. There is, however, a lack of available procedures in commonly used statistical packages. In the present study, a generalization of the aligned rank test for the two-way interaction is proposed for the analysis of the typical sources of variation in a three-way analysis of variance (ANOVA) with repeated measures. It can be implemented in the usual statistical packages. Its statistical properties are tested by using simulation methods with two sample sizes (n = 30 and n = 10) and three distributions (normal, exponential and double exponential). Results indicate substantial increases in power for non-normal distributions in comparison with the usual parametric tests. Similar levels of Type I error for both parametric and aligned rank ANOVA were obtained with non-normal distributions and large sample sizes. Degrees-of-freedom adjustments for Type I error control in small samples are proposed. The procedure is applied to a case study with 30 participants per group where it detects gender differences in linguistic abilities in blind children not shown previously by other methods.
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Ismet DOGAN
2015-10-01
Full Text Available Objective: Choosing the most efficient statistical test is one of the essential problems of statistics. Asymptotic relative efficiency is a notion which enables to implement in large samples the quantitative comparison of two different tests used for testing of the same statistical hypothesis. The notion of the asymptotic efficiency of tests is more complicated than that of asymptotic efficiency of estimates. This paper discusses the effect of sample size on expected values and variances of non-parametric tests for independent two samples and determines the most effective test for different sample sizes using Fraser efficiency value. Material and Methods: Since calculating the power value in comparison of the tests is not practical most of the time, using the asymptotic relative efficiency value is favorable. Asymptotic relative efficiency is an indispensable technique for comparing and ordering statistical test in large samples. It is especially useful in nonparametric statistics where there exist numerous heuristic tests such as the linear rank tests. In this study, the sample size is determined as 2 ≤ n ≤ 50. Results: In both balanced and unbalanced cases, it is found that, as the sample size increases expected values and variances of all the tests discussed in this paper increase as well. Additionally, considering the Fraser efficiency, Mann-Whitney U test is found as the most efficient test among the non-parametric tests that are used in comparison of independent two samples regardless of their sizes. Conclusion: According to Fraser efficiency, Mann-Whitney U test is found as the most efficient test.
Marmarelis, Vasilis Z; Shin, Dae C; Zhang, Yaping; Kautzky-Willer, Alexandra; Pacini, Giovanni; D'Argenio, David Z
2013-07-01
Modeling studies of the insulin-glucose relationship have mainly utilized parametric models, most notably the minimal model (MM) of glucose disappearance. This article presents results from the comparative analysis of the parametric MM and a nonparametric Laguerre based Volterra Model (LVM) applied to the analysis of insulin modified (IM) intravenous glucose tolerance test (IVGTT) data from a clinical study of gestational diabetes mellitus (GDM). An IM IVGTT study was performed 8 to 10 weeks postpartum in 125 women who were diagnosed with GDM during their pregnancy [population at risk of developing diabetes (PRD)] and in 39 control women with normal pregnancies (control subjects). The measured plasma glucose and insulin from the IM IVGTT in each group were analyzed via a population analysis approach to estimate the insulin sensitivity parameter of the parametric MM. In the nonparametric LVM analysis, the glucose and insulin data were used to calculate the first-order kernel, from which a diagnostic scalar index representing the integrated effect of insulin on glucose was derived. Both the parametric MM and nonparametric LVM describe the glucose concentration data in each group with good fidelity, with an improved measured versus predicted r² value for the LVM of 0.99 versus 0.97 for the MM analysis in the PRD. However, application of the respective diagnostic indices of the two methods does result in a different classification of 20% of the individuals in the PRD. It was found that the data based nonparametric LVM revealed additional insights about the manner in which infused insulin affects blood glucose concentration. © 2013 Diabetes Technology Society.
Testing Equality of Nonparametric Functions in Two Partially Linear Models%检验两个部分线性模型中非参函数相等
Institute of Scientific and Technical Information of China (English)
施三支; 宋立新; 杨华
2008-01-01
We propose the test statistic to check whether the nonparametric func-tions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.
Directory of Open Access Journals (Sweden)
Kühnast, Corinna
2008-04-01
Full Text Available Background: Although non-normal data are widespread in biomedical research, parametric tests unnecessarily predominate in statistical analyses. Methods: We surveyed five biomedical journals and – for all studies which contain at least the unpaired t-test or the non-parametric Wilcoxon-Mann-Whitney test – investigated the relationship between the choice of a statistical test and other variables such as type of journal, sample size, randomization, sponsoring etc. Results: The non-parametric Wilcoxon-Mann-Whitney was used in 30% of the studies. In a multivariable logistic regression the type of journal, the test object, the scale of measurement and the statistical software were significant. The non-parametric test was more common in case of non-continuous data, in high-impact journals, in studies in humans, and when the statistical software is specified, in particular when SPSS was used.
Chakrabarty, Dalia
2013-01-01
In lieu of direct detection of dark matter, estimation of the distribution of the gravitational mass in distant galaxies is of crucial importance in Astrophysics. Typically, such estimation is performed using small samples of noisy, partially missing measurements - only some of the three components of the velocity and location vectors of individual particles that live in the galaxy are measurable. Such limitations of the available data in turn demands that simplifying model assumptions be undertaken. Thus, assuming that the phase space of a galaxy manifests simple symmetries - such as isotropy - allows for the learning of the density of the gravitational mass in galaxies. This is equivalent to assuming that the phase space $pdf$ from which the velocity and location vectors of galactic particles are sampled from, is an isotropic function of these vectors. We present a new non-parametric test of hypothesis that tests for relative support in two or more measured data sets of disparate sizes, for the undertaken m...
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Donald W. Zimmerman
2004-01-01
Full Text Available It is well known that the two-sample Student t test fails to maintain its significance level when the variances of treatment groups are unequal, and, at the same time, sample sizes are unequal. However, introductory textbooks in psychology and education often maintain that the test is robust to variance heterogeneity when sample sizes are equal. The present study discloses that, for a wide variety of non-normal distributions, especially skewed distributions, the Type I error probabilities of both the t test and the Wilcoxon-Mann-Whitney test are substantially inflated by heterogeneous variances, even when sample sizes are equal. The Type I error rate of the t test performed on ranks replacing the scores (rank-transformed data is inflated in the same way and always corresponds closely to that of the Wilcoxon-Mann-Whitney test. For many probability densities, the distortion of the significance level is far greater after transformation to ranks and, contrary to known asymptotic properties, the magnitude of the inflation is an increasing function of sample size. Although nonparametric tests of location also can be sensitive to differences in the shape of distributions apart from location, the Wilcoxon-Mann-Whitney test and rank-transformation tests apparently are influenced mainly by skewness that is accompanied by specious differences in the means of ranks.
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
[Use of nonparametric methods in medicine. V. A probability test using iteration].
Gerylovová, A; Holcík, J
1990-10-01
The authors give an account of the so-called Wald-Wolfowitz test of iteration of two types of elements by means of which it is possible to test the probability of the pattern of two types of elements. To facilitate the application of the test five percent critical values are given for the number of iterations for left-sided, right-sided and bilateral alternative hypotheses. The authors present also tables of critical values for up and down iterations which are obtained when we replace the originally assessed sequence of observations by a sequence +1 and -1, depending on the sign of the consecutive differences. The application of the above tests is illustrated on examples.
Non-Parametric, Closed-Loop Testing of Autonomy in Unmanned Aircraft Systems Project
National Aeronautics and Space Administration — The proposed Phase I program aims to develop new methods to support safety testing for integration of Unmanned Aircraft Systems into the National Airspace (NAS) with...
Fujita, André; Takahashi, Daniel Y; Patriota, Alexandre G; Sato, João R
2014-12-10
Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting 'significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically tests whether two or more populations are equally clustered. The proposed method allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest. Copyright © 2014 John Wiley & Sons, Ltd.
A Nonparametric Test of Interaction in the General Two-Way Layout
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John ÖHRVIK
2010-01-01
Full Text Available The two-way layout is frequently occurring, e.g. blocking is used to reduce the between subject variability when comparing treatments or many medical centers are included in a clinical trial to recruit a sufficient number of patients. In epidemiological studies, it is common to study the interactions between genetic and environmental factors. This paper is concerned with the statistical analysis of data arising in these situations when assumptions like normality do not necessarily apply. The main objective of this paper is to propose a test for interactions for continuous data based on joint ranking of all observations after iteratively eliminating the two main effects. The validity of the significance levels of the test when using a finite sample version of the asymptotic distribution of the test statistic is manifested and the power against different alternatives illustrated by extensive simulation experiments. The proposed test is compared with competitors on published data sets. Data from the Survey of Adolescent Life in Vestmanland (SALVe project are analyzed both with our test and Brunner and Puri's proposal. The test shows good asymptotic and small sample properties. It can be used in the general two-way layout and multiple comparisons can be performed in a straightforward way. The analyses of the SALVe project show that our proposal can be useful in such studies. It has the potential to pick-up interactions hidden in the noise of gross errors when using standard ANOVA. The test may become a valuable tool and alternative to other proposals in exploring interactions.
A new non-parametric stationarity test of time series in the time domain
Jin, Lei
2014-11-07
© 2015 The Royal Statistical Society and Blackwell Publishing Ltd. We propose a new double-order selection test for checking second-order stationarity of a time series. To develop the test, a sequence of systematic samples is defined via Walsh functions. Then the deviations of the autocovariances based on these systematic samples from the corresponding autocovariances of the whole time series are calculated and the uniform asymptotic joint normality of these deviations over different systematic samples is obtained. With a double-order selection scheme, our test statistic is constructed by combining the deviations at different lags in the systematic samples. The null asymptotic distribution of the statistic proposed is derived and the consistency of the test is shown under fixed and local alternatives. Simulation studies demonstrate well-behaved finite sample properties of the method proposed. Comparisons with some existing tests in terms of power are given both analytically and empirically. In addition, the method proposed is applied to check the stationarity assumption of a chemical process viscosity readings data set.
CURRENT STATUS OF NONPARAMETRIC STATISTICS
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Orlov A. I.
2015-02-01
Full Text Available Nonparametric statistics is one of the five points of growth of applied mathematical statistics. Despite the large number of publications on specific issues of nonparametric statistics, the internal structure of this research direction has remained undeveloped. The purpose of this article is to consider its division into regions based on the existing practice of scientific activity determination of nonparametric statistics and classify investigations on nonparametric statistical methods. Nonparametric statistics allows to make statistical inference, in particular, to estimate the characteristics of the distribution and testing statistical hypotheses without, as a rule, weakly proven assumptions about the distribution function of samples included in a particular parametric family. For example, the widespread belief that the statistical data are often have the normal distribution. Meanwhile, analysis of results of observations, in particular, measurement errors, always leads to the same conclusion - in most cases the actual distribution significantly different from normal. Uncritical use of the hypothesis of normality often leads to significant errors, in areas such as rejection of outlying observation results (emissions, the statistical quality control, and in other cases. Therefore, it is advisable to use nonparametric methods, in which the distribution functions of the results of observations are imposed only weak requirements. It is usually assumed only their continuity. On the basis of generalization of numerous studies it can be stated that to date, using nonparametric methods can solve almost the same number of tasks that previously used parametric methods. Certain statements in the literature are incorrect that nonparametric methods have less power, or require larger sample sizes than parametric methods. Note that in the nonparametric statistics, as in mathematical statistics in general, there remain a number of unresolved problems
Parametric and nonparametric Granger causality testing: Linkages between international stock markets
de Gooijer, J.G.; Sivarajasingham, S.
2008-01-01
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 t
2013-03-01
Mendenhall , and Sheaffer [25]. For the remainder of this paper, however, we will make use of the Wilcoxon rank sum test for purposes of comparison with the...B. W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman & Hall/CRC, 1986, p. 48. [25] D. D. Wackerly, W. Mendenhall III and R
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.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2014-01-01
Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.
Nonparametric statistics for social and behavioral sciences
Kraska-MIller, M
2013-01-01
Introduction to Research in Social and Behavioral SciencesBasic Principles of ResearchPlanning for ResearchTypes of Research Designs Sampling ProceduresValidity and Reliability of Measurement InstrumentsSteps of the Research Process Introduction to Nonparametric StatisticsData AnalysisOverview of Nonparametric Statistics and Parametric Statistics Overview of Parametric Statistics Overview of Nonparametric StatisticsImportance of Nonparametric MethodsMeasurement InstrumentsAnalysis of Data to Determine Association and Agreement Pearson Chi-Square Test of Association and IndependenceContingency
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Lee, L.; Helsel, D.
2007-01-01
Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.
Nonparametric Econometrics: The np Package
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Tristen Hayﬁeld
2008-07-01
Full Text Available We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of signiﬁcance and consistent model speciﬁcation tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.
Quantal Response: Nonparametric Modeling
2017-01-01
spline N−spline Fig. 3 Logistic regression 7 Approved for public release; distribution is unlimited. 5. Nonparametric QR Models Nonparametric linear ...stimulus and probability of response. The Generalized Linear Model approach does not make use of the limit distribution but allows arbitrary functional...7. Conclusions and Recommendations 18 8. References 19 Appendix A. The Linear Model 21 Appendix B. The Generalized Linear Model 33 Appendix C. B
Introduction to nonparametric statistics for the biological sciences using R
MacFarland, Thomas W
2016-01-01
This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses a...
Nonparametric identification of copula structures
Li, Bo
2013-06-01
We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process.We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets. © 2013 American Statistical Association.
Nonparametric Inference for Periodic Sequences
Sun, Ying
2012-02-01
This article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.
Nonparametric statistical methods
Hollander, Myles; Chicken, Eric
2013-01-01
Praise for the Second Edition"This book should be an essential part of the personal library of every practicing statistician."-Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given sit
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Nonparametric Cooperative Spectrum Sensing Algorithm Based on Friedman Test%基于Friedman检验的非参数协作频谱感知方法
Institute of Scientific and Technical Information of China (English)
王炯滔; 金明; 李有明; 高洋
2014-01-01
Covariance matrix based spectrum sensing encounters performance degradation when there the antenna correlation is low. To overcome this drawback, a nonparametric cooperative spectrum sensing algorithm based on Friedman test is proposed. Distributed sensors possess the effect of space diversity, so that the signal power among the sensors at the same time may not be completely equal. Based on this feature, the spectrum sensing is realized by comparing signal powers among the sensors. For the nonparametric approach is adopted, the proposed algorithm is robust to noise uncertainty and is suitable for noise of any statistical distribution. The theoretical expression of decision threshold is also derived, which shows that the decision threshold has no relationship with the sample number. As a result, the threshold does not need to be reset when the sample number changes. Simulation results demonstrate the effectiveness of the algorithm.%协方差矩阵频谱感知方法在天线相关性低时感知性能较差，该文针对这一问题提出一种基于Friedman检验的非参数协作频谱感知方法。分布式放置的感知节点具有空间分集的特性，因此在同一时刻感知节点上的信号功率不完全相同。利用这一特点，提出通过比较各感知节点的信号功率水平来实现频谱感知。由于采用了非参数化表示，该方法对噪声不确定性稳定，且适用于任意统计分布的噪声。另外，推导了所提方法判决门限的理论表达式，结果显示判决门限与采样点数无关，因此在采样点数变化的情况下无需重新设置判决门限。仿真结果验证了上述理论分析的有效性。
A Nonparametric Analogy of Analysis of Covariance
Burnett, Thomas D.; Barr, Donald R.
1977-01-01
A nonparametric test of the hypothesis of no treatment effect is suggested for a situation where measures of the severity of the condition treated can be obtained and ranked both pre- and post-treatment. The test allows the pre-treatment rank to be used as a concomitant variable. (Author/JKS)
Directory of Open Access Journals (Sweden)
César Merino Soto
2009-05-01
Full Text Available Resumen:La presente investigación hace un estudio psicométrico de un nuevo sistema de calificación de la Prueba Gestáltica del Bendermodificada para niños, que es el Sistema de Calificación Cualitativa (Brannigan y Brunner, 2002, en un muestra de 244 niñosingresantes a primer grado de primaria en cuatro colegios públicos, ubicados en Lima. El enfoque usado es un análisis noparamétrico de ítems mediante el programa Testgraf (Ramsay, 1991. Los resultados indican niveles apropiados deconsistencia interna, identificándose la unidimensionalidad, y el buen nivel discriminativo de las categorías de calificación deeste Sistema Cualitativo. No se hallaron diferencias demográficas respecto al género ni la edad. Se discuten los presenteshallazgos en el contexto del potencial uso del Sistema de Calificación Cualitativa y del análisis no paramétrico de ítems en lainvestigación psicométrica.AbstracThis research designs a psychometric study of a new scoring system of the Bender Gestalt test modified to children: it is theQualitative Scoring System (Brannigan & Brunner, 2002, in a sample of 244 first grade children of primary level, in four public school of Lima. The approach aplied is the nonparametric item analysis using The test graft computer program (Ramsay, 1991. Our findings point to good levels of internal consistency, unidimensionality and good discriminative level ofthe categories of scoring from the Qualitative Scoring System. There are not demographic differences between gender or age.We discuss our findings within the context of the potential use of the Qualitative Scoring System and of the nonparametricitem analysis approach in the psychometric research.
A Censored Nonparametric Software Reliability Model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This paper analyses the effct of censoring on the estimation of failure rate, and presents a framework of a censored nonparametric software reliability model. The model is based on nonparametric testing of failure rate monotonically decreasing and weighted kernel failure rate estimation under the constraint of failure rate monotonically decreasing. Not only does the model have the advantages of little assumptions and weak constraints, but also the residual defects number of the software system can be estimated. The numerical experiment and real data analysis show that the model performs well with censored data.
Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh
Bari, Sheikh Hefzul; Rahman, M. Tauhid Ur; Hoque, Muhammad Azizul; Hussain, Md. Manjurul
2016-07-01
The aim of the present study was to investigate 50 years (1964-2013) of seasonal and annual rainfall trends and their fluctuation over time in northern Bangladesh. After testing the autocorrelation, non-parametric Mann-Kendall test along with Sen Slope estimator was used to examine rainfall trends and their magnitudes. The sequential Mann-Kendall test was used to identify any fluctuations in the trends over time and to detect the possible points of change in the rainfall series. We found that pre-monsoon and post-monsoon rainfall is increasing in most of the rainfall stations. The only decrement in pre-monsoon rainfall was found for Ishurdi (1.28 mm/year). However, the sequential Mann-Kendall test detected decreasing pre-monsoon rainfall trend after early the 1990s. Monsoon rainfall showed a decreasing trend in the majority of the area studied. The maximum decrement in monsoon rainfall was found for Sylhet station (8.10 mm/year) and minimum in Mymensingh (1.53 mm/year). An upward monsoon rainfall trend was found for Rangpur (2.02 mm/year). Annual rainfall followed the monsoon rainfall trend. However, all of the positive and negative trends were found statistically non-significant at 95% confidence limit with the only exception for monsoon and annual rainfall at Rajshahi station. Rajshahi station was the only region where the monsoon and annual rainfall has a significant negative trend at 95% confidence limit. The sequential Mann-Kendall test detected several non-significant points of change for seasonal and annual rainfall at most of the stations. Periodic fluctuations were also detected. We observed that there were decreasing seasonal rainfall trend after early the 1990s for the majority of the stations.
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the trea
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the
2nd Conference of the International Society for Nonparametric Statistics
Manteiga, Wenceslao; Romo, Juan
2016-01-01
This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cádiz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers...
Nonparametric statistical methods using R
Kloke, John
2014-01-01
A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.The book first gives an overview of the R language and basic statistical c
Non-parametric approach to the study of phenotypic stability.
Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P
2016-02-19
The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.
Semi- and Nonparametric ARCH Processes
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Oliver B. Linton
2011-01-01
Full Text Available ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about the general properties of such processes, including stationary conditions, ergodic conditions and mixing conditions. The last part is on the estimation methods in ARCH/GARCH processes.
Nonparametric estimation of ultrasound pulses
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Leeman, Sidney
1994-01-01
An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori...
Nonparametric inferences for kurtosis and conditional kurtosis
Institute of Scientific and Technical Information of China (English)
XIE Xiao-heng; HE You-hua
2009-01-01
Under the assumption of strictly stationary process, this paper proposes a nonparametric model to test the kurtosis and conditional kurtosis for risk time series. We apply this method to the daily returns of S&P500 index and the Shanghai Composite Index, and simulate GARCH data for verifying the efficiency of the presented model. Our results indicate that the risk series distribution is heavily tailed, but the historical information can make its future distribution light-tailed. However the far future distribution's tails are little affected by the historical data.
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders
DEFF Research Database (Denmark)
Nielsen, Morten Ørregaard
2009-01-01
In this paper a nonparametric variance ratio testing approach is proposed for determining the number of cointegrating relations in fractionally integrated systems. The test statistic is easily calculated without prior knowledge of the integration order of the data, the strength of the cointegrating...
Comparing parametric and nonparametric regression methods for panel data
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
Mura, Maria Chiara; De Felice, Marco; Morlino, Roberta; Fuselli, Sergio
2010-01-01
In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6), concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a) node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b) node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c) node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW) non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%); most important, they suggest a possible procedure to optimize network design.
Directory of Open Access Journals (Sweden)
Maria Chiara Mura
2010-12-01
Full Text Available In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6, concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%; most important, they suggest a possible procedure to optimize network design.
Nonparametric dark energy reconstruction from supernova data.
Holsclaw, Tracy; Alam, Ujjaini; Sansó, Bruno; Lee, Herbert; Heitmann, Katrin; Habib, Salman; Higdon, David
2010-12-10
Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.
Nonparametric k-nearest-neighbor entropy estimator.
Lombardi, Damiano; Pant, Sanjay
2016-01-01
A nonparametric k-nearest-neighbor-based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering nonuniform probability densities in the region of k-nearest neighbors around each sample point. It aims to improve the classical estimators in three situations: first, when the dimensionality of the random variable is large; second, when near-functional relationships leading to high correlation between components of the random variable are present; and third, when the marginal variances of random variable components vary significantly with respect to each other. Heuristics on the error of the proposed and classical estimators are presented. Finally, the proposed estimator is tested for a variety of distributions in successively increasing dimensions and in the presence of a near-functional relationship. Its performance is compared with a classical estimator, and a significant improvement is demonstrated.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Nonparametric Regression with Common Shocks
Directory of Open Access Journals (Sweden)
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....
An asymptotically optimal nonparametric adaptive controller
Institute of Scientific and Technical Information of China (English)
郭雷; 谢亮亮
2000-01-01
For discrete-time nonlinear stochastic systems with unknown nonparametric structure, a kernel estimation-based nonparametric adaptive controller is constructed based on truncated certainty equivalence principle. Global stability and asymptotic optimality of the closed-loop systems are established without resorting to any external excitations.
Nonparametric Detection of Geometric Structures Over Networks
Zou, Shaofeng; Liang, Yingbin; Poor, H. Vincent
2017-10-01
Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exist, all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary and unknown. The goal is to design statistically consistent tests with probability of errors converging to zero as the network size becomes asymptotically large. Kernel-based tests are proposed based on maximum mean discrepancy that measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space. Detection of an anomalous interval over a line network is first studied. Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent. It is also shown that certain necessary conditions must hold to guarantee any test to be universally consistent. Comparison of sufficient and necessary conditions yields that the proposed test is order-level optimal and nearly optimal respectively in terms of minimum and maximum sizes of candidate anomalous intervals. Generalization of the results to other networks is further developed. Numerical results are provided to demonstrate the performance of the proposed tests.
ANALYSIS OF TIED DATA: AN ALTERNATIVE NON-PARAMETRIC APPROACH
Directory of Open Access Journals (Sweden)
I. C. A. OYEKA
2012-02-01
Full Text Available This paper presents a non-parametric statistical method of analyzing two-sample data that makes provision for the possibility of ties in the data. A test statistic is developed and shown to be free of the effect of any possible ties in the data. An illustrative example is provided and the method is shown to compare favourably with its competitor; the Mann-Whitney test and is more powerful than the latter when there are ties.
Nonparametric statistical testing of coherence differences
Maris, E.; Schoffelen, J.M.; Fries, P.
2007-01-01
Many important questions in neuroscience are about interactions between neurons or neuronal groups. These interactions are often quantified by coherence, which is a frequency-indexed measure that quantifies the extent to which two signals exhibit a consistent phase relation. In this paper, we consid
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...
Bayesian nonparametric duration model with censorship
Directory of Open Access Journals (Sweden)
Joseph Hakizamungu
2007-10-01
Full Text Available This paper is concerned with nonparametric i.i.d. durations models censored observations and we establish by a simple and unified approach the general structure of a bayesian nonparametric estimator for a survival function S. For Dirichlet prior distributions, we describe completely the structure of the posterior distribution of the survival function. These results are essentially supported by prior and posterior independence properties.
Bootstrap Estimation for Nonparametric Efficiency Estimates
1995-01-01
This paper develops a consistent bootstrap estimation procedure to obtain confidence intervals for nonparametric measures of productive efficiency. Although the methodology is illustrated in terms of technical efficiency measured by output distance functions, the technique can be easily extended to other consistent nonparametric frontier models. Variation in estimated efficiency scores is assumed to result from variation in empirical approximations to the true boundary of the production set. ...
Nonparametric inference procedures for multistate life table analysis.
Dow, M M
1985-01-01
Recent generalizations of the classical single state life table procedures to the multistate case provide the means to analyze simultaneously the mobility and mortality experience of 1 or more cohorts. This paper examines fairly general nonparametric combinatorial matrix procedures, known as quadratic assignment, as an analysis technic of various transitional patterns commonly generated by cohorts over the life cycle course. To some degree, the output from a multistate life table analysis suggests inference procedures. In his discussion of multstate life table construction features, the author focuses on the matrix formulation of the problem. He then presents several examples of the proposed nonparametric procedures. Data for the mobility and life expectancies at birth matrices come from the 458 member Cayo Santiago rhesus monkey colony. The author's matrix combinatorial approach to hypotheses testing may prove to be a useful inferential strategy in several multidimensional demographic areas.
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...
Why preferring parametric forecasting to nonparametric methods?
Jabot, Franck
2015-05-07
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.
Categorical and nonparametric data analysis choosing the best statistical technique
Nussbaum, E Michael
2014-01-01
Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain
Nonparametric TOA estimators for low-resolution IR-UWB digital receiver
Institute of Scientific and Technical Information of China (English)
Yanlong Zhang; Weidong Chen
2015-01-01
Nonparametric time-of-arrival (TOA) estimators for im-pulse radio ultra-wideband (IR-UWB) signals are proposed. Non-parametric detection is obviously useful in situations where de-tailed information about the statistics of the noise is unavailable or not accurate. Such TOA estimators are obtained based on condi-tional statistical tests with only a symmetry distribution assumption on the noise probability density function. The nonparametric es-timators are attractive choices for low-resolution IR-UWB digital receivers which can be implemented by fast comparators or high sampling rate low resolution analog-to-digital converters (ADCs), in place of high sampling rate high resolution ADCs which may not be available in practice. Simulation results demonstrate that nonparametric TOA estimators provide more effective and robust performance than typical energy detection (ED) based estimators.
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major......This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural...... currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis....
Institute of Scientific and Technical Information of China (English)
LIU Yong-jian; DUAN Chuan; TIAN Meng-liang; HU Er-liang; HUANG Yu-bi
2010-01-01
Analysis of multi-environment trials (METs) of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction (GEI) to 14 maize (Zea mays L.) genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEI and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability.Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX 168 as desirable and ND 108, CM 12, CN36, and NK6661 as undesirable genotypes.
Recent Advances and Trends in Nonparametric Statistics
Akritas, MG
2003-01-01
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection o
Correlated Non-Parametric Latent Feature Models
Doshi-Velez, Finale
2012-01-01
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural...... breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major...
Nonparametric statistics a step-by-step approach
Corder, Gregory W
2014-01-01
"…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca
Thirty years of nonparametric item response theory
Molenaar, W.
2001-01-01
Relationships between a mathematical measurement model and its real-world applications are discussed. A distinction is made between large data matrices commonly found in educational measurement and smaller matrices found in attitude and personality measurement. Nonparametric methods are evaluated fo
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Decompounding random sums: A nonparametric approach
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Pitts, Susan M.
review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Nonparametric Transient Classification using Adaptive Wavelets
Varughese, Melvin M; Stephanou, Michael; Bassett, Bruce A
2015-01-01
Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind ...
A Bayesian nonparametric meta-analysis model.
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G
2015-03-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
Nonparametric Bayes analysis of social science data
Kunihama, Tsuyoshi
Social science data often contain complex characteristics that standard statistical methods fail to capture. Social surveys assign many questions to respondents, which often consist of mixed-scale variables. Each of the variables can follow a complex distribution outside parametric families and associations among variables may have more complicated structures than standard linear dependence. Therefore, it is not straightforward to develop a statistical model which can approximate structures well in the social science data. In addition, many social surveys have collected data over time and therefore we need to incorporate dynamic dependence into the models. Also, it is standard to observe massive number of missing values in the social science data. To address these challenging problems, this thesis develops flexible nonparametric Bayesian methods for the analysis of social science data. Chapter 1 briefly explains backgrounds and motivations of the projects in the following chapters. Chapter 2 develops a nonparametric Bayesian modeling of temporal dependence in large sparse contingency tables, relying on a probabilistic factorization of the joint pmf. Chapter 3 proposes nonparametric Bayes inference on conditional independence with conditional mutual information used as a measure of the strength of conditional dependence. Chapter 4 proposes a novel Bayesian density estimation method in social surveys with complex designs where there is a gap between sample and population. We correct for the bias by adjusting mixture weights in Bayesian mixture models. Chapter 5 develops a nonparametric model for mixed-scale longitudinal surveys, in which various types of variables can be induced through latent continuous variables and dynamic latent factors lead to flexibly time-varying associations among variables.
Bayesian nonparametric estimation for Quantum Homodyne Tomography
Naulet, Zacharie; Barat, Eric
2016-01-01
We estimate the quantum state of a light beam from results of quantum homodyne tomography noisy measurements performed on identically prepared quantum systems. We propose two Bayesian nonparametric approaches. The first approach is based on mixture models and is illustrated through simulation examples. The second approach is based on random basis expansions. We study the theoretical performance of the second approach by quantifying the rate of contraction of the posterior distribution around ...
NONPARAMETRIC ESTIMATION OF CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS
Directory of Open Access Journals (Sweden)
Orlov A. I.
2015-10-01
Full Text Available The article is devoted to the nonparametric point and interval estimation of the characteristics of the probabilistic distribution (the expectation, median, variance, standard deviation, variation coefficient of the sample results. Sample values are regarded as the implementation of independent and identically distributed random variables with an arbitrary distribution function having the desired number of moments. Nonparametric analysis procedures are compared with the parametric procedures, based on the assumption that the sample values have a normal distribution. Point estimators are constructed in the obvious way - using sample analogs of the theoretical characteristics. Interval estimators are based on asymptotic normality of sample moments and functions from them. Nonparametric asymptotic confidence intervals are obtained through the use of special output technology of the asymptotic relations of Applied Statistics. In the first step this technology uses the multidimensional central limit theorem, applied to the sums of vectors whose coordinates are the degrees of initial random variables. The second step is the conversion limit multivariate normal vector to obtain the interest of researcher vector. At the same considerations we have used linearization and discarded infinitesimal quantities. The third step - a rigorous justification of the results on the asymptotic standard for mathematical and statistical reasoning level. It is usually necessary to use the necessary and sufficient conditions for the inheritance of convergence. This article contains 10 numerical examples. Initial data - information about an operating time of 50 cutting tools to the limit state. Using the methods developed on the assumption of normal distribution, it can lead to noticeably distorted conclusions in a situation where the normality hypothesis failed. Practical recommendations are: for the analysis of real data we should use nonparametric confidence limits
portfolio optimization based on nonparametric estimation methods
Directory of Open Access Journals (Sweden)
mahsa ghandehari
2017-03-01
Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.
a Multivariate Downscaling Model for Nonparametric Simulation of Daily Flows
Molina, J. M.; Ramirez, J. A.; Raff, D. A.
2011-12-01
A multivariate, stochastic nonparametric framework for stepwise disaggregation of seasonal runoff volumes to daily streamflow is presented. The downscaling process is conditional on volumes of spring runoff and large-scale ocean-atmosphere teleconnections and includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. The non-parametric and assumption-free character of the framework allows consideration of the random nature and nonlinearities of daily flows, which parametric models are unable to account for adequately. This paper examines statistical links between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a periodicity analysis of climate patterns to detect coherences of their cyclic behavior in the frequency domain. We explore the use of such relationships and selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices, NPGO, SOI and PDO, respectively) in the proposed data-driven framework by means of a combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. In addition, we present a new and simple algorithm, based on nonparametric resampling, that overcomes the common limitation of lack of preservation of historical correlation between daily flows across months. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and produces synthetic series that are statistically indistinguishable from the observations. We present evidence showing that both
Using Mathematica to build Non-parametric Statistical Tables
Directory of Open Access Journals (Sweden)
Gloria Perez Sainz de Rozas
2003-01-01
Full Text Available In this paper, I present computational procedures to obtian statistical tables. The tables of the asymptotic distribution and the exact distribution of Kolmogorov-Smirnov statistic Dn for one population, the table of the distribution of the runs R, the table of the distribution of Wilcoxon signed-rank statistic W+ and the table of the distribution of Mann-Whitney statistic Ux using Mathematica, Version 3.9 under Window98. I think that it is an interesting cuestion because many statistical packages give the asymptotic significance level in the statistical tests and with these porcedures one can easily calculate the exact significance levels and the left-tail and right-tail probabilities with non-parametric distributions. I have used mathematica to make these calculations because one can use symbolic language to solve recursion relations. It's very easy to generate the format of the tables, and it's possible to obtain any table of the mentioned non-parametric distributions with any precision, not only with the standard parameters more used in Statistics, and without transcription mistakes. Furthermore, using similar procedures, we can generate tables for the following distribution functions: Binomial, Poisson, Hypergeometric, Normal, x2 Chi-Square, T-Student, F-Snedecor, Geometric, Gamma and Beta.
Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes
Zhou, Wei-Xing; Sornette, Didier
We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(tc-t) variable, where tc is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study
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Anestis Antoniadis
2001-06-01
Full Text Available Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced.
Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures
Li, Quanbao; Wei, Fajie; Zhou, Shenghan
2017-05-01
The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.
Multivariate nonparametric regression and visualization with R and applications to finance
Klemelä, Jussi
2014-01-01
A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio
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.
A nonparametric and diversified portfolio model
Shirazi, Yasaman Izadparast; Sabiruzzaman, Md.; Hamzah, Nor Aishah
2014-07-01
Traditional portfolio models, like mean-variance (MV) suffer from estimation error and lack of diversity. Alternatives, like mean-entropy (ME) or mean-variance-entropy (MVE) portfolio models focus independently on the issue of either a proper risk measure or the diversity. In this paper, we propose an asset allocation model that compromise between risk of historical data and future uncertainty. In the new model, entropy is presented as a nonparametric risk measure as well as an index of diversity. Our empirical evaluation with a variety of performance measures shows that this model has better out-of-sample performances and lower portfolio turnover than its competitors.
Non-Parametric Estimation of Correlation Functions
DEFF Research Database (Denmark)
Brincker, Rune; Rytter, Anders; Krenk, Steen
In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are pointed...... out, and methods to prevent bias are presented. The techniques are evaluated by comparing their speed and accuracy on the simple case of estimating auto-correlation functions for the response of a single degree-of-freedom system loaded with white noise....
Lottery spending: a non-parametric analysis.
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Lottery spending: a non-parametric analysis.
Directory of Open Access Journals (Sweden)
Skip Garibaldi
Full Text Available We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
Preliminary results on nonparametric facial occlusion detection
Directory of Open Access Journals (Sweden)
Daniel LÓPEZ SÁNCHEZ
2016-10-01
Full Text Available The problem of face recognition has been extensively studied in the available literature, however, some aspects of this field require further research. The design and implementation of face recognition systems that can efficiently handle unconstrained conditions (e.g. pose variations, illumination, partial occlusion... is still an area under active research. This work focuses on the design of a new nonparametric occlusion detection technique. In addition, we present some preliminary results that indicate that the proposed technique might be useful to face recognition systems, allowing them to dynamically discard occluded face parts.
Trend of monthly temperature and daily extreme temperature during 1951-2012 in New Zealand
Caloiero, Tommaso
2017-07-01
Among several variables affecting climate change and climate variability, temperature plays a crucial role in the process because its variations in monthly and extreme values can impact on the global hydrologic cycle and energy balance through thermal forcing. In this study, an analysis of temperature data has been performed over 22 series observed in New Zealand. In particular, to detect possible trends in the time series, the Mann-Kendall non-parametric test was first applied at monthly scale and then to several indices of extreme daily temperatures computed since 1951. The results showed a positive trend in both the maximum and the minimum temperatures, in particular, in the autumn-winter period. This increase has been evaluated faster in maximum temperature than in minimum one. The trend analysis of the temperature indices suggests that there has been an increase in the frequency and intensity of hot extremes, while most of the cold extremes showed a downward tendency.
Minaei, Masoud; Irannezhad, Masoud
2016-10-01
This study analyses spatio-temporal trends in precipitation, temperature, and river discharge in the northeast of Iran during recent decades (1953-2013). The Pettitt, SNHT, Buishand, Box-Pierce, Ljung-Box, and McLeod-Li methods were applied to examine homogeneity in time series studied. The nonparametric Mann-Kendall and Sen's slope estimator tests were used to detect possible significant (p Iran during 1953-2013 are in response to warming temperatures, which increase the rate of evapotranspiration. Differences between the results of our comprehensive large-scale study and those of previous researches confirm the necessity for more model-based local studies on climatic and environmental changes across the northeast of Iran.
Nonparametric Estimation of Distributions in Random Effects Models
Hart, Jeffrey D.
2011-01-01
We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.
Jang, Eunice Eunhee; Roussos, Louis
2007-01-01
This article reports two studies to illustrate methodologies for conducting a conditional covariance-based nonparametric dimensionality assessment using data from two forms of the Test of English as a Foreign Language (TOEFL). Study 1 illustrates how to assess overall dimensionality of the TOEFL including all three subtests. Study 2 is aimed at…
Bayesian Nonparametric Clustering for Positive Definite Matrices.
Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos
2016-05-01
Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.
Comparison of reliability techniques of parametric and non-parametric method
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C. Kalaiselvan
2016-06-01
Full Text Available Reliability of a product or system is the probability that the product performs adequately its intended function for the stated period of time under stated operating conditions. It is function of time. The most widely used nano ceramic capacitor C0G and X7R is used in this reliability study to generate the Time-to failure (TTF data. The time to failure data are identified by Accelerated Life Test (ALT and Highly Accelerated Life Testing (HALT. The test is conducted at high stress level to generate more failure rate within the short interval of time. The reliability method used to convert accelerated to actual condition is Parametric method and Non-Parametric method. In this paper, comparative study has been done for Parametric and Non-Parametric methods to identify the failure data. The Weibull distribution is identified for parametric method; Kaplan–Meier and Simple Actuarial Method are identified for non-parametric method. The time taken to identify the mean time to failure (MTTF in accelerating condition is the same for parametric and non-parametric method with relative deviation.
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
Local Component Analysis for Nonparametric Bayes Classifier
Khademi, Mahmoud; safayani, Meharn
2010-01-01
The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with co...
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Nonparametric Maximum Entropy Estimation on Information Diagrams
Martin, Elliot A; Meinke, Alexander; Děchtěrenko, Filip; Davidsen, Jörn
2016-01-01
Maximum entropy estimation is of broad interest for inferring properties of systems across many different disciplines. In this work, we significantly extend a technique we previously introduced for estimating the maximum entropy of a set of random discrete variables when conditioning on bivariate mutual informations and univariate entropies. Specifically, we show how to apply the concept to continuous random variables and vastly expand the types of information-theoretic quantities one can condition on. This allows us to establish a number of significant advantages of our approach over existing ones. Not only does our method perform favorably in the undersampled regime, where existing methods fail, but it also can be dramatically less computationally expensive as the cardinality of the variables increases. In addition, we propose a nonparametric formulation of connected informations and give an illustrative example showing how this agrees with the existing parametric formulation in cases of interest. We furthe...
Nonparametric estimation of employee stock options
Institute of Scientific and Technical Information of China (English)
FU Qiang; LIU Li-an; LIU Qian
2006-01-01
We proposed a new model to price employee stock options (ESOs). The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modify BlackScholes formula. The model overcomes the limits of Black-Scholes formula in handling option prices with varied volatility. It disposes the effects of ESOs self-characteristics such as non-tradability, the longer term for expiration, the early exercise feature, the restriction on shorting selling and the employee's risk aversion on risk neutral pricing condition, and can be applied to ESOs valuation with the explanatory variable in no matter the certainty case or random case.
On Parametric (and Non-Parametric Variation
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Neil Smith
2009-11-01
Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
A nonparametric dynamic additive regression model for longitudinal data
DEFF Research Database (Denmark)
Martinussen, Torben; Scheike, Thomas H.
2000-01-01
dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models......dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models...
Nonparametric Bayesian inference for multidimensional compound Poisson processes
S. Gugushvili; F. van der Meulen; P. Spreij
2015-01-01
Given a sample from a discretely observed multidimensional compound Poisson process, we study the problem of nonparametric estimation of its jump size density r0 and intensity λ0. We take a nonparametric Bayesian approach to the problem and determine posterior contraction rates in this context, whic
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
施沛德; 王海燕; 张利华
2000-01-01
For regression analysis, some useful Information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literat黵e, but the optimal rates of global convergence have not been obtained yet. Because of the possible Information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression f unction based on right-censored response data, and proves, under some regularity condi-tions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtai
Analysis of spatial and temporal rainfall trends in Sicily during the 1921-2012 period
Liuzzo, Lorena; Bono, Enrico; Sammartano, Vincenzo; Freni, Gabriele
2016-10-01
Precipitation patterns worldwide are changing under the effects of global warming. The impacts of these changes could dramatically affect the hydrological cycle and, consequently, the availability of water resources. In order to improve the quality and reliability of forecasting models, it is important to analyse historical precipitation data to account for possible future changes. For these reasons, a large number of studies have recently been carried out with the aim of investigating the existence of statistically significant trends in precipitation at different spatial and temporal scales. In this paper, the existence of statistically significant trends in rainfall from observational datasets, which were measured by 245 rain gauges over Sicily (Italy) during the 1921-2012 period, was investigated. Annual, seasonal and monthly time series were examined using the Mann-Kendall non-parametric statistical test to detect statistically significant trends at local and regional scales, and their significance levels were assessed. Prior to the application of the Mann-Kendall test, the historical dataset was completed using a geostatistical spatial interpolation technique, the residual ordinary kriging, and then processed to remove the influence of serial correlation on the test results, applying the procedure of trend-free pre-whitening. Once the trends at each site were identified, the spatial patterns of the detected trends were examined using spatial interpolation techniques. Furthermore, focusing on the 30 years from 1981 to 2012, the trend analysis was repeated with the aim of detecting short-term trends or possible changes in the direction of the trends. Finally, the effect of climate change on the seasonal distribution of rainfall during the year was investigated by analysing the trend in the precipitation concentration index. The application of the Mann-Kendall test to the rainfall data provided evidence of a general decrease in precipitation in Sicily during the
Roshan, Gholamreza; Yousefi, Robabe; Kovács, Attila; Matzarakis, Andreas
2016-10-01
As a preliminary and major step for land use planning of the coming years, the study of variability of the past decades' climatic conditions with comprehensive indicators is of high importance. Given the fact that one of the affected areas by climatic change includes variability of thermal comfort, this study uses the physiologically equivalent temperature (PET) to identify and evaluate bioclimatic conditions of 40 meteorological stations in Iran. In this study, PET changes for the period of 1960 to 2010 are analyzed, with the use of Mann-Kendall non-parametric test and Pearson parametric method. The study focuses particularly on the diversity in spatio-temporal distribution of Iran's bioclimatic conditions. The findings show that the mean frequency percentage of days with comfort is 12.9 % according to the total number of selected stations. The maximum and minimum frequency percentage with values of 17.4 and 10.3 belong to Kerman and Chabahar stations, respectively. The findings of long-term trend analysis for the period of 1960-2010 show that 55 % of the stations have significant increasing trend in terms of thermal comfort class based on the Pearson method, while it is 40 % based on Mann-Kendall test. The results indicate that the highest frequency of days with thermal comfort in the southern coasts of Iran relates to the end of autumn and winter, nevertheless, such ideal conditions for the coastal cities of Caspian Sea and even central stations of Iran relate to mid-spring and mid-autumn. Late summer and early autumn along with late spring can be identified as the most ideal times in the west and northwest part of Iran. In addition, the most important inhibiting factors of thermal comfort prove to be different across the regions of Iran. For instance, in the southern coasts, warm to very hot bioclimatic events and in the west and northwest regions, cold to very cold conditions turn out to be the most important inhibiting factors. When considering the variations
National Research Council Canada - National Science Library
Arbel, Julyan; King, Catherine K; Raymond, Ben; Winsley, Tristrom; Mengersen, Kerrie L
2015-01-01
...‐species toxicity tests. In this study, we apply a Bayesian nonparametric model to a soil microbial data set acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica...
Nonparametric methods in actigraphy: An update
Directory of Open Access Journals (Sweden)
Bruno S.B. Gonçalves
2014-09-01
Full Text Available Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm results for each time interval. Simulated data showed that (1 synchronization analysis depends on sample size, and (2 fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization.
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
Nonparametric methods in actigraphy: An update
Gonçalves, Bruno S.B.; Cavalcanti, Paula R.A.; Tavares, Gracilene R.; Campos, Tania F.; Araujo, John F.
2014-01-01
Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm) results for each time interval. Simulated data showed that (1) synchronization analysis depends on sample size, and (2) fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization. PMID:26483921
Jiang, Chong; Li, Daiqing; Gao, Yanni; Liu, Wenfeng; Zhang, Linbo
2016-05-01
Under the impacts of climate variability and human activities, there is violent fluctuation for streamflow in the large basins in China. Therefore, it is crucial to separate the impacts of climate variability and human activities on streamflow fluctuation for better water resources planning and management. In this study, the Three Rivers Headwater Region (TRHR) was chosen as the study area. Long-term hydrological data for the TRHR were collected in order to investigate the changes in annual runoff during the period of 1956-2012. The nonparametric Mann-Kendall test, moving t test, Pettitt test, Mann-Kendall-Sneyers test, and the cumulative anomaly curve were used to identify trends and change points in the hydro-meteorological variables. Change point in runoff was identified in the three basins, which respectively occurred around the years 1989 and 1993, dividing the long-term runoff series into a natural period and a human-induced period. Then, the hydrologic sensitivity analysis method was employed to evaluate the effects of climate variability and human activities on mean annual runoff for the human-induced period based on precipitation and potential evapotranspiration. In the human-induced period, climate variability was the main factor that increased (reduced) runoff in LRB and YARB (YRB) with contribution of more than 90 %, while the increasing (decreasing) percentage due to human activities only accounted for less than 10 %, showing that runoff in the TRHR is more sensitive to climate variability than human activities. The intra-annual distribution of runoff shifted gradually from a double peak pattern to a single peak pattern, which was mainly influenced by atmospheric circulation in the summer and autumn. The inter-annual variation in runoff was jointly controlled by the East Asian monsoon, the westerly, and Tibetan Plateau monsoons.
Jiang, Chong; Li, Daiqing; Gao, Yanni; Liu, Wenfeng; Zhang, Linbo
2017-07-01
Under the impacts of climate variability and human activities, there is violent fluctuation for streamflow in the large basins in China. Therefore, it is crucial to separate the impacts of climate variability and human activities on streamflow fluctuation for better water resources planning and management. In this study, the Three Rivers Headwater Region (TRHR) was chosen as the study area. Long-term hydrological data for the TRHR were collected in order to investigate the changes in annual runoff during the period of 1956-2012. The nonparametric Mann-Kendall test, moving t test, Pettitt test, Mann-Kendall-Sneyers test, and the cumulative anomaly curve were used to identify trends and change points in the hydro-meteorological variables. Change point in runoff was identified in the three basins, which respectively occurred around the years 1989 and 1993, dividing the long-term runoff series into a natural period and a human-induced period. Then, the hydrologic sensitivity analysis method was employed to evaluate the effects of climate variability and human activities on mean annual runoff for the human-induced period based on precipitation and potential evapotranspiration. In the human-induced period, climate variability was the main factor that increased (reduced) runoff in LRB and YARB (YRB) with contribution of more than 90 %, while the increasing (decreasing) percentage due to human activities only accounted for less than 10 %, showing that runoff in the TRHR is more sensitive to climate variability than human activities. The intra-annual distribution of runoff shifted gradually from a double peak pattern to a single peak pattern, which was mainly influenced by atmospheric circulation in the summer and autumn. The inter-annual variation in runoff was jointly controlled by the East Asian monsoon, the westerly, and Tibetan Plateau monsoons.
Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations
Gugushvili, S.; Spreij, P.
2014-01-01
We consider nonparametric Bayesian estimation of the drift coefficient of a multidimensional stochastic differential equation from discrete-time observations on the solution of this equation. Under suitable regularity conditions, we establish posterior consistency in this context.
A non-parametric approach to investigating fish population dynamics
National Research Council Canada - National Science Library
Cook, R.M; Fryer, R.J
2001-01-01
.... Using a non-parametric model for the stock-recruitment relationship it is possible to avoid defining specific functions relating recruitment to stock size while also providing a natural framework to model process error...
Nonparametric Bayesian Modeling for Automated Database Schema Matching
Energy Technology Data Exchange (ETDEWEB)
Ferragut, Erik M [ORNL; Laska, Jason A [ORNL
2015-01-01
The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.
PV power forecast using a nonparametric PV model
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...
Trend of suspended sediment load in the Velika Morava River in the period 1967-2007
Directory of Open Access Journals (Sweden)
Mustafić Sanja
2014-01-01
Full Text Available The paper is concerned with identifying changes in the time series of discharge (Q, suspended sediment concentration (SSC and sediment load (Qs of the Velika Morva River. The catchment area on farthest hydrological profile Ljubičevski most on Velika Morava River is approximately 35,496 km2. In this profile were carried out daily measurements of flow and concetration of silt in the period from 1967 to 2007. Average perennial transport of suspended sediment is 2.57ˣ106 t (72.4 t/km2/yr and ranged from 0.17ˣ106 t (4.8 t/km2/yr to 10.02ˣ106 t (282.2 t/km2/yr. Trends determined for Q, SSC and Qs are statistically obtained using the non-parametric Mann-Kendall test. Results of Mann-Kendall test show that Q has a slight declining trend of annual values which do not show statistical significance. Decline in trendline SSC and Qs is a significant at the level of 0.01. Calculating the standardized regression coefficients, it was found that the relative impact of SSC on sediment load is 3.1 time higher than the impact of discharge. From 1967 to 2007 the average decrease in sediment load at the mouth of the Velika Morava into the Danube was 3.1 t/km2/yr. [Projekat Ministarstva nauke Republike Srbije, br. 43007: The Research on Climate Change Influences on Environment: Influence Monitoring, Adaptation and Mitigation, subproject No. 9: Torrential Floods Frequency, Soil and Water Degradation as the Consequence of Global Changes
Spline Nonparametric Regression Analysis of Stress-Strain Curve of Confined Concrete
Directory of Open Access Journals (Sweden)
Tavio Tavio
2008-01-01
Full Text Available Due to enormous uncertainties in confinement models associated with the maximum compressive strength and ductility of concrete confined by rectilinear ties, the implementation of spline nonparametric regression analysis is proposed herein as an alternative approach. The statistical evaluation is carried out based on 128 large-scale column specimens of either normal-or high-strength concrete tested under uniaxial compression. The main advantage of this kind of analysis is that it can be applied when the trend of relation between predictor and response variables are not obvious. The error in the analysis can, therefore, be minimized so that it does not depend on the assumption of a particular shape of the curve. This provides higher flexibility in the application. The results of the statistical analysis indicates that the stress-strain curves of confined concrete obtained from the spline nonparametric regression analysis proves to be in good agreement with the experimental curves available in literatures
Applications of non-parametric statistics and analysis of variance on sample variances
Myers, R. H.
1981-01-01
Nonparametric methods that are available for NASA-type applications are discussed. An attempt will be made here to survey what can be used, to attempt recommendations as to when each would be applicable, and to compare the methods, when possible, with the usual normal-theory procedures that are avavilable for the Gaussion analog. It is important here to point out the hypotheses that are being tested, the assumptions that are being made, and limitations of the nonparametric procedures. The appropriateness of doing analysis of variance on sample variances are also discussed and studied. This procedure is followed in several NASA simulation projects. On the surface this would appear to be reasonably sound procedure. However, difficulties involved center around the normality problem and the basic homogeneous variance assumption that is mase in usual analysis of variance problems. These difficulties discussed and guidelines given for using the methods.
Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida
Sayemuzzaman, M.; Ye, M.
2015-12-01
The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface
A New Non-Parametric Approach to Galaxy Morphological Classification
Lotz, J M; Madau, P; Lotz, Jennifer M.; Primack, Joel; Madau, Piero
2003-01-01
We present two new non-parametric methods for quantifying galaxy morphology: the relative distribution of the galaxy pixel flux values (the Gini coefficient or G) and the second-order moment of the brightest 20% of the galaxy's flux (M20). We test the robustness of G and M20 to decreasing signal-to-noise and spatial resolution, and find that both measures are reliable to within 10% at average signal-to-noise per pixel greater than 3 and resolutions better than 1000 pc and 500 pc, respectively. We have measured G and M20, as well as concentration (C), asymmetry (A), and clumpiness (S) in the rest-frame near-ultraviolet/optical wavelengths for 150 bright local "normal" Hubble type galaxies (E-Sd) galaxies and 104 0.05 < z < 0.25 ultra-luminous infrared galaxies (ULIRGs).We find that most local galaxies follow a tight sequence in G-M20-C, where early-types have high G and C and low M20 and late-type spirals have lower G and C and higher M20. The majority of ULIRGs lie above the normal galaxy G-M20 sequence...
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
For regression analysis, some useful information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literature, but the optimal rates of global convergence have not been obtained yet. Because of the possible information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression function based on right_censored response data, and proves, under some regularity conditions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtain the asymptotic optimality of AIC, AICC, GCV, Cp and FPE criteria in the process of selecting the parameters.
Rediscovery of Good-Turing estimators via Bayesian nonparametrics.
Favaro, Stefano; Nipoti, Bernardo; Teh, Yee Whye
2016-03-01
The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good-Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library.
Comparing parametric and nonparametric regression methods for panel data
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb-Douglas and......We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs...... rejects both the Cobb-Douglas and the Translog functional form, while a recently developed nonparametric kernel regression method with a fully nonparametric panel data specification delivers plausible results. On average, the nonparametric regression results are similar to results that are obtained from...
Directory of Open Access Journals (Sweden)
Rita de C. F. Damé
2013-04-01
Full Text Available Scientific evidence on climate changes at global level has gained increasing interest in the scientific community in general. The impacts of climate change as well as anthropogenic actions may cause errors in hydro-agricultural projects existent in the watershed under study. This study aimed to identify the presence or absence of trend in total annual precipitation series of the watershed of the Mirim Lagoon, state of Rio Grande do Sul-RS / Brazil / Uruguay (Brazilian side as well as to detect the period in which they occurred. For that, it was analyzed the precipitation data belonging to 14 weather stations. To detect the existence of monotonic trend and change points, it was used the nonparametric tests of Mann-Kendall and Mann-Whitney, the "t" test of Student for two samples of unpaired data (parametric, as well as the technique of progressive mean. The Weather Station 3152014 (Pelotas presented changes in the trend in the series of annual precipitation in the period from 1953 to 2007. The methodologies that use subdivided series were more efficient in detecting change in trend when compared with the Mann-Kendall test, which uses the complete series (from 1921 to 2007.Evidências científicas sobre mudanças climáticas em nível mundial vêm despertando interesses crescentes na comunidade científica. Os impactos causados pelas alterações climáticas e ações antropogênicas podem ocasionar erros nos projetos hidroagrícolas na bacia hidrográfica em estudo. O presente trabalho foi conduzido com o objetivo de identificar a presença ou a ausência de tendência nas séries de precipitação total anual da bacia hidrográfica da lagoa Mirim- RS/Brasil/Uruguai (lado brasileiro, bem como detectar o período em que estas ocorreram. Para tanto, foram analisados os dados de precipitação pertencentes a 14 estações climatológicas. Para detectar a existência de tendência monotônica e de pontos de mudança, foram utilizados os testes n
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Directory of Open Access Journals (Sweden)
Saerom Park
Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Park, Saerom; Lee, Jaewook; Son, Youngdoo
2016-01-01
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Akhtar, Naveed; Mian, Ajmal
2017-10-03
We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.
Nonparametric estimation of a convex bathtub-shaped hazard function.
Jankowski, Hanna K; Wellner, Jon A
2009-11-01
In this paper, we study the nonparametric maximum likelihood estimator (MLE) of a convex hazard function. We show that the MLE is consistent and converges at a local rate of n(2/5) at points x(0) where the true hazard function is positive and strictly convex. Moreover, we establish the pointwise asymptotic distribution theory of our estimator under these same assumptions. One notable feature of the nonparametric MLE studied here is that no arbitrary choice of tuning parameter (or complicated data-adaptive selection of the tuning parameter) is required.
Non-parametric analysis of rating transition and default data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...... but that this dependence vanishes after 2-3 years....
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects
Qian, Minghui; Hu, Ridong; Chen, Jianwei
2016-01-01
Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…
Uniform Consistency for Nonparametric Estimators in Null Recurrent Time Series
DEFF Research Database (Denmark)
Gao, Jiti; Kanaya, Shin; Li, Degui
2015-01-01
This paper establishes uniform consistency results for nonparametric kernel density and regression estimators when time series regressors concerned are nonstationary null recurrent Markov chains. Under suitable regularity conditions, we derive uniform convergence rates of the estimators. Our...... results can be viewed as a nonstationary extension of some well-known uniform consistency results for stationary time series....
Non-parametric Bayesian inference for inhomogeneous Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
With reference to a specific data set, we consider how to perform a flexible non-parametric Bayesian analysis of an inhomogeneous point pattern modelled by a Markov point process, with a location dependent first order term and pairwise interaction only. A priori we assume that the first order term...
Investigating the cultural patterns of corruption: A nonparametric analysis
Halkos, George; Tzeremes, Nickolaos
2011-01-01
By using a sample of 77 countries our analysis applies several nonparametric techniques in order to reveal the link between national culture and corruption. Based on Hofstede’s cultural dimensions and the corruption perception index, the results reveal that countries with higher levels of corruption tend to have higher power distance and collectivism values in their society.
Coverage Accuracy of Confidence Intervals in Nonparametric Regression
Institute of Scientific and Technical Information of China (English)
Song-xi Chen; Yong-song Qin
2003-01-01
Point-wise confidence intervals for a nonparametric regression function with random design points are considered. The confidence intervals are those based on the traditional normal approximation and the empirical likelihood. Their coverage accuracy is assessed by developing the Edgeworth expansions for the coverage probabilities. It is shown that the empirical likelihood confidence intervals are Bartlett correctable.
Non-parametric analysis of rating transition and default data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move...
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
A nonparametric approach for relevance determination
Shahbaba, Babak
2010-01-01
The problem of evaluating a large number of factors in terms of their relevance to an outcome of interest arises in many research areas such as genetics, image processing, astrophysics, and neuroscience. In this paper, we argue that treating such problems as large-scale hypothesis testing does not reflect the usual motivation behind these studies, which is to select a subset of promising factors for further investigation. and leads investigators to rely on arbitrary selection mechanisms (e.g., setting the false discovery rate at 0.05) or unrealistic loss functions. Moreover, while we might be able to justify simplifying assumptions (e.g., parametric distributional forms for test statistics under the null and alternative hypotheses) for classic hypothesis testing situations (i.e., one hypothesis at a time), generalizing such assumptions to large-scale studies is restrictive and unnecessary. In accordance with the objective of such studies, we propose to treat them as relevance determination problems. This way,...
Directory of Open Access Journals (Sweden)
Akhtar R. Siddique
2000-03-01
Full Text Available This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices. This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices.
Zamani, Reza; Mirabbasi, Rasoul; Abdollahi, Sajjad; Jhajharia, Deepak
2017-07-01
Due to the substantial decrease of water resources as well as the increase in demand and climate change phenomenon, analyzing the trend of hydrological parameters is of paramount importance. In the present study, investigations were carried out to identify the trends in streamflow at 20 hydrometric stations and 11 rainfall gauging stations located in Karkheh River Basin (KRB), Iran, in monthly, seasonal, and annual time scales during the last 38 years from 1974 to 2011. This study has been conducted using two versions of Mann-Kendall tests, including (i) Mann-Kendall test by considering all the significant autocorrelation structure (MK3) and (ii) Mann-Kendall test by considering LTP and Hurst coefficient (MK4). The results indicate that the KRB streamflow trend (using both test versions) has decreased in all three time scales. There is a significant decreasing trend in 78 and 73 % of the monthly cases using the MK3 and MK4 tests, respectively, while these percentages changed to 80 and 70 % on seasonal and annual time scales, respectively. Investigation of the trend line slope using Theil-Sen's estimator showed a negative trend in all three time scales. The use of MK4 test instead of the MK3 test has caused a decrease in the significance level of Mann-Kendall Z-statistic values. The results of the precipitation trends indicate both increasing and decreasing trends. Also, the correlation between the area average streamflow and precipitation shows a strong correlation in annual time scale in the KRB.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben
2017-06-06
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
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
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....
Non-parametric trend analysis of water quality data of rivers in Kansas
Yu, Y.-S.; Zou, S.; Whittemore, D.
1993-01-01
Surface water quality data for 15 sampling stations in the Arkansas, Verdigris, Neosho, and Walnut river basins inside the state of Kansas were analyzed to detect trends (or lack of trends) in 17 major constituents by using four different non-parametric methods. The results show that concentrations of specific conductance, total dissolved solids, calcium, total hardness, sodium, potassium, alkalinity, sulfate, chloride, total phosphorus, ammonia plus organic nitrogen, and suspended sediment generally have downward trends. Some of the downward trends are related to increases in discharge, while others could be caused by decreases in pollution sources. Homogeneity tests show that both station-wide trends and basinwide trends are non-homogeneous. ?? 1993.
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
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...
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
other aspects, the properties of a method for parameter estimation in stochastic differential equations is considered within the field of heat dynamics of buildings. In the second paper a lack-of-fit test for stochastic differential equations is presented. The test can be applied to both linear and non-linear...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...... stochastic differential equations. Some applications are presented in the papers. In the summary report references are made to a number of other applications. Resumé på dansk: Nærværende afhandling består af ti artikler publiceret i perioden 1996-1999 samt et sammendrag og en perspektivering heraf. I...
Comparison of Rank Analysis of Covariance and Nonparametric Randomized Blocks Analysis.
Porter, Andrew C.; McSweeney, Maryellen
The relative power of three possible experimental designs under the condition that data is to be analyzed by nonparametric techniques; the comparison of the power of each nonparametric technique to its parametric analogue; and the comparison of relative powers using nonparametric and parametric techniques are discussed. The three nonparametric…
Nonparametric Inference for the Cosmic Microwave Background
Genovese, C R; Nichol, R C; Arjunwadkar, M; Wasserman, L; Genovese, Christopher R.; Miller, Christopher J.; Nichol, Robert C.; Arjunwadkar, Mihir; Wasserman, Larry
2004-01-01
The Cosmic Microwave Background (CMB), which permeates the entire Universe, is the radiation left over from just 380,000 years after the Big Bang. On very large scales, the CMB radiation field is smooth and isotropic, but the existence of structure in the Universe - stars, galaxies, clusters of galaxies - suggests that the field should fluctuate on smaller scales. Recent observations, from the Cosmic Microwave Background Explorer to the Wilkinson Microwave Anisotropy Project, have strikingly confirmed this prediction. CMB fluctuations provide clues to the Universe's structure and composition shortly after the Big Bang that are critical for testing cosmological models. For example, CMB data can be used to determine what portion of the Universe is composed of ordinary matter versus the mysterious dark matter and dark energy. To this end, cosmologists usually summarize the fluctuations by the power spectrum, which gives the variance as a function of angular frequency. The spectrum's shape, and in particular the ...
Non-parametric estimation of Fisher information from real data
Shemesh, Omri Har; Miñano, Borja; Hoekstra, Alfons G; Sloot, Peter M A
2015-01-01
The Fisher Information matrix is a widely used measure for applications ranging from statistical inference, information geometry, experiment design, to the study of criticality in biological systems. Yet there is no commonly accepted non-parametric algorithm to estimate it from real data. In this rapid communication we show how to accurately estimate the Fisher information in a nonparametric way. We also develop a numerical procedure to minimize the errors by choosing the interval of the finite difference scheme necessary to compute the derivatives in the definition of the Fisher information. Our method uses the recently published "Density Estimation using Field Theory" algorithm to compute the probability density functions for continuous densities. We use the Fisher information of the normal distribution to validate our method and as an example we compute the temperature component of the Fisher Information Matrix in the two dimensional Ising model and show that it obeys the expected relation to the heat capa...
International Conference on Robust Rank-Based and Nonparametric Methods
McKean, Joseph
2016-01-01
The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...
Nonparametric instrumental regression with non-convex constraints
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
Combined parametric-nonparametric identification of block-oriented systems
Mzyk, Grzegorz
2014-01-01
This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.
Estimation of Stochastic Volatility Models by Nonparametric Filtering
DEFF Research Database (Denmark)
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases......A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Nonparametric Regression Estimation for Multivariate Null Recurrent Processes
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.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Right-Censored Nonparametric Regression: A Comparative Simulation Study
Directory of Open Access Journals (Sweden)
Dursun Aydın
2016-11-01
Full Text Available This paper introduces the operating of the selection criteria for right-censored nonparametric regression using smoothing spline. In order to transform the response variable into a variable that contains the right-censorship, we used the KaplanMeier weights proposed by [1], and [2]. The major problem in smoothing spline method is to determine a smoothing parameter to obtain nonparametric estimates of the regression function. In this study, the mentioned parameter is chosen based on censored data by means of the criteria such as improved Akaike information criterion (AICc, Bayesian (or Schwarz information criterion (BIC and generalized crossvalidation (GCV. For this purpose, a Monte-Carlo simulation study is carried out to illustrate which selection criterion gives the best estimation for censored data.
Poverty and life cycle effects: A nonparametric analysis for Germany
Stich, Andreas
1996-01-01
Most empirical studies on poverty consider the extent of poverty either for the entire society or for separate groups like elderly people.However, these papers do not show what the situation looks like for persons of a certain age. In this paper poverty measures depending on age are derived using the joint density of income and age. The density is nonparametrically estimated by weighted Gaussian kernel density estimation. Applying the conditional density of income to several poverty measures ...
Nonparametric estimation of Fisher information from real data
Har-Shemesh, Omri; Quax, Rick; Miñano, Borja; Hoekstra, Alfons G.; Sloot, Peter M. A.
2016-02-01
The Fisher information matrix (FIM) is a widely used measure for applications including statistical inference, information geometry, experiment design, and the study of criticality in biological systems. The FIM is defined for a parametric family of probability distributions and its estimation from data follows one of two paths: either the distribution is assumed to be known and the parameters are estimated from the data or the parameters are known and the distribution is estimated from the data. We consider the latter case which is applicable, for example, to experiments where the parameters are controlled by the experimenter and a complicated relation exists between the input parameters and the resulting distribution of the data. Since we assume that the distribution is unknown, we use a nonparametric density estimation on the data and then compute the FIM directly from that estimate using a finite-difference approximation to estimate the derivatives in its definition. The accuracy of the estimate depends on both the method of nonparametric estimation and the difference Δ θ between the densities used in the finite-difference formula. We develop an approach for choosing the optimal parameter difference Δ θ based on large deviations theory and compare two nonparametric density estimation methods, the Gaussian kernel density estimator and a novel density estimation using field theory method. We also compare these two methods to a recently published approach that circumvents the need for density estimation by estimating a nonparametric f divergence and using it to approximate the FIM. We use the Fisher information of the normal distribution to validate our method and as a more involved example we compute the temperature component of the FIM in the two-dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.
A Bayesian nonparametric method for prediction in EST analysis
Directory of Open Access Journals (Sweden)
Prünster Igor
2007-09-01
Full Text Available Abstract Background Expressed sequence tags (ESTs analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library. Results In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b the number of new unique genes to be observed in a future sample; c the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries, previously studied with frequentist methods, are analyzed in detail. Conclusion The Bayesian nonparametric approach we undertake yields valuable tools for gene capture and prediction in EST libraries. The estimators we obtain do not feature the kind of drawbacks associated with frequentist estimators and are reliable for any size of the additional sample.
Fusion of Hard and Soft Information in Nonparametric Density Estimation
2015-06-10
estimation exploiting, in concert, hard and soft information. Although our development, theoretical and numerical, makes no distinction based on sample...Fusion of Hard and Soft Information in Nonparametric Density Estimation∗ Johannes O. Royset Roger J-B Wets Department of Operations Research...univariate density estimation in situations when the sample ( hard information) is supplemented by “soft” information about the random phenomenon. These
Nonparametric estimation for hazard rate monotonously decreasing system
Institute of Scientific and Technical Information of China (English)
Han Fengyan; Li Weisong
2005-01-01
Estimation of density and hazard rate is very important to the reliability analysis of a system. In order to estimate the density and hazard rate of a hazard rate monotonously decreasing system, a new nonparametric estimator is put forward. The estimator is based on the kernel function method and optimum algorithm. Numerical experiment shows that the method is accurate enough and can be used in many cases.
Non-parametric versus parametric methods in environmental sciences
Directory of Open Access Journals (Sweden)
Muhammad Riaz
2016-01-01
Full Text Available This current report intends to highlight the importance of considering background assumptions required for the analysis of real datasets in different disciplines. We will provide comparative discussion of parametric methods (that depends on distributional assumptions (like normality relative to non-parametric methods (that are free from many distributional assumptions. We have chosen a real dataset from environmental sciences (one of the application areas. The findings may be extended to the other disciplines following the same spirit.
A non-parametric Bayesian approach for clustering and tracking non-stationarities of neural spikes.
Shalchyan, Vahid; Farina, Dario
2014-02-15
Neural spikes from multiple neurons recorded in a multi-unit signal are usually separated by clustering. Drifts in the position of the recording electrode relative to the neurons over time cause gradual changes in the position and shapes of the clusters, challenging the clustering task. By dividing the data into short time intervals, Bayesian tracking of the clusters based on Gaussian cluster model has been previously proposed. However, the Gaussian cluster model is often not verified for neural spikes. We present a Bayesian clustering approach that makes no assumptions on the distribution of the clusters and use kernel-based density estimation of the clusters in every time interval as a prior for Bayesian classification of the data in the subsequent time interval. The proposed method was tested and compared to Gaussian model-based approach for cluster tracking by using both simulated and experimental datasets. The results showed that the proposed non-parametric kernel-based density estimation of the clusters outperformed the sequential Gaussian model fitting in both simulated and experimental data tests. Using non-parametric kernel density-based clustering that makes no assumptions on the distribution of the clusters enhances the ability of tracking cluster non-stationarity over time with respect to the Gaussian cluster modeling approach. Copyright © 2013 Elsevier B.V. All rights reserved.
Kisi, Ozgur; Ay, Murat
2016-07-01
We thank discusser for his comments on our recent papers about application of innovative trend method (Şen, 2012) for water quality parameters (Kisi and Ay, 2014) and evaporation (Kisi, 2015) and are grateful for having the opportunity to explain and discuss the concerns raised. Illustration of the innovative Şen's trend method with some trend possibilities in the comments by discusser (in Fig. 3 in his discussion) has simple data seem to have normal distribution. However, some data may have highly skewed distribution and indicating trends in these data similar to the Figure 3 as suggested by discusser is impossible. Innovative trend analysis (Şen, 2012) of annual low flows for the duration 14-day (Q14) and 60-day (Q60) in Peterek and Dutdere stations are illustrated in Fig. 1 as an example. In Fig. 1, both half time series are sorted in ascending order. Therefore, Kisi and Ay (2014) provided "a general view" or "imaginative illustration" for the innovative Şen's trend method in Fig. 2 in their study. By this figure, general trend possibilities for any data having different distributions can be shown.
Rotondi, R.
2009-04-01
According to the unified scaling theory the probability distribution function of the recurrence time T is a scaled version of a base function and the average value of T can be used as a scale parameter for the distribution. The base function must belong to the scale family of distributions: tested on different catalogues and for different scale levels, for Corral (2005) the (truncated) generalized gamma distribution is the best model, for German (2006) the Weibull distribution. The scaling approach should overcome the difficulty of estimating distribution functions over small areas but theorical limitations and partial instability of the estimated distributions have been pointed out in the literature. Our aim is to analyze the recurrence time of strong earthquakes that occurred in the Italian territory. To satisfy the hypotheses of independence and identical distribution we have evaluated the times between events that occurred in each area of the Database of Individual Seismogenic Sources and then we have gathered them by eight tectonically coherent regions, each of them dominated by a well characterized geodynamic process. To solve problems like: paucity of data, presence of outliers and uncertainty in the choice of the functional expression for the distribution of t, we have followed a nonparametric approach (Rotondi (2009)) in which: (a) the maximum flexibility is obtained by assuming that the probability distribution is a random function belonging to a large function space, distributed as a stochastic process; (b) nonparametric estimation method is robust when the data contain outliers; (c) Bayesian methodology allows to exploit different information sources so that the model fitting may be good also to scarce samples. We have compared the hazard rates evaluated through the parametric and nonparametric approach. References Corral A. (2005). Mixing of rescaled data and Bayesian inference for earthquake recurrence times, Nonlin. Proces. Geophys., 12, 89
Application of the LSQR algorithm in non-parametric estimation of aerosol size distribution
He, Zhenzong; Qi, Hong; Lew, Zhongyuan; Ruan, Liming; Tan, Heping; Luo, Kun
2016-05-01
Based on the Least Squares QR decomposition (LSQR) algorithm, the aerosol size distribution (ASD) is retrieved in non-parametric approach. The direct problem is solved by the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law. An optimal wavelength selection method is developed to improve the retrieval accuracy of the ASD. The proposed optimal wavelength set is selected by the method which can make the measurement signals sensitive to wavelength and decrease the degree of the ill-condition of coefficient matrix of linear systems effectively to enhance the anti-interference ability of retrieval results. Two common kinds of monomodal and bimodal ASDs, log-normal (L-N) and Gamma distributions, are estimated, respectively. Numerical tests show that the LSQR algorithm can be successfully applied to retrieve the ASD with high stability in the presence of random noise and low susceptibility to the shape of distributions. Finally, the experimental measurement ASD over Harbin in China is recovered reasonably. All the results confirm that the LSQR algorithm combined with the optimal wavelength selection method is an effective and reliable technique in non-parametric estimation of ASD.
Passenger Flow Prediction of Subway Transfer Stations Based on Nonparametric Regression Model
Directory of Open Access Journals (Sweden)
Yujuan Sun
2014-01-01
Full Text Available Passenger flow is increasing dramatically with accomplishment of subway network system in big cities of China. As convergence nodes of subway lines, transfer stations need to assume more passengers due to amount transfer demand among different lines. Then, transfer facilities have to face great pressure such as pedestrian congestion or other abnormal situations. In order to avoid pedestrian congestion or warn the management before it occurs, it is very necessary to predict the transfer passenger flow to forecast pedestrian congestions. Thus, based on nonparametric regression theory, a transfer passenger flow prediction model was proposed. In order to test and illustrate the prediction model, data of transfer passenger flow for one month in XIDAN transfer station were used to calibrate and validate the model. By comparing with Kalman filter model and support vector machine regression model, the results show that the nonparametric regression model has the advantages of high accuracy and strong transplant ability and could predict transfer passenger flow accurately for different intervals.
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.
Panel data nonparametric estimation of production risk and risk preferences
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...... approaches for obtaining firm-specific measures of risk attitudes. We found that Polish dairy farmers are risk averse regarding production risk and price uncertainty. According to our results, Polish dairy farmers perceive the production risk as being more significant than the risk related to output price...
Digital spectral analysis parametric, non-parametric and advanced methods
Castanié, Francis
2013-01-01
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a
Nonparametric statistical structuring of knowledge systems using binary feature matches
DEFF Research Database (Denmark)
Mørup, Morten; Glückstad, Fumiko Kano; Herlau, Tue
2014-01-01
statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary......Structuring knowledge systems with binary features is often based on imposing a similarity measure and clustering objects according to this similarity. Unfortunately, such analyses can be heavily influenced by the choice of similarity measure. Furthermore, it is unclear at which level clusters have...
1st Conference of the International Society for Nonparametric Statistics
Lahiri, S; Politis, Dimitris
2014-01-01
This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for NonParametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI, and other organizations. M.G. Akritas, S.N. Lahiri, and D.N. Politis are the first executive committee members of ISNPS, and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao, and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world. The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the wo...
Non-parametric Morphologies of Mergers in the Illustris Simulation
Bignone, Lucas A; Sillero, Emanuel; Pedrosa, Susana E; Pellizza, Leonardo J; Lambas, Diego G
2016-01-01
We study non-parametric morphologies of mergers events in a cosmological context, using the Illustris project. We produce mock g-band images comparable to observational surveys from the publicly available Illustris simulation idealized mock images at $z=0$. We then measure non parametric indicators: asymmetry, Gini, $M_{20}$, clumpiness and concentration for a set of galaxies with $M_* >10^{10}$ M$_\\odot$. We correlate these automatic statistics with the recent merger history of galaxies and with the presence of close companions. Our main contribution is to assess in a cosmological framework, the empirically derived non-parametric demarcation line and average time-scales used to determine the merger rate observationally. We found that 98 per cent of galaxies above the demarcation line have a close companion or have experienced a recent merger event. On average, merger signatures obtained from the $G-M_{20}$ criteria anticorrelate clearly with the elapsing time to the last merger event. We also find that the a...
Genomic breeding value estimation using nonparametric additive regression models
Directory of Open Access Journals (Sweden)
Solberg Trygve
2009-01-01
Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization
Lee, Kyungbook; Song, Seok Goo
2016-10-01
Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events (M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.
A non-parametric framework for estimating threshold limit values
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.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb-Douglas a......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...... to estimate production functions without the specification of a functional form. Therefore, they avoid possible misspecification errors due to the use of an unsuitable functional form. In this paper, we use parametric and non-parametric methods to identify the optimal size of Polish crop farms...
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
Computing Economies of Scope Using Robust Partial Frontier Nonparametric Methods
Directory of Open Access Journals (Sweden)
Pedro Carvalho
2016-03-01
Full Text Available This paper proposes a methodology to examine economies of scope using the recent order-α nonparametric method. It allows us to investigate economies of scope by comparing the efficient order-α frontiers of firms that produce two or more goods with the efficient order-α frontiers of firms that produce only one good. To accomplish this, and because the order-α frontiers are irregular, we suggest to linearize them by the DEA estimator. The proposed methodology uses partial frontier nonparametric methods that are more robust than the traditional full frontier methods. By using a sample of 67 Portuguese water utilities for the period 2002–2008 and, also, a simulated sample, we prove the usefulness of the approach adopted and show that if only the full frontier methods were used, they would lead to different results. We found evidence of economies of scope in the provision of water supply and wastewater services simultaneously by water utilities in Portugal.
Bayesian nonparametric dictionary learning for compressed sensing MRI.
Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping
2014-12-01
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
DEFF Research Database (Denmark)
Effraimidis, Georgios; Dahl, Christian Møller
In this paper, we develop a fully nonparametric approach for the estimation of the cumulative incidence function with Missing At Random right-censored competing risks data. We obtain results on the pointwise asymptotic normality as well as the uniform convergence rate of the proposed nonparametric...... estimator. A simulation study that serves two purposes is provided. First, it illustrates in details how to implement our proposed nonparametric estimator. Secondly, it facilitates a comparison of the nonparametric estimator to a parametric counterpart based on the estimator of Lu and Liang (2008...
Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie
2015-09-01
Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems.
Takamizawa, Hisashi; Itoh, Hiroto; Nishiyama, Yutaka
2016-10-01
In order to understand neutron irradiation embrittlement in high fluence regions, statistical analysis using the Bayesian nonparametric (BNP) method was performed for the Japanese surveillance and material test reactor irradiation database. The BNP method is essentially expressed as an infinite summation of normal distributions, with input data being subdivided into clusters with identical statistical parameters, such as mean and standard deviation, for each cluster to estimate shifts in ductile-to-brittle transition temperature (DBTT). The clusters typically depend on chemical compositions, irradiation conditions, and the irradiation embrittlement. Specific variables contributing to the irradiation embrittlement include the content of Cu, Ni, P, Si, and Mn in the pressure vessel steels, neutron flux, neutron fluence, and irradiation temperatures. It was found that the measured shifts of DBTT correlated well with the calculated ones. Data associated with the same materials were subdivided into the same clusters even if neutron fluences were increased.
Comparison of non-parametric methods for ungrouping coarsely aggregated data
DEFF Research Database (Denmark)
Rizzi, Silvia; Thinggaard, Mikael; Engholm, Gerda
2016-01-01
Background Histograms are a common tool to estimate densities non-parametrically. They are extensively encountered in health sciences to summarize data in a compact format. Examples are age-specific distributions of death or onset of diseases grouped in 5-years age classes with an open-ended age...... methods for ungrouping count data. We compare the performance of two spline interpolation methods, two kernel density estimators and a penalized composite link model first via a simulation study and then with empirical data obtained from the NORDCAN Database. All methods analyzed can be used to estimate...... composite link model performs the best. Conclusion We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized...
Emura, Takeshi; Konno, Yoshihiko; Michimae, Hirofumi
2015-07-01
Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset.
Determining the Mass of Kepler-78b with Nonparametric Gaussian Process Estimation
Grunblatt, Samuel Kai; Howard, Andrew; Haywood, Raphaëlle
2016-01-01
Kepler-78b is a transiting planet that is 1.2 times the radius of Earth and orbits a young, active K dwarf every 8 hr. The mass of Kepler-78b has been independently reported by two teams based on radial velocity (RV) measurements using the HIRES and HARPS-N spectrographs. Due to the active nature of the host star, a stellar activity model is required to distinguish and isolate the planetary signal in RV data. Whereas previous studies tested parametric stellar activity models, we modeled this system using nonparametric Gaussian process (GP) regression. We produced a GP regression of relevant Kepler photometry. We then use the posterior parameter distribution for our photometric fit as a prior for our simultaneous GP + Keplerian orbit models of the RV data sets. We tested three simple kernel functions for our GP regressions. Based on a Bayesian likelihood analysis, we selected a quasi-periodic kernel model with GP hyperparameters coupled between the two RV data sets, giving a Doppler amplitude of 1.86 ± 0.25 m s-1 and supporting our belief that the correlated noise we are modeling is astrophysical. The corresponding mass of 1.87-0.26+0.27 ME is consistent with that measured in previous studies, and more robust due to our nonparametric signal estimation. Based on our mass and the radius measurement from transit photometry, Kepler-78b has a bulk density of 6.0-1.4+1.9 g cm-3. We estimate that Kepler-78b is 32% ± 26% iron using a two-component rock-iron model. This is consistent with an Earth-like composition, with uncertainty spanning Moon-like to Mercury-like compositions.
Robust Depth-Weighted Wavelet for Nonparametric Regression Models
Institute of Scientific and Technical Information of China (English)
Lu LIN
2005-01-01
In the nonpaxametric regression models, the original regression estimators including kernel estimator, Fourier series estimator and wavelet estimator are always constructed by the weighted sum of data, and the weights depend only on the distance between the design points and estimation points. As a result these estimators are not robust to the perturbations in data. In order to avoid this problem, a new nonparametric regression model, called the depth-weighted regression model, is introduced and then the depth-weighted wavelet estimation is defined. The new estimation is robust to the perturbations in data, which attains very high breakdown value close to 1/2. On the other hand, some asymptotic behaviours such as asymptotic normality are obtained. Some simulations illustrate that the proposed wavelet estimator is more robust than the original wavelet estimator and, as a price to pay for the robustness, the new method is slightly less efficient than the original method.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P
2016-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: 1. Deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, that not only remove limitations that seriously degrade the inference on large networks, but also reveal s...
Analyzing single-molecule time series via nonparametric Bayesian inference.
Hines, Keegan E; Bankston, John R; Aldrich, Richard W
2015-02-03
The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.
Prior processes and their applications nonparametric Bayesian estimation
Phadia, Eswar G
2016-01-01
This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and P...
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb......-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Nonparametric forecasting of low-dimensional dynamical systems.
Berry, Tyrus; Giannakis, Dimitrios; Harlim, John
2015-03-01
This paper presents a nonparametric modeling approach for forecasting stochastic dynamical systems on low-dimensional manifolds. The key idea is to represent the discrete shift maps on a smooth basis which can be obtained by the diffusion maps algorithm. In the limit of large data, this approach converges to a Galerkin projection of the semigroup solution to the underlying dynamics on a basis adapted to the invariant measure. This approach allows one to quantify uncertainties (in fact, evolve the probability distribution) for nontrivial dynamical systems with equation-free modeling. We verify our approach on various examples, ranging from an inhomogeneous anisotropic stochastic differential equation on a torus, the chaotic Lorenz three-dimensional model, and the Niño-3.4 data set which is used as a proxy of the El Niño Southern Oscillation.
Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.
Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z
2017-03-01
A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.
Nonparametric estimation of stochastic differential equations with sparse Gaussian processes
García, Constantino A.; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G.
2017-08-01
The application of stochastic differential equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a nonparametric method for estimating the drift and diffusion terms of SDEs from a densely observed discrete time series. The use of Gaussian processes as priors permits working directly in a function-space view and thus the inference takes place directly in this space. To cope with the computational complexity that requires the use of Gaussian processes, a sparse Gaussian process approximation is provided. This approximation permits the efficient computation of predictions for the drift and diffusion terms by using a distribution over a small subset of pseudosamples. The proposed method has been validated using both simulated data and real data from economy and paleoclimatology. The application of the method to real data demonstrates its ability to capture the behavior of complex systems.
Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees
Directory of Open Access Journals (Sweden)
Savic Vladimir
2010-01-01
Full Text Available Nonparametric belief propagation (NBP is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST created by breadth first search (BFS method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks. Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.
Revealing components of the galaxy population through nonparametric techniques
Bamford, Steven P; Nichol, Robert C; Miller, Christopher J; Wasserman, Larry; Genovese, Christopher R; Freeman, Peter E
2008-01-01
The distributions of galaxy properties vary with environment, and are often multimodal, suggesting that the galaxy population may be a combination of multiple components. The behaviour of these components versus environment holds details about the processes of galaxy development. To release this information we apply a novel, nonparametric statistical technique, identifying four components present in the distribution of galaxy H$\\alpha$ emission-line equivalent-widths. We interpret these components as passive, star-forming, and two varieties of active galactic nuclei. Independent of this interpretation, the properties of each component are remarkably constant as a function of environment. Only their relative proportions display substantial variation. The galaxy population thus appears to comprise distinct components which are individually independent of environment, with galaxies rapidly transitioning between components as they move into denser environments.
Multi-Directional Non-Parametric Analysis of Agricultural Efficiency
DEFF Research Database (Denmark)
Balezentis, Tomas
This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...... of stochasticity associated with Lithuanian family farm performance. The former technique showed that the farms differed in terms of the mean values and variance of the efficiency scores over time with some clear patterns prevailing throughout the whole research period. The fuzzy Free Disposal Hull showed...
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Evaluation of Nonparametric Probabilistic Forecasts of Wind Power
DEFF Research Database (Denmark)
Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008;
likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...... of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind......Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...
Equity and efficiency in private and public education: a nonparametric comparison
L. Cherchye; K. de Witte; E. Ooghe; I. Nicaise
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Equity and efficiency in private and public education: a nonparametric comparison
Cherchye, L.; de Witte, K.; Ooghe, E.; Nicaise, I.
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
Semi-parametric regression: Efficiency gains from modeling the nonparametric part
Yu, Kyusang; Park, Byeong U; 10.3150/10-BEJ296
2011-01-01
It is widely admitted that structured nonparametric modeling that circumvents the curse of dimensionality is important in nonparametric estimation. In this paper we show that the same holds for semi-parametric estimation. We argue that estimation of the parametric component of a semi-parametric model can be improved essentially when more structure is put into the nonparametric part of the model. We illustrate this for the partially linear model, and investigate efficiency gains when the nonparametric part of the model has an additive structure. We present the semi-parametric Fisher information bound for estimating the parametric part of the partially linear additive model and provide semi-parametric efficient estimators for which we use a smooth backfitting technique to deal with the additive nonparametric part. We also present the finite sample performances of the proposed estimators and analyze Boston housing data as an illustration.
Takara, K. T.
2015-12-01
This paper describes a non-parametric frequency analysis method for hydrological extreme-value samples with a size larger than 100, verifying the estimation accuracy with a computer intensive statistics (CIS) resampling such as the bootstrap. Probable maximum values are also incorporated into the analysis for extreme events larger than a design level of flood control. Traditional parametric frequency analysis methods of extreme values include the following steps: Step 1: Collecting and checking extreme-value data; Step 2: Enumerating probability distributions that would be fitted well to the data; Step 3: Parameter estimation; Step 4: Testing goodness of fit; Step 5: Checking the variability of quantile (T-year event) estimates by the jackknife resampling method; and Step_6: Selection of the best distribution (final model). The non-parametric method (NPM) proposed here can skip Steps 2, 3, 4 and 6. Comparing traditional parameter methods (PM) with the NPM, this paper shows that PM often underestimates 100-year quantiles for annual maximum rainfall samples with records of more than 100 years. Overestimation examples are also demonstrated. The bootstrap resampling can do bias correction for the NPM and can also give the estimation accuracy as the bootstrap standard error. This NPM has advantages to avoid various difficulties in above-mentioned steps in the traditional PM. Probable maximum events are also incorporated into the NPM as an upper bound of the hydrological variable. Probable maximum precipitation (PMP) and probable maximum flood (PMF) can be a new parameter value combined with the NPM. An idea how to incorporate these values into frequency analysis is proposed for better management of disasters that exceed the design level. The idea stimulates more integrated approach by geoscientists and statisticians as well as encourages practitioners to consider the worst cases of disasters in their disaster management planning and practices.
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:
Directory of Open Access Journals (Sweden)
Santiago Ostengo
2011-12-01
considered in a breeding program. It is for that reason that in sugar cane breeding, multienvironmental trials (MET are conducted at the last stage of the selection process. There exist different approaches to study genotype-environment interaction. One of these is the non-parametric technique, a valid and useful tool which allows making an initial exploration that can be easily interpreted. The non-parametric technique called relative consistency of performance enables the classification of genotypes into the following four categories: (i consistently superior; (ii inconsistently superior; (iii inconsistently inferior and (iv consistently inferior. This work aims to evaluate the consistency of performance of TUC 95-10 variety across different agro-ecological environments in the province of Tucumán (Argentina, as regards the variable tons of sugar per hectare and considering different crop ages. Data were obtained from MET of the Sugarcane Breeding Program of Estación Experimental Agroindustrial Obispo Colombres (EEAOC from Tucumán (Argentina, conducted at six sites through four crop ages. Results showed that TUC 95-10, recently released by EEAOC, can be labeled as consistently superior at all ages, i.e. it held the top position in sugar production in all tested environments. Therefore, it can be concluded that TUC 95-10 shows an excellent performance and good adaptation to different agro-ecological environments in Tucumán, at all crop ages.
Long-term trend and variability of precipitation in Chhattisgarh State, India
Meshram, Sarita Gajbhiye; Singh, Vijay P.; Meshram, Chandrashekhar
2017-08-01
Spatial and temporal precipitation variability in Chhattisgarh State in India was examined by using monthly precipitation data for 102 years (1901-2002) from 16 stations. The homogeneity of precipitation data was evaluated by the double-mass curve approach and the presence of serial correlation by lag-1 autocorrelation coefficient. Linear regression analysis, the conventional Mann-Kendall (MK) test, and Spearman's rho were employed to identify trends and Sen's slope to estimate the slope of trend line. The coefficient of variation (CV) was used to analyze precipitation variability. Spatial interpolation was done by a Kriging process using ArcGIS 9.3. Results of both parametric and non-parametric tests and trend tests showed that at 5 % significance level, annual precipitation exhibited a decreasing trend at all stations except Bilaspur and Dantewada. For both annual and monsoon precipitation, Sen's test showed a decreasing trend for all stations, except Bilaspur and Dantewada. The highest percentage of variability was observed in winter precipitation (88.75 %) and minimum percentage variability in annual series (14.01 %) over the 102-year periods.
Long-term trend and variability of precipitation in Chhattisgarh State, India
Meshram, Sarita Gajbhiye; Singh, Vijay P.; Meshram, Chandrashekhar
2016-04-01
Spatial and temporal precipitation variability in Chhattisgarh State in India was examined by using monthly precipitation data for 102 years (1901-2002) from 16 stations. The homogeneity of precipitation data was evaluated by the double-mass curve approach and the presence of serial correlation by lag-1 autocorrelation coefficient. Linear regression analysis, the conventional Mann-Kendall (MK) test, and Spearman's rho were employed to identify trends and Sen's slope to estimate the slope of trend line. The coefficient of variation (CV) was used to analyze precipitation variability. Spatial interpolation was done by a Kriging process using ArcGIS 9.3. Results of both parametric and non-parametric tests and trend tests showed that at 5 % significance level, annual precipitation exhibited a decreasing trend at all stations except Bilaspur and Dantewada. For both annual and monsoon precipitation, Sen's test showed a decreasing trend for all stations, except Bilaspur and Dantewada. The highest percentage of variability was observed in winter precipitation (88.75 %) and minimum percentage variability in annual series (14.01 %) over the 102-year periods.
Chatterjee, Soumendu; Khan, Ansar; Akbari, Hashem; Wang, Yupeng
2016-12-01
This paper intended to investigate spatio-temporal monotonic trend and shift in concentration of monsoon precipitation across West Bengal, India, by analysing the time series of monthly precipitation from 18 weather stations during the period from 1901 to 2002. In dealing with, the inhomogeneity in the precipitation series, RHtestsV4 software package is used to detect, and adjust for, multiple change points (shifts) that could exist in data series. Finally, the cumulative deviation test was applied at 5% significant level to check the homogeneity (presence of historic changes by cumulative deviations test). Afterward, non-parametric Mann-Kendall (MK) test and Theil-Sen estimator (TSE) was applied to detect of nature and slope of trends; and, Sequential Mann Kendall (SQMK) test was applied for detection of turning point and magnitude of change in trends. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation in precipitation data series. Four indices- precipitation concentration index (PCI), precipitation concentration degree (PCD), precipitation concentration period (PCP) and fulcrum (centre of gravity) were used to detect precipitation concentration and the spatial pattern in it. The application of the above-mentioned procedures has shown very notable statewide monotonic trend for monsoon precipitation time series. Regional cluster analysis by SQMK found increasing precipitation in mountain and coastal regions in general, except during the non- monsoon seasons. The results show that higher PCI values were mainly observed in South Bengal, whereas lower PCI values were mostly detected in North Bengal. The PCI values are noticeably larger in places where both monsoon total precipitation and span of rainy season are lower. The results of PCP reveal that precipitation in Gangetic Bengal mostly occurs in summer (monsoon season), and the rainy season arrives earlier in North Bengal than South Bengal
Mapping trends of large and medium size carnivores of conservation interest in Romania
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Mihai Cristian Adamescu
2014-07-01
Full Text Available We analysed yearly estimates of population size data during 2001-2012 for five carnivores species of conservation interest (Ursus arctos, Canis lupus, Lynx lynx, Felis silvestris and Canis aureus. Population size estimations were done by the game management authorities and integrated by the competent authorities on the Ministry of Environment and Climate Change. Trends in data were detected using non-parametric Mann-Kendall test. This test was chosen considering the short length of data series and its usefulness for non-normal distributed data. The trend was tested at three spatial scales: game management units (n=1565, biogeographical region (n=5 and national. Trends depicted for each game management unit were plotted using ArcGIS, resulting species trend distribution maps. For the studied period increasing population trends were observed for Ursus arctos, Canis lupus, Canis aureus and Lynx lynx, while for Felis silvestris there was no trend recorded. Such an analysis in especially useful for conservation proposes, game management and reporting obligations under article 17 of the EC Habitat Directive, using population trend as a proxy for population dynamics. We conclude that the status of the five carnivore species is favourable during the study period.
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.
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2017-01-18
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.
The Utility of Nonparametric Transformations for Imputation of Survey Data
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Robbins Michael W.
2014-12-01
Full Text Available Missing values present a prevalent problem in the analysis of establishment survey data. Multivariate imputation algorithms (which are used to fill in missing observations tend to have the common limitation that imputations for continuous variables are sampled from Gaussian distributions. This limitation is addressed here through the use of robust marginal transformations. Specifically, kernel-density and empirical distribution-type transformations are discussed and are shown to have favorable properties when used for imputation of complex survey data. Although such techniques have wide applicability (i.e., they may be easily applied in conjunction with a wide array of imputation techniques, the proposed methodology is applied here with an algorithm for imputation in the USDA’s Agricultural Resource Management Survey. Data analysis and simulation results are used to illustrate the specific advantages of the robust methods when compared to the fully parametric techniques and to other relevant techniques such as predictive mean matching. To summarize, transformations based upon parametric densities are shown to distort several data characteristics in circumstances where the parametric model is ill fit; however, no circumstances are found in which the transformations based upon parametric models outperform the nonparametric transformations. As a result, the transformation based upon the empirical distribution (which is the most computationally efficient is recommended over the other transformation procedures in practice.
Nonparametric identification of structural modifications in Laplace domain
Suwała, G.; Jankowski, Ł.
2017-02-01
This paper proposes and experimentally verifies a Laplace-domain method for identification of structural modifications, which (1) unlike time-domain formulations, allows the identification to be focused on these parts of the frequency spectrum that have a high signal-to-noise ratio, and (2) unlike frequency-domain formulations, decreases the influence of numerical artifacts related to the particular choice of the FFT exponential window decay. In comparison to the time-domain approach proposed earlier, advantages of the proposed method are smaller computational cost and higher accuracy, which leads to reliable performance in more difficult identification cases. Analytical formulas for the first- and second-order sensitivity analysis are derived. The approach is based on a reduced nonparametric model, which has the form of a set of selected structural impulse responses. Such a model can be collected purely experimentally, which obviates the need for design and laborious updating of a parametric model, such as a finite element model. The approach is verified experimentally using a 26-node lab 3D truss structure and 30 identification cases of a single mass modification or two concurrent mass modifications.
Nonparametric Bayes modeling for case control studies with many predictors.
Zhou, Jing; Herring, Amy H; Bhattacharya, Anirban; Olshan, Andrew F; Dunson, David B
2016-03-01
It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.
Chairez, Isaac
2016-04-05
This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.
Nonparametric estimation of quantum states, processes and measurements
Lougovski, Pavel; Bennink, Ryan
Quantum state, process, and measurement estimation methods traditionally use parametric models, in which the number and role of relevant parameters is assumed to be known. When such an assumption cannot be justified, a common approach in many disciplines is to fit the experimental data to multiple models with different sets of parameters and utilize an information criterion to select the best fitting model. However, it is not always possible to assume a model with a finite (countable) number of parameters. This typically happens when there are unobserved variables that stem from hidden correlations that can only be unveiled after collecting experimental data. How does one perform quantum characterization in this situation? We present a novel nonparametric method of experimental quantum system characterization based on the Dirichlet Process (DP) that addresses this problem. Using DP as a prior in conjunction with Bayesian estimation methods allows us to increase model complexity (number of parameters) adaptively as the number of experimental observations grows. We illustrate our approach for the one-qubit case and show how a probability density function for an unknown quantum process can be estimated.
Non-parametric and least squares Langley plot methods
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P. W. Kiedron
2015-04-01
Full Text Available Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer–Lambert–Beer law V=V>/i>0e−τ ·m, where a plot of ln (V voltage vs. m air mass yields a straight line with intercept ln (V0. This ln (V0 subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The eleven techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln (V0's are smoothed and interpolated with median and mean moving window filters.
Pivotal Estimation of Nonparametric Functions via Square-root Lasso
Belloni, Alexandre; Wang, Lie
2011-01-01
In a nonparametric linear regression model we study a variant of LASSO, called square-root LASSO, which does not require the knowledge of the scaling parameter $\\sigma$ of the noise or bounds for it. This work derives new finite sample upper bounds for prediction norm rate of convergence, $\\ell_1$-rate of converge, $\\ell_\\infty$-rate of convergence, and sparsity of the square-root LASSO estimator. A lower bound for the prediction norm rate of convergence is also established. In many non-Gaussian noise cases, we rely on moderate deviation theory for self-normalized sums and on new data-dependent empirical process inequalities to achieve Gaussian-like results provided log p = o(n^{1/3}) improving upon results derived in the parametric case that required log p = O(log n). In addition, we derive finite sample bounds on the performance of ordinary least square (OLS) applied tom the model selected by square-root LASSO accounting for possible misspecification of the selected model. In particular, we provide mild con...
A global-scale investigation of trends in annual maximum streamflow
Do, Hong X.; Westra, Seth; Leonard, Michael
2017-09-01
This study investigates the presence of trends in annual maximum daily streamflow data from the Global Runoff Data Centre database, which holds records of 9213 stations across the globe. The records were divided into three reference datasets representing different compromises between spatial coverage and minimum record length, followed by further filtering based on continent, Köppen-Weiger climate classification, presence of dams, forest cover changes and catchment size. Trends were evaluated using the Mann-Kendall nonparametric trend test at the 10% significance level, combined with a field significance test. The analysis found substantial differences between reference datasets in terms of the specific stations that exhibited significant increasing or decreasing trends, showing the need for careful construction of statistical methods. The results were more consistent at the continental scale, with decreasing trends for a large number of stations in western North America and the data-covered regions of Australia, and increasing trends in parts of Europe, eastern North America, parts of South America and southern Africa. Interestingly, neither the presence of dams nor changes in forest cover had a large effect on the trend results, but the catchment size was important, as catchments exhibiting increasing (decreasing) trends tended to be smaller (larger). Finally, there were more stations with significant decreasing trends than significant increasing trends across all the datasets analysed, indicating that limited evidence exists for the hypothesis that flood hazard is increasing when averaged across the data-covered regions of the globe.
Institute of Scientific and Technical Information of China (English)
LI WeiHong; CHEN YaNing; HAO XingMing; HUANG Xiang; CHEN YaPeng
2007-01-01
A case study on the responses of streamflow to climate change in the Toutun River basin was carried out based on data analysis of streamflow, precipitation, and temperatures during the past 50 years.Temporal series of the streamflow change in the Toutun River basin was analyzed and tested using the Mann-Kendall nonparametric test. Results revealed that the annual runoff of the Toutun River had been in a monotonic decreasing trend for the past 50 years. Compared with the 1950s and 1960s, the annual runoff in the 1990s decreased by 4.0×105 m3 and 7.2×105 m3. The precipitation did not show monotonic trend during the past 50 years, but the annual temperature increased by 1.12℃ since the 1950s. Further data analysis indicated that the monthly runoff of the Toutun River decreased significantly from August to October, with precipitation displaying the similar pattern of seasonal change. Analysis suggests that the reduction of streamflow in the Toutun River basin is possibly caused by the seasonal change of precipitation, especially the precipitation reduction in summer, and temperature increases.
Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe
Muchuru, Shepherd; Botai, Joel O.; Botai, Christina M.; Landman, Willem A.; Adeola, Abiodun M.
2016-04-01
In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.
Climate Change in Jordan: A Comprehensive Examination Approach
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Moshrik R. Hamdi
2009-01-01
Full Text Available Problem statement: Jordan is experiencing harsh water shortage in different parts of the country due to high fluctuations in annual precipitation; the only source of water. In addition, heat waves are becoming more frequent in the region. Precipitation decrement and summer heat waves are being blamed on global warming. This study aimed to detect trends in weather parameters in Jordan. Approach: Data from six meteorological stations distributed around Jordan were analyzed using several parametric and nonparametric statistical approaches including Mann-Kendall, Linear Regression, Cusum, Rank Sum, Student's t-test, Rank Difference, Auto Correlation and Skewness-Kurtosis Normality test. Results: The results indicated that there are no visible trends indicating an increase or decrease in the annual precipitation and maximum temperature. However, there are good to strong trends indicating that annual minimum temperature has increased in the last decade while annual temperature range has decreased. Conclusion: Decreasing temperature range proved that the Earths atmosphere is becoming more efficient in trapping terrestrial infrared radiation, which is accountable for the global warming.
Analysis of temporal and spatial trends of hydro-climatic variables in the Wei River Basin.
Zhao, Jing; Huang, Qiang; Chang, Jianxia; Liu, Dengfeng; Huang, Shengzhi; Shi, Xiaoyu
2015-05-01
The Wei River is the largest tributary of the Yellow River in China. The relationship between runoff and precipitation in the Wei River Basin has been changed due to the changing climate and increasingly intensified human activities. In this paper, we determine abrupt changes in hydro-climatic variables and identify the main driving factors for the changes in the Wei River Basin. The nature of the changes is analysed based on data collected at twenty-one weather stations and five hydrological stations in the period of 1960-2010. The sequential Mann-Kendall test analysis is used to capture temporal trends and abrupt changes in the five sub-catchments of the Wei River Basin. A non-parametric trend test at the basin scale for annual data shows a decreasing trend of precipitation and runoff over the past fifty-one years. The temperature exhibits an increase trend in the entire period. The potential evaporation was calculated based on the Penman-Monteith equation, presenting an increasing trend of evaporation since 1990. The stations with a significant decreasing trend in annual runoff mainly are located in the west of the Wei River primarily interfered by human activities. Regression analysis indicates that human activity was possibly the main cause of the decline of runoff after 1970.
Spatiotemporal distribution of reference evapotranspiration in the Republic of Moldova
Piticar, Adrian; Mihăilă, Dumitru; Lazurca, Liliana Gina; Bistricean, Petruţ-Ionel; Puţuntică, Anatolie; Briciu, Andrei-Emil
2016-05-01
The main objectives of this study are to investigate the spatial distribution and changes in reference evapotranspiration ( ET 0) in the Republic of Moldova. Monthly data of maximum and minimum air temperature, sunshine duration, relative humidity, and wind speed recorded at 14 weather stations over a period of 52 years (1961-2012) were used. ET 0 was computed based on the FAO Penman-Monteith formula. Annual and growing seasons of winter wheat and maize time series were analyzed for the 1981-2012 period as well as for the 1961-1980. The trends and their statistical significance in ET 0 series were detected using Mann-Kendall test and T test, while the magnitude of the trends was estimated using Sen's slope and linear regression. For the 1981-2012 period, the results indicated that annual ET 0 had a positive trend in more than 90 % of the time series according to both parametric and nonparametric methods. The magnitude of positive trends in annual ET 0 series ranged between 13.80 and 72.07 mm/decade. In the growing seasons of winter wheat and maize, the results are similar to those found in the annual series. Significant decreasing trends dominated over the 1961-1980 period.
Institute of Scientific and Technical Information of China (English)
LINGNeng-xiang; DUXue-qiao
2005-01-01
In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.
Kernel bandwidth estimation for non-parametric density estimation: a comparative study
CSIR Research Space (South Africa)
Van der Walt, CM
2013-12-01
Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...
Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models
De Blasi, Pierpaolo; Lau, John W; 10.3150/09-BEJ233
2011-01-01
This paper develops nonparametric estimation for discrete choice models based on the mixed multinomial logit (MMNL) model. It has been shown that MMNL models encompass all discrete choice models derived under the assumption of random utility maximization, subject to the identification of an unknown distribution $G$. Noting the mixture model description of the MMNL, we employ a Bayesian nonparametric approach, using nonparametric priors on the unknown mixing distribution $G$, to estimate choice probabilities. We provide an important theoretical support for the use of the proposed methodology by investigating consistency of the posterior distribution for a general nonparametric prior on the mixing distribution. Consistency is defined according to an $L_1$-type distance on the space of choice probabilities and is achieved by extending to a regression model framework a recent approach to strong consistency based on the summability of square roots of prior probabilities. Moving to estimation, slightly different te...
Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change
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Hae-Bum Yun
2011-01-01
Full Text Available A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models
Fan, Jianqing; Song, Rui
2011-01-01
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under the nonparametric additive models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, an iterative nonparametric independence screening (INIS) is also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data a...
Directory of Open Access Journals (Sweden)
Saeed Banihashemi
2015-12-01
Full Text Available In line with the growing global trend toward energy efficiency in buildings, this paper aims to first; investigate the energy performance of double-glazed windows in different climates and second; analyze the most dominant used parametric and non-parametric tests in dimension reduction for simulating this component. A four-story building representing the conventional type of residential apartments for four climates of cold, temperate, hot-arid and hot-humid was selected for simulation. 10 variables of U-factor, SHGC, emissivity, visible transmittance, monthly average dry bulb temperature, monthly average percent humidity, monthly average wind speed, monthly average direct solar radiation, monthly average diffuse solar radiation and orientation constituted the parameters considered in the calculation of cooling and heating loads of the case. Design of Experiment and Principal Component Analysis methods were applied to find the most significant factors and reduction dimension of initial variables. It was observed that in two climates of temperate and hot-arid, using double glazed windows was beneficial in both cold and hot months whereas in cold and hot-humid climates where heating and cooling loads are dominant respectively, they were advantageous in only those dominant months. Furthermore, an inconsistency was revealed between parametric and non-parametric tests in terms of identifying the most significant variables.
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.
Examples of the Application of Nonparametric Information Geometry to Statistical Physics
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Giovanni Pistone
2013-09-01
Full Text Available We review a nonparametric version of Amari’s information geometry in which the set of positive probability densities on a given sample space is endowed with an atlas of charts to form a differentiable manifold modeled on Orlicz Banach spaces. This nonparametric setting is used to discuss the setting of typical problems in machine learning and statistical physics, such as black-box optimization, Kullback-Leibler divergence, Boltzmann-Gibbs entropy and the Boltzmann equation.
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2008-01-01
Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.
A robust nonparametric method for quantifying undetected extinctions.
Chisholm, Ryan A; Giam, Xingli; Sadanandan, Keren R; Fung, Tak; Rheindt, Frank E
2016-06-01
How many species have gone extinct in modern times before being described by science? To answer this question, and thereby get a full assessment of humanity's impact on biodiversity, statistical methods that quantify undetected extinctions are required. Such methods have been developed recently, but they are limited by their reliance on parametric assumptions; specifically, they assume the pools of extant and undetected species decay exponentially, whereas real detection rates vary temporally with survey effort and real extinction rates vary with the waxing and waning of threatening processes. We devised a new, nonparametric method for estimating undetected extinctions. As inputs, the method requires only the first and last date at which each species in an ensemble was recorded. As outputs, the method provides estimates of the proportion of species that have gone extinct, detected, or undetected and, in the special case where the number of undetected extant species in the present day is assumed close to zero, of the absolute number of undetected extinct species. The main assumption of the method is that the per-species extinction rate is independent of whether a species has been detected or not. We applied the method to the resident native bird fauna of Singapore. Of 195 recorded species, 58 (29.7%) have gone extinct in the last 200 years. Our method projected that an additional 9.6 species (95% CI 3.4, 19.8) have gone extinct without first being recorded, implying a true extinction rate of 33.0% (95% CI 31.0%, 36.2%). We provide R code for implementing our method. Because our method does not depend on strong assumptions, we expect it to be broadly useful for quantifying undetected extinctions. © 2016 Society for Conservation Biology.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P.
2017-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
Shivam; Goyal, Manish Kumar; Sarma, Arup Kumar
2017-08-01
This study focuses on changes in the maximum and minimum temperature over the Subansiri River basin for different climate change scenarios. For the study, dataset from Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) (i.e., coupled model intercomparison project phase five (CMIP5) dataset with representative concentration pathway (RCP) scenarios) were utilized. Long-term (2011-2100) maximum temperature ( T max) and minimum temperature (Tmin) time series were generated using the statistical downscaling technique for low emission scenario (RCP2.6), moderate emission scenario (RCP6.0), and extreme emission scenario (RCP8.5). Trends and change of magnitude in T max, T min, and diurnal temperature range (DTR) were analyzed for different interdecadal time scales (2011-2100, 2011-2040, 2041-2070, 2070-2100) using Mann-Kendall non-parametric test and Sen's slope estimator, respectively. The temperature data series for the observed duration (1981-2000) has been found to show increasing trends in T max and T min at both annual and monthly scale. Trend analysis of downscaled temperature for the period 2011-2100 shows increase in annual maximum temperature and annual minimum temperature for all the selected RCP scenarios; however, on the monthly scale, T max and T min have been seen to have decreasing trends in some months.
Modelling the Effects of Land-Use Changes on Climate: a Case Study on Yamula DAM
Köylü, Ü.; Geymen, A.
2016-10-01
Dams block flow of rivers and cause artificial water reservoirs which affect the climate and the land use characteristics of the river basin. In this research, the effect of the huge water body obtained by Yamula Dam in Kızılırmak Basin is analysed over surrounding spatial's land use and climate change. Mann Kendal non-parametrical statistical test, Theil&Sen Slope method, Inverse Distance Weighting (IDW), Soil Conservation Service-Curve Number (SCS-CN) methods are integrated for spatial and temporal analysis of the research area. For this research humidity, temperature, wind speed, precipitation observations which are collected in 16 weather stations nearby Kızılırmak Basin are analyzed. After that these statistical information is combined by GIS data over years. An application is developed for GIS analysis in Python Programming Language and integrated with ArcGIS software. Statistical analysis calculated in the R Project for Statistical Computing and integrated with developed application. According to the statistical analysis of extracted time series of meteorological parameters, statistical significant spatiotemporal trends are observed for climate change and land use characteristics. In this study, we indicated the effect of big dams in local climate on semi-arid Yamula Dam.
Gao, Xiaoli; Peng, Shizhang; Wang, Weiguang; Xu, Junzeng; Yang, Shihong
2016-10-01
Reference evapotranspiration (ET0) is the essential part of hydrological cycle and crop irrigation system (watering system framework). To reveal the change pattern of annual and seasonal ET0 and its corresponding dominated factors aspects of karst regions in China, daily meteorological data of 19 meteorological stations during 1959-2011 in Guizhou Province were collected. ET0 trends in seasonal and annual time series were detected by nonparametric Mann-Kendall test. The results indicated (demonstrated) that the annual ET0 for the entire Guizhou Province performed a decreasing trend by 0.4476 mm/year. The seasonal ET0 in spring and summer performed the an alike decreasing trend, while it presented an increasing trend in autumn and winter. According to the rotated empirical orthogonal function clustering method, Guizhou Province was divided into four parts. Annual ET0 performed (executed) the ascendant trend in western area, while the descendent trend occurred in middle, northwestern, and southwestern areas. The sensitivity and the attribution analysis indicated (specified) that the relative humidity was identified as the most sensitive variable to the annual ET0 and sunshine duration as the essential main thrusts for the declined annual ET0 in Guizhou Province. In addition, the more severe of karst landform development, the more obvious decreasing trend in ET0.
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Jin-liang REN
2012-06-01
Full Text Available Evaporation, which is an important factor in the water balance at the basin scale, is a critical variable in the determination of local available water resources. Since the potential evaporation is mainly influenced by meteorological variables, it is necessary to investigate the extent to which different meteorological variables affect the potential evaporation. The aim of this study was to explore the variation trends of different meteorological variables, and their impacts on the potential evaporation. This study selected the Hailar Meteorological Station of the Hailar region, which is situated in a cold, semi-arid, and sub-humid region, as a case study site. Based on observed daily meteorological data from 1951 to 2009, the potential evaporation was calculated with the Penman formula, and the variations of meteorological variables were investigated with the nonparametric Mann-Kendall test. The correlation between the potential evaporation and each meteorological variable at annual and seasonal scales was also analyzed. The results show that the annual and seasonal potential evaporation and air temperature present increasing trends, whereas the wind speed, sunshine duration, and relative humidity present decreasing trends. Among the meteorological variables, the air temperature and relative humidity are the key factors that affect potential evaporation at different time scales, and the impacts of other meteorological variables on the potential evaporation are not significant and vary with time scales.
Impacts of Climatic Change on River Runoff in Northern Xinjiang of China over Last Fifty Years
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YANG Yuhui; CHEN Yaning; LI Weihong; WANG Minzhong; SUN Guili
2010-01-01
The characteristics of climatic change and river runoff, as well as the response of river runoff to climatic change in the northern Xinjiang are analyzed on the basis of the hydrological and meteorological data over the last 50 years by the methods of Mann-Kendall nonparametric test and the nonlinear regression model. The results show that: 1) The temperature and the precipitation increased significantly in the whole northern Xinjiang, but the precipitation displayed no obvious change, or even a decreasing trend in the northern mountainous area of the northern Xinjiang. 2) River runoff varied in different regions in the northern Xinjiang. It significantly increased in the northern slope of the Tianshan Mountains and the north of the northern Xinjiang (p=0.05), while slightly increased in the west of the northern Xinjiang. 3) North Atlantic Oscillation (NAO) affects river runoff by influencing temperature and precipita-tion. The NAO and precipitation had apparent significant correlations with the river runoff, but the temperature did not in the northern Xinjiang. Since the mid-1990s river runoff increase was mainly caused by the increasing temperature in the northern slope of the Tianshan Mountains and the north of the northern Xinjiang. Increased precipitation resulted in increased river runoff in the west of the northern Xinjiang.
A Multi-Satellite Approach for Water Storage Monitoring in an Arid Watershed
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Dawit T. Ghebreyesus
2016-07-01
Full Text Available The objective of this study was to use satellite imagery to monitor the water budget of Al Ain region in the United Arab Emirates (UAE. Inflows and outflows were estimated and the trend of water storage variation in the study area was examined from 2005 to 2014. Evapotranspiration was estimated using the simplified Penman-Monteith equation. Landsat images were used to determine the extent of agricultural and green areas. Time series of gravity recovery and climate experiment (GRACE observations over the study area were used to assess the inferred water storage variation from satellite data. The change of storage inferred from the Water Budget Equation showed a decreasing trend at an average rate of 2.57 Mm3 annually. Moreover, GRACE readings showed a decreasing trend at a rate of 0.35 cm of water depth annually. Mann-Kendal, a non-parametric trend test, proved the presence of significant negative trends in both time series at a 5% significance level. A two-month lag resulted in a better agreement (R2 = 0.55 between the change in water storage and GRACE anomalies within the study area. These results suggest that water storage in the study area is being depleted significantly. Moreover, the potential of remote sensing in water resource management, especially in remote and arid areas, was demonstrated.
THE INFLUENCE OF EUROPEAN CLIMATE VARIABILITY MECHANISM ON AIR TEMPERATURE IN ROMANIA
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M. MATEI
2013-03-01
Full Text Available The main objective of the present paper is to analyze the temporal and spatial variability of air-temperature in Romania, by using mean air-temperature values provided by the ECA&D project (http://eca.knmi.nl/. These data sets will be filtered by means of the EOF (Empirical Orthogonal Function analysis, which describes various modes of space variability and time coefficient series (PC series. The EOF analysis will also be used to identify the main way of action of the European climate variability mechanism, by using multiple variables in grid points, provided by the National Centre of Atmospheric Research (NCAR, USA. The variables considered here are: sea level pressure (SLP, geopotential height at 500 mb (H500 and air temperature at 850 mb (T850, for the summer and winter seasons. The linear trends and shift points of considered variables are then assessed by means of the Mann-Kendall and Pettitt non-parametric tests. By interpreting the results, we can infer that there is causal relationship between the large-scale analyzed parameters and temperature variability in Romania. These results are consistent with those presented by Busuioc et al., 2010, where the main variation trends of the principal European variables are shown.
Institute of Scientific and Technical Information of China (English)
Nnyaladzi BATISANI
2011-01-01
The fourth assessment report of the IPCC highlights that the global average surface temperature is projected to increase by 1.8 to 4.0℃ by the year 2100 compared to current climate. Given that climate is the most important driver of the hydrological cycle, the rise in temperature could cause changes in occurrence patterns of extreme hydrologic events like streamflow droughts. An increase in frequency and severity of these events could pose serious challenges for sustainable management of water resources particular in arid regions.However, the understanding of water resources dynamics and the possible impacts of climate change on these dynamics is hindered by uncertainties in climate change models and complex hydrological responses of streams and catchments to climatic changes. Therefore observational evidence of streamflow dynamics at the local scale could play a crucial role in addressing these uncertainties and achieving a fuller reconciliation between model-based scenarios and ground truth. This paper determines spatial and temporal changes in streamflow volumes and their association with climatic factors based on the non-parametric Mann-Kendall test and ANOVA to determine possible changes in streamflow over the years and their relation to climatic factors. Streamflow is generally stochastic highlighting the importance of factoring in temporal flow variability in water resources planning. There is no clear evidence that changes in climatic variables are related to streamflow behaviour.
Spatiotemporal variation of precipitation based on three indicators in the flood season in Beijing
Chen, Y.; Wang, J. H.; Xiao, W. H.; Zhou, Y. Y.; Wang, L. T.; Song, X. Y.
2016-08-01
This paper attempts to analyze the spatiotemporal variation of precipitation in Beijing in terms of three indicators, i.e., the total precipitation, the maximum daily precipitation, and the maximum 5h precipitation in the flood season, with data collected from 20 meteorological stations from 1975 to 2012 (June to September). The Mann-Kendall nonparametric test, the linear regression, and five-year moving average were used. The results showed that: (i) the three indicators showed a decreasing trend; however, from 1975 to 1995 and from 1995 to 2012, the precipitation in the flood season showed an increasing trend with an increasing rate of 3.36 and 2.90mm/a, respectively. (ii) The indicators demonstrated a similar spatial variation. The high value precipitation areas were mainly in the northern and central parts of the city. The central districts had increasing centers with maximum increasing rates of 0.35, 0.65, and 0.49mm/a, respectively. However, the northern part exhibited different degrees of decreasing trends. (iii) The increase in the precipitation in the city center was mainly due to the urban heat island effect and the change in the underlying surface. The temperature gradient was intensified due to the topography in the city center and the northern suburb, causing a decreasing trend of precipitation.
Li, Zhenwei; Xu, Xianli; Yu, Bofu; Xu, Chaohao; Liu, Meixian; Wang, Kelin
2016-11-01
Quantifying the impacts of climate and human activities on water and sediment discharge has become a central topic in climate and hydrologic research. This issue, however, has so far received little attention in karst regions around the world. Seven karst catchments located in southwest China were chosen to explore water and sediment discharge responses to different driving factors during the period from the 1950s to 2011. The non-parametric Mann-Kendall test was used to detect both the trends and abrupt changes in water and sediment discharge. The double mass curve method was used to quantify the effects of climate and human activities on water and sediment discharge. Results indicated that the annual water discharge showed a decreasing trend in all catchments (-0.21 to -3.68 × 108 m3 yr-1), and the sediment discharge exhibited a significant decreasing trend (-7 to -101 × 104 t yr-1) for six out of the seven catchments. A rapid decline (abrupt change) in sediment discharge occurred since 2000 for all except Liujiang catchment where the sediment discharge has a slight increase since 1983 as no large dams were constructed in this catchment. Specifically, the magnitude of reduction in sediment discharge (%) significantly increases with the extent of flow regulation as measured by the ratio of the area upstream the dam to the total catchment area for the seven catchments (R2 = 0.98, P China.
Shivam; Goyal, Manish Kumar; Sarma, Arup Kumar
2016-06-01
This study focuses on changes in the maximum and minimum temperature over the Subansiri River basin for different climate change scenarios. For the study, dataset from Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) (i.e., coupled model intercomparison project phase five (CMIP5) dataset with representative concentration pathway (RCP) scenarios) were utilized. Long-term (2011-2100) maximum temperature (T max) and minimum temperature (Tmin) time series were generated using the statistical downscaling technique for low emission scenario (RCP2.6), moderate emission scenario (RCP6.0), and extreme emission scenario (RCP8.5). Trends and change of magnitude in T max, T min, and diurnal temperature range (DTR) were analyzed for different interdecadal time scales (2011-2100, 2011-2040, 2041-2070, 2070-2100) using Mann-Kendall non-parametric test and Sen's slope estimator, respectively. The temperature data series for the observed duration (1981-2000) has been found to show increasing trends in T max and T min at both annual and monthly scale. Trend analysis of downscaled temperature for the period 2011-2100 shows increase in annual maximum temperature and annual minimum temperature for all the selected RCP scenarios; however, on the monthly scale, T max and T min have been seen to have decreasing trends in some months.
Spatial and Temporal Variability of Rainfall in the Gandaki River Basin of Nepal Himalaya
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Jeeban Panthi
2015-03-01
Full Text Available Landslides, floods, and droughts are recurring natural disasters in Nepal related to too much or too little water. The summer monsoon contributes more than 80% of annual rainfall, and rainfall spatial and inter-annual variation is very high. The Gandaki River, one of the three major rivers of Nepal and one of the major tributaries of the Ganges River, covers all agro-ecological zones in the central part of Nepal. Time series tests were applied for different agro-ecological zones of the Gandaki River Basin (GRB for rainfall trends of four seasons (pre-monsoon, monsoon, post-monsoon and winter from 1981 to 2012. The non-parametric Mann-Kendall and Sen’s methods were used to determine the trends. Decadal anomalies relative to the long-term average were analyzed using the APHRODITE precipitation product. Trends in number of rainy days and timing of the monsoon were also analyzed. We found that the post-monsoon, pre-monsoon and winter rainfalls are decreasing significantly in most of the zones but monsoon rainfall is increasing throughout the basin. In the hill region, the annual rainfall is increasing but the rainy days do not show any trend. There is a tendency toward later departure of monsoon from Nepal, indicating an increase in its duration. These seasonally and topographically variable trends may have significant impacts for the agriculture and livestock smallholders that form the majority of the population in the GRB.
River discharge changes in the Qinghai-Tibet Plateau
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Annual mean discharge data of the five large rivers in the exorheic region of the Qinghai-Tibet Plateau from 1956 to 2000 are analyzed for trends with the Mann-Kendall nonparametric trend test. The results reveal that though in general no increasing trends exist in the total river discharges, significant regional differences of river discharge exist, reflecting the decreasing trends of discharge in the Yellow River and the Tongtian River (upper Changjiang River), an increasing trend in Yalong River, and inverted change in the Lancang River and Yarlung Zangbo River. Based on analyses of the seasonal discharge, it is found that climatic change had a significant effect on the seasonal variation of river discharge in the Qinghai-Tibet Plateau. In spring (from March to May) the discharge increased significantly, especially in the source area of the Yellow River. Together with the analyses on data of the mean temperature in the Northern Hemisphere and climatic data within the river basins, the relationship between discharges and mean temperature of the Northern Hemisphere is explored, which indicates that there is no increase in the stream discharge in the Qinghai-Tibet Plateau with global warming. It is probably the increasing evaporation, caused by rising temperature that offsets the hydrological effect of increasing precipitation.
Observed changes in seasonal heat waves and warm temperature extremes in the Romanian Carpathians
Micu, Dana; Birsan, Marius-Victor; Dumitrescu, Alexandru; Cheval, Sorin
2015-04-01
Extreme high temperature have a large impact on environment and human activities, especially in high elevation areas particularly sensitive to the recent climate warming. The climate of the Romanian Carpathians became warmer particularly in winter, spring and summer, exibiting a significant increasing frequency of warm extremes. The paper investigates the seasonal changes in the frequency, duration and intensity of heat waves in relation to the shifts in the daily distribution of maximum temperatures over a 50-year period of meteorological observations (1961-2010). The paper uses the heat wave definition recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) and exploits the gridded daily dataset of maximum temperature at 0.1° resolution (~10 km) developed in the framework of the CarpatClim project (www.carpatclim.eu). The seasonal changes in heat waves behavior were identified using the Mann-Kendall non-parametric trend test. The results suggest an increase in heat wave frequency and a lengthening of intervals affected by warm temperature extremes all over the study region, which are explained by the shifts in the upper (extreme) tail of the daily maximum temperature distribution in most seasons. The trends are consistent across the region and are well correlated to the positive phases of the East Atlantic Oscillation. Our results are in good agreement with the previous temperature-related studies concerning the Carpathian region. This study was realized within the framework of the project GENCLIM, financed by UEFISCDI, code PN-II 151/2014.
Kim, Daeha; Rhee, Jinyoung
2016-04-01
Evapotranspiration (ET) has received a great attention in drought assessment as it is closely related to atmospheric water demand. The hypothetical potential ET (ETp) has been predominantly used, nonetheless it does not actually exist in the hydrologic cycle. In this work, we used a complementary method for ET estimation to obtain wet-environment ET (ETw) and actual ET (ETa) from routinely observed climatic data. By combining ET deficits (ETw minus ETa) and the structure of the Standardized Precipitation-Evapotranspiration Index (SPEI), we proposed a novel ET-based drought index, the Standardized Evapotranspiration Deficit Index (SEDI). We carried out historical drought identification for the contiguous United States using temperature datasets of the PRISM Climate Group. SEDI presented spatial distributions of drought areas similar to the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) for major drought events. It indicates that SEDI can be used for validating other drought indices. Using the non-parametric Mann-Kendall test, we found a significant decreasing trend of SEDI (increasing drought risk) similar to PDSI and SPI in the western United States. This study suggests a potential of ET-based indices for drought quantification even with no involvement of precipitation data.
A novel nonparametric confidence interval for differences of proportions for correlated binary data.
Duan, Chongyang; Cao, Yingshu; Zhou, Lizhi; Tan, Ming T; Chen, Pingyan
2016-11-16
Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance. © The Author(s) 2016.
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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.
NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
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Richard C. Zink
2012-07-01
Full Text Available Analysis of covariance serves two important purposes in a randomized clinical trial. First, there is a reduction of variance for the treatment effect which provides more powerful statistical tests and more precise confidence intervals. Second, it provides estimates of the treatment effect which are adjusted for random imbalances of covariates between the treatment groups. The nonparametric analysis of covariance method of Koch, Tangen, Jung, and Amara (1998 defines a very general methodology using weighted least-squares to generate covariate-adjusted treatment effects with minimal assumptions. This methodology is general in its applicability to a variety of outcomes, whether continuous, binary, ordinal, incidence density or time-to-event. Further, its use has been illustrated in many clinical trial settings, such as multi-center, dose-response and non-inferiority trials.NParCov3 is a SAS/IML macro written to conduct the nonparametric randomization-based covariance analyses of Koch et al. (1998. The software can analyze a variety of outcomes and can account for stratification. Data from multiple clinical trials will be used for illustration.
Institute of Scientific and Technical Information of China (English)
ZHOU Yue; LU Xixi; HUANG Ying; ZHU Yunmei
2004-01-01
The sediment flux data, measured from a dry-hot valley of the Longchuan River, a tributary of the lower Jinsha River, were analyzed with Mann-Kendall test, Seasonal Mann-Kendall test and Sen's test. In both the upper reaches (Xiaohekou) and the lower reaches (Xiaohuangguayuan), the sediment fluxes showed a significant increase from 1970 to 2001, despite the fact that the water discharge did not change significantly during the period and numerous reservoir constructions which contribute to the trap of sediment. This can be attributed to the intensification of human activities, especially the activities related to land surface disturbances such as deforestation and afforestation, expansion of agriculture land, and road constructions. This increase is more significant in the lower reaches of the river observed at the place of Xiaohuangguayuan due to the dry-hot climate. The profound increase in sediment flux has significant implications for effective management of the sedimentation problems of the on-going Three Gorges Reservoir.
Comparison of non-parametric methods for ungrouping coarsely aggregated data
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Silvia Rizzi
2016-05-01
Full Text Available Abstract Background Histograms are a common tool to estimate densities non-parametrically. They are extensively encountered in health sciences to summarize data in a compact format. Examples are age-specific distributions of death or onset of diseases grouped in 5-years age classes with an open-ended age group at the highest ages. When histogram intervals are too coarse, information is lost and comparison between histograms with different boundaries is arduous. In these cases it is useful to estimate detailed distributions from grouped data. Methods From an extensive literature search we identify five methods for ungrouping count data. We compare the performance of two spline interpolation methods, two kernel density estimators and a penalized composite link model first via a simulation study and then with empirical data obtained from the NORDCAN Database. All methods analyzed can be used to estimate differently shaped distributions; can handle unequal interval length; and allow stretches of 0 counts. Results The methods show similar performance when the grouping scheme is relatively narrow, i.e. 5-years age classes. With coarser age intervals, i.e. in the presence of open-ended age groups, the penalized composite link model performs the best. Conclusion We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized composite link model when data are grouped in wide age classes.
Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel
2017-10-01
This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.
Nonparametric signal processing validation in T-wave alternans detection and estimation.
Goya-Esteban, R; Barquero-Pérez, O; Blanco-Velasco, M; Caamaño-Fernández, A J; García-Alberola, A; Rojo-Álvarez, J L
2014-04-01
Although a number of methods have been proposed for T-Wave Alternans (TWA) detection and estimation, their performance strongly depends on their signal processing stages and on their free parameters tuning. The dependence of the system quality with respect to the main signal processing stages in TWA algorithms has not yet been studied. This study seeks to optimize the final performance of the system by successive comparisons of pairs of TWA analysis systems, with one single processing difference between them. For this purpose, a set of decision statistics are proposed to evaluate the performance, and a nonparametric hypothesis test (from Bootstrap resampling) is used to make systematic decisions. Both the temporal method (TM) and the spectral method (SM) are analyzed in this study. The experiments were carried out in two datasets: first, in semisynthetic signals with artificial alternant waves and added noise; second, in two public Holter databases with different documented risk of sudden cardiac death. For semisynthetic signals (SNR = 15 dB), after the optimization procedure, a reduction of 34.0% (TM) and 5.2% (SM) of the power of TWA amplitude estimation errors was achieved, and the power of error probability was reduced by 74.7% (SM). For Holter databases, appropriate tuning of several processing blocks, led to a larger intergroup separation between the two populations for TWA amplitude estimation. Our proposal can be used as a systematic procedure for signal processing block optimization in TWA algorithmic implementations.
Zoffoli, Luca; Ditroilo, Massimiliano; Federici, Ario; Lucertini, Francesco
2017-09-09
This study used surface electromyography (EMG) to investigate the regions and patterns of activity of the external oblique (EO), erector spinae longissimus (ES), multifidus (MU) and rectus abdominis (RA) muscles during walking (W) and pole walking (PW) performed at different speeds and grades. Eighteen healthy adults undertook W and PW on a motorized treadmill at 60% and 100% of their walk-to-run preferred transition speed at 0% and 7% treadmill grade. The Teager-Kaiser energy operator was employed to improve the muscle activity detection and statistical non-parametric mapping based on paired t-tests was used to highlight statistical differences in the EMG patterns corresponding to different trials. The activation amplitude of all trunk muscles increased at high speed, while no differences were recorded at 7% treadmill grade. ES and MU appeared to support the upper body at the heel-strike during both W and PW, with the latter resulting in elevated recruitment of EO and RA as required to control for the longer stride and the push of the pole. Accordingly, the greater activity of the abdominal muscles and the comparable intervention of the spine extensors supports the use of poles by walkers seeking higher engagement of the lower trunk region. Copyright © 2017 Elsevier Ltd. All rights reserved.
Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling
Briley, Daniel A.; Harden, K. Paige; Bates, Timothy C.; Tucker-Drob, Elliot M.
2017-01-01
Gene × Environment (G×E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G×E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G×E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status (SES) interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G×E interaction with sufficient power to detect statistically significant G×E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R. PMID:26318287
Directory of Open Access Journals (Sweden)
Weiß, Verena
2015-10-01
Full Text Available Introduction: For survival data the coefficient of determination cannot be used to describe how good a model fits to the data. Therefore, several measures of explained variation for survival data have been proposed in recent years.Methods: We analyse an existing measure of explained variation with regard to minimisation aspects and demonstrate that these are not fulfilled for the measure.Results: In analogy to the least squares method from linear regression analysis we develop a novel measure for categorical covariates which is based only on the Kaplan-Meier estimator. Hence, the novel measure is a completely nonparametric measure with an easy graphical interpretation. For the novel measure different weighting possibilities are available and a statistical test of significance can be performed. Eventually, we apply the novel measure and further measures of explained variation to a dataset comprising persons with a histopathological papillary thyroid carcinoma.Conclusion: We propose a novel measure of explained variation with a comprehensible derivation as well as a graphical interpretation, which may be used in further analyses with survival data.
Directory of Open Access Journals (Sweden)
Antonio Canale
2017-06-01
Full Text Available msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric inference introduced by Canale and Dunson (2016. The method, based on mixtures of multiscale beta dictionary densities, overcomes the drawbacks of Pólya trees and inherits many of the advantages of Dirichlet process mixture models. The key idea is that an infinitely-deep binary tree is introduced, with a beta dictionary density assigned to each node of the tree. Using a multiscale stick-breaking characterization, stochastically decreasing weights are assigned to each node. The result is an infinite mixture model. The package msBP implements a series of basic functions to deal with this family of priors such as random densities and numbers generation, creation and manipulation of binary tree objects, and generic functions to plot and print the results. In addition, it implements the Gibbs samplers for posterior computation to perform multiscale density estimation and multiscale testing of group differences described in Canale and Dunson (2016.
Non-Parametric Evolutionary Algorithm for Estimating Root Zone Soil Moisture
Mohanty, B.; Shin, Y.; Ines, A. M.
2013-12-01
Prediction of root zone soil moisture is critical for water resources management. In this study, we explored a non-parametric evolutionary algorithm for estimating root zone soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term root zone soil moisture dynamics at different soil depths. Also, we analyzed rainfall occurrence probabilities and dry/wet spell lengths reproduced by this approach. The HMMGA was used to estimate the optimal state sequences (weather states) based on the precipitation history. Historical root zone soil moisture statistics were then determined based on the weather state conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km x 130 km) and Illinois (300 km x 500 km), during 1995 to 2009 and 1994 to 2010, respectively. We found that the newly developed framework performed well in predicting root zone soil moisture dynamics at both the spatial scales. Also, the reproduced rainfall occurrence probabilities and dry/wet spell lengths matched well with the observations at the spatio-temporal scales. Since the proposed algorithm requires only precipitation and historical soil moisture data from existing, established weather stations, it can serve an attractive alternative for predicting root zone soil moisture in the future using climate change scenarios and root zone soil moisture history.
Modular autopilot design and development featuring Bayesian non-parametric adaptive control
Stockton, Jacob
Over the last few decades, Unmanned Aircraft Systems, or UAS, have become a critical part of the defense of our nation and the growth of the aerospace sector. UAS have a great potential for the agricultural industry, first response, and ecological monitoring. However, the wide range of applications require many mission-specific vehicle platforms. These platforms must operate reliably in a range of environments, and in presence of significant uncertainties. The accepted practice for enabling autonomously flying UAS today relies on extensive manual tuning of the UAS autopilot parameters, or time consuming approximate modeling of the dynamics of the UAS. These methods may lead to overly conservative controllers or excessive development times. A comprehensive approach to the development of an adaptive, airframe-independent controller is presented. The control algorithm leverages a nonparametric, Bayesian approach to adaptation, and is used as a cornerstone for the development of a new modular autopilot. Promising simulation results are presented for the adaptive controller, as well as, flight test results for the modular autopilot.
DEFF Research Database (Denmark)
Rochon, Justine; Gondan, Matthias; Kieser, Meinhard
2012-01-01
the null hypothesis of normality, a nonparametric test is applied in the main analysis. Methods: Equally sized samples were drawn from exponential, uniform, and normal distributions. The two-sample t test was conducted if either both samples (Strategy I) or the collapsed set of residuals from both samples...
Trend analysis of rainfall time series for Sindh river basin in India
Gajbhiye, Sarita; Meshram, Chandrashekhar; Mirabbasi, Rasoul; Sharma, S. K.
2016-08-01
The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water such as 83 % water used for agriculture sector, 12 % for industry sector and only 5 % for domestic sector. In this study, the historical rainfall data for the periods 1901-2002 and 1942-2002 of the Sindh river basin, India, were analysed for monthly, seasonal and annual trends. The conventional Mann-Kendall test (MK) and Mann-Kendall test (MMK), after the removal of the effect of all significant autocorrelation coefficients, and Sen's slope estimator were used to identify the trends. Kriging technique was used for interpolating the spatial pattern using Arc GIS 9.3. The analysis suggested significant increase in the trend of rainfall for seasonal and annual series in the Sindh basin during 1901-2002.
Shi, Yang; Chinnaiyan, Arul M; Jiang, Hui
2015-07-01
High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data. The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/. jianghui@umich.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator
Energy Technology Data Exchange (ETDEWEB)
Huang, Shengzhi; Huang, Qiang; Leng, Guoyong; Chang, Jianxia
2016-06-01
This study develops a nonparametric multivariate drought index, namely, the Nonparametric Multivariate Standardized Drought Index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing Nonparametric Multivariate Drought Index, we use the nonparametric kernel estimator to derive the joint distribution of precipitation and streamflow, thus providing additional insights in drought index development. The proposed NMSDI are applied in the Wei River Basin (WRB), based on which the drought evolution characteristics are investigated. Results indicate: (1) generally, NMSDI captures the drought onset similar to Standardized Precipitation Index (SPI) and drought termination and persistence similar to Standardized Streamflow Index (SSFI). The drought events identified by NMSDI match well with historical drought records in the WRB. The performances are also consistent with that by an existing Multivariate Standardized Drought Index (MSDI) at various timescales, confirming the validity of the newly constructed NMSDI in drought detections (2) An increasing risk of drought has been detected for the past decades, and will be persistent to a certain extent in future in most areas of the WRB; (3) the identified change points of annual NMSDI are mainly concentrated in the early 1970s and middle 1990s, coincident with extensive water use and soil reservation practices. This study highlights the nonparametric multivariable drought index, which can be used for drought detections and predictions efficiently and comprehensively.
Dingzhi Peng; Linghua Qiu; Jing Fang; Zhongyuan Zhang
2016-01-01
Although a fragile climate region, the Taihu Lake Basin is among the most developed regions in China and is subjected to intense anthropogenic interference. In this basin, water resources encounter major challenges (e.g., floods, typhoons, and water pollution). In this study, the impacts of climate changes and human activities on hydrological processes were estimated to aid water resource management in developed regions in China. The Mann-Kendall test and cumulative anomaly curve were applied...
Erhui Li; Xingmin Mu; Guangju Zhao; Peng Gao; Hongbo Shao
2014-01-01
Precipitation is very important to the formation of runoff, and studying of runoff variation and its response to precipitation has practical significance to sustainable utilization of water resources. The study used Mann-Kendall test, anomaly accumulation method, and precipitation elasticity of runoff method to analyze the changes in the relation of precipitation and runoff and the contribution of precipitation to runoff change in the Hekou-Longmen region (from 1957 to 2010), Huangfuchuan wat...
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.
Directory of Open Access Journals (Sweden)
Archer Kellie J
2008-02-01
Full Text Available Abstract Background With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN to those with normal functioning allograft. Results The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. Conclusion We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been
Kong, Xiangrong; Mas, Valeria; Archer, Kellie J
2008-02-26
With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN) to those with normal functioning allograft. The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been reported to be relevant to renal diseases. Further study on the
DEFF Research Database (Denmark)
Ramirez, José Rangel; Sørensen, John Dalsgaard
2011-01-01
This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian...... updating approach and be integrated in the reliability analysis by a third-order polynomial chaos expansion approximation. Although Classical Bayesian updating approaches are often used because of its parametric formulation, non-parametric approaches are better alternatives for multi-parametric updating...... with a non-conjugating formulation. The results in this paper show the influence on the time dependent updated reliability when non-parametric and classical Bayesian approaches are used. Further, the influence on the reliability of the number of updated parameters is illustrated....
Non-parametric seismic hazard analysis in the presence of incomplete data
Yazdani, Azad; Mirzaei, Sajjad; Dadkhah, Koroush
2017-01-01
The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.
An Evaluation of Parametric and Nonparametric Models of Fish Population Response.
Energy Technology Data Exchange (ETDEWEB)
Haas, Timothy C.; Peterson, James T.; Lee, Danny C.
1999-11-01
Predicting the distribution or status of animal populations at large scales often requires the use of broad-scale information describing landforms, climate, vegetation, etc. These data, however, often consist of mixtures of continuous and categorical covariates and nonmultiplicative interactions among covariates, complicating statistical analyses. Using data from the interior Columbia River Basin, USA, we compared four methods for predicting the distribution of seven salmonid taxa using landscape information. Subwatersheds (mean size, 7800 ha) were characterized using a set of 12 covariates describing physiography, vegetation, and current land-use. The techniques included generalized logit modeling, classification trees, a nearest neighbor technique, and a modular neural network. We evaluated model performance using out-of-sample prediction accuracy via leave-one-out cross-validation and introduce a computer-intensive Monte Carlo hypothesis testing approach for examining the statistical significance of landscape covariates with the non-parametric methods. We found the modular neural network and the nearest-neighbor techniques to be the most accurate, but were difficult to summarize in ways that provided ecological insight. The modular neural network also required the most extensive computer resources for model fitting and hypothesis testing. The generalized logit models were readily interpretable, but were the least accurate, possibly due to nonlinear relationships and nonmultiplicative interactions among covariates. Substantial overlap among the statistically significant (P<0.05) covariates for each method suggested that each is capable of detecting similar relationships between responses and covariates. Consequently, we believe that employing one or more methods may provide greater biological insight without sacrificing prediction accuracy.
Modern nonparametric, robust and multivariate methods festschrift in honour of Hannu Oja
Taskinen, Sara
2015-01-01
Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.
Directory of Open Access Journals (Sweden)
Rabia Ece OMAY
2013-06-01
Full Text Available In this study, relationship between gross domestic product (GDP per capita and sulfur dioxide (SO2 and particulate matter (PM10 per capita is modeled for Turkey. Nonparametric fixed effect panel data analysis is used for the modeling. The panel data covers 12 territories, in first level of Nomenclature of Territorial Units for Statistics (NUTS, for period of 1990-2001. Modeling of the relationship between GDP and SO2 and PM10 for Turkey, the non-parametric models have given good results.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
Recent Trends in Temperature and Precipitation in the Langat River Basin, Malaysia
Directory of Open Access Journals (Sweden)
Mahdi Amirabadizadeh
2015-01-01
Full Text Available A study was undertaken to detect long-term trends in the annual and seasonal series of maximum and minimum temperatures. Measurements were taken at 11 meteorological stations located in the Langat River Basin in Malaysia. The rainfall and maximum and minimum temperature data were obtained from the Malaysia Meteorological Department (MMD and the Department of Irrigation and Drainage (DID Malaysia. The procedures used included the Mann-Kendall test, the Mann-Kendall rank statistic test, and the Theil-Sen’s slope method. The analytical results indicated that when there were increasing and decreasing trends in the annual and seasonal precipitation and temperature, only the increasing trends were significant at the 95% confidence level. The Theil-Sen’s slope method showed that the rate of increment in the annual precipitation is greater than the seasonal precipitation. A bootstrap technique was applied to explore uncertainty about significant slope values for rainfall, as well as the maximum and minimum temperatures. The Mann-Kendall rank statistics test indicated that most of the trends in the annual and seasonal time series started in the year 2000. All of the annual and seasonal significant trends were obtained at the stations located in the north, east, and northeast portions of the Langat River Basin.
Amirataee, Babak; Montaseri, Majid; Sanikhani, Hadi
2016-10-01
Reference evapotranspiration (ET0) is considered a key parameter for evaluating the climatic changes as well as spatial and temporal patterns of parameters influencing the eco-hydrological processes. The analysis of trend variations of this index can be used to determine appropriate strategies in planning and management of water resources. In this paper, the trend variations of monthly and annual ET0 in Urmia Lake basin, located in the northwest of Iran, have been analyzed using data from 14 synoptic stations in the study area. Regarding the significant effect of autocorrelation coefficients with different lags on trend variations of ET0, this paper has resorted to modified Mann-Kendall test via eliminating the significance effect of autocorrelation coefficients with different lags to analyze the trend variations. Furthermore, Theil-Sen estimator has been used to determine the slope of trend line of ET0. The results indicated an increasing trend in ET0 values at all the studied stations. Having used the modified Mann-Kendall test, the values of significant increasing (positive) trend, which were estimated using common Mann-Kendall test, dramatically decreased. As such, the values of only 7 stations have been significant at 95 % level. The results confirmed the need for eliminating the significance effect of autocorrelation coefficients with different lags to determine and evaluate the trend of hydrological variables.
Testing for Subcellular Randomness
Okunoye, Babatunde O
2008-01-01
Statistical tests were conducted on 1,000 numbers generated from the genome of Bacteriophage T4, obtained from GenBank with accession number AF158101.The numbers passed the non-parametric, distribution-free tests.Deoxyribonucleic acid was discovered to be a random number generator, existent in nature.
A Non-Parametric and Entropy Based Analysis of the Relationship between the VIX and S&P 500
Directory of Open Access Journals (Sweden)
Abhay K. Singh
2013-10-01
Full Text Available This paper features an analysis of the relationship between the S&P 500 Index and the VIX using daily data obtained from the CBOE website and SIRCA (The Securities Industry Research Centre of the Asia Pacific. We explore the relationship between the S&P 500 daily return series and a similar series for the VIX in terms of a long sample drawn from the CBOE from 1990 to mid 2011 and a set of returns from SIRCA’s TRTH datasets from March 2005 to-date. This shorter sample, which captures the behavior of the new VIX, introduced in 2003, is divided into four sub-samples which permit the exploration of the impact of the Global Financial Crisis. We apply a series of non-parametric based tests utilizing entropy based metrics. These suggest that the PDFs and CDFs of these two return distributions change shape in various subsample periods. The entropy and MI statistics suggest that the degree of uncertainty attached to these distributions changes through time and using the S&P 500 return as the dependent variable, that the amount of information obtained from the VIX changes with time and reaches a relative maximum in the most recent period from 2011 to 2012. The entropy based non-parametric tests of the equivalence of the two distributions and their symmetry all strongly reject their respective nulls. The results suggest that parametric techniques do not adequately capture the complexities displayed in the behavior of these series. This has practical implications for hedging utilizing derivatives written on the VIX.
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any func
Revisiting the Distance Duality Relation using a non-parametric regression method
Rana, Akshay; Jain, Deepak; Mahajan, Shobhit; Mukherjee, Amitabha
2016-07-01
The interdependence of luminosity distance, DL and angular diameter distance, DA given by the distance duality relation (DDR) is very significant in observational cosmology. It is very closely tied with the temperature-redshift relation of Cosmic Microwave Background (CMB) radiation. Any deviation from η(z)≡ DL/DA (1+z)2 =1 indicates a possible emergence of new physics. Our aim in this work is to check the consistency of these relations using a non-parametric regression method namely, LOESS with SIMEX. This technique avoids dependency on the cosmological model and works with a minimal set of assumptions. Further, to analyze the efficiency of the methodology, we simulate a dataset of 020 points of η (z) data based on a phenomenological model η(z)= (1+z)epsilon. The error on the simulated data points is obtained by using the temperature of CMB radiation at various redshifts. For testing the distance duality relation, we use the JLA SNe Ia data for luminosity distances, while the angular diameter distances are obtained from radio galaxies datasets. Since the DDR is linked with CMB temperature-redshift relation, therefore we also use the CMB temperature data to reconstruct η (z). It is important to note that with CMB data, we are able to study the evolution of DDR upto a very high redshift z = 2.418. In this analysis, we find no evidence of deviation from η=1 within a 1σ region in the entire redshift range used in this analysis (0 < z <= 2.418).
Johnson, H.O.; Gupta, S.C.; Vecchia, A.V.; Zvomuya, F.
2009-01-01
Excessive loading of sediment and nutrients to rivers is a major problem in many parts of the United States. In this study, we tested the non-parametric Seasonal Kendall (SEAKEN) trend model and the parametric USGS Quality of Water trend program (QWTREND) to quantify trends in water quality of the Minnesota River at Fort Snelling from 1976 to 2003. Both methods indicated decreasing trends in flow-adjusted concentrations of total suspended solids (TSS), total phosphorus (TP), and orthophosphorus (OP) and a generally increasing trend in flow-adjusted nitrate plus nitrite-nitrogen (NO3-N) concentration. The SEAKEN results were strongly influenced by the length of the record as well as extreme years (dry or wet) earlier in the record. The QWTREND results, though influenced somewhat by the same factors, were more stable. The magnitudes of trends between the two methods were somewhat different and appeared to be associated with conceptual differences between the flow-adjustment processes used and with data processing methods. The decreasing trends in TSS, TP, and OP concentrations are likely related to conservation measures implemented in the basin. However, dilution effects from wet climate or additional tile drainage cannot be ruled out. The increasing trend in NO3-N concentrations was likely due to increased drainage in the basin. Since the Minnesota River is the main source of sediments to the Mississippi River, this study also addressed the rapid filling of Lake Pepin on the Mississippi River and found the likely cause to be increased flow due to recent wet climate in the region. Copyright ?? 2009 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.
Nonparametric estimation of population density for line transect sampling using FOURIER series
Crain, B.R.; Burnham, K.P.; Anderson, D.R.; Lake, J.L.
1979-01-01
A nonparametric, robust density estimation method is explored for the analysis of right-angle distances from a transect line to the objects sighted. The method is based on the FOURIER series expansion of a probability density function over an interval. With only mild assumptions, a general population density estimator of wide applicability is obtained.
A non-parametric peak finder algorithm and its application in searches for new physics
Chekanov, S
2011-01-01
We have developed an algorithm for non-parametric fitting and extraction of statistically significant peaks in the presence of statistical and systematic uncertainties. Applications of this algorithm for analysis of high-energy collision data are discussed. In particular, we illustrate how to use this algorithm in general searches for new physics in invariant-mass spectra using pp Monte Carlo simulations.
Nonparametric estimation of the stationary M/G/1 workload distribution function
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted
2005-01-01
In this paper it is demonstrated how a nonparametric estimator of the stationary workload distribution function of the M/G/1-queue can be obtained by systematic sampling the workload process. Weak convergence results and bootstrap methods for empirical distribution functions for stationary associ...
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Madsen, Kristoffer H.; Siebner, Hartwig Roman
2014-01-01
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian...
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-parametric system identification from non-linear stochastic response
DEFF Research Database (Denmark)
Rüdinger, Finn; Krenk, Steen
2001-01-01
An estimation method is proposed for identification of non-linear stiffness and damping of single-degree-of-freedom systems under stationary white noise excitation. Non-parametric estimates of the stiffness and damping along with an estimate of the white noise intensity are obtained by suitable p...
DEFF Research Database (Denmark)
Ramirez, José Rangel; Sørensen, John Dalsgaard
2011-01-01
This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian u...
Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
A non-parametric method for correction of global radiation observations
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Perers, Bengt;
2013-01-01
in the observations are corrected. These are errors such as: tilt in the leveling of the sensor, shadowing from surrounding objects, clipping and saturation in the signal processing, and errors from dirt and wear. The method is based on a statistical non-parametric clear-sky model which is applied to both...
Nonparametric estimation in an "illness-death" model when all transition times are interval censored
DEFF Research Database (Denmark)
Frydman, Halina; Gerds, Thomas; Grøn, Randi
2013-01-01
We develop nonparametric maximum likelihood estimation for the parameters of an irreversible Markov chain on states {0,1,2} from the observations with interval censored times of 0 → 1, 0 → 2 and 1 → 2 transitions. The distinguishing aspect of the data is that, in addition to all transition times ...
A Comparison of Shewhart Control Charts based on Normality, Nonparametrics, and Extreme-Value Theory
Ion, R.A.; Does, R.J.M.M.; Klaassen, C.A.J.
2000-01-01
Several control charts for individual observations are compared. The traditional ones are the well-known Shewhart control charts with estimators for the spread based on the sample standard deviation and the average of the moving ranges. The alternatives are nonparametric control charts, based on emp
Non-parametric production analysis of pesticides use in the Netherlands
Oude Lansink, A.G.J.M.; Silva, E.
2004-01-01
Many previous empirical studies on the productivity of pesticides suggest that pesticides are under-utilized in agriculture despite the general held believe that these inputs are substantially over-utilized. This paper uses data envelopment analysis (DEA) to calculate non-parametric measures of the
An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models
Liang, Tie; Wells, Craig S.; Hambleton, Ronald K.
2014-01-01
As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting…
Agasisti, Tommaso
2011-01-01
The objective of this paper is an efficiency analysis concerning higher education systems in European countries. Data have been extracted from OECD data-sets (Education at a Glance, several years), using a non-parametric technique--data envelopment analysis--to calculate efficiency scores. This paper represents the first attempt to conduct such an…
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models.
Fan, Jianqing; Feng, Yang; Song, Rui
2011-06-01
A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under general nonparametric models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, a data-driven thresholding and an iterative nonparametric independence screening (INIS) are also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data analysis demonstrate that the proposed procedure works well with moderate sample size and large dimension and performs better than competing methods.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models.
Fan, Jianqing; Ma, Yunbei; Dai, Wei
2014-01-01
The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high dimensional varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications.
Low default credit scoring using two-class non-parametric kernel density estimation
CSIR Research Space (South Africa)
Rademeyer, E
2016-12-01
Full Text Available This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes’ rule, to include either...
Measuring the influence of networks on transaction costs using a non-parametric regression technique
DEFF Research Database (Denmark)
Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Do Former College Athletes Earn More at Work? A Nonparametric Assessment
Henderson, Daniel J.; Olbrecht, Alexandre; Polachek, Solomon W.
2006-01-01
This paper investigates how students' collegiate athletic participation affects their subsequent labor market success. By using newly developed techniques in nonparametric regression, it shows that on average former college athletes earn a wage premium. However, the premium is not uniform, but skewed so that more than half the athletes actually…
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Gao, Oliver H.; Holmén, Britt A.; Niemeier, Debbie A.
The Ozone Weekend Effect (OWE) has become increasingly more frequent and widespread in southern California since the mid-1970s. Although a number of hypotheses have been suggested to explain the effect, there remains uncertainty associated with the root factors contributing to elevated weekend ozone concentrations. Targeting the time window of the 1997 Southern California Ozone Study (SCOS97), this paper examines traffic activity data for 14 vehicle classes at 27 weigh-in-motion (WIM) stations in southern California. Nonparametric factorial analyses of light-duty vehicle (LDV) and heavy-duty truck (HDT) traffic volumes indicate significant differences in daily volumes by day of week and between the weekly patterns of daily LDV and HDT volumes. Across WIM stations, the daily LDV volume was highest on Friday and decreased by 10% on weekends compared to that on midweek days. In contrast, daily HDT volumes showed dramatic weekend drops of 53% on Saturday and 64% on Sunday. As a result, LDV to HDT ratios increased by 145% on weekends. Nonparametric tests also suggest that weekly traffic patterns varied significantly between WIM stations located close to (central) and far from (peripheral) the Los Angeles Metro area. Weekend increases in LDV/HDT ratios were more pronounced at central WIM sites due to greater weekend declines of HDT relative to LDV traffic. The implications of these weekly traffic patterns for the OWE in southern California were investigated by estimating daily WIM traffic on-road running exhaust emissions of total organic gas (TOG) and oxides of nitrogen (NO x) using EMFAC2002 emission factors. The results support the California Air Resource Board's (CARB's) NO x reduction hypothesis that greater weekend NO x reductions relative to volatile organic compound (VOC) emissions, in combinations with the VOC-limited ozone system, contribute to the OWE observed in the region. The results from this study can be used to develop weekend on-road mobile emission
T. Kuosmanen (Timo); G.T. Post (Thierry)
2001-01-01
textabstractWe develop a nonparametric test of productive efficiency that accounts for the possibility of errors-in-variables. The test allows for statistical inference based on the extreme value distribution of the L?? norm. In contrast to the test proposed by Varian, H (1985): 'Nonparametric
T. Kuosmanen (Timo); G.T. Post (Thierry)
2001-01-01
textabstractWe develop a nonparametric test of productive efficiency that accounts for the possibility of errors-in-variables. The test allows for statistical inference based on the extreme value distribution of the L?? norm. In contrast to the test proposed by Varian, H (1985): 'Nonparametric Analy
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...
A nonparametric approach to the analysis of dichotomous item responses
Mokken, R.J.; Lewis, C.
1982-01-01
An item response theory is discussed which is based on purely ordinal assumptions about the probabilities that people respond positively to items. It is considered as a natural generalization of both Guttman scaling and classical test theory. A distinction is drawn between construction and evaluatio
A Nonparametric Approach to Estimate Classification Accuracy and Consistency
Lathrop, Quinn N.; Cheng, Ying
2014-01-01
When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…
Subpixel Snow Cover Mapping from MODIS Data by Nonparametric Regression Splines
Akyurek, Z.; Kuter, S.; Weber, G. W.
2016-12-01
Spatial extent of snow cover is often considered as one of the key parameters in climatological, hydrological and ecological modeling due to its energy storage, high reflectance in the visible and NIR regions of the electromagnetic spectrum, significant heat capacity and insulating properties. A significant challenge in snow mapping by remote sensing (RS) is the trade-off between the temporal and spatial resolution of satellite imageries. In order to tackle this issue, machine learning-based subpixel snow mapping methods, like Artificial Neural Networks (ANNs), from low or moderate resolution images have been proposed. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression tool that can build flexible models for high dimensional and complex nonlinear data. Although MARS is not often employed in RS, it has various successful implementations such as estimation of vertical total electron content in ionosphere, atmospheric correction and classification of satellite images. This study is the first attempt in RS to evaluate the applicability of MARS for subpixel snow cover mapping from MODIS data. Total 16 MODIS-Landsat ETM+ image pairs taken over European Alps between March 2000 and April 2003 were used in the study. MODIS top-of-atmospheric reflectance, NDSI, NDVI and land cover classes were used as predictor variables. Cloud-covered, cloud shadow, water and bad-quality pixels were excluded from further analysis by a spatial mask. MARS models were trained and validated by using reference fractional snow cover (FSC) maps generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also developed. The mutual comparison of obtained MARS and ANN models was accomplished on independent test areas. The MARS model performed better than the ANN model with an average RMSE of 0.1288 over the independent test areas; whereas the average RMSE of the ANN model
Validation of two (parametric vs non-parametric) daily weather generators
Dubrovsky, M.; Skalak, P.
2015-12-01
As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series
Fault prediction of fighter based on nonparametric density estimation
Institute of Scientific and Technical Information of China (English)
Zhang Zhengdao; Hu Shousong
2005-01-01
Fighters and other complex engineering systems have many characteristics such as difficult modeling and testing, multiple working situations, and high cost. Aim at these points, a new kind of real-time fault predictor is designed based on an improved k-nearest neighbor method, which needs neither the math model of system nor the training data and prior knowledge. It can study and predict while system's running, so that it can overcome the difficulty of data acquirement. Besides, this predictor has a fast prediction speed, and the false alarm rate and missing alarm rate can be adjusted randomly. The method is simple and universalizable. The result of simulation on fighter F-16 proved the efficiency.
missForest: Nonparametric missing value imputation using random forest
Stekhoven, Daniel J.
2015-05-01
missForest imputes missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation and can be run in parallel to save computation time. missForest has been used to, among other things, impute variable star colors in an All-Sky Automated Survey (ASAS) dataset of variable stars with no NOMAD match.
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
The Nonlinear Dynamic Relationship of Exchange Rates: Parametric and Nonparametric Causality testing
Bekiros, S.D.; Diks, C.
2007-01-01
The present study investigates the long-term linear and nonlinear causal linkages among six currencies, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD and USD/CAD. The prime motivation for choosing these exchange rates comes from the fact that they are the most liquid and widely traded, covering
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
Non-parametric Bayesian human motion recognition using a single MEMS tri-axial accelerometer.
Ahmed, M Ejaz; Song, Ju Bin
2012-09-27
In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer
Directory of Open Access Journals (Sweden)
M. Ejaz Ahmed
2012-09-01
Full Text Available In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
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...... to avoid this problem. The main objective is to investigate the applicability of the nonparametric kernel regression method in applied production analysis. The focus of the empirical analyses included in this thesis is the agricultural sector in Poland. Data on Polish farms are used to investigate...... practically and politically relevant problems and to illustrate how nonparametric regression methods can be used in applied microeconomic production analysis both in panel data and cross-section data settings. The thesis consists of four papers. The first paper addresses problems of parametric...
Stahel-Donoho kernel estimation for fixed design nonparametric regression models
Institute of Scientific and Technical Information of China (English)
LIN; Lu
2006-01-01
This paper reports a robust kernel estimation for fixed design nonparametric regression models.A Stahel-Donoho kernel estimation is introduced,in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points.Based on a local approximation,a computational technique is given to approximate to the incomputable depths of the errors.As a result the new estimator is computationally efficient.The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error.Unlike the depth-weighted estimator for parametric regression models,this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one.Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.
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.
Directory of Open Access Journals (Sweden)
Mustafa Koroglu
2016-02-01
Full Text Available This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries’ openness to trade, respectively.
Saad, Walid; Poor, H Vincent; Başar, Tamer; Song, Ju Bin
2012-01-01
This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users(PUs), to cooperate and use \\emph{Bayesian nonparametric} techniques to estimate the distributions of the PUs' activity pattern, assumed to be completely unknown. In the proposed model, each cognitive node may have its own individual view on each PU's distribution, and, hence, seeks to find partners having a correlated perception. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use a combination of Bayesian nonparametric models such as the Dirichlet process and statistical goodness of fit techniques ...
非参数判别模型%Nonparametric discriminant model
Institute of Scientific and Technical Information of China (English)
谢斌锋; 梁飞豹
2011-01-01
提出了一类新的判别分析方法,主要思想是将非参数回归模型推广到判别分析中,形成相应的非参数判别模型.通过实例与传统判别法相比较,表明非参数判别法具有更广泛的适用性和较高的回代正确率.%In this paper, the author puts forth a new class of discriminant method, which the main idea is applied non- parametric regression model to discriminant analysis and forms the corresponding nonparametric discriminant model. Compared with the traditional discriminant methods by citing an example, the nonparametric discriminant method has more comprehensive adaptability and higher correct rate of back subsitution.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
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...... function. However, the a priori specification of a functional form involves the risk of choosing one that is not similar to the “true” but unknown relationship between the regressors and the dependent variable. This problem, known as parametric misspecification, can result in biased parameter estimates...... 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...
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J.
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction
Institute of Scientific and Technical Information of China (English)
WENG Jian-cheng; HU Zhong-wei; YU Quan; REN Fu-tian
2007-01-01
A K-nearest neighbor (K-NN) based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series,collected from the floating car data (FCD) system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.
Testing for additivity with B-splines
Institute of Scientific and Technical Information of China (English)
Heng-jian CUI; Xu-ming HE; Li LIU
2007-01-01
Regression splines are often used for fitting nonparametric functions, and they work especially well for additivity models. In this paper, we consider two simple tests of additivity: an adaptation of Tukey's one degree of freedom test and a nonparametric version of Rao's score test. While the Tukey-type test can detect most forms of the local non-additivity at the parametric rate of O(n-1/2), the score test is consistent for all alternative at a nonparametric rate. The asymptotic distribution of these test statistics is derived under both the null and local alternative hypotheses. A simulation study is conducted to compare their finite-sample performances with some existing kernelbased tests. The score test is found to have a good overall performance.
Testing for additivity with B-splines
Institute of Scientific and Technical Information of China (English)
2007-01-01
Regression splines are often used for fitting nonparametric functions, and they work especially well for additivity models. In this paper, we consider two simple tests of additivity: an adaptation of Tukey’s one degree of freedom test and a nonparametric version of Rao’s score test. While the Tukey-type test can detect most forms of the local non-additivity at the parametric rate of O(n-1/2), the score test is consistent for all alternative at a nonparametric rate. The asymptotic distribution of these test statistics is derived under both the null and local alternative hypotheses. A simulation study is conducted to compare their finite-sample performances with some existing kernel-based tests. The score test is found to have a good overall performance.
Variable selection in identification of a high dimensional nonlinear non-parametric system
Institute of Scientific and Technical Information of China (English)
Er-Wei BAI; Wenxiao ZHAO; Weixing ZHENG
2015-01-01
The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.
Estimating Financial Risk Measures for Futures Positions:A Non-Parametric Approach
Cotter, John; dowd, kevin
2011-01-01
This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and the...
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access to no...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Asymmetry Effects in Chinese Stock Markets Volatility: A Generalized Additive Nonparametric Approach
Hou, Ai Jun
2007-01-01
The unique characteristics of the Chinese stock markets make it difficult to assume a particular distribution for innovations in returns and the specification form of the volatility process when modeling return volatility with the parametric GARCH family models. This paper therefore applies a generalized additive nonparametric smoothing technique to examine the volatility of the Chinese stock markets. The empirical results indicate that an asymmetric effect of negative news exists in the Chin...
Using a nonparametric PV model to forecast AC power output of PV plants
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
In this paper, a methodology using a nonparametric model is used to forecast AC power output of PV plants using as inputs several forecasts of meteorological variables from a Numerical Weather Prediction (NWP) model and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast the AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, an...
An exact predictive recursion for Bayesian nonparametric analysis of incomplete data
Garibaldi, Ubaldo; Viarengo, Paolo
2010-01-01
This paper presents a new derivation of nonparametric distribution estimation with right-censored data. It is based on an extension of the predictive inferences to compound evidence. The estimate is recursive and exact, and no stochastic approximation is needed: it simply requires that the censored data are processed in decreasing order. Only in this case the recursion provides exact posterior predictive distributions for subsequent samples under a Dirichlet process prior. The resulting estim...
Essays on parametric and nonparametric modeling and estimation with applications to energy economics
Gao, Weiyu
My dissertation research is composed of two parts: a theoretical part on semiparametric efficient estimation and an applied part in energy economics under different dynamic settings. The essays are related in terms of their applications as well as the way in which models are constructed and estimated. In the first essay, efficient estimation of the partially linear model is studied. We work out the efficient score functions and efficiency bounds under four stochastic restrictions---independence, conditional symmetry, conditional zero mean, and partially conditional zero mean. A feasible efficient estimation method for the linear part of the model is developed based on the efficient score. A battery of specification test that allows for choosing between the alternative assumptions is provided. A Monte Carlo simulation is also conducted. The second essay presents a dynamic optimization model for a stylized oilfield resembling the largest developed light oil field in Saudi Arabia, Ghawar. We use data from different sources to estimate the oil production cost function and the revenue function. We pay particular attention to the dynamic aspect of the oil production by employing petroleum-engineering software to simulate the interaction between control variables and reservoir state variables. Optimal solutions are studied under different scenarios to account for the possible changes in the exogenous variables and the uncertainty about the forecasts. The third essay examines the effect of oil price volatility on the level of innovation displayed by the U.S. economy. A measure of innovation is calculated by decomposing an output-based Malmquist index. We also construct a nonparametric measure for oil price volatility. Technical change and oil price volatility are then placed in a VAR system with oil price and a variable indicative of monetary policy. The system is estimated and analyzed for significant relationships. We find that oil price volatility displays a significant
Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat
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Luca Scrucca
2001-06-01
Full Text Available In this paper we describe the Xlisp-Stat version of the sm library, a software for applying nonparametric kernel smoothing methods. The original version of the sm library was written by Bowman and Azzalini in S-Plus, and it is documented in their book Applied Smoothing Techniques for Data Analysis (1997. This is also the main reference for a complete description of the statistical methods implemented. The sm library provides kernel smoothing methods for obtaining nonparametric estimates of density functions and regression curves for different data structures. Smoothing techniques may be employed as a descriptive graphical tool for exploratory data analysis. Furthermore, they can also serve for inferential purposes as, for instance, when a nonparametric estimate is used for checking a proposed parametric model. The Xlisp-Stat version includes some extensions to the original sm library, mainly in the area of local likelihood estimation for generalized linear models. The Xlisp-Stat version of the sm library has been written following an object-oriented approach. This should allow experienced Xlisp-Stat users to implement easily their own methods and new research ideas into the built-in prototypes.
Kianisarkaleh, Azadeh; Ghassemian, Hassan
2016-09-01
Feature extraction plays a crucial role in improvement of hyperspectral images classification. Nonparametric feature extraction methods show better performance compared to parametric ones when distribution of classes is non normal-like. Moreover, they can extract more features than parametric methods do. In this paper, a new nonparametric linear feature extraction method is introduced for classification of hyperspectral images. The proposed method has no free parameter and its novelty can be discussed in two parts. First, neighbor samples are specified by using Parzen window idea for determining local mean. Second, two new weighting functions are used. Samples close to class boundaries will have more weight in the between-class scatter matrix formation and samples close to class mean will have more weight in the within-class scatter matrix formation. The experimental results on three real hyperspectral data sets, Indian Pines, Salinas and Pavia University, demonstrate that the proposed method has better performance in comparison with some other nonparametric and parametric feature extraction methods.
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale.
Mircioiu, Constantin; Atkinson, Jeffrey
2017-05-10
A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple "yes" or "no" but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with "large" (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 "very important" and 4 "essential"), a "score analysis" based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.
Non-parametric foreground subtraction for 21cm epoch of reionization experiments
Harker, Geraint; Bernardi, Gianni; Brentjens, Michiel A; De Bruyn, A G; Ciardi, Benedetta; Jelic, Vibor; Koopmans, Leon V E; Labropoulos, Panagiotis; Mellema, Garrelt; Offringa, Andre; Pandey, V N; Schaye, Joop; Thomas, Rajat M; Yatawatta, Sarod
2009-01-01
An obstacle to the detection of redshifted 21cm emission from the epoch of reionization (EoR) is the presence of foregrounds which exceed the cosmological signal in intensity by orders of magnitude. We argue that in principle it would be better to fit the foregrounds non-parametrically - allowing the data to determine their shape - rather than selecting some functional form in advance and then fitting its parameters. Non-parametric fits often suffer from other problems, however. We discuss these before suggesting a non-parametric method, Wp smoothing, which seems to avoid some of them. After outlining the principles of Wp smoothing we describe an algorithm used to implement it. We then apply Wp smoothing to a synthetic data cube for the LOFAR EoR experiment. The performance of Wp smoothing, measured by the extent to which it is able to recover the variance of the cosmological signal and to which it avoids leakage of power from the foregrounds, is compared to that of a parametric fit, and to another non-parame...
The properties and mechanism of long-term memory in nonparametric volatility
Li, Handong; Cao, Shi-Nan; Wang, Yan
2010-08-01
Recent empirical literature documents the presence of long-term memory in return volatility. But the mechanism of the existence of long-term memory is still unclear. In this paper, we investigate the origin and properties of long-term memory with nonparametric volatility, using high-frequency time series data of the Chinese Shanghai Composite Stock Price Index. We perform Detrended Fluctuation Analysis (DFA) on three different nonparametric volatility estimators with different sampling frequencies. For the same volatility series, the Hurst exponents reduce as the sampling time interval increases, but they are still larger than 1/2, which means that no matter how the interval changes, it still cannot change the existence of long memory. RRV presents a relatively stable property on long-term memory and is less influenced by sampling frequency. RV and RBV have some evolutionary trends depending on time intervals, which indicating that the jump component has no significant impact on the long-term memory property. This suggests that the presence of long-term memory in nonparametric volatility can be contributed to the integrated variance component. Considering the impact of microstructure noise, RBV and RRV still present long-term memory under various time intervals. We can infer that the presence of long-term memory in realized volatility is not affected by market microstructure noise. Our findings imply that the long-term memory phenomenon is an inherent characteristic of the data generating process, not a result of microstructure noise or volatility clustering.
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.
Xu, Yanxun; Müller, Peter; Wahed, Abdus S; Thall, Peter F
2016-01-01
We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at https://www.ma.utexas.edu/users/yxu/.
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/.
Directory of Open Access Journals (Sweden)
Andreas H. Melcher
2012-09-01
Full Text Available This study analyses multidimensional spawning habitat suitability of the fish species “Nase” (latin: Chondrostoma nasus. This is the first time non-parametric methods were used to better understand biotic habitat use in theory and practice. In particular, we tested (1 the Decision Tree technique, Chi-squared Automatic Interaction Detectors (CHAID, to identify specific habitat types and (2 Prediction-Configural Frequency Analysis (P-CFA to test for statistical significance. The combination of both non-parametric methods, CHAID and P-CFA, enabled the identification, prediction and interpretation of most typical significant spawning habitats, and we were also able to determine non-typical habitat types, e.g., types in contrast to antitypes. The gradual combination of these two methods underlined three significant habitat types: shaded habitat, fine and coarse substrate habitat depending on high flow velocity. The study affirmed the importance for fish species of shading and riparian vegetation along river banks. In addition, this method provides a weighting of interactions between specific habitat characteristics. The results demonstrate that efficient river restoration requires re-establishing riparian vegetation as well as the open river continuum and hydro-morphological improvements to habitats.
Characterizing major agricultural land change trends in the Western Corn Belt
Shao, Yang; Taff, Gregory N.; Ren, Jie; Campbell, James B.
2016-12-01
In this study we developed annual corn/soybean maps for the Western Corn Belt within the United States using multi-temporal MODIS NDVI products from 2001 to 2015 to support long-term cropland change analysis. Based on the availability of training data (cropland data layer from the USDA-NASS), we designed a cross-validation scheme for 2006-2015 MODIS data to examine the spatial generalization capability of a neural network classifier. Training data points were derived from a three-state sub-region consisting of North Dakota, Nebraska, and Iowa. Trained neural networks were applied to the testing sub-region (South Dakota, Kansas, Minnesota, and Missouri) to generate corn/soybean maps. Using a default threshold value (neural network output signal ⩾ 0.5), the neural networks performed well for South Dakota and Minnesota. Overall accuracy was higher than 80% (kappa > 0.55) for all testing years from 2006 to 2015. However, we observed high variation of classification performance for Kansas (overall accuracy: 0.71-0.82) and Missouri (overall accuracy: 0.65-0.77) for various testing years. We developed a threshold-moving method that decreases/increases threshold values of neural network output signals to match MODIS-derived corn/soybean acreage with the NASS acreage statistics. Over 70% of testing states and years showed improved classification performance compared to the use of a default 0.5 threshold. The largest improvement of kappa value was about 0.08. This threshold-moving method was used to generate MODIS-based annual corn/soybean map products for 2001-2015. A non-parametric Mann-Kendall test was then used to identify areas that showed significant (p North Dakota, South Dakota, and the west portion of Minnesota. The highest annual increase rate for a 5-km moving window was about 6.8%.
Mavrommatis, A. P.; Segall, P.; Uchida, N.; Johnson, K. M.
2015-12-01
Changes in the recurrence intervals of repeating earthquakes offshore northern Japan in the period 1996 to 2011 imply long-term acceleration of aseismic slip preceding the 2011 M9 Tohoku-oki earthquake, confirming a previous inference from completely independent GPS data (Mavrommatis et al., 2014, GRL). We test whether sequences of repeating earthquakes exhibit a statistically significant monotonic trend in recurrence interval by applying the nonparametric Mann-Kendall test. Offshore northern Tohoku, all sequences that pass the test exhibit decelerating recurrence, consistent with decaying afterslip following the 1994 M7.7 Sanriku earthquake. On the other hand, offshore south-central Tohoku, all sequences that pass the test exhibit accelerating recurrence, consistent with long-term accelerating creep prior to the 2011 Μ9 earthquake. Using a physical model of repeating earthquake recurrence, we produce time histories of cumulative slip on the plate interface. After correcting for afterslip following several M~7 earthquakes in the period 2003-2011, we find that all but one sequence exhibit statistically significant slip accelerations. Offshore south-central Tohoku, the estimated slip acceleration is on average 2.9 mm/yr2, consistent with the range of 2.6-4.0 mm/yr2 estimated from independent GPS data (Mavrommatis et al., 2014). From a joint inversion of GPS and seismicity data, we infer that a substantial portion of the plate interface experienced accelerating creep in the 15 years prior to the M9 Tohoku-oki earthquake. The large slip area of the Tohoku-oki earthquake appears to be partly bounded by accelerating creep, implying that most of the rupture area of the M9 earthquake was either locked or creeping at a constant rate during this time period. Accelerating creep would result in increasing stressing rate on locked parts of the interface, thereby promoting nucleation of moderate to large earthquakes.
Institute of Scientific and Technical Information of China (English)
赵文芝; 田铮; 夏志明
2009-01-01
A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed.The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise.Moreover,the consistence of the test is proved while the rate of convergence is given.The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data.
Zhu, Feng; Feng, Weiyue; Wang, Huajian; Huang, Shaosen; Lv, Yisong; Chen, Yong
2013-01-01
X-ray spectral imaging provides quantitative imaging of trace elements in biological sample with high sensitivity. We propose a novel algorithm to promote the signal-to-noise ratio (SNR) of X-ray spectral images that have low photon counts. Firstly, we estimate the image data area that belongs to the homogeneous parts through confidence interval testing. Then, we apply the Poisson regression through its maximum likelihood estimation on this area to estimate the true photon counts from the Poisson noise corrupted data. Unlike other denoising methods based on regression analysis, we use the bootstrap resampling methods to ensure the accuracy of regression estimation. Finally, we use a robust local nonparametric regression method to estimate the baseline and subsequently subtract it from the X-ray spectral data to further improve the SNR of the data. Experiments on several real samples show that the proposed method performs better than some state-of-the-art approaches to ensure accuracy and precision for quantit...
Directory of Open Access Journals (Sweden)
Vangelis Sakkalis
2008-01-01
Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.
Cliff´s Delta Calculator: A non-parametric effect size program for two groups of observations
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Guillermo Macbeth
2011-05-01
Full Text Available The Cliff´s Delta statistic is an effect size measure that quantifies the amount of difference between two non-parametric variables beyond p-values interpretation. This measure can be understood as a useful complementary analysis for the corresponding hypothesis testing. During the last two decades the use of effect size measures has been strongly encouraged by methodologists and leading institutions of behavioral sciences. The aim of this contribution is to introduce the Cliff´s Delta Calculator software that performs such analysis and offers some interpretation tips. Differences and similarities with the parametric case are analysed and illustrated. The implementation of this free program is fully described and compared with other calculators. Alternative algorithmic approaches are mathematically analysed and a basic linear algebra proof of its equivalence is formally presented. Two worked examples in cognitive psychology are commented. A visual interpretation of Cliff´s Delta is suggested. Availability, installation and applications of the program are presented and discussed.
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Rohin Anhal
2013-10-01
Full Text Available The aim of this paper is to examine the direction of causality between real GDP on the one hand and final energy and coal consumption on the other in India, for the period from 1970 to 2011. The methodology adopted is the non-parametric bootstrap procedure, which is used to construct the critical values for the hypothesis of causality. The results of the bootstrap tests show that for total energy consumption, there exists no causal relationship in either direction with GDP of India. However, if coal consumption is considered, we find evidence in support of unidirectional causality running from coal consumption to GDP. This clearly has important implications for the Indian economy. The most important implication is that curbing coal consumption in order to reduce carbon emissions would in turn have a limiting effect on economic growth. Our analysis contributes to the literature in three distinct ways. First, this is the first paper to use the bootstrap method to examine the growth-energy connection for the Indian economy. Second, we analyze data for the time period 1970 to 2011, thereby utilizing recently available data that has not been used by others. Finally, in contrast to the recently done studies, we adopt a disaggregated approach for the analysis of the growth-energy nexus by considering not only aggregate energy consumption, but coal consumption as well.
Non-parametric change-point method for differential gene expression detection.
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Yao Wang
Full Text Available BACKGROUND: We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short, by using a single equation for detecting differential gene expression (DGE in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability. METHODOLOGY: NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods. CONCLUSIONS: Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods.
Temperature extremes in Western Europe and associated atmospheric anomalies
Carvalho, V. A.; Santos, J. A.
2009-09-01
This worḱs focal point is the analysis of temperature extremes over Western Europe in the period 1957-2007 and their relationship to large-scale anomalies in the atmospheric circulation patterns. The study is based on temperature daily time series recorded at a set of meteorological stations covering the target area. The large-scale anomalies are analyzed using data from the National Centers for Environmental Prediction reanalysis project. Firstly, a preliminary statistical analysis was undertaken in order to identify data gaps and erroneous values and to check the homogeneity of the time series, using not only elementary statistical approaches (e.g., chronograms, box-plots, scatter-plots), but also a set of non-parametric statistical tests particularly suitable for the analysis of monthly and seasonal mean temperature time series (e.g., Wald-Wolfowitz serial correlation test, Spearman and Mann-Kendall trend tests). Secondly, based on previous results, a selection of the highest quality time series was carried out. Aiming at identifying temperature extremes, we then proceed to the isolation of months with temperature values above or below pre-selected thresholds based on the empirical distribution of each time series. In particular, thresholds are based on percentiles specifically computed for each individual temperature record (data adaptive) and not on fixed values. As a result, a calendar of extremely high and extremely low monthly mean temperatures is obtained and the large-scale atmospheric conditions during each extreme are analyzed. Several atmospheric fields are considered in this study (e.g., 2-m maximum and minimum air temperature, sea level pressure, geopotential height, zonal and meridional wind components, vorticity, relative humidity) at different isobaric levels. Results show remarkably different synoptic conditions for temperature extremes in different parts of Western Europe, highlighting the different dynamical mechanisms underlying their
Directory of Open Access Journals (Sweden)
H. Zareabyaneh
2014-12-01
Full Text Available Any planning for the future requires estimates of future conditions. It is possible to study changes over time series. In this study, changes of production and cultivated area of 17 field crops of Khorasan Razavi province in a 25-year period were determined with Mann - Kendall test, Sen’s Estimator Slope and linear regression. Analysis of the three tests showed that performance of 76.5% from yield, 88.2% from area under cultivation and 55.8% from agricultural production were significant at the 0.01 and 0.05 level. On the other hand, trend of yields 58.8% was increase, 17.7% was reduced and 23.5% was no significant trend. Similarly, trend of 23.5% from area under cultivation was acreage, 64.7% was reduction, and 11.8% was no significant trend. For production variable, 29.4% was significantly increased and 29.4% was significant reduction. More detailed analysis showed that performance, production and area under cultivation of three crops of cotton, grain and tomatoes increased significantly. Results of all three methods showed the highest trend of negatively performance and area under cultivation variation is related to pea and melon respectively. Furthermore, most of the positive trend in production of tomatoes and grain, performance in onions, potatoes and tomatoes and area under cultivation in tomato observed. The results showed that linear trend and the nonparametric tests of important products of province: wheat, barley, sugar beet, cotton, melons, watermelons and tomatoes in 0.01 were significant. This result shows the importance of these yields in gross state province product.
Ohkubo, Jun
2011-12-01
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.
Blundell, Richard; Horowitz, Joel L.; Parey, Matthias
2011-01-01
This paper develops a new method for estimating a demand function and the welfare consequences of price changes. The method is applied to gasoline demand in the U.S. and is applicable to other goods. The method uses shape restrictions derived from economic theory to improve the precision of a nonparametric estimate of the demand function. Using data from the U.S. National Household Travel Survey, we show that the restrictions are consistent with the data on gasoline demand and remove the anom...
Nonparametric variance estimation in the analysis of microarray data: a measurement error approach.
Carroll, Raymond J; Wang, Yuedong
2008-01-01
This article investigates the effects of measurement error on the estimation of nonparametric variance functions. We show that either ignoring measurement error or direct application of the simulation extrapolation, SIMEX, method leads to inconsistent estimators. Nevertheless, the direct SIMEX method can reduce bias relative to a naive estimator. We further propose a permutation SIMEX method which leads to consistent estimators in theory. The performance of both SIMEX methods depends on approximations to the exact extrapolants. Simulations show that both SIMEX methods perform better than ignoring measurement error. The methodology is illustrated using microarray data from colon cancer patients.
López Fontán, J L; Costa, J; Ruso, J M; Prieto, G; Sarmiento, F
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found.
Energy Technology Data Exchange (ETDEWEB)
Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)
Nonparametric bootstrap analysis with applications to demographic effects in demand functions.
Gozalo, P L
1997-12-01
"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt
Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling
Knowles, David
2010-01-01
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated datasets based on a known sparse connectivity matrix for E. Coli, and on three biological datasets of increasing complexity.
Noise and speckle reduction in synthetic aperture radar imagery by nonparametric Wiener filtering.
Caprari, R S; Goh, A S; Moffatt, E K
2000-12-10
We present a Wiener filter that is especially suitable for speckle and noise reduction in multilook synthetic aperture radar (SAR) imagery. The proposed filter is nonparametric, not being based on parametrized analytical models of signal statistics. Instead, the Wiener-Hopf equation is expressed entirely in terms of observed signal statistics, with no reference to the possibly unobservable pure signal and noise. This Wiener filter is simple in concept and implementation, exactly minimum mean-square error, and directly applicable to signal-dependent and multiplicative noise. We demonstrate the filtering of a genuine two-look SAR image and show how a nonnegatively constrained version of the filter substantially reduces ringing.
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images
2010-04-01
OF EACH CELL ARE RESULTS OF KSVD AND BPFA, RESPECTIVELY. σ C.man House Peppers Lena Barbara Boats F.print Couple Hill 5 37.87 39.37 37.78 38.60 38.08...INTERPOLATION PSNR RESULTS, USING PATCH SIZE 8× 8. BOTTOM: BPFA RGB IMAGE INTERPOLATION PSNR RESULTS, USING PATCH SIZE 7× 7. data ratio C.man House Peppers Lena...of subspaces. IEEE Trans. Inform. Theory, 2009. [16] T. Ferguson . A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1:209–230
BOOTSTRAP WAVELET IN THE NONPARAMETRIC REGRESSION MODEL WITH WEAKLY DEPENDENT PROCESSES
Institute of Scientific and Technical Information of China (English)
林路; 张润楚
2004-01-01
This paper introduces a method of bootstrap wavelet estimation in a nonparametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of the bootstrap wavelet estimators are given in the fixed design model. The conditional normality for a modified version of the bootstrap wavelet estimators is obtained in the fixed model. The consistency for the bootstrap wavelet estimator is also proved in the random design model. These results show that the bootstrap wavelet method is valid for the model with weakly dependent processes.
An adaptive nonparametric method in benchmark analysis for bioassay and environmental studies.
Bhattacharya, Rabi; Lin, Lizhen
2010-12-01
We present a novel nonparametric method for bioassay and benchmark analysis in risk assessment, which averages isotonic MLEs based on disjoint subgroups of dosages. The asymptotic theory for the methodology is derived, showing that the MISEs (mean integrated squared error) of the estimates of both the dose-response curve F and its inverse F(-1) achieve the optimal rate O(N(-4/5)). Also, we compute the asymptotic distribution of the estimate ζ~p of the effective dosage ζ(p) = F(-1) (p) which is shown to have an optimally small asymptotic variance.
Trends in ocean colour and chlorophyll concentration from 1889 to 2000, worldwide.
Directory of Open Access Journals (Sweden)
Marcel R Wernand
Full Text Available Marine primary productivity is an important agent in the global cycling of carbon dioxide, a major 'greenhouse gas', and variations in the concentration of the ocean's phytoplankton biomass can therefore explain trends in the global carbon budget. Since the launch of satellite-mounted sensors globe-wide monitoring of chlorophyll, a phytoplankton biomass proxy, became feasible. Just as satellites, the Forel-Ule (FU scale record (a hardly explored database of ocean colour has covered all seas and oceans--but already since 1889. We provide evidence that changes of ocean surface chlorophyll can be reconstructed with confidence from this record. The EcoLight radiative transfer numerical model indicates that the FU index is closely related to chlorophyll concentrations in open ocean regions. The most complete FU record is that of the North Atlantic in terms of coverage over space and in time; this dataset has been used to test the validity of colour changes that can be translated to chlorophyll. The FU and FU-derived chlorophyll data were analysed for monotonously increasing or decreasing trends with the non-parametric Mann-Kendall test, a method to establish the presence of a consistent trend. Our analysis has not revealed a globe-wide trend of increase or decrease in chlorophyll concentration during the past century; ocean regions have apparently responded differentially to changes in meteorological, hydrological and biological conditions at the surface, including potential long-term trends related to global warming. Since 1889, chlorophyll concentrations have decreased in the Indian Ocean and in the Pacific; increased in the Atlantic Ocean, the Mediterranean, the Chinese Sea, and in the seas west and north-west of Japan. This suggests that explanations of chlorophyll changes over long periods should focus on hydrographical and biological characteristics typical of single ocean regions, not on those of 'the' ocean.
Trends in ocean colour and chlorophyll concentration from 1889 to 2000, worldwide.
Wernand, Marcel R; van der Woerd, Hendrik J; Gieskes, Winfried W C
2013-01-01
Marine primary productivity is an important agent in the global cycling of carbon dioxide, a major 'greenhouse gas', and variations in the concentration of the ocean's phytoplankton biomass can therefore explain trends in the global carbon budget. Since the launch of satellite-mounted sensors globe-wide monitoring of chlorophyll, a phytoplankton biomass proxy, became feasible. Just as satellites, the Forel-Ule (FU) scale record (a hardly explored database of ocean colour) has covered all seas and oceans--but already since 1889. We provide evidence that changes of ocean surface chlorophyll can be reconstructed with confidence from this record. The EcoLight radiative transfer numerical model indicates that the FU index is closely related to chlorophyll concentrations in open ocean regions. The most complete FU record is that of the North Atlantic in terms of coverage over space and in time; this dataset has been used to test the validity of colour changes that can be translated to chlorophyll. The FU and FU-derived chlorophyll data were analysed for monotonously increasing or decreasing trends with the non-parametric Mann-Kendall test, a method to establish the presence of a consistent trend. Our analysis has not revealed a globe-wide trend of increase or decrease in chlorophyll concentration during the past century; ocean regions have apparently responded differentially to changes in meteorological, hydrological and biological conditions at the surface, including potential long-term trends related to global warming. Since 1889, chlorophyll concentrations have decreased in the Indian Ocean and in the Pacific; increased in the Atlantic Ocean, the Mediterranean, the Chinese Sea, and in the seas west and north-west of Japan. This suggests that explanations of chlorophyll changes over long periods should focus on hydrographical and biological characteristics typical of single ocean regions, not on those of 'the' ocean.
Spatiotemporal investigation of long-term seasonal temperature variability in Libya
Elsharkawy, S. G.; Elmallah, E. S.
2016-09-01
Throughout this work, spatial and temporal variations of seasonal surface air temperature have been investigated. Moreover, the effects of relative internal (teleconnection) and external (solar) forcing on surface air temperature variability have been examined. Seasonal temperature time series covering 30 different meteorological locations and lasting over the last century are considered. These locations are classified into two groups based on their spatial distribution. One represents Coast Libya Surface Air Temperature (CLSAT), contains 19 locations, and the other represents Desert Libya Surface Air Temperature (DLSAT), contains 11 locations. Average temperature departure test is applied to investigate the nature of temperature variations. Temperature trends are analyzed using the nonparametric Mann-Kendall test and their coefficients are calculated using Sen's slope estimate. Cross-correlation and spectral analysis techniques are also applied. Our results showed temperature deviation from average within a band of ± 2°C at coast region, while ± 4°C at desert region. Extreme behavior intensions between summer and winter temperatures at coast region are noticed. Segmentation process declared reversal cooling/warming behavior within temperature records for all seasons. Desert region shows warming trend for all seasons with higher coefficients than obtained at coast region. Results obtained for spectral analysis show different short and medium signals and concluded that not only the spectral properties are different for different geographical regions but also different for different climatic seasons on regional scale as well. Cross-correlation results showed that highest influence for Rz upon coastal temperature is always in conjunction with highest influence of NAO upon coastal temperature during the period 1981-2010. Desert region does not obey this phenomenon, where highest temperature-NAO correlations at desert during autumn and winter seasons are not
Zuo, Depeng; Xu, Zongxue; Yao, Wenyi; Jin, Shuangyan; Xiao, Peiqing; Ran, Dachuan
2016-02-15
The changes in runoff and sediment load in the Loess Plateau of China have received considerable attention owing to their dramatic decline during recent decades. In this paper, the impacts of land-use and climate changes on water and sediment yields in the Huangfuchuan River basin (HFCRB) of the Loess Plateau are investigated by combined usage of statistical tests, hydrological modeling, and land-use maps. The temporal trends and abrupt changes in runoff and sediment loads during 1954-2012 are detected by using non-parametric Mann-Kendall and Pettitt tests. The land-use changes between 1980 and 2005 are determined by using transition matrix analysis, and the effects of land-use and climate changes on water and sediment yields are assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model and four scenarios, respectively. The results show significant decreasing trends in both annual runoff and sediment loads, whereas slightly decreasing and significantly increasing trends are detected for annual precipitation and air temperature, respectively. 1984 is identified as the dividing year of the study period. The land-use changes between 1980 and 2005 show significant effects of the Grain for Green Project in China. Both land-use change and climate change have greater impact on the reduction of sediment yield than that of water. Water and sediment yields in the upstream region show more significant decreases than those in the downstream region under different effects. The results obtained in this study can provide useful information for water resource planning and management as well as soil and water conservation in the Loess Plateau region.
Historical Climate and Streamflow Trends in Santa Ana River Basin
Mejia, D.; Sultana, R.; Tang, V.
2015-12-01
Santa Ana River watershed, located in Southern California, is the home of more than 5 million people. Population is projected to double within the next 50 years in the 2,650 square miles watershed. With prolonged drought conditions, and projected climate change, a strong concern exists about sustainable water supply of the area. In this study, historic climate and streamflow trend from water year 1965 to 2014 is analyzed using the nonparametric Mann-Kendall test. Climate trends are studied using annual rainfall, and annual average maximum and minimum temperature at 5 and 4 weather stations, respectively. Three of the precipitation stations show precipitation is decreasing in the watershed while minimum and maximum temperature has an increasing trend at three stations (p < 0.05). To assess whether streamflow and stream-channel characteristics are tended to increase or decrease monotonically with time, four variables - (1) annual maximum peak, (2) annual mean, (3) low to moderate and (4) moderate to high maximum peak streamflow were tested at 20 stream gauge sites. Only at 5 stream gage stations, significant streamflow trend is observed. At two stream gages, annual peak and annual average streamflow is increasing and at two stations, annual average streamflow has a decreasing trend. Low to moderate peak streamflow is increasing at two gage locations but there is no monotonic trend in moderate to high flows. As precipitation is decreasing in some part of the watershed, the effect of increasing urbanization in the area can be attributed for the localized increase in mean and peak streamflow. The trend analysis in weather and stream gage data will be presented in detail.
Temporal precipitation trend analysis at the Langat River Basin, Selangor, Malaysia
Palizdan, Narges; Falamarzi, Yashar; Huang, Yuk Feng; Lee, Teang Shui; Ghazali, Abdul Halim
2015-12-01
The Langat River Basin provides fresh water for about 1.2 million people in the Langat and Klang valleys. Any change in the pattern of rainfall could affect the quantity of water in the basin. Studying the pattern of change in rainfall is crucial for managing the available water resources in the basin. Thus, in this study, for the first time, both parametric and non-parametric methods were employed to detect rainfall trend in the basin for the period 1982-2011. The trends were determined at 30 rainfall stations using the Mann-Kendall (MK) test, the Sen's slope estimator and the linear regression analysis. Lag-1 approach was utilized to test the serial correlation of the series. On the annual scale, it was found that most of the stations in the basin were characterized with insignificant trends. The significant trends were found only at the four stations, namely 44301, 44305, 44320 and 2719001. The results of the seasonal trend analysis showed that most of the stations during the northeast monsoon (NEM) and the inter monsoon 1 (INT1) seasons and half of the stations during the southwest monsoon (SWM) season experienced insignificant positive trends. To the contrary, for the inter monsoon 2 (INT2) season, majority of the stations showed negative trends. It was found that during the NEM season the station 44301, for the INT1 season stations 44301, 2719001 and 3118069 were established as having significant changes, while in the SWM season station 2917001 and during the INT2 season, the stations 2615131 and 44301 showed significant trends. It is worth mentioning that the maximum rainfall occurs in inter-monsoon seasons.
A Critical Evaluation of the Nonparametric Approach to Estimate Terrestrial Evaporation
Directory of Open Access Journals (Sweden)
Yongmin Yang
2016-01-01
Full Text Available Evapotranspiration (ET estimation has been one of the most challenging problems in recent decades for hydrometeorologists. In this study, a nonparametric approach to estimate terrestrial evaporation was evaluated using both model simulation and measurements from three sites. Both the model simulation and the in situ evaluation at the Tiger Bush Site revealed that this approach would greatly overestimate ET under dry conditions (evaporative fraction smaller than 0.4. For the evaluation at the Tiger Bush Site, the difference between ET estimates and site observations could be as large as 130 W/m2. However, this approach provided good estimates over the two crop sites. The Nash-Sutcliffe coefficient (E was 0.9 and 0.94, respectively, for WC06 and Yingke. A further theoretical analysis indicates the nonparametric approach is very close to the equilibrium evaporation equation under wet conditions, and this can explain the good performance of this approach at the two crop sites in this study. The evaluation indicates that this approach needs more careful appraisal and that its application in dry conditions should be avoided.
Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting
Directory of Open Access Journals (Sweden)
Jelena Fiosina
2017-01-01
Full Text Available Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression. However, nonparametric and semiparametric methods, which enable forecasting by building nonlinear data models, are computationally intensive and lack sufficient scalability to cope with big datasets to extract successful results in a reasonable time. We present distributed parallel versions of some nonparametric and semiparametric regression models. We used MapReduce paradigm and describe the algorithms in terms of SPARK data structures to parallelize the calculations. The forecasting accuracy of the proposed algorithms is compared with the linear regression model, which is the only forecasting model currently having parallel distributed realization within the SPARK framework to address big data problems. The advantages of the parallelization of the algorithm are also provided. We validate our models conducting various numerical experiments: evaluating the goodness of fit, analyzing how increasing dataset size influences time consumption, and analyzing time consumption by varying the degree of parallelism (number of workers in the distributed realization.
Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance
Bhattacharjee, A.; Sutradhar, A.
2012-12-01
Modern close loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a frequency domain nonparametric identification of the nonlinear glucose-insulin process in an insulin dependent diabetes mellitus patient that captures the process dynamics in presence of uncertainties and parameter variations. An online frequency domain kernel estimation method has been proposed that uses the input-output data from the 19th order first principle model of the patient in intravenous route. Volterra equations up to second order kernels with extended input vector for a Hammerstein model are solved online by adaptive recursive least square (ARLS) algorithm. The frequency domain kernels are estimated using the harmonic excitation input data sequence from the virtual patient model. A short filter memory length of M = 2 was found sufficient to yield acceptable accuracy with lesser computation time. The nonparametric models are useful for closed loop control, where the frequency domain kernels can be directly used as the transfer function. The validation results show good fit both in frequency and time domain responses with nominal patient as well as with parameter variations.
A web application for evaluating Phase I methods using a non-parametric optimal benchmark.
Wages, Nolan A; Varhegyi, Nikole
2017-06-01
In evaluating the performance of Phase I dose-finding designs, simulation studies are typically conducted to assess how often a method correctly selects the true maximum tolerated dose under a set of assumed dose-toxicity curves. A necessary component of the evaluation process is to have some concept for how well a design can possibly perform. The notion of an upper bound on the accuracy of maximum tolerated dose selection is often omitted from the simulation study, and the aim of this work is to provide researchers with accessible software to quickly evaluate the operating characteristics of Phase I methods using a benchmark. The non-parametric optimal benchmark is a useful theoretical tool for simulations that can serve as an upper limit for the accuracy of maximum tolerated dose identification based on a binary toxicity endpoint. It offers researchers a sense of the plausibility of a Phase I method's operating characteristics in simulation. We have developed an R shiny web application for simulating the benchmark. The web application has the ability to quickly provide simulation results for the benchmark and requires no programming knowledge. The application is free to access and use on any device with an Internet browser. The application provides the percentage of correct selection of the maximum tolerated dose and an accuracy index, operating characteristics typically used in evaluating the accuracy of dose-finding designs. We hope this software will facilitate the use of the non-parametric optimal benchmark as an evaluation tool in dose-finding simulation.
LSTA, Rawane Samb
2010-01-01
This thesis deals with the nonparametric estimation of density f of the regression error term E of the model Y=m(X)+E, assuming its independence with the covariate X. The difficulty linked to this study is the fact that the regression error E is not observed. In a such setup, it would be unwise, for estimating f, to use a conditional approach based upon the probability distribution function of Y given X. Indeed, this approach is affected by the curse of dimensionality, so that the resulting estimator of the residual term E would have considerably a slow rate of convergence if the dimension of X is very high. Two approaches are proposed in this thesis to avoid the curse of dimensionality. The first approach uses the estimated residuals, while the second integrates a nonparametric conditional density estimator of Y given X. If proceeding so can circumvent the curse of dimensionality, a challenging issue is to evaluate the impact of the estimated residuals on the final estimator of the density f. We will also at...
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...
Non-parametric transformation for data correlation and integration: From theory to practice
Energy Technology Data Exchange (ETDEWEB)
Datta-Gupta, A.; Xue, Guoping; Lee, Sang Heon [Texas A& M Univ., College Station, TX (United States)
1997-08-01
The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require a priori functional relationship between response and predictor variables which is the case with traditional multiple regression. The transformations are very general, computationally efficient and can easily handle mixed data types for example, continuous variables such as porosity, permeability and categorical variables such as rock type, lithofacies. The power of the non-parametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we utilize these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its fullest potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus, greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes.
Directory of Open Access Journals (Sweden)
Urbi Garay
2016-03-01
Full Text Available We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return and betas (to a choice set of explanatory factors in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers.
Identification and well-posedness in a class of nonparametric problems
Zinde-Walsh, Victoria
2010-01-01
This is a companion note to Zinde-Walsh (2010), arXiv:1009.4217v1[MATH.ST], to clarify and extend results on identification in a number of problems that lead to a system of convolution equations. Examples include identification of the distribution of mismeasured variables, of a nonparametric regression function under Berkson type measurement error, some nonparametric panel data models, etc. The reason that identification in different problems can be considered in one approach is that they lead to the same system of convolution equations; moreover the solution can be given under more general assumptions than those usually considered, by examining these equations in spaces of generalized functions. An important issue that did not receive sufficient attention is that of well-posedness. This note gives conditions under which well-posedness obtains, an example that demonstrates that when well-posedness does not hold functions that are far apart can give rise to observable arbitrarily close functions and discusses ...
Kim, Junmo; Fisher, John W; Yezzi, Anthony; Cetin, Müjdat; Willsky, Alan S
2005-10-01
In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training.
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.
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
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.
Palacios, Julia A; Minin, Vladimir N
2013-03-01
Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method.
A non-parametric approach to estimate the total deviation index for non-normal data.
Perez-Jaume, Sara; Carrasco, Josep L
2015-11-10
Concordance indices are used to assess the degree of agreement between different methods that measure the same characteristic. In this context, the total deviation index (TDI) is an unscaled concordance measure that quantifies to which extent the readings from the same subject obtained by different methods may differ with a certain probability. Common approaches to estimate the TDI assume data are normally distributed and linearity between response and effects (subjects, methods and random error). Here, we introduce a new non-parametric methodology for estimation and inference of the TDI that can deal with any kind of quantitative data. The present study introduces this non-parametric approach and compares it with the already established methods in two real case examples that represent situations of non-normal data (more specifically, skewed data and count data). The performance of the already established methodologies and our approach in these contexts is assessed by means of a simulation study. Copyright © 2015 John Wiley & Sons, Ltd.
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.
Freiman, M; Kronman, A; Esses, S J; Joskowicz, L; Sosna, J
2010-01-01
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.
Montiel, Ariadna; Sendra, Irene; Escamilla-Rivera, Celia; Salzano, Vincenzo
2014-01-01
In this work we present a nonparametric approach, which works on minimal assumptions, to reconstruct the cosmic expansion of the Universe. We propose to combine a locally weighted scatterplot smoothing method and a simulation-extrapolation method. The first one (Loess) is a nonparametric approach that allows to obtain smoothed curves with no prior knowledge of the functional relationship between variables nor of the cosmological quantities. The second one (Simex) takes into account the effect of measurement errors on a variable via a simulation process. For the reconstructions we use as raw data the Union2.1 Type Ia Supernovae compilation, as well as recent Hubble parameter measurements. This work aims to illustrate the approach, which turns out to be a self-sufficient technique in the sense we do not have to choose anything by hand. We examine the details of the method, among them the amount of observational data needed to perform the locally weighted fit which will define the robustness of our reconstructio...
MEASURING DARK MATTER PROFILES NON-PARAMETRICALLY IN DWARF SPHEROIDALS: AN APPLICATION TO DRACO
Energy Technology Data Exchange (ETDEWEB)
Jardel, John R.; Gebhardt, Karl [Department of Astronomy, The University of Texas, 2515 Speedway, Stop C1400, Austin, TX 78712-1205 (United States); Fabricius, Maximilian H.; Williams, Michael J. [Max-Planck Institut fuer extraterrestrische Physik, Giessenbachstrasse, D-85741 Garching bei Muenchen (Germany); Drory, Niv, E-mail: jardel@astro.as.utexas.edu [Instituto de Astronomia, Universidad Nacional Autonoma de Mexico, Avenida Universidad 3000, Ciudad Universitaria, C.P. 04510 Mexico D.F. (Mexico)
2013-02-15
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 {alpha} = -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.
Evolution of the CMB Power Spectrum Across WMAP Data Releases: A Nonparametric Analysis
Aghamousa, Amir; Souradeep, Tarun
2011-01-01
We present a comparative analysis of the WMAP 1-, 3-, 5-, and 7-year data releases for the CMB angular power spectrum, with respect to the following three key questions: (a) How well is the angular power spectrum determined by the data alone? (b) How well is the Lambda-CDM model supported by a model-independent, data-driven analysis? (c) What are the realistic uncertainties on peak/dip locations and heights? Our analysis is based on a nonparametric function estimation methodology [1,2]. Our results show that the height of the power spectrum is well determined by data alone for multipole index l approximately less than 600 (1-year), 800 (3-year), and 900 (5- and 7-year data realizations). We also show that parametric fits based on the Lambda-CDM model are remarkably close to our nonparametric fit in l-regions where the data are sufficiently precise. A contrasting example is provided by an H-Lambda-CDM model: As the data become precise with successive data realizations, the H-Lambda-CDM angular power spectrum g...
A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.
Johnson, Timothy D; Liu, Zhuqing; Bartsch, Andreas J; Nichols, Thomas E
2013-08-01
The Potts model has enjoyed much success as a prior model for image segmentation. Given the individual classes in the model, the data are typically modeled as Gaussian random variates or as random variates from some other parametric distribution. In this article, we present a non-parametric Potts model and apply it to a functional magnetic resonance imaging study for the pre-surgical assessment of peritumoral brain activation. In our model, we assume that the Z-score image from a patient can be segmented into activated, deactivated, and null classes, or states. Conditional on the class, or state, the Z-scores are assumed to come from some generic distribution which we model non-parametrically using a mixture of Dirichlet process priors within the Bayesian framework. The posterior distribution of the model parameters is estimated with a Markov chain Monte Carlo algorithm, and Bayesian decision theory is used to make the final classifications. Our Potts prior model includes two parameters, the standard spatial regularization parameter and a parameter that can be interpreted as the a priori probability that each voxel belongs to the null, or background state, conditional on the lack of spatial regularization. We assume that both of these parameters are unknown, and jointly estimate them along with other model parameters. We show through simulation studies that our model performs on par, in terms of posterior expected loss, with parametric Potts models when the parametric model is correctly specified and outperforms parametric models when the parametric model in misspecified.
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.
Institute of Scientific and Technical Information of China (English)
Yong Liu,; Wei Zou
2011-01-01
Extending the income dynamics approach in Quah （2003）, the present paper studies the enlarging income inequality in China over the past three decades from the viewpoint of rural-urban migration and economic transition. We establish non-parametric estimations of rural and urban income distribution functions in China, and aggregate a population- weighted, nationwide income distribution function taking into account rural-urban differences in technological progress and price indexes. We calculate 12 inequality indexes through non-parametric estimation to overcome the biases in existingparametric estimation and, therefore, provide more accurate measurement of income inequalitY. Policy implications have been drawn based on our research.
Directory of Open Access Journals (Sweden)
S. Simic
2008-12-01
Full Text Available The influence of variability of atmospheric parameters on short- and long-term changes of spectral UV irradiance measured at the Sonnblick observatory (47.03° N, 12.57° E, 3106 m during the period from 1994 to 2006 is studied. Measurements were performed with the Brewer #093 single-monochromator spectrophotometer and with a Bentham DM 150 spectroradiometer (double-monochromator.
The influence of ozone, albedo, snowline and clouds on UV variability is evaluated for each parameter separately using 10-year climatology. It is found that the effect of total ozone on short-term variability of UV irradiance at 305 nm can be more than 200% and on average more than 50%. Clouds can cause variability of 150% or more and on average 35%. Variability caused by albedo reaches a maximum of 32% in April (6% on average. In summer and autumn, total ozone and clouds strongly influence the variability of UV radiation, whereas in winter and spring ozone has the more pronounced effect. A decrease in snowline height from 3000 m to 800 m a.s.l. enhances the UV irradiance by a factor of 1.24 for clear sky conditions and by a factor of 1.7 for 8/8 cloud cover.
Long-term trends are investigated for the time period from 1994 to 2006 based on clear-sky measurements, using the non-parametric Mann-Kendall trend test. Significant downward trends (99% confidence level are found for solar zenith angle 55° at wavelengths from 305 nm to 324 nm and erythemally weighted irradiance according to CIE, which are caused by an increase in sunshine duration during periods of high total column ozone. Significant trends (90% confidence level were also found for other combinations of wavelength and SZA.
Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang
2016-09-01
As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for
Directory of Open Access Journals (Sweden)
J. C. Refsgaard
2013-02-01
Full Text Available This study uses a 133 yr data set from the 1055 km2 Skjern River catchment in a western Danish catchment to evaluate: long-term past climate changes in the area; the capability of a conceptual hydrological model NAM to simulate climate change impacts on river discharge; and the occurrences of droughts and floods in a changing climate. The degree of change in the climatic variables is examined using the non-parametric Mann-Kendall test. During the last 133 yr the area has experienced a significant change in precipitation of 46% and a temperature change of 1.3 °C leading to (simulated increases in discharge of 103% and groundwater recharge of 172%. Only a small part of the past climatic changes was found to be correlated to the climatic drivers: NAO, SCA and AMO. The NAM model was calibrated on the period 1961–1970 and showed generally an excellent match between simulated and observed discharge. The capability of the hydrological model to predict climate change impact was investigated by looking at performances outside the calibration period. The results showed a reduced model fit, especially for the modern time periods (after the 1970s, and not all hydrological changes could be explained. This might indicate that hydrological models cannot be expected to predict climate change impacts on discharge as accurately in the future, as they perform under present conditions, where they can be calibrated. The (simulated stream discharge was subsequently analyzed using flood and drought indices based on the threshold method. The extreme signal was found to depend highly on the period chosen as reference to normal. The analysis, however, indicated enhanced amplitude of the hydrograph towards the drier extremes superimposed on the overall discharge increase leading to more relative drought periods.
Directory of Open Access Journals (Sweden)
Pablo A. Méndez-Lázaro
2014-06-01
Full Text Available Climate variability is a threat to water resources on a global scale and in tropical regions in particular. Rainfall events and patterns are associated worldwide with natural disasters like mudslides and landslides, meteorological phenomena like hurricanes, risks/hazards including severe storms and flooding, and health effects like vector-borne and waterborne diseases. Therefore, in the context of global change, research on rainfall patterns and their variations presents a challenge to the scientific community. The main objective of this research was to analyze recent trends in precipitation in the San Juan metropolitan area in Puerto Rico and their relationship with regional and global climate variations. The statistical trend analysis of precipitation was performed with the nonparametric Mann-Kendall test. All stations showed positive trends of increasing annual rainfall between 1955 and 2009. The winter months of January and February had an increase in monthly rainfall, although winter is normally a dry season on the island. Regarding dry days, we found an annual decreasing trend, also specifically in winter. In terms of numbers of severe rainfall events described as more than 78 mm in 24 hours, 63 episodes have occurred in the San Juan area in the last decade, specifically in the 2000-2009 time frame, with an average of 6 severe events per year. The majority of the episodes occurred in summer, more frequently in August and September. These results can be seen as a clear example of the complexity of spatial and temporal of rainfall distribution over a tropical city.
Precipitation variability and the sugarcane climate demand in Brazil
Pereira, V. R.; de Avila, A. M. H.; Blain, G.; Zullo, J., Jr.
2014-12-01
This study presents the precipitation variability in São Paulo state/Brazil considering the climate demand for high sugarcane productivity. The Brazilian sugarcane and the bioethanol chain are facing an increase demand in response of the biofuel industry expansion. The productivity improvement is the key point to face the challenges about the land expansion in the Brazilian agriculture. The sugarcane phenology is climate dependent even being efficient in the decarboxylation process. The sprouting, growing, yield and the sugar content are determined by the climate. The accumulated rainy days during the pre harvest or more than 180 days of dry period can reduce the sugar content during the maturation process. Daily rainfall time series for the period 1960-2003 from 210 rain gauges at São Paulo state - the major Brazilian producer - are used. We subset the time series in the annual, seasonal, ten-day totals and dry and wet spells analysis. We used the mann- kendall non-parametric test to calculate the trends. The annual, the seasonal totals and the dry and wet spells did not showed a significant change in time. However, the ten-day total analysis in the beginning of the rainy season - i.e. in October - showed an interesting changing pattern - 24% of gauges showed a significant negative trend (p_value<0.1). These gauges are located in specific regions with the highest sugarcane production. Also, the October totals showed significant and negative trends (p_value<0.1) for more than 95% of precipitation gauges. These results are strongly indicating a longer dry season in the last twenty years. These changes in the precipitation variability can be related with the instability of the sugarcane market in Brazil in the last years.
Liu, Huiyu; Xu, Xiaojuan; Lin, Zhenshan; Zhang, Mingyang; Mi, Ying; Huang, Changchun; Yang, Hao
2016-12-01
With the ensemble Empirical Mode Decomposition Method (EEMD) and the non-parametric Mann-Kendall Test, the quasi-periodic and abrupt changes of sedimentation rate at multiple time scales, and their relations to climate changes and human activities from 1951 to 2010 in Meiliang Bay of Lake Taihu (China) were studied. The results showed the following. (1) The change in sedimentation rate can be completely decomposed into three quasi-periodic changes on 3.7, 6.4, and 24-yr time scales, and a long-term trend. (2) The quasi-periodic changes in sedimentation rate are significantly and positively related to changes in annual average temperature at 6.4 and 24-yr time scales and human activities at 3.7-yr time scales, and not significantly related to precipitation at these time scales. The trend of sedimentation rate has a negative relation with temperature, but positive relations with precipitation and human activities. As a whole, the total variance contribution of climate changes to the quasi-periodic changes of sedimentation rate is close to that of human activities; (3) Temperature and precipitation are possibly related to the abrupt change of sedimentation rate as a whole. Floods have significant impacts on abrupt changes in the sedimentation rate at 3.7, 6.4 and 24-yr time scales. Moreover, some abrupt changes of sedimentation rate at 3.7- and 6.4-yr time scales are partly related to the changes of precipitation at 3.1-yr time scale and temperature at 5-yr time scale. The results of this study will help identify the impacts of climate change and human activities on lake sedimentation at different time scales, and will be available for use as a guide for reasonable development and effective protection of lake resources.
Hoogestraat, Galen K.; Stamm, John F.
2015-11-02
Upward trends in precipitation and streamflow have been observed in the northeastern Missouri River Basin during the past century, including the area of eastern South Dakota. Some of the identified upward trends were anomalously large relative to surrounding parts of the northern Great Plains. Forcing factors for streamflow trends in eastern South Dakota are not well understood, and it is not known whether streamflow trends are driven primarily by climatic changes or various land-use changes. Understanding the effects that climate (specifically precipitation and temperature) has on streamflow characteristics within a region will help to better understand additional factors such as land-use alterations that may affect the hydrology of the region. To aid in this understanding, a study was completed by the U.S. Geological Survey, in cooperation with the East Dakota Water Development District and James River Water Development District, to assess trends in climate and streamflow characteristics at 10 selected streamgages in eastern South Dakota for water years (WYs) 1945–2013 (69 years) and WYs 1980–2013 (34 years). A WY is the 12-month period, October 1 through September 30, and is designated by the calendar year in which it ends. One streamgage is on the Whetstone River, a tributary to the Minnesota River, and the other streamgages are in the James, Big Sioux, and Vermillion River Basins. The watersheds for two of the James River streamgages extend into North Dakota, and parts of the watersheds for two of the Big Sioux River streamgages extend into Minnesota and Iowa. The objectives of this study were to document trends in streamflow and precipitation in these watersheds, and characterize the residual streamflow variability that might be attributed to factors other than precipitation. Residuals were computed as the departure from a locally-weighted scatterplot smoothing (LOWESS) model. Significance of trends was based on the Mann-Kendall nonparametric test at a 0
Spatiotemporal analysis of snow trends in Austria
Koch, Roland; Schöner, Wolfgang
2015-04-01
This study presents the spatiotemporal analysis of Austrian snow observations. A set of consistent and reliable long-term time series of snow depth on a daily scale from selected meteorological sites across Austria is used. The time series were collected by the Central Institute for Meteorology and Geodynamics (ZAMG) and the Hydrographical Central Bureau of Austria (HZB). The data cover a time period from the late nineteenth century until today. In the first part of the study spatiotemporal characteristics of seasonal snow depth observations were investigated by the method of principal component analysis (PCA). Furthermore, the spatial patterns of variability have been used for a regionalisation, identifying regions with similar conditions during the base period 1961 to 2010. The results show a clear separation of four major regions including various sub-regions. However, the regionalisation was limited due to sparse data coverage. The non-parametric Mann-Kendall statistical test had been used to assess the significance of trends in snow indices, e.g. snow depth, maximum snow depth, snow cover duration, at monthly and seasonal time scales. In order to remove the influence of the lag-1 serial correlation from the snow data, the trend-free pre-whitening approach was applied. In the monthly and seasonal time series during the period 1961-2010, negative trends in snow indices were significant at the 95% confidence level primarily at stations in the Western and Southern part of Austria. In addition, the correlation between snow observations and gridded HISTALP winter temperature and precipitation fields was investigated. The analysis has shown an increased temperature and decreased precipitation during the 1990s, yielding a pronounced reduction in snow depth and duration. As a matter of fact, the results indicate major shifts of the snow depth and snow cover duration around the 1970s and especially the 1990s, which are predominantly responsible for trends.
Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang
2016-09-01
As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for
Prevailing trends of climatic extremes across Indus-Delta of Sindh-Pakistan
Abbas, Farhat; Rehman, Iqra; Adrees, Muhammad; Ibrahim, Muhammad; Saleem, Farhan; Ali, Shafaqat; Rizwan, Muhammad; Salik, Muhammad Raza
2017-01-01
This study examines the variability and change in the patterns of climatic extremes experienced in Indus-Delta of Sindh province of Pakistan, comprising regions of Karachi, Badin, Mohenjodaro, and Rohri. The homogenized daily minimum and maximum temperature and precipitation data for a 36-year period were used to calculate 13 and 11 indices of temperature and precipitation extremes with the help of RClimDex, a program written in the statistical software package R. A non-parametric Mann-Kendall test and Sen's slope estimates were used to determine the statistical significance and magnitude of the calculated trend. Temperatures of summer days and tropical nights increased in the region with overall significant warming trends for monthly maximum temperature as well as for warm days and nights reflecting dry conditions in the study area. The warm extremes and nighttime temperature indices showed greater trends than cold extremes and daytime indices depicting an overall warming trends in the Delta. Historic decrease in the acreage of major crops and over 33% decrease in agriculture credit for Sindh are the indicators of adverse impacts of warmer and drier weather on Sindh agriculture. Trends reported for Karachi and Badin are expected to decrease rice cultivation, hatching of fisheries, and mangroves forest surrounding these cities. Increase in the prevailing temperature trends will lead to increasingly hotter and drier summers resulting to constraints on cotton, wheat, and rice yield in Rohri and Mohenjodaro areas due to increased crop water requirements that may be met with additional groundwater pumping; nonetheless, the depleted groundwater resources would have a direct impact on the region's economy.
Li, Mao-Fen; Luo, Wei; Li, Hailiang; Liu, Enping; Li, Yuping
2017-03-01
Frequent occurrences of extreme precipitation events have significant impacts on agricultural production. Tropical agriculture has been playing an important role in national economy in China. A precise understanding of variability in extreme precipitation indices and their impacts on crop yields are of great value for farmers and policy makers at county level, particularly in tropical China where almost all agriculture is rainfed. This research has studied observed trends in extreme precipitation indices (a total of 10) during 1988-2013 over Hainan island, tropical China. Mann-Kendall nonparametric test was adopted for trend detection and the results showed that most of precipitation indices showed increasing trend. Since rice is the most important staple food in Hainan island, the impacts of extreme precipitation indices on rice yields were also analyzed through simple correlations. In general, the rainy days and rain intensity in late rice growing season showed increasing trend over Hainan island. The rice yield presented ninth-degree polynomial technological trend at all stations and increasing trend for early rice yield. Late rice yield showed a decreasing trend in some parts of Hainan island. Spearman rank correlation coefficient indicated that the correlation was more pronounced between extreme precipitation indices and yields at Haikou site for early rice, and Haikou, Sanya, and Qionghai stations for late rice, respectively. Further results also indicated that there were statistically significant positive trends of R10 and R20 (number of days with precipitation ≥10 mm and precipitation ≥20 mm, respectively) from July to November at Haikou (located in north of Hainan island), and this positive trend may be a disadvantage for late rice yield. The cut-off value of extreme precipitation indices and its correlation with rice yield anomaly indices for Hainan island provided a foundation for vulnerability assessment as well as a contribution to set up
Multi-daily rainfall climatology and change for water resource management
Gajic-Capka, M.; Capka, B.
2009-09-01
The ongoing study on precipitation regime in the Croatian Drava River basin is intended to be used by decision makers in water and energy resource management. One side aspect of this study is the variability in daily and multi-daily amounts (2- and 5-days), the indices defined by WMO and amended by the end-users, which are critical for applications in assessment and maintenance of flooding. The daily rainfall series are from the period 1961-2000, collected at two rain gauges: Varazdin in the upper part of the Croatian Drava River basin and Osijek in the low one, not far from its mouse into the Danube River. The applied Gamma probability distribution makes it possible to estimate the likelihood of rainfall at different durations within a specific range. The generalized extreme value distribution is employed to compute the design values of annual maximal 1-, 2- and 5-day precipitation amounts with different return periods indicating at precipitation risk. In addition to the extreme precipitation characteristics of the current climate, the evolution of trends in annual maximal 1-, 2- and 5-day rainfall amounts are derived from linear regression during the period 1901-2007 at Osijek and for the both stations since 1951. Statistical significance at the 5% confidence level is established using the Mann-Kendall rank non-parametric test. The coefficients of variation, indicating interannual variability, are then computed for consecutive 30-year moving time windows (1 year step) for each parameter. Trends for 30-year periods with the 10-year step (1901-1930, 1911-1940, ....) were computed to detect the possible changes in variability. At the level of the synoptic measurements, the analysis of weather types related to the extreme rainfall events would be deduced.
Recent trend of thermal extremes across the agro ecological zones of Pakistan
Hina, Saadia; Saleem, Farhan; Lu, Riyu
2017-04-01
Annual trends of temperature extremes in different zones spatially distributed over Pakistan were studied using homogenized daily minimum and maximum temperatures data for the period of 1981-2010. Extreme temperature indices calculated on annual timescale were chosen from the list of core climate extreme indices recommended by the World Meteorological Organization - Commission for Climatology (WMO - CCL) and the research project on Climate Variability and Predictability (CLIVAR) of the World Climate Research Programme (WCRP). Furthermore, a nonparametric Mann-Kendall test and Sen's slope estimator method were applied to compute magnitude and significance level of annually averaged trends of temperature extremes. Analysis revealed a decreasing trend of DTR (Diurnal temperature range) and CSDI (cold spell duration index) over the Country, with the regional averages of -0.15 (oC decade-1) and -1.83 (days decade-1). The spatial averaged regional trend of WSDI (Warm spell duration index) is 1.05 (days decade-1) which were found statistically significant for the zones of Northern Dry Mountains (1.75 days decade-1), Western Dry Mountains (5.15 days decade-1) and Dry Western Plateau (3.95 days decade-1) respectively. The positive trends of SU25 (4.58 days decade-1) and TR20 (3.51 nights decade-1) were calculated in the region. The zones of Western Dry Mountains (7.56 days decade-1) and Barani/Rainfall (7.24 nights decade-1) experienced the most frequent numbers of SU25 and TR20. The indices of thermal extremes depicted trend of significant warming in the region of Pakistan.
Fan, Chao; Myint, Soe W.; Rey, Sergio J.; Li, Wenwen
2017-06-01
Urbanization is a natural and social process involving simultaneous changes to the Earth's land systems, energy flow, demographics, and the economy. Understanding the spatiotemporal pattern of urbanization is increasingly important for policy formulation, decision making, and natural resource management. A combination of satellite remote sensing and patch-based models has been widely adopted to characterize landscape changes at various spatial and temporal scales. Nevertheless, the validity of this type of framework in identifying long-term changes, especially subtle or gradual land modifications is seriously challenged. In this paper, we integrate annual image time series, continuous spatial indices, and non-parametric trend analysis into a spatiotemporal study of landscape dynamics over the Phoenix metropolitan area from 1991 to 2010. We harness local indicators of spatial dependence and modified Mann-Kendall test to describe the monotonic trends in the quantity and spatial arrangement of two important land use land cover types: vegetation and built-up areas. Results suggest that declines in vegetation and increases in built-up areas are the two prevalent types of changes across the region. Vegetation increases mostly occur at the outskirts where new residential areas are developed from natural desert. A sizable proportion of vegetation declines and built-up increases are seen in the central and southeast part. Extensive land conversion from agricultural fields into urban land use is one important driver of vegetation declines. The xeriscaping practice also contributes to part of vegetation loss and an increasingly heterogeneous landscape. The quantitative framework proposed in this study provides a pathway to effective landscape mapping and change monitoring from a spatial statistical perspective.
Directory of Open Access Journals (Sweden)
DRAGOTĂ CARMEN-SOFIA
2015-03-01
Full Text Available Recent summer heat waves (2003, 2010 had a strong socio-economic impact in different parts of the continent by means of crop shortfalls and forest fires. Sustained hot days became more frequent in the recent decades in many European regions, affecting human health and leading to additional deaths. This signal has been outlined in many studies conducted in Romania, suggesting that the southern region of Romania is particularly subject to large temperature increase. This work investigates the changing annual and seasonal heat waves at regional scale of the Romanian Plain, over period 1961-2014. Daily maximum temperature recorded at six weather stations available from the ECA&D project (European Climate Assessment and Datasets were analyzed. The changes in the seasonal frequency, duration and intensity of heat waves were studied using the Mann-Kendall nonparametric trend test, as recommended by the scientific expert team on climate change detection. The likelyhood of higher maximum temperatures rise, particularly after the mid 1980s, and the changes in the upper tail of the probability density functions of these temperatures, within the extreme domain (beyond the 95% percentile level, explain the persistence and intensity of heat waves. The upward trends are dominant most of the year, and many of the calculated decadal slopes were found statistically significant (relative to the 5% level, proving an ongoing and strong warming all over the region. Our findings are in good agreement with several recent studies carried out at European and national scale and pledge for further scientific analyses i.e. heat stress impact on public health and agriculture.
DEFF Research Database (Denmark)
Bendixen, Carsten
2014-01-01
Bidrag med en kortfattet, introducerende, perspektiverende og begrebsafklarende fremstilling af begrebet test i det pædagogiske univers.......Bidrag med en kortfattet, introducerende, perspektiverende og begrebsafklarende fremstilling af begrebet test i det pædagogiske univers....
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Mauro Gasparini
2013-05-01
Full Text Available We present an application of nonparametric estimation of survival in the presence of left-truncated and right-censored data. We confirm the well-known unstable behavior of the survival estimates when the risk set is small and there are too few early deaths. How ever, in our real scenario where only few death times are necessarily available, the proper nonparametric maximum likelihood estimator, and its usual modification, behave less badly than alternative methods proposed in the literature. The relative merits of the different estimators are discussed in a simulation study extending the settings of the case study to more general scenarios.
Link, J M; Alimonti, G; Anjos, J C; Arena, V; Barberis, S; Bediaga, I; Benussi, L; Bianco, S; Boca, G; Bonomi, G; Boschini, M; Butler, J N; Carrillo, S; Casimiro, E; Castromonte, C; Cawlfield, C; Cerutti, A; Cheung, H W K; Chiodini, G; Cho, K; Chung, Y S; Cinquini, L; Cuautle, E; Cumalat, J P; D'Angelo, P; Davenport, T F; De Miranda, J M; Di Corato, M; Dini, P; Dos Reis, A C; Edera, L; Engh, D; Erba, S; Fabbri, F L; Frisullo, V; Gaines, I; Garbincius, P H; Gardner, R; Garren, L A; Gianini, G; Gottschalk, E; Göbel, C; Handler, T; Hernández, H; Hosack, M; Inzani, P; Johns, W E; Kang, J S; Kasper, P H; Kim, D Y; Ko, B R; Kreymer, A E; Kryemadhi, A; Kutschke, R; Kwak, J W; Lee, K B; Leveraro, F; Liguori, G; Lopes-Pegna, D; Luiggi, E; López, A M; Machado, A A; Magnin, J; Malvezzi, S; Massafferri, A; Menasce, D; Merlo, M M; Mezzadri, M; Mitchell, R; Moroni, L; Méndez, H; Nehring, M; O'Reilly, B; Otalora, J; Pantea, D; Paris, A; Park, H; Pedrini, D; Pepe, I M; Polycarpo, E; Pontoglio, C; Prelz, F; Quinones, J; Rahimi, A; Ramírez, J E; Ratti, S P; Reyes, M; Riccardi, C; Rovere, M; Sala, S; Segoni, I; Sheaff, M; Sheldon, P D; Stenson, K; Sánchez-Hernández, A; Uribe, C; Vaandering, E W; Vitulo, P; Vázquez, F; Wang, M; Webster, M; Wilson, J R; Wiss, J; Yager, P M; Zallo, A; Zhang, Y
2007-01-01
Using a large sample of \\dpkkpi{} decays collected by the FOCUS photoproduction experiment at Fermilab, we present the first non-parametric analysis of the \\kpi{} amplitudes in \\dpkkpi{} decay. The technique is similar to the technique used for our non-parametric measurements of the \\krzmndk{} form factors. Although these results are in rough agreement with those of E687, we observe a wider S-wave contribution for the \\ksw{} contribution than the standard, PDG \\cite{pdg} Breit-Wigner parameterization. We have some weaker evidence for the existence of a new, D-wave component at low values of the $K^- \\pi^+$ mass.
Institute of Scientific and Technical Information of China (English)
Zara Ghodsi; Emmanuel Sirimal Silva; Hossein Hassani
2015-01-01
The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.
Non-parametric star formation histories for 5 dwarf spheroidal galaxies of the local group
Hernández, X; Valls-Gabaud, D; Gilmore, Gerard; Valls-Gabaud, David
2000-01-01
We use recent HST colour-magnitude diagrams of the resolved stellar populations of a sample of local dSph galaxies (Carina, LeoI, LeoII, Ursa Minor and Draco) to infer the star formation histories of these systems, $SFR(t)$. Applying a new variational calculus maximum likelihood method which includes a full Bayesian analysis and allows a non-parametric estimate of the function one is solving for, we infer the star formation histories of the systems studied. This method has the advantage of yielding an objective answer, as one need not assume {\\it a priori} the form of the function one is trying to recover. The results are checked independently using Saha's $W$ statistic. The total luminosities of the systems are used to normalize the results into physical units and derive SN type II rates. We derive the luminosity weighted mean star formation history of this sample of galaxies.
Forecasting of Households Consumption Expenditure with Nonparametric Regression: The Case of Turkey
Directory of Open Access Journals (Sweden)
Aydin Noyan
2016-11-01
Full Text Available The relationship between household income and expenditure is important for understanding how the shape of the economic dynamics of the households. In this study, the relationship between household consumption expenditure and household disposable income were analyzed by Locally Weighted Scatterplot Smoothing Regression which is a nonparametric method using R programming. This study aimed to determine relationship between variables directly, unlike making any assumptions are commonly used as in the conventional parametric regression. According to the findings, effect on expenditure with increasing of income and household size together increased rapidly at first, and then speed of increase decreased. This increase can be explained by having greater compulsory consumption expenditure relatively in small households. Besides, expenditure is relatively higher in middle and high income levels according to low income level. However, the change in expenditure is limited in middle and is the most limited in high income levels when household size changes.
Using nonparametrics to specify a model to measure the value of travel time
DEFF Research Database (Denmark)
Fosgerau, Mogens
2007-01-01
Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source...... of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing...... unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory....
Directory of Open Access Journals (Sweden)
Anass Benjebbour
2008-02-01
Full Text Available In the future, there will be a growing need for more flexible but efficient utilization of radio resources. Increased flexibility in radio transmission, however, yields a higher likelihood of interference owing to limited coordination among users. In this paper, we address the problem of flexible spectrum sharing where a wideband single carrier modulated signal is spectrally overlapped by unknown narrowband interference (NBI and where a cyclic Wiener filter is utilized for nonparametric NBI suppression at the receiver. The pulse shape design for the wideband signal is investigated to improve the NBI suppression capability of cyclic Wiener filtering. Specifically, two pulse shaping schemes, which outperform existing raised cosine pulse shaping schemes even for the same amount of excess bandwidth, are proposed. Based on computer simulation, the interference suppression capability of cyclic Wiener filtering is evaluated for both the proposed and existing pulse shaping schemes under several interference conditions and over both AWGN and Rayleigh fading channels.
Directory of Open Access Journals (Sweden)
Benjebbour Anass
2008-01-01
Full Text Available Abstract In the future, there will be a growing need for more flexible but efficient utilization of radio resources. Increased flexibility in radio transmission, however, yields a higher likelihood of interference owing to limited coordination among users. In this paper, we address the problem of flexible spectrum sharing where a wideband single carrier modulated signal is spectrally overlapped by unknown narrowband interference (NBI and where a cyclic Wiener filter is utilized for nonparametric NBI suppression at the receiver. The pulse shape design for the wideband signal is investigated to improve the NBI suppression capability of cyclic Wiener filtering. Specifically, two pulse shaping schemes, which outperform existing raised cosine pulse shaping schemes even for the same amount of excess bandwidth, are proposed. Based on computer simulation, the interference suppression capability of cyclic Wiener filtering is evaluated for both the proposed and existing pulse shaping schemes under several interference conditions and over both AWGN and Rayleigh fading channels.
Nonparametric Multivariate CUSUM Chart and EWMA Chart Based on Simplicial Data Depth
Institute of Scientific and Technical Information of China (English)
Yanting; Li Zhaojun Wang
2003-01-01
Simplicial data depth is a useful tool for describing how central a vector is in a multivariate distribution. If the average simplicial depth of a subgroup of observations from a multivariate distribution is too small, it may indicate that a shift in its location or/both scale occurs. In this paper, we propose two new types of nonparametric control charts which are one-sided CUSUM and EWMA control schemes based on simplicial data depth. We also compute the Average Run Length of the CUSUM chart and the EWMA chart by Markov chain method. Recommendations on how to choose the optimal reference value and the smoothing parameter are also given. Comparisons between these two proposed control schemes and the multivariate EWMA are presented.
Non-parametric co-clustering of large scale sparse bipartite networks on the GPU
DEFF Research Database (Denmark)
Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai
2011-01-01
Co-clustering is a problem of both theoretical and practical importance, e.g., market basket analysis and collaborative filtering, and in web scale text processing. We state the co-clustering problem in terms of non-parametric generative models which can address the issue of estimating the number...... of row and column clusters from a hypothesis space of an infinite number of clusters. To reach large scale applications of co-clustering we exploit that parameter inference for co-clustering is well suited for parallel computing. We develop a generic GPU framework for efficient inference on large scale......-life large scale collaborative filtering data and web scale text corpora, demonstrating that latent mesoscale structures extracted by the co-clustering problem as formulated by the Infinite Relational Model (IRM) are consistent across consecutive runs with different initializations and also relevant...
Assessing T cell clonal size distribution: a non-parametric approach.
Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V
2014-01-01
Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Assessing T cell clonal size distribution: a non-parametric approach.
Directory of Open Access Journals (Sweden)
Olesya V Bolkhovskaya
Full Text Available Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Szabo, Zoltan
2010-01-01
The goal of this paper is to extend independent subspace analysis (ISA) to the case of (i) nonparametric, not strictly stationary source dynamics and (ii) unknown source component dimensions. We make use of functional autoregressive (fAR) processes to model the temporal evolution of the hidden sources. An extension of the ISA separation principle--which states that the ISA problem can be solved by traditional independent component analysis (ICA) and clustering of the ICA elements--is derived for the solution of the defined fAR independent process analysis task (fAR-IPA): applying fAR identification we reduce the problem to ISA. A local averaging approach, the Nadaraya-Watson kernel regression technique is adapted to obtain strongly consistent fAR estimation. We extend the Amari-index to different dimensional components and illustrate the efficiency of the fAR-IPA approach by numerical examples.
A non-parametric method for correction of global radiation observations
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Perers, Bengt;
2013-01-01
This paper presents a method for correction and alignment of global radiation observations based on information obtained from calculated global radiation, in the present study one-hour forecast of global radiation from a numerical weather prediction (NWP) model is used. Systematical errors detected...... 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...... University. The method can be useful for optimized use of solar radiation observations for forecasting, monitoring, and modeling of energy production and load which are affected by solar radiation....
A Level Set Analysis and A Nonparametric Regression on S&P 500 Daily Return
Directory of Open Access Journals (Sweden)
Yipeng Yang
2016-02-01
Full Text Available In this paper, a level set analysis is proposed which aims to analyze the S&P 500 return with a certain magnitude. It is found that the process of large jumps/drops of return tend to have negative serial correlation, and volatility clustering phenomenon can be easily seen. Then, a nonparametric analysis is performed and new patterns are discovered. An ARCH model is constructed based on the patterns we discovered and it is capable of manifesting the volatility skew in option pricing. A comparison of our model with the GARCH(1,1 model is carried out. The explanation of the validity on our model through prospect theory is provided, and, as a novelty, we linked the volatility skew phenomenon to the prospect theory in behavioral finance.
Non-parametric Reconstruction of Cluster Mass Distribution from Strong Lensing Modelling Abell 370
Abdel-Salam, H M; Williams, L L R
1997-01-01
We describe a new non-parametric technique for reconstructing the mass distribution in galaxy clusters with strong lensing, i.e., from multiple images of background galaxies. The observed positions and redshifts of the images are considered as rigid constraints and through the lens (ray-trace) equation they provide us with linear constraint equations. These constraints confine the mass distribution to some allowed region, which is then found by linear programming. Within this allowed region we study in detail the mass distribution with minimum mass-to-light variation; also some others, such as the smoothest mass distribution. The method is applied to the extensively studied cluster Abell 370, which hosts a giant luminous arc and several other multiply imaged background galaxies. Our mass maps are constrained by the observed positions and redshifts (spectroscopic or model-inferred by previous authors) of the giant arc and multiple image systems. The reconstructed maps obtained for A370 reveal a detailed mass d...
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access...... to nonpublic information. Our analysis shows that information networks have an impact on the level of TAC. Many resources that are sacrificed for TAC are inputs that also enter the technical production process. As most production data do not separate between these two usages of inputs, high transaction costs...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling.
Karsch, Kevin; Liu, Ce; Kang, Sing Bing
2014-11-01
We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
Nonparametric analysis of competing risks data with event category missing at random.
Gouskova, Natalia A; Lin, Feng-Chang; Fine, Jason P
2017-03-01
In competing risks setup, the data for each subject consist of the event time, censoring indicator, and event category. However, sometimes the information about the event category can be missing, as, for example, in a case when the date of death is known but the cause of death is not available. In such situations, treating subjects with missing event category as censored leads to the underestimation of the hazard functions. We suggest nonparametric estimators for the cumulative cause-specific hazards and the cumulative incidence functions which use the Nadaraya-Watson estimator to obtain the contribution of an event with missing category to each of the cause-specific hazards. We derive the propertied of the proposed estimators. Optimal bandwidth is determined, which minimizes the mean integrated squared errors of the proposed estimators over time. The methodology is illustrated using data on lung infections in patients from the United States Cystic Fibrosis Foundation Patient Registry. © 2016, The International Biometric Society.
Crainiceanu, Ciprian M; Caffo, Brian S; Di, Chong-Zhi; Punjabi, Naresh M
2009-06-01
We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS.
Ghodsi, Zara; Silva, Emmanuel Sirimal; Hassani, Hossein
2015-06-01
The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.