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Correcting for measurement error when estimating the density of a routinely collected biomedical variable is an important issue when describing reference values for both healthy and pathological states. The present work addresses the problem of estimating the density of a biomedical variable observed with measurement error without any a priori knowledge on the error density. Assuming the availability of a sample of replicate observations, either internal or external, which is generally easily obtained in clinical settings, we propose an estimator based on the non-parametric deconvolution theory with an adaptive procedure for cutoff selection, the replicates being used for an estimation of the error density. We illustrate this approach in two applicative examples: (i) the systolic blood pressure distribution density, using the Framingham Study data set, and (ii) the distribution of the timing of onset of pregnancy within the female cycle, using ultrasound measurements in the first trimester of pregnancy. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Questions Many methods have been developed to estimate species richness but few are useful for estimating regional richness. We compared the performance of commonly used non-parametric and area-based estimators with a particular focus on testing a newly developed but little tested maximum entropy method (MaxEnt). Location Tropical forest of Jianfengling Reserve, Hainan Island, China. Methods We extrapolated species richness on 12 estimators up to a larger regional scale - the reserve (472 km2) - where 164 25 m × 25 m quadrats were distributed on a grid of 160 km2 within the tropical forest. We also analysed the effects of base (or 'anchor') scale A0 on the species richness estimated ( Sest) with MaxEnt. Results Six non-parametric methods underestimated the species richness, while six area-based methods overestimated the species richness. The accuracy of the MaxEnt estimate ( Sest) was improved with the increase of base scale A0. Conclusions Our findings suggest non-parametric methods should not be used to estimate richness across heterogeneous landscapes but can be used in well-defined sampling areas. Jack2 is the best of the six non-parametric methods, while the logistic model and the MaxEnt method seem to be the best of the six area-based methods. Improvements to the MaxEnt method are possible but that will require reformulation of the method by considering species-abundance distributions other than log-series and more general spatial allocation rules. [ABSTRACT FROM AUTHOR]

The forecast performance of the empirical ESTAR model of Taylor et al. () is examined for 4 bilateral real exchange rate series over an out-of-sample evaluation period of nearly 12 years. Point as well as density forecasts are constructed, considering forecast horizons of 1 to 22 steps head. The study finds that no forecast gains over a simple AR(1) specification exist at any of the forecast horizons that are considered, regardless of whether point or density forecasts are utilised in the evaluation. Non-parametric methods are used in conjunction with simulation techniques to learn about the models and their forecasts. It is shown graphically that the nonlinearity in the conditional means (or point forecasts) of the ESTAR model decreases as the forecast horizon increases. The non-parametric methods show also that the multiple steps ahead forecast densities are normal looking with no signs of bi-modality, skewness or kurtosis. [ABSTRACT FROM AUTHOR]

Copyright of Studies of Socio-Economics & Humanities / Socioekonomické a Humanitní Studie is the property of College of Banking, Banking Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Popular non-parametric methods like k-nearest neighbor classifier and density based clustering method like DBSCAN show good performance when data set sizes are large. The time complexity to find a density at a point in the data set is O(n) where n is the size of the data set, hence these non-parametric methods are not scalable for large data sets. A two level rough fuzzy weighted leader based classifier has been developed which is a scalable and efficient method for classification. However, a generalized model does not exist to estimate density non-parametrically that can be used for density based classification and clustering. This paper presents a generalized model which proposes a single level rough fuzzy weighted leader clustering method to condense data set inorder to reduce computational burden and use these rough-fuzzy weighted leaders to estimate density at a point in the data set for classification and clustering. We show that the proposed rough fuzzy weighted leader based non-parametric methods are fast and efficient when compared with related existing methods interms of accuracy and computational time. [Copyright &y& Elsevier]

The author suggests a heuristic method for detecting the dependence of random time series that can be used in the case when this dependence is relatively weak, such that the traditional methods are not effective. The method requires comparison of some special functionals on the sample characteristic functions with the same functionals computed for the benchmark time series with a known degree of correlation. Some experiments for financial time series are presented. [ABSTRACT FROM AUTHOR]

Abstract: When analyzing the nonlinear Duffing oscillator, the weak nonlinearity is basically dependent on the amplitude range of the input excitation. The nonlinear differential equation models of such nonlinear oscillators, which can be transformed into the frequency domain, can generally only provide Volterra modeling and analysis in the frequency-domain over a fraction of the entire framework of weak nonlinearity. When the amplitude of the excitation exceeds a certain value, the underlying nonlinear differential equation will become invalid in the frequency domain. This paper discusses the problem of using a new non-parametric routine to extend the capability of Volterra analysis, in the frequency domain, to weakly nonlinear Duffing systems at a much wider range of excitation amplitude range which the current underlying nonlinear differential equation models fail to address. [Copyright &y& Elsevier]

Real-time data on national accounts statistics typically undergo an extensive revision process, leading to multiple vintages on the same generic variable. The time between the publication of the initial and final data is a lengthy one and raises the question of how to model and forecast the final vintage of data - an issue that dates from seminal articles by Mankiw et al. [51], Mankiw and Shapiro [52] and Nordhaus [57]. To solve this problem, we develop the non-parametric method of multivariate singular spectrum analysis (MSSA) for multi-vintage data. MSSA is much more flexible than the standard methods of modelling that involve at least one of the restrictive assumptions of linearity, normality and stationarity. The benefits are illustrated with data on the UK index of industrial production: neither the preliminary vintages nor the competing models are as accurate as the forecasts using MSSA. [ABSTRACT FROM AUTHOR]

Abstract: Non-parametric option pricing models, such as artificial neural networks, are often found to outperform their parametric counterparts in empirical option pricing exercises. In this context, non-parametric models are viewed as more flexible and amenable to adaptive learning. However, the main drawback of non-parametric approaches is their lack of stability, which is detrimental to out-of-sample performance. This is the key reason why one may prefer a parsimonious parametric model. This paper proposes a parametric Takagi–Sugeno–Kang (TSK) fuzzy rule-based option pricing model that requires only a small number of rules to describe highly complex non-linear functions. The findings for this data-driven approach indicate that the TSK model presents a robust option pricing tool that is superior to an array of well-known parametric models from the literature. In addition, its predictive performance is consistently no worse than that of a non-parametric feedforward neural network model. [Copyright &y& Elsevier]

The paper assesses the difference in the nature of relationship between efficiency and its determinants of Indian cement producing firms in the presence and absence of environmental regulation. It provides an intuitive construct to identify the determinants of efficiency, which is followed by an empirical verification using a two-stage stochastic data envelopment analysis. Important quantitative determinants of efficiency include factor intensities of outputs, levels of pollution abatements, gross value added, whereas size, age, ownership type, development indicators of the place of location of a firm are among the qualitative determinants. An initial phase and a matured phase of regulation are considered in the paper. The main results suggest that capital intensity of output plays a positive role in increasing efficiency under regulation in the initial phase, material intensity in both phases and labour intensity, a negative role, in the subsequent phase. Higher levels of pollution abatements cause higher efficiency under regulation, while in the initial phase private firms report higher efficiencies under regulation; it is just the reverse in the subsequent phase. Size, age and development indicator of the state in which a firm is located can play a positive role to achieve higher efficiencies in the matured phase of regulation. [ABSTRACT FROM AUTHOR]

Teaching experiments have generated several hypotheses concerning the construction of fraction schemes and operations and relationships among them. In particular, researchers have hypothesized that children's construction of splitting operations is crucial to their construction of more advanced fractions concepts (Steife, 2002). The authors propose that splitting constitutes a psychological structure similar to that of a mathematical group (Piaget, 1970b): a structure that introduces mutual reversibility of students' partitioning and iterating operations that the authors refer to as the splitting loope. Data consisted of 66 sixth-grade students' written performance on 20 tasks designed to provoke responses that would indicate particular fractions schemes and operations. Findings are consistent with hypotheses from related teaching experiments. In particular, they demonstrate—consistent with the notion of the splitting loope—that equipartitioning and the partitive unit fraction scheme mediate the construction of splitting from partitioning and iterating operations. [ABSTRACT FROM AUTHOR]

Economic theory emphasizes that risk sharing makes it possible to exploit benefits from comparative advantages and economies of scale. Unlike previous studies we test (and reject) the assumption of parameter homogeneity across geographical units in measuring risk sharing. The estimated regional-specific index of risk sharing is then used as a covariate in a model of industrial specialization for the EU15 regions. By estimating a number of nonparametric additive spatial autocovariance models, allowing for nonlinearities and spatial dependence, we show that industrial specialization is positively affected by risk-sharing measures even controlling for other relevant regressors. [ABSTRACT FROM AUTHOR]

1. There are two reasons for wanting to compare measurers or methods of measurement. One is to calibrate one method or measurer against another; the other is to detect bias. Fixed bias is present when one method gives higher (or lower) values across the whole range of measurement. Proportional bias is present when one method gives values that diverge progressively from those of the other. 2. Linear regression analysis is a popular method for comparing methods of measurement, but the familiar ordinary least squares (OLS) method is rarely acceptable. The OLS method requires that the x values are fixed by the design of the study, whereas it is usual that both y and x values are free to vary and are subject to error. In this case, special regression techniques must be used. 3. Clinical chemists favour techniques such as major axis regression (‘Deming’s method’), the Passing–Bablok method or the bivariate least median squares method. Other disciplines, such as allometry, astronomy, biology, econometrics, fisheries research, genetics, geology, physics and sports science, have their own preferences. 4. Many Monte Carlo simulations have been performed to try to decide which technique is best, but the results are almost uninterpretable. 5. I suggest that pharmacologists and physiologists should use ordinary least products regression analysis (geometric mean regression, reduced major axis regression): it is versatile, can be used for calibration or to detect bias and can be executed by hand-held calculator or by using the loss function in popular, general-purpose, statistical software. [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Mexicana de Biodiversidad is the property of Universidad Nacional Autonoma de Mexico, Instituto de Biologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A successful class of image denoising methods is based on Bayesian approaches working in wavelet representations. The performance of these methods improves when relations among the local frequency coefficients are explicitly included. However, in these techniques, analytical estimates can be obtained only for particular combinations of analytical models of signal and noise, thus precluding its straightforward extension to deal with other arbitrary noise sources. In this paper, we propose an alternative non-explicit way to take into account the relations among natural image wavelet coefficients for denoising: we use support vector regression (SVR) in the wavelet domain to enforce these relations in the estimated signal. Since relations among the coefficients are specific to the signal, the regularization property of SVR is exploited to remove the noise, which does not share this feature. The specific signal relations are encoded in an anisotropic kernel obtained from mutual information measures computed on a representative image database. In the proposed scheme, training considers minimizing the Kullback-Leibler divergence (KLD) between the estimated and actual probability functions (or histograms) of signal and noise in order to enforce similarity up to the higher (computationally estimable) order. Due to its non-parametric nature, the method can eventually cope with different noise sources without the need of an explicit re-formulation, as it is strictly necessary under parametric Bayesian formalisms. Results under several noise levels and noise sources show that: (1) the proposed method outperforms conventional wavelet methods that assume coefficient independence, (2) it is similar to state-of-the-art methods that do explicitly include these relations when the noise source is Gaussian, and (3) it gives better numerical and visual performance when more complex, realistic noise sources are considered. Therefore, the proposed machine learning approach can be seen as a more flexible (model-free) alternative to the explicit description of wavelet coefficient relations for image denoising. [ABSTRACT FROM AUTHOR]

This paper formalizes the process of forecasting unbalanced monthly datasets in order to obtain robust nowcasts and forecasts of quarterly gross domestic product (GDP) growth rate through a semi-parametric modeling. This innovative approach lies in the use of non-parametric methods, based on nearest neighbors and on radial basis function approaches, to forecast the monthly variables involved in the parametric modeling of GDP using bridge equations. A real-time experience is carried out on euro area vintage data in order to anticipate, with an advance ranging from 6 to 1 months, the GDP flash estimate for the whole zone. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: A widely accepted magnetic resonance imaging (MRI) model states that the observed voxel intensity is a piecewise constant signal intensity function corresponding to the tissue spatial distribution, corrupted with multiplicative and additive noise. The multiplicative noise is assumed to be a smooth bias field, it is called intensity inhomogeneity (IIH) field. Our approach to IIH correction is based on the definition of an energy function that incorporates some smoothness constraints into the conventional classification error function of the IIH corrected image. The IIH field estimation algorithm is a gradient descent of this energy function relative to the IIH field. We call it adaptive field rule (AFR). We comment on the likeness of our approach to the self-organizing map (SOM) learning rule, on the basis of the neighboring function that controls the influence of the neighborhood on each voxel''s IIH estimation. We discuss the convergence properties of the algorithm. Experimental results show that AFR compares well with state of the art algorithms. Moreover, the mean signal intensity corresponding to each class of tissue can be estimated from the image data applying the gradient descent of the proposed energy function relative to the intensity class means. We test several variations of this gradient descent approach, which embody diverse assumptions about available a priori information. [Copyright &y& Elsevier]

Copyright of Computer Science (1407-7493) is the property of RTU Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents a new methodology for forecasting volatility. It relies on a weighted mean of short and long estimates of variance, based on a Moving Average framework. The quality of the predictions obtained with the proposed formula was checked with both simulated and real data. When applied to the analysis of simulated data, the new formula provides the least reliable forecast when a Random Walk is used as Data Generating Process (DGP) and the forecast variance is a simple Moving Average. This is also the case when the DGP belongs to the ARCH model family and the associated forecast formula is used. However, compared to existing approaches, the new methodology allows for the most reliable forecast on 5-day and 20-day horizons, when it is applied to Index, Fixed Income and Foreign Exchange data series.Journal of Asset Management (2009) 10, 124–136. doi:10.1057/jam.2009.2 [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the non-parametric estimation of conditional moments, which is useful for applications in global sensitivity analysis (GSA) and in the more general emulation framework. The estimation is based on the state-dependent parameter (SDP) estimation approach and allows for the estimation of conditional moments of order larger than unity. This allows one to identify a wider spectrum of parameter sensitivities with respect to the variance-based main effects, like shifts in the variance, skewness or kurtosis of the model output, so adding valuable information for the analyst, at a small computational cost. [Copyright &y& Elsevier]

We present an approach to association studies involving a dozen or so 'response' variables and a few hundred 'explanatory' variables which emphasizes transparency, simplicity, and protection against spurious results. The methods proposed are largely non-parametric, and they are systematically rounded-off by the Benjamini-Hochberg method of multiple testing. An application to the detection of associations between risk factors of heart disease and genetic polymorphisms using the REGRESS dataset provides ample illustration of our approach. Special attention is paid to book-keeping and information-management aspects of data analysis, which allow the creation of an informative and reasonably digestible 'map of relationships'--the end-product of an association study as far as statistics is concerned. [ABSTRACT FROM AUTHOR]

Copyright of Gazi University Journal of Economics & Administrative Sciences is the property of Gazi University Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Random sets form a well-established, general tool for modelling epistemic uncertainty in engineering. They can be seen as encompassing probability theory, fuzzy sets and interval analysis. Random set models for data uncertainty are typically used to obtain robust upper and lower bounds for the reliability of structures in engineering models. The goal of this paper is to show how random set models can be constructed from measurement data by non-parametric methods using inequalities of Tchebycheff type. Relations with sensitivity analysis will also be high-lighted. We demonstrate the application of the methods in an FE-model for the excavation of a cantilever sheet pile wall. [Copyright &y& Elsevier]

Copyright of Ensayos - Revista de Economía is the property of Ensayos Revista de Economia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On-line auctions pose many challenges for the empirical researcher, one of which is the effective and reliable modelling of price paths. We propose a novel way of modelling price paths in eBay's on-line auctions by using functional data analysis. One of the practical challenges is that the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data, which can be difficult if the data are irregularly distributed. We present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. As well as being able to handle sparse and unevenly distributed data, the model also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's on-line auctions. On-line auctions produce monotonic increasing price curves that are often correlated across auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also manages to capture the underlying monotonic trend in the data without imposing model constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an on-line auction, our approach also results in more accurate price predictions compared with standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants. [ABSTRACT FROM AUTHOR]

Non-independent evolution of amino acid sites has become a noticeable limitation of most methods aimed at identifying selective constraints at functionally important amino acid sites or protein regions. The need for a generalised framework to account for non-independence of amino acid sites has fuelled the design and development of new mathematical models and computational tools centred on resolving this problem. Molecular coevolution is one of the most active areas of research, with an increasing rate of new models and methods being developed everyday. Both parametric and nonparametric methods have been developed to account for correlated variability of amino acid sites. These methods have been utilised for detecting phylogenetic, functional and structural coevolution as well as to identify surfaces of amino acid sites involved in protein-protein interactions. Here we discuss and briefly describe these methods, and identify their advantages and limitations. [ABSTRACT FROM AUTHOR]

The article assesses the hospital performance in terms of health care spending in various hospitals situated across the Organization for Economic Cooperation and Development (OECD) countries. The assessment is based on the results gathered by three different approaches to international comparison: unit costs for standard hospital treatments, overall efficiency levels in a set of paired countries, and within-country dispersion in individual hospital efficiency. After data analysis, it is claimed that countries could potentially reduce unit costs by between five percent and 48 percent if the same be likewise reduced to the level of the best performer for each intervention. There are also differences found in the evidence on within-country dispersion.

Many economic and financial time series exhibit heteroskedasticity, where the variability changes are often based on recent past shocks, which cause large or small fluctuations to cluster together. Classical ways of modelling the changing variance include the use of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and Neural Networks models. The paper starts with a comparative study of these two models, both in terms of capturing the non-linear or heteroskedastic structure and forecasting performance. Monthly and daily exchange rates for three different countries are implemented. The paper continues with different methods for combining forecasts of the volatility from the competing models, in order to improve forecasting accuracy. Traditional methods for combining the predicted values from different models, using various weighting schemes are considered, such as the simple average or methods that find the best weights in terms of minimizing the squared forecast error. The main purpose of the paper is, however, to propose an alternative methodology for combining forecasts effectively. The new, hereby-proposed non-linear, non-parametric, kernel-based method, is shown to have the basic advantage of not being affected by outliers, structural breaks or shocks to the system and it does not require a specific functional form for the combination. [ABSTRACT FROM AUTHOR]

Abstract: Several methods (Bayesian, testing, confidence) are presented for evaluating the probability ‘p’ that the outputs of a computer code, given random inputs, will meet a set of evaluation criteria. They all lead to the same expression for the confidence β that p exceeds some desired value q, based on the results of running the code independently N times. This expression is independent of the number of evaluation criteria. The ‘coverage’ approach to sampling, which yields a confidence β co that the extreme values of the outputs from N independent runs of the code bound a region into which a single run of the code will fall with at least a probability γ, is shown to solve a different problem and to be unsuitable as a method for evaluating ‘p’, except in the special case when there is only one evaluation criterion. The method has broad application. It is developed in this paper in the context of evaluating the likelihood that a nuclear plant will meet the applicable safety criteria. [Copyright &y& Elsevier]

Abstract: The orthogonal/similar character of 38 chromatographic systems using retention factor data of 68 drug substances was determined using different parametric and non-parametric methods. Non-parametric methods can be recommended, as the majority of retention data cannot be considered as normally distributed. The generalized pairwise correlation method (GPCM) with different statistical tests was applied to examine the similarity/orthogonality of the tested systems. Beside this, the Pearson''s (product moment) correlation coefficient, the Spearman''s rho and the Kendall''s tau were also used as conventional correlation parameters. The Williams’ t-test (as a parametric statistical test), and the Conditional Fisher''s, the McNemar''s and the Chi-square tests (as non-parametric statistical tests) were used for hypotheses testing. Except for the selection using correlation coefficients all other measures are non-parametric. A new procedure was applied to establish orthogonality of the chromatographic systems. From the different correlation measures so-called orthogonality ratios were calculated. The ratios originating from GPCM with McNemar''s test was found to be the best to select orthogonal (dissimilar) and similar systems. The method was compared with various alternatives to validate the findings (among others with the ratios from Pearson''s correlation coefficients). The non-parametric options for correlation coefficients (Spearman''s rho and Kendall''s tau) are found not to be sensitive enough to define the orthogonality of these chromatographic systems. [Copyright &y& Elsevier]

Non-parametric estimation of functional relationships is an important part of data analysis, particularly in the exploratory stages. This paper considers non-parametric estimation of the mean functions in family studies using weighted robust estimating equations while retaining a fully parametric model for the covariance structure. The proposed procedure allows an exploratory examination of complex pedigree data that is an invaluable aid in determining appropriate models. This is illustrated by an examination of the relationship between IQ and the level of a particular protein in individuals collected as part of a large family study. [ABSTRACT FROM AUTHOR]

Population L-moments have been proposed as alternatives to central moments for describing distribution location, dispersion and shape, and their sample estimates are unbiased. However, only asymptotic variances and covariances of their estimates have been reported. In this article, we derive expressions for exact variances and covariances of sample L-moments for any sample size n in terms of first- and second-order moments of order statistics from small sample sizes which do not depend on n. Various applications of these result are discussed.We also derive distribution-free unbiased estimators of the variances and covariances of sample L-moments, and report the results of a simulation study to investigate and compare the sampling distributions of standardised L-moments using exact, asymptotic and estimated standard errors. In particular, a new test of symmetry is investigated. Also, approximate standard errors of ratios of sample L-moments, used to estimate ratios of population L-moments analogous to classical scaled measures of skewness and kurtosis, are exemplified. [Copyright &y& Elsevier]

We apply methods from order statistics to the problem of satisfying regulations that specify individual criteria to be met by each of a number of outputs, k, from a computer code simulating nuclear accidents. The regulations are assumed to apply to an ‘extent’, γk, (such as 95%) of the cumulative probability distribution of each output, k, that is obtained by randomly varying the inputs to the code over their ranges of uncertainty. We use a ‘bracketing’ approach to obtain expressions for the confidence, β, or probability that these desired extents will be covered in N runs of the code. Detailed results are obtained for k=1,2,3, with equal extents, γ, and are shown to depend on the degree of correlation of the outputs. They reduce to the proper expressions in limiting cases. These limiting cases are also analyzed for an arbitrary number of outputs, k.The bracketing methodology is contrasted with the traditional ‘coverage’ approach in which the objective is to obtain a range of outputs that enclose a total fraction, γ, of all possible outputs, without regard to the extent of individual outputs. For the case of two outputs we develop an alternate formulation and show that the confidence, β, depends on the degree of correlation between outputs. The alternate formulation reduces to the single output case when the outputs are so well correlated that the coverage criterion is always met in a single run of the code if either output lies beyond an extent γ, it reduces to Wilks'' expression for un-correlated variables when the outputs are independent, and it reduces to Wald''s result when the outputs are so negatively correlated that the coverage criterion could never be met by the two outputs of a single run of the code. The predictions of both formulations are validated by comparison with Monte Carlo simulations. [Copyright &y& Elsevier]

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value. [ABSTRACT FROM AUTHOR]

Water quality variables – Turbidity, pH, Electrical Conductivity (EC), Chlorides and Total Hardness (TH) were monitored at a downstream location in the Tamiraparani River during 1978–1992. The observations were made at weekly intervals in a water treatment and supply plant using standard methods. Graphical and statistical analyses were used for data exploration, trend detection and assessment. Box-Whisker plots of annual and seasonal changes in variables indicated apparent trends being present in the data and their response to the seasonal influence of the monsoon rainfall. Further, the examination of the median values of the variables indicated that changes in the direction of trend occurred during 1985–1986, especially in pH, EC and TH. The statistical analyses were done using non-parametric methods, the ANCOVA on rank transformed data and the Seasonal Man-Kendall test. The presence of monotonic trend in all the water quality variables was confirmed, however, with independent direction of change. The trend line was fitted by the method of least squares. The estimated values indicated significant increases in EC (28 μS cm-1) while significant decreases were observed in turbidity (90 NTU), pH (0.78), and total hardness (23 ppm) in a span of 15 years. The changes induced in river flow by the addition of a stabilizing reservoir, the influence of seasonal and spatial pattern of monsoon rainfall across the river basin and the increased agriculture appear causative factors for the water quality trends seen in the Tamiraparani River system. [ABSTRACT FROM AUTHOR]

In this paper, recent techniques of estimating implied information from derivatives markets are presented and applied empirically to the French derivatives market. We determine nonparametric implied volatility functions, state–price densities and historical densities from a high–frequency CAC 40 stock index option dataset. Moreover, we construct an estimator of the risk aversion function implied by the joint observation of the cross–section of option prices and time–series of underlying asset value. We report a decreasing implied volatility curve with the moneyness of the option. The estimated relative risk aversion functions are positive and globally consistent with the decreasing relative risk aversion assumption. [ABSTRACT FROM AUTHOR]

The article discusses a study that examined the effectiveness of the use of the transformation of data for the application of models of analysis of variance in different plant and animal experiments. A number of scientific journals published between 1987 and 1997 were used in this study. According to results of the study, transformations were applied in 86 articles prior to the use of parametric methods. The study also found that transformation were unduly applied in 28.2% of the cases.

All variables of several large data sets from regional geochemical and environmental surveys were tested for a normal or lognormal data distribution. As a general rule, almost all variables (up to more than 50 analysed chemical elements per data set) show neither a normal or a lognormal data distribution. Even when different transformation methods are used more than 70 % of all variables in every single data set do not approach a normal distribution. Distributions are usually skewed, have outliers and originate from more than one process. When dealing with regional geochemical or environmental data normal and/or lognormal distributions are an exception and not the rule. This observation has serious consequences for the further statistical treatment of geochemical and environmental data. The most widely used statistical methods are all based on the assumption that the studied data show a normal or lognormal distribution. Neglecting that geochemcial and environmental data show neither a normal or lognormal distribution will lead to biased or faulty results when such techniques are used. [ABSTRACT FROM AUTHOR]

The load flow problem is fundamental to characterize the equilibrium behavior of a power system. Uncertain power injections such as those due to demand variations and intermittent renewable resources will change the system's equilibrium unexpectedly, and thus potentially jeopardizing the system's reliability and stability. Understanding load flow solutions under uncertainty becomes imperative to ensure the seamless operation of a power system. In this work, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on the Gaussian Process (GP) learning to understand the power system behavior under uncertainty for better operational decisions. The technique can provide " semi-explicit " form of load flow solutions by implementing the learning and testing steps that map control variables to inputs. The proposed NP-PLF leverages upon GP upper confidence bound (GP-UCB) sampling algorithm. The salient features of this NP-PLF method are: i) applicable for power flow problem having power injection uncertainty with an unknown class of distribution; ii) providing probabilistic learning bound (PLB) which further provides control over the error and convergence; iii) capable of handling intermittent distributed generation as well as load uncertainties. The simulation results performed on the IEEE 30-bus and IEEE 118-bus system show that the proposed method can learn the voltage function over the power injection subspace using a small number of training samples. Further, the testing with different input uncertainty distributions indicates that complete statistical information can be obtained for the probabilistic load flow problem with an average percentage relative error of the order of 1 0 − 3 % on 50,000 test points. • Novel non-parametric probabilistic load flow for uncertain power injections. • No need of information about the type of uncertainty distribution. • A semi-explicit form of power flow solutions. • Accurate statistical information on power flow. • Probabilistic learning bound (PLB) for convergence criteria. [ABSTRACT FROM AUTHOR]