Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor
2012-06-29
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.
2012-01-01
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.
2010-01-01
The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.
Kromhout, D.
2009-01-01
Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements of the
Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data
Ulbrich, N.
2015-01-01
An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
International Nuclear Information System (INIS)
Vogt, Frank
2013-01-01
Graphical abstract: Analysis Task: Determine the albumin (= protein) concentration in microalgae cells as a function of the cells’ nutrient availability. Left Panel: The predicted albumin concentrations as obtained by conventional principal component regression features low reproducibility and are partially higher than the concentrations of algae in which albumin is contained. Right Panel: Augmenting an incomplete PCR calibration with additional expert information derives reasonable albumin concentrations which now reveal a significant dependency on the algae's nutrient situation. -- Highlights: •Make quantitative analyses of compounds embedded in largely unknown chemical matrices robust. •Improved concentration prediction with originally insufficient calibration models. •Chemometric approach for incorporating expertise from other fields and/or researchers. •Ensure chemical, biological, or medicinal meaningfulness of quantitative analyses. -- Abstract: Incomplete calibrations are encountered in many applications and hamper chemometric data analyses. Such situations arise when target analytes are embedded in a chemically complex matrix from which calibration concentrations cannot be determined with reasonable efforts. In other cases, the samples’ chemical composition may fluctuate in an unpredictable way and thus cannot be comprehensively covered by calibration samples. The reason for calibration model to fail is the regression principle itself which seeks to explain measured data optimally in terms of the (potentially incomplete) calibration model but does not consider chemical meaningfulness. This study presents a novel chemometric approach which is based on experimentally feasible calibrations, i.e. concentration series of the target analytes outside the chemical matrix (‘ex situ calibration’). The inherent lack-of-information is then compensated by incorporating additional knowledge in form of regression constraints. Any outside knowledge can be
Forecasting exchange rates: a robust regression approach
Preminger, Arie; Franck, Raphael
2005-01-01
The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...
A statistical approach to instrument calibration
Robert R. Ziemer; David Strauss
1978-01-01
Summary - It has been found that two instruments will yield different numerical values when used to measure identical points. A statistical approach is presented that can be used to approximate the error associated with the calibration of instruments. Included are standard statistical tests that can be used to determine if a number of successive calibrations of the...
DEFF Research Database (Denmark)
Tybjærg-Hansen, Anne
2009-01-01
Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements...... of the risk factors are observed on a subsample. We extend the multivariate RC techniques to a meta-analysis framework where multiple studies provide independent repeat measurements and information on disease outcome. We consider the cases where some or all studies have repeat measurements, and compare study......-specific, averaged and empirical Bayes estimates of RC parameters. Additionally, we allow for binary covariates (e.g. smoking status) and for uncertainty and time trends in the measurement error corrections. Our methods are illustrated using a subset of individual participant data from prospective long-term studies...
On the calibration process of film dosimetry: OLS inverse regression versus WLS inverse prediction
International Nuclear Information System (INIS)
Crop, F; Thierens, H; Rompaye, B Van; Paelinck, L; Vakaet, L; Wagter, C De
2008-01-01
The purpose of this study was both putting forward a statistically correct model for film calibration and the optimization of this process. A reliable calibration is needed in order to perform accurate reference dosimetry with radiographic (Gafchromic) film. Sometimes, an ordinary least squares simple linear (in the parameters) regression is applied to the dose-optical-density (OD) curve with the dose as a function of OD (inverse regression) or sometimes OD as a function of dose (inverse prediction). The application of a simple linear regression fit is an invalid method because heteroscedasticity of the data is not taken into account. This could lead to erroneous results originating from the calibration process itself and thus to a lower accuracy. In this work, we compare the ordinary least squares (OLS) inverse regression method with the correct weighted least squares (WLS) inverse prediction method to create calibration curves. We found that the OLS inverse regression method could lead to a prediction bias of up to 7.3 cGy at 300 cGy and total prediction errors of 3% or more for Gafchromic EBT film. Application of the WLS inverse prediction method resulted in a maximum prediction bias of 1.4 cGy and total prediction errors below 2% in a 0-400 cGy range. We developed a Monte-Carlo-based process to optimize calibrations, depending on the needs of the experiment. This type of thorough analysis can lead to a higher accuracy for film dosimetry
Ulbrich, N.; Volden, T.
2018-01-01
Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods.
Energy Technology Data Exchange (ETDEWEB)
Keilacker, H; Becker, G; Ziegler, M; Gottschling, H D [Zentralinstitut fuer Diabetes, Karlsburg (German Democratic Republic)
1980-10-01
In order to handle all types of radioimmunoassay (RIA) calibration curves obtained in the authors' laboratory in the same way, they tried to find a non-linear expression for their regression which allows calibration curves with different degrees of curvature to be fitted. Considering the two boundary cases of the incubation protocol they derived a hyperbolic inverse regression function: x = a/sub 1/y + a/sub 0/ + asub(-1)y/sup -1/, where x is the total concentration of antigen, asub(i) are constants, and y is the specifically bound radioactivity. An RIA evaluation procedure based on this function is described providing a fitted inverse RIA calibration curve and some statistical quality parameters. The latter are of an order which is normal for RIA systems. There is an excellent agreement between fitted and experimentally obtained calibration curves having a different degree of curvature.
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll
2003-01-01
This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...
Agogo, George O; van der Voet, Hilko; van't Veer, Pieter; Ferrari, Pietro; Leenders, Max; Muller, David C; Sánchez-Cantalejo, Emilio; Bamia, Christina; Braaten, Tonje; Knüppel, Sven; Johansson, Ingegerd; van Eeuwijk, Fred A; Boshuizen, Hendriek
2014-01-01
In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.
International Nuclear Information System (INIS)
Seo, In Yong; Ha, Bok Nam; Lee, Sung Woo; Shin, Chang Hoon; Kim, Seong Jun
2010-01-01
In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method
DEM Calibration Approach: design of experiment
Boikov, A. V.; Savelev, R. V.; Payor, V. A.
2018-05-01
The problem of DEM models calibration is considered in the article. It is proposed to divide models input parameters into those that require iterative calibration and those that are recommended to measure directly. A new method for model calibration based on the design of the experiment for iteratively calibrated parameters is proposed. The experiment is conducted using a specially designed stand. The results are processed with technical vision algorithms. Approximating functions are obtained and the error of the implemented software and hardware complex is estimated. The prospects of the obtained results are discussed.
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
Delwiche, Stephen R; Reeves, James B
2010-01-01
In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various
bayesQR: A Bayesian Approach to Quantile Regression
Directory of Open Access Journals (Sweden)
Dries F. Benoit
2017-01-01
Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.
Approaches to Low Fuel Regression Rate in Hybrid Rocket Engines
Directory of Open Access Journals (Sweden)
Dario Pastrone
2012-01-01
Full Text Available Hybrid rocket engines are promising propulsion systems which present appealing features such as safety, low cost, and environmental friendliness. On the other hand, certain issues hamper the development hoped for. The present paper discusses approaches addressing improvements to one of the most important among these issues: low fuel regression rate. To highlight the consequence of such an issue and to better understand the concepts proposed, fundamentals are summarized. Two approaches are presented (multiport grain and high mixture ratio which aim at reducing negative effects without enhancing regression rate. Furthermore, fuel material changes and nonconventional geometries of grain and/or injector are presented as methods to increase fuel regression rate. Although most of these approaches are still at the laboratory or concept scale, many of them are promising.
Nucleonic gauges in Poland and new approach to their calibration
International Nuclear Information System (INIS)
Urbanski, P.
2000-01-01
The current status of manufacturing and application of radioisotope gauges in Poland is presented. Metrological performance of the gauges is briefly described and their expected future prospects on the market of the industrial measuring instruments are discussed. Progress in electronic engineering and common use of the microprocessor systems in the radioisotope gauges made possible application of the sophisticated methods of signal processing and data treatment, as for example statistical multivariate analysis. Some examples of the multivariate calibration of nucleonic gauges are presented. Application of the partial least square regression (PLS) and artificial neural network (ANN) for calibration of the gauges has been shown. (author)
Stepwise Regression Analysis of MDOE Balance Calibration Data Acquired at DNW
DeLoach, RIchard; Philipsen, Iwan
2007-01-01
This paper reports a comparison of two experiment design methods applied in the calibration of a strain-gage balance. One features a 734-point test matrix in which loads are varied systematically according to a method commonly applied in aerospace research and known in the literature of experiment design as One Factor At a Time (OFAT) testing. Two variations of an alternative experiment design were also executed on the same balance, each with different features of an MDOE experiment design. The Modern Design of Experiments (MDOE) is an integrated process of experiment design, execution, and analysis applied at NASA's Langley Research Center to achieve significant reductions in cycle time, direct operating cost, and experimental uncertainty in aerospace research generally and in balance calibration experiments specifically. Personnel in the Instrumentation and Controls Department of the German Dutch Wind Tunnels (DNW) have applied MDOE methods to evaluate them in the calibration of a balance using an automated calibration machine. The data have been sent to Langley Research Center for analysis and comparison. This paper reports key findings from this analysis. The chief result is that a 100-point calibration exploiting MDOE principles delivered quality comparable to a 700+ point OFAT calibration with significantly reduced cycle time and attendant savings in direct and indirect costs. While the DNW test matrices implemented key MDOE principles and produced excellent results, additional MDOE concepts implemented in balance calibrations at Langley Research Center are also identified and described.
Analysing inequalities in Germany a structured additive distributional regression approach
Silbersdorff, Alexander
2017-01-01
This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators. This new approach allows the observed discrepancy between the individuals’ realities and the abstract representation of those realities to be explicitly taken into consideration using the arithmetic mean alone. In turn, the method is applied to the question of economic inequality in Germany.
Loeza-Quintana, Tzitziki; Adamowicz, Sarah J
2018-02-01
During the past 50 years, the molecular clock has become one of the main tools for providing a time scale for the history of life. In the era of robust molecular evolutionary analysis, clock calibration is still one of the most basic steps needing attention. When fossil records are limited, well-dated geological events are the main resource for calibration. However, biogeographic calibrations have often been used in a simplistic manner, for example assuming simultaneous vicariant divergence of multiple sister lineages. Here, we propose a novel iterative calibration approach to define the most appropriate calibration date by seeking congruence between the dates assigned to multiple allopatric divergences and the geological history. Exploring patterns of molecular divergence in 16 trans-Bering sister clades of echinoderms, we demonstrate that the iterative calibration is predominantly advantageous when using complex geological or climatological events-such as the opening/reclosure of the Bering Strait-providing a powerful tool for clock dating that can be applied to other biogeographic calibration systems and further taxa. Using Bayesian analysis, we observed that evolutionary rate variability in the COI-5P gene is generally distributed in a clock-like fashion for Northern echinoderms. The results reveal a large range of genetic divergences, consistent with multiple pulses of trans-Bering migrations. A resulting rate of 2.8% pairwise Kimura-2-parameter sequence divergence per million years is suggested for the COI-5P gene in Northern echinoderms. Given that molecular rates may vary across latitudes and taxa, this study provides a new context for dating the evolutionary history of Arctic marine life.
A Novel Approach to Calibrating Multifunctional Binocular Stereovision Sensor
International Nuclear Information System (INIS)
Xue, T; Zhu, J G; Wu, B; Ye, S H
2006-01-01
We present a novel multifunctional binocular stereovision sensor for various threedimensional (3D) inspection tasks. It not only avoids the so-called correspondence problem of passive stereo vision, but also possesses the uniform mathematical model. We also propose a novel approach to estimating all the sensor parameters with free-position planar reference object. In this technique, the planar pattern can be moved freely by hand. All the camera intrinsic and extrinsic parameters with coefficient of lens radial and tangential distortion are estimated, and sensor parameters are calibrated based on the 3D measurement model and optimized with the feature point constraint algorithm using the same views in the camera calibration stage. The proposed approach greatly reduces the cost of the calibration equipment, and it is flexible and practical for the vision measurement. It shows that this method has high precision by experiment, and the sensor measured relative error of space length excels 0.3%
Bayesian approach to errors-in-variables in regression models
Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad
2017-05-01
In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.
A standardized plate meter for measuring pasture mass was calibrated at the Agroforestry Research and Demonstration Site in Blacksburg, VA, using six ungrazed plots of established tall fescue (Festuca arundinaceae) overseeded with orchardgrass (Dactylis glomerata). Each plot was interplanted with b...
Chen, Baojiang; Qin, Jing
2014-05-10
In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.
A computational approach to compare regression modelling strategies in prediction research.
Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H
2016-08-25
It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.
International Nuclear Information System (INIS)
Greacen, E.L.; Correll, R.L.; Cunningham, R.B.; Johns, G.G.; Nicolls, K.D.
1981-01-01
Procedures common to different methods of calibration of neutron moisture meters are outlined and laboratory and field calibration methods compared. Gross errors which arise from faulty calibration techniques are described. The count rate can be affected by the dry bulk density of the soil, the volumetric content of constitutional hydrogen and other chemical components of the soil and soil solution. Calibration is further complicated by the fact that the neutron meter responds more strongly to the soil properties close to the detector and source. The differences in slope of calibration curves for different soils can be as much as 40%
Wind Tunnel Strain-Gage Balance Calibration Data Analysis Using a Weighted Least Squares Approach
Ulbrich, N.; Volden, T.
2017-01-01
A new approach is presented that uses a weighted least squares fit to analyze wind tunnel strain-gage balance calibration data. The weighted least squares fit is specifically designed to increase the influence of single-component loadings during the regression analysis. The weighted least squares fit also reduces the impact of calibration load schedule asymmetries on the predicted primary sensitivities of the balance gages. A weighting factor between zero and one is assigned to each calibration data point that depends on a simple count of its intentionally loaded load components or gages. The greater the number of a data point's intentionally loaded load components or gages is, the smaller its weighting factor becomes. The proposed approach is applicable to both the Iterative and Non-Iterative Methods that are used for the analysis of strain-gage balance calibration data in the aerospace testing community. The Iterative Method uses a reasonable estimate of the tare corrected load set as input for the determination of the weighting factors. The Non-Iterative Method, on the other hand, uses gage output differences relative to the natural zeros as input for the determination of the weighting factors. Machine calibration data of a six-component force balance is used to illustrate benefits of the proposed weighted least squares fit. In addition, a detailed derivation of the PRESS residuals associated with a weighted least squares fit is given in the appendices of the paper as this information could not be found in the literature. These PRESS residuals may be needed to evaluate the predictive capabilities of the final regression models that result from a weighted least squares fit of the balance calibration data.
Statistical methods in regression and calibration analysis of chromosome aberration data
International Nuclear Information System (INIS)
Merkle, W.
1983-01-01
The method of iteratively reweighted least squares for the regression analysis of Poisson distributed chromosome aberration data is reviewed in the context of other fit procedures used in the cytogenetic literature. As an application of the resulting regression curves methods for calculating confidence intervals on dose from aberration yield are described and compared, and, for the linear quadratic model a confidence interval is given. Emphasis is placed on the rational interpretation and the limitations of various methods from a statistical point of view. (orig./MG)
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Energy Technology Data Exchange (ETDEWEB)
Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)
2012-02-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
A Practical pedestrian approach to parsimonious regression with inaccurate inputs
Directory of Open Access Journals (Sweden)
Seppo Karrila
2014-04-01
Full Text Available A measurement result often dictates an interval containing the correct value. Interval data is also created by roundoff, truncation, and binning. We focus on such common interval uncertainty in data. Inaccuracy in model inputs is typically ignored on model fitting. We provide a practical approach for regression with inaccurate data: the mathematics is easy, and the linear programming formulations simple to use even in a spreadsheet. This self-contained elementary presentation introduces interval linear systems and requires only basic knowledge of algebra. Feature selection is automatic; but can be controlled to find only a few most relevant inputs; and joint feature selection is enabled for multiple modeled outputs. With more features than cases, a novel connection to compressed sensing emerges: robustness against interval errors-in-variables implies model parsimony, and the input inaccuracies determine the regularization term. A small numerical example highlights counterintuitive results and a dramatic difference to total least squares.
Does intense monitoring matter? A quantile regression approach
Directory of Open Access Journals (Sweden)
Fekri Ali Shawtari
2017-06-01
Full Text Available Corporate governance has become a centre of attention in corporate management at both micro and macro levels due to adverse consequences and repercussion of insufficient accountability. In this study, we include the Malaysian stock market as sample to explore the impact of intense monitoring on the relationship between intellectual capital performance and market valuation. The objectives of the paper are threefold: i to investigate whether intense monitoring affects the intellectual capital performance of listed companies; ii to explore the impact of intense monitoring on firm value; iii to examine the extent to which the directors serving more than two board committees affects the linkage between intellectual capital performance and firms' value. We employ two approaches, namely, the Ordinary Least Square (OLS and the quantile regression approach. The purpose of the latter is to estimate and generate inference about conditional quantile functions. This method is useful when the conditional distribution does not have a standard shape such as an asymmetric, fat-tailed, or truncated distribution. In terms of variables, the intellectual capital is measured using the value added intellectual coefficient (VAIC, while the market valuation is proxied by firm's market capitalization. The findings of the quantile regression shows that some of the results do not coincide with the results of OLS. We found that intensity of monitoring does not influence the intellectual capital of all firms. It is also evident that intensity of monitoring does not influence the market valuation. However, to some extent, it moderates the relationship between intellectual capital performance and market valuation. This paper contributes to the existing literature as it presents new empirical evidences on the moderating effects of the intensity of monitoring of the board committees on the relationship between performance and intellectual capital.
Bias and Uncertainty in Regression-Calibrated Models of Groundwater Flow in Heterogeneous Media
DEFF Research Database (Denmark)
Cooley, R.L.; Christensen, Steen
2006-01-01
small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear...... are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis....
Bayesian logistic regression approaches to predict incorrect DRG assignment.
Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural
2018-05-07
Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.
Regression Benchmarking: An Approach to Quality Assurance in Performance
Bulej, Lubomír
2005-01-01
The paper presents a short summary of our work in the area of regression benchmarking and its application to software development. Specially, we explain the concept of regression benchmarking, the requirements for employing regression testing in a software project, and methods used for analyzing the vast amounts of data resulting from repeated benchmarking. We present the application of regression benchmarking on a real software project and conclude with a glimpse at the challenges for the fu...
Bias and Uncertainty in Regression-Calibrated Models of Groundwater Flow in Heterogeneous Media
DEFF Research Database (Denmark)
Cooley, R.L.; Christensen, Steen
2006-01-01
by a lumped or smoothed m-dimensional approximation γθ*, where γ is an interpolation matrix and θ* is a stochastic vector of parameters. Vector θ* has small enough dimension to allow its estimation with the available data. The consequence of the replacement is that model function f(γθ*) written in terms......Groundwater models need to account for detailed but generally unknown spatial variability (heterogeneity) of the hydrogeologic model inputs. To address this problem we replace the large, m-dimensional stochastic vector β that reflects both small and large scales of heterogeneity in the inputs...... small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear...
Testing for Stock Market Contagion: A Quantile Regression Approach
S.Y. Park (Sung); W. Wang (Wendun); N. Huang (Naijing)
2015-01-01
markdownabstract__Abstract__ Regarding the asymmetric and leptokurtic behavior of financial data, we propose a new contagion test in the quantile regression framework that is robust to model misspecification. Unlike conventional correlation-based tests, the proposed quantile contagion test
Identifying predictors of physics item difficulty: A linear regression approach
Mesic, Vanes; Muratovic, Hasnija
2011-06-01
Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary difficult and very easy items. Knowing the factors that influence physics item difficulty makes it possible to model the item difficulty even before the first pilot study is conducted. Thus, by identifying predictors of physics item difficulty, we can improve the test-design process. Furthermore, we get additional qualitative feedback regarding the basic aspects of student cognitive achievement in physics that are directly responsible for the obtained, quantitative test results. In this study, we conducted a secondary analysis of data that came from two large-scale assessments of student physics achievement at the end of compulsory education in Bosnia and Herzegovina. Foremost, we explored the concept of “physics competence” and performed a content analysis of 123 physics items that were included within the above-mentioned assessments. Thereafter, an item database was created. Items were described by variables which reflect some basic cognitive aspects of physics competence. For each of the assessments, Rasch item difficulties were calculated in separate analyses. In order to make the item difficulties from different assessments comparable, a virtual test equating procedure had to be implemented. Finally, a regression model of physics item difficulty was created. It has been shown that 61.2% of item difficulty variance can be explained by factors which reflect the automaticity, complexity, and modality of the knowledge structure that is relevant for generating the most probable correct solution, as well as by the divergence of required thinking and interference effects between intuitive and formal physics knowledge
Identifying predictors of physics item difficulty: A linear regression approach
Directory of Open Access Journals (Sweden)
Hasnija Muratovic
2011-06-01
Full Text Available Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary difficult and very easy items. Knowing the factors that influence physics item difficulty makes it possible to model the item difficulty even before the first pilot study is conducted. Thus, by identifying predictors of physics item difficulty, we can improve the test-design process. Furthermore, we get additional qualitative feedback regarding the basic aspects of student cognitive achievement in physics that are directly responsible for the obtained, quantitative test results. In this study, we conducted a secondary analysis of data that came from two large-scale assessments of student physics achievement at the end of compulsory education in Bosnia and Herzegovina. Foremost, we explored the concept of “physics competence” and performed a content analysis of 123 physics items that were included within the above-mentioned assessments. Thereafter, an item database was created. Items were described by variables which reflect some basic cognitive aspects of physics competence. For each of the assessments, Rasch item difficulties were calculated in separate analyses. In order to make the item difficulties from different assessments comparable, a virtual test equating procedure had to be implemented. Finally, a regression model of physics item difficulty was created. It has been shown that 61.2% of item difficulty variance can be explained by factors which reflect the automaticity, complexity, and modality of the knowledge structure that is relevant for generating the most probable correct solution, as well as by the divergence of required thinking and interference effects between intuitive and formal
International Nuclear Information System (INIS)
High, F. William; Stubbs, Christopher W.; Rest, Armin; Stalder, Brian; Challis, Peter
2009-01-01
We present stellar locus regression (SLR), a method of directly adjusting the instrumental broadband optical colors of stars to bring them into accord with a universal stellar color-color locus, producing accurately calibrated colors for both stars and galaxies. This is achieved without first establishing individual zero points for each passband, and can be performed in real-time at the telescope. We demonstrate how SLR naturally makes one wholesale correction for differences in instrumental response, for atmospheric transparency, for atmospheric extinction, and for Galactic extinction. We perform an example SLR treatment of Sloan Digital Sky Survey data over a wide range of Galactic dust values and independently recover the direction and magnitude of the canonical Galactic reddening vector with 14-18 mmag rms uncertainties. We then isolate the effect of atmospheric extinction, showing that SLR accounts for this and returns precise colors over a wide range of air mass, with 5-14 mmag rms residuals. We demonstrate that SLR-corrected colors are sufficiently accurate to allow photometric redshift estimates for galaxy clusters (using red sequence galaxies) with an uncertainty σ(z)/(1 + z) = 0.6% per cluster for redshifts 0.09 < z < 0.25. Finally, we identify our objects in the 2MASS all-sky catalog, and produce i-band zero points typically accurate to 18 mmag using only SLR. We offer open-source access to our IDL routines, validated and verified for the implementation of this technique, at http://stellar-locus-regression.googlecode.com.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
Wan, Jian; Chen, Yi-Chieh; Morris, A Julian; Thennadil, Suresh N
2017-07-01
Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant
The price sensitivity of Medicare beneficiaries: a regression discontinuity approach.
Buchmueller, Thomas C; Grazier, Kyle; Hirth, Richard A; Okeke, Edward N
2013-01-01
We use 4 years of data from the retiree health benefits program of the University of Michigan to estimate the effect of price on the health plan choices of Medicare beneficiaries. During the period of our analysis, changes in the University's premium contribution rules led to substantial price changes. A key feature of this 'natural experiment' is that individuals who had retired before a certain date were exempted from having to pay any premium contributions. This 'grandfathering' creates quasi-experimental variation that is ideal for estimating the effect of price. Using regression discontinuity methods, we compare the plan choices of individuals who retired just after the grandfathering cutoff date and were therefore exposed to significant price changes to the choices of a 'control group' of individuals who retired just before that date and therefore did not experience the price changes. The results indicate a statistically significant effect of price, with a $10 increase in monthly premium contributions leading to a 2 to 3 percentage point decrease in a plan's market share. Copyright © 2012 John Wiley & Sons, Ltd.
Efficient and robust cell detection: A structured regression approach.
Xie, Yuanpu; Xing, Fuyong; Shi, Xiaoshuang; Kong, Xiangfei; Su, Hai; Yang, Lin
2018-02-01
Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and large variations in cell sizes and shapes. In addition, the ever-increasing amount of available datasets and the high resolution of whole-slice scanned images pose a further demand for efficient processing algorithms. In this paper, we present a novel structured regression model based on a proposed fully residual convolutional neural network for efficient cell detection. For each testing image, our model learns to produce a dense proximity map that exhibits higher responses at locations near cell centers. Our method only requires a few training images with weak annotations (just one dot indicating the cell centroids). We have extensively evaluated our method using four different datasets, covering different microscopy staining methods (e.g., H & E or Ki-67 staining) or image acquisition techniques (e.g., bright-filed image or phase contrast). Experimental results demonstrate the superiority of our method over existing state of the art methods in terms of both detection accuracy and running time. Copyright © 2017. Published by Elsevier B.V.
Neighborhood Effects in Wind Farm Performance: A Regression Approach
Directory of Open Access Journals (Sweden)
Matthias Ritter
2017-03-01
Full Text Available The optimization of turbine density in wind farms entails a trade-off between the usage of scarce, expensive land and power losses through turbine wake effects. A quantification and prediction of the wake effect, however, is challenging because of the complex aerodynamic nature of the interdependencies of turbines. In this paper, we propose a parsimonious data driven regression wake model that can be used to predict production losses of existing and potential wind farms. Motivated by simple engineering wake models, the predicting variables are wind speed, the turbine alignment angle, and distance. By utilizing data from two wind farms in Germany, we show that our models can compete with the standard Jensen model in predicting wake effect losses. A scenario analysis reveals that a distance between turbines can be reduced by up to three times the rotor size, without entailing substantial production losses. In contrast, an unfavorable configuration of turbines with respect to the main wind direction can result in production losses that are much higher than in an optimal case.
Drabinová, Adéla; Martinková, Patrícia
2017-01-01
In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…
Directory of Open Access Journals (Sweden)
Kowal Robert
2016-12-01
Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Multiple areas of research function within its scope. One of the many fundamental questions in the model concerns proving the efficiency of the most commonly used OLS estimators and examining their properties. In the literature of the subject one can find taking back to this scope and certain solutions in that regard. Methodically, they are borrowed from the multiple regression model or also from a boundary partial model. Not everything, however, is here complete and consistent. In the paper a completely new scheme is proposed, based on the implementation of the Cauchy-Schwarz inequality in the arrangement of the constraint aggregated from calibrated appropriately secondary constraints of unbiasedness which in a result of choice the appropriate calibrator for each variable directly leads to showing this property. A separate range-is a matter of choice of such a calibrator. These deliberations, on account of the volume and kinds of the calibration, were divided into a few parts. In the one the efficiency of OLS estimators is proven in a mixed scheme of the calibration by averages, that is preliminary, and in the most basic frames of the proposed methodology. In these frames the future outlines and general premises constituting the base of more distant generalizations are being created.
New approach for calibration the efficiency of HPGe detectors
International Nuclear Information System (INIS)
Alnour, I.A.; Wagiran, H.; Suhaimi Hamzah; Siong, W.B.; Mohd Suhaimi Elias
2013-01-01
Full-text: This work evaluates the efficiency calibrating of HPGe detector coupled with Canberra GC3018 with Genie 2000 software and Ortec GEM25-76-XLB-C with Gamma Vision software; available at Neutron activation analysis laboratory in Malaysian Nuclear Agency (NM). The efficiency calibration curve was constructed from measurement of an IAEA, standard gamma point sources set composed by 214 Am, 57 Co, 133 Ba, 152 Eu, 137 Cs and 60 Co. The efficiency calibrations were performed for three different geometries: 5, 10 and 15 cm distances from the end cap detector. The polynomial parameters functions were simulated through a computer program, MATLAB in order to find an accurate fit to the experimental data points. The efficiency equation was established from the known fitted parameters which allow for the efficiency evaluation at particular energy of interest. The study shows that significant deviations in the efficiency, depending on the source-detector distance and photon energy. (author)
Design and analysis of experiments classical and regression approaches with SAS
Onyiah, Leonard C
2008-01-01
Introductory Statistical Inference and Regression Analysis Elementary Statistical Inference Regression Analysis Experiments, the Completely Randomized Design (CRD)-Classical and Regression Approaches Experiments Experiments to Compare Treatments Some Basic Ideas Requirements of a Good Experiment One-Way Experimental Layout or the CRD: Design and Analysis Analysis of Experimental Data (Fixed Effects Model) Expected Values for the Sums of Squares The Analysis of Variance (ANOVA) Table Follow-Up Analysis to Check fo
A New Approach for Checking and Complementing CALIPSO Lidar Calibration
Josset, Damien B.; Vaughan, Mark A.; Hu, Yongxiang; Avery, Melody A.; Powell, Kathleen A.; Hunt, William H.; Winker, David M.; Pelon, Jacques; Trepte, Charles R.; Lucker, Patricia L.;
2010-01-01
We have been studying the backscatter ratio of the two CALIPSO wavelengths for 3 different targets. We are showing the ratio of integrate attenuated backscatter coefficient for cirrus clouds, ocean surface and liquid. Water clouds for one month of nightime data (left:July,right:December), Only opaque cirrus classified as randomly oriented ice[1] are used. For ocean and water clouds, only the clearest shots, determined by a threshold on integrated attenuated backscatter are used. Two things can be immediately observed: 1. A similar trend (black dotted line) is visible using all targets, the color ratio shows a tendency to be higher north and lower south for those two months. 2. The water clouds average value is around 15% lower than ocean surface and cirrus clouds. This is due to the different multiple scattering at 532 nm and 1064 nm [2] which strongly impact the water cloud retrieval. Conclusion: Different targets can be used to improve CALIPSO 1064 nm calibration accuracy. All of them show the signature of an instrumental calibration shift. Multiple scattering introduce a bias in liquid water cloud signal but it still compares very well with all other methods and should not be overlooked. The effect of multiple scattering in liquid and ice clouds will be the subject of future research. If there really is a sampling issue. Combining all methods to increase the sampling, mapping the calibration coefficient or trying to reach an orbit per orbit calibration seems an appropriate way.
Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.
Yelland, Lisa N; Salter, Amy B; Ryan, Philip
2011-10-15
Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.
Fuzzy multinomial logistic regression analysis: A multi-objective programming approach
Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan
2017-05-01
Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.
DEFF Research Database (Denmark)
Schlechtingen, Meik; Santos, Ilmar
2011-01-01
This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal...
Christiansen, Bo
2015-04-01
Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.
Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant
International Nuclear Information System (INIS)
Chan, Yea-Kuang; Tsai, Yu-Ching
2017-01-01
The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.
Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant
Energy Technology Data Exchange (ETDEWEB)
Chan, Yea-Kuang; Tsai, Yu-Ching [Institute of Nuclear Energy Research, Taoyuan City, Taiwan (China). Nuclear Engineering Division
2017-03-15
The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.
A novel approach for absolute radar calibration: formulation and theoretical validation
Directory of Open Access Journals (Sweden)
C. Merker
2015-06-01
Full Text Available The theoretical framework of a novel approach for absolute radar calibration is presented and its potential analysed by means of synthetic data to lay out a solid basis for future practical application. The method presents the advantage of an absolute calibration with respect to the directly measured reflectivity, without needing a previously calibrated reference device. It requires a setup comprising three radars: two devices oriented towards each other, measuring reflectivity along the same horizontal beam and operating within a strongly attenuated frequency range (e.g. K or X band, and one vertical reflectivity and drop size distribution (DSD profiler below this connecting line, which is to be calibrated. The absolute determination of the calibration factor is based on attenuation estimates. Using synthetic, smooth and geometrically idealised data, calibration is found to perform best using homogeneous precipitation events with rain rates high enough to ensure a distinct attenuation signal (reflectivity above ca. 30 dBZ. Furthermore, the choice of the interval width (in measuring range gates around the vertically pointing radar, needed for attenuation estimation, is found to have an impact on the calibration results. Further analysis is done by means of synthetic data with realistic, inhomogeneous precipitation fields taken from measurements. A calibration factor is calculated for each considered case using the presented method. Based on the distribution of the calculated calibration factors, the most probable value is determined by estimating the mode of a fitted shifted logarithmic normal distribution function. After filtering the data set with respect to rain rate and inhomogeneity and choosing an appropriate length of the considered attenuation path, the estimated uncertainty of the calibration factor is of the order of 1 to 11 %, depending on the chosen interval width. Considering stability and accuracy of the method, an interval of
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
Application of heuristic and machine-learning approach to engine model calibration
Cheng, Jie; Ryu, Kwang R.; Newman, C. E.; Davis, George C.
1993-03-01
Automation of engine model calibration procedures is a very challenging task because (1) the calibration process searches for a goal state in a huge, continuous state space, (2) calibration is often a lengthy and frustrating task because of complicated mutual interference among the target parameters, and (3) the calibration problem is heuristic by nature, and often heuristic knowledge for constraining a search cannot be easily acquired from domain experts. A combined heuristic and machine learning approach has, therefore, been adopted to improve the efficiency of model calibration. We developed an intelligent calibration program called ICALIB. It has been used on a daily basis for engine model applications, and has reduced the time required for model calibrations from many hours to a few minutes on average. In this paper, we describe the heuristic control strategies employed in ICALIB such as a hill-climbing search based on a state distance estimation function, incremental problem solution refinement by using a dynamic tolerance window, and calibration target parameter ordering for guiding the search. In addition, we present the application of a machine learning program called GID3* for automatic acquisition of heuristic rules for ordering target parameters.
Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K
2011-10-01
To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods
The Wally plot approach to assess the calibration of clinical prediction models.
Blanche, Paul; Gerds, Thomas A; Ekstrøm, Claus T
2017-12-06
A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. Typically, the calibration assumption is assessed graphically but in practice it is often challenging to judge whether a "disappointing" calibration plot is the consequence of a departure from the calibration assumption, or alternatively just "bad luck" due to sampling variability. We propose a graphical approach which enables the visualization of how much a calibration plot agrees with the calibration assumption to address this issue. The approach is mainly based on the idea of generating new plots which mimic the available data under the calibration assumption. The method handles the common non-trivial situations in which the data contain censored observations and occurrences of competing events. This is done by building on ideas from constrained non-parametric maximum likelihood estimation methods. Two examples from large cohort data illustrate our proposal. The 'wally' R package is provided to make the methodology easily usable.
A theoretical approach to calibrate radiation portal monitor (RPM) systems
International Nuclear Information System (INIS)
Nafee, Sherif S.; Abbas, Mahmoud I.
2008-01-01
Radiation portal monitor (RPM) systems are widely used at international border crossings, where they are applied to the task of detecting nuclear devices, special nuclear material, and radiation dispersal device materials that could appear at borders. The requirements and constraints on RPM systems deployed at high-volume border crossings are significantly different from those at weapons facilities or steel recycling plants, the former being required to rapidly detect localized sources of radiation with a very high detection probability and low false-alarm rate, while screening all of the traffic without impeding the flow of commerce [Chambers, W.H., Atwater, H.F., Fehlau, P.E., Hastings, R.D., Henry, C.N., Kunz, W.E., Sampson, T.E., Whittlesey, T.H., Worth, G.M., 1974. Portal Monitor for Diversion Safeguards. LA-5681, Los Alamos Scientific Laboratory, Los Alamos, NM]. In the present work, compact analytical formulae are derived and used to calibrate two RPM systems with isotropic radiating sources: (i) polyvinyltoluene (PVT) or plastic and (ii) thallium-doped crystalline sodium iodide, NaI(Tl), gamma-ray detector materials. The calculated efficiencies are compared to measured values reported in the literatures, showing very good agreement
Directory of Open Access Journals (Sweden)
Hsin-Lun Wu
Full Text Available Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT. Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2 required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006. Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001. Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.
Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches
Energy Technology Data Exchange (ETDEWEB)
Yoo S.; Yang, Y.; Carbonell, J.
2011-10-24
Email overload, even after spam filtering, presents a serious productivity challenge for busy professionals and executives. One solution is automated prioritization of incoming emails to ensure the most important are read and processed quickly, while others are processed later as/if time permits in declining priority levels. This paper presents a study of machine learning approaches to email prioritization into discrete levels, comparing ordinal regression versus classier cascades. Given the ordinal nature of discrete email priority levels, SVM ordinal regression would be expected to perform well, but surprisingly a cascade of SVM classifiers significantly outperforms ordinal regression for email prioritization. In contrast, SVM regression performs well -- better than classifiers -- on selected UCI data sets. This unexpected performance inversion is analyzed and results are presented, providing core functionality for email prioritization systems.
A game-theoretic approach for calibration of low-cost magnetometers under noise uncertainty
Siddharth, S.; Ali, A. S.; El-Sheimy, N.; Goodall, C. L.; Syed, Z. F.
2012-02-01
Pedestrian heading estimation is a fundamental challenge in Global Navigation Satellite System (GNSS)-denied environments. Additionally, the heading observability considerably degrades in low-speed mode of operation (e.g. walking), making this problem even more challenging. The goal of this work is to improve the heading solution when hand-held personal/portable devices, such as cell phones, are used for positioning and to improve the heading estimation in GNSS-denied signal environments. Most smart phones are now equipped with self-contained, low cost, small size and power-efficient sensors, such as magnetometers, gyroscopes and accelerometers. A magnetometer needs calibration before it can be properly employed for navigation purposes. Magnetometers play an important role in absolute heading estimation and are embedded in many smart phones. Before the users navigate with the phone, a calibration is invoked to ensure an improved signal quality. This signal is used later in the heading estimation. In most of the magnetometer-calibration approaches, the motion modes are seldom described to achieve a robust calibration. Also, suitable calibration approaches fail to discuss the stopping criteria for calibration. In this paper, the following three topics are discussed in detail that are important to achieve proper magnetometer-calibration results and in turn the most robust heading solution for the user while taking care of the device misalignment with respect to the user: (a) game-theoretic concepts to attain better filter parameter tuning and robustness in noise uncertainty, (b) best maneuvers with focus on 3D and 2D motion modes and related challenges and (c) investigation of the calibration termination criteria leveraging the calibration robustness and efficiency.
A game-theoretic approach for calibration of low-cost magnetometers under noise uncertainty
International Nuclear Information System (INIS)
Siddharth, S; Ali, A S; El-Sheimy, N; Goodall, C L; Syed, Z F
2012-01-01
Pedestrian heading estimation is a fundamental challenge in Global Navigation Satellite System (GNSS)-denied environments. Additionally, the heading observability considerably degrades in low-speed mode of operation (e.g. walking), making this problem even more challenging. The goal of this work is to improve the heading solution when hand-held personal/portable devices, such as cell phones, are used for positioning and to improve the heading estimation in GNSS-denied signal environments. Most smart phones are now equipped with self-contained, low cost, small size and power-efficient sensors, such as magnetometers, gyroscopes and accelerometers. A magnetometer needs calibration before it can be properly employed for navigation purposes. Magnetometers play an important role in absolute heading estimation and are embedded in many smart phones. Before the users navigate with the phone, a calibration is invoked to ensure an improved signal quality. This signal is used later in the heading estimation. In most of the magnetometer-calibration approaches, the motion modes are seldom described to achieve a robust calibration. Also, suitable calibration approaches fail to discuss the stopping criteria for calibration. In this paper, the following three topics are discussed in detail that are important to achieve proper magnetometer-calibration results and in turn the most robust heading solution for the user while taking care of the device misalignment with respect to the user: (a) game-theoretic concepts to attain better filter parameter tuning and robustness in noise uncertainty, (b) best maneuvers with focus on 3D and 2D motion modes and related challenges and (c) investigation of the calibration termination criteria leveraging the calibration robustness and efficiency. (paper)
International Nuclear Information System (INIS)
Yuan, Haibo; Liu, Xiaowei; Xiang, Maosheng; Huang, Yang; Zhang, Huihua; Chen, Bingqiu
2015-01-01
In this paper we propose a spectroscopy-based stellar color regression (SCR) method to perform accurate color calibration for modern imaging surveys, taking advantage of millions of stellar spectra now available. The method is straightforward, insensitive to systematic errors in the spectroscopically determined stellar atmospheric parameters, applicable to regions that are effectively covered by spectroscopic surveys, and capable of delivering an accuracy of a few millimagnitudes for color calibration. As an illustration, we have applied the method to the Sloan Digital Sky Survey (SDSS) Stripe 82 data. With a total number of 23,759 spectroscopically targeted stars, we have mapped out the small but strongly correlated color zero-point errors present in the photometric catalog of Stripe 82, and we improve the color calibration by a factor of two to three. Our study also reveals some small but significant magnitude dependence errors in the z band for some charge-coupled devices (CCDs). Such errors are likely to be present in all the SDSS photometric data. Our results are compared with those from a completely independent test based on the intrinsic colors of red galaxies presented by Ivezić et al. The comparison, as well as other tests, shows that the SCR method has achieved a color calibration internally consistent at a level of about 5 mmag in u – g, 3 mmag in g – r, and 2 mmag in r – i and i – z. Given the power of the SCR method, we discuss briefly the potential benefits by applying the method to existing, ongoing, and upcoming imaging surveys
Helmreich, James E.; Krog, K. Peter
2018-01-01
We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…
Time series modeling by a regression approach based on a latent process.
Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice
2009-01-01
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.
Barimani, Shirin; Kleinebudde, Peter
2017-10-01
A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.
A video-based approach to calibrating car-following parameters in VISSIM for urban traffic
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Zhengyang Lu
2016-08-01
Full Text Available Microscopic simulation models need to be calibrated to represent realistic local traffic conditions. Traditional calibration methods are conducted by searching for the model parameter set that minimizes the discrepancies of certain macroscopic metrics between simulation results and field observations. However, this process could easily lead to inappropriate selection of calibration parameters and thus erroneous simulation results. This paper proposes a video-based approach to incorporate direct measurements of car-following parameters into the process of VISSIM model calibration. The proposed method applies automated video processing techniques to extract vehicle trajectory data and utilizes the trajectory data to determine values of certain car-following parameters in VISSIM. This paper first describes the calibration procedure step by step, and then applies the method to a case study of simulating traffic at a signalized intersection in VISSIM. From the field-collected video footage, trajectories of 1229 through-movement vehicles were extracted and analyzed to calibrate three car-following parameters regarding desired speed, desired acceleration, and safe following distance, respectively. The case study demonstrates the advantages and feasibility of the proposed approach.
Directory of Open Access Journals (Sweden)
Suduan Chen
2014-01-01
Full Text Available As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.
Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De
2014-01-01
As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.
A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant
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Baofeng Shi
2015-01-01
Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.
Ubuntu and Capabilities Approach: Basic Doctrines for Calibrating Humanitarian Action
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Rapatsa Mashele
2016-06-01
Full Text Available This article explores prospects of using Ubuntu and Capabilities Approach to expand the scope of humanitarian action, to design one which serves humanity better even in the absence of disaster to essentially fulfil human development needs. It is considerate of the fact that humanitarian works contributes immensely in determining the extent to which humanity thrives. The traditional view on humanitarianism presupposes action-driven initiatives geared towards devising interventions to restore or reinforce human social order, improve livelihoods and quality of life. In sociological terms, human development is dependent on realizing and safeguarding, amongst others, human well-being, civil liberties and social security. The article utilizes core values enshrined in Ubuntu, Africa’s historic philosophy of life, and Amartya Sen’s Capabilities Approach as tools of analysis, with the view to expressing how to operationalize what should be considered stable humanitarian conditions and human well-being. Owing to persistent socio-economic challenges, especially the poverty problem, it is asserted that humanitarian action ought to depart from being a post-disaster intervention strategy, to being a pro-active and preventative pre-disaster orientated action, intended to nurture well-being and resultantly enable human development.
Linear Calibration – Is It so Simple?
International Nuclear Information System (INIS)
Arsova, Diana; Babanova, Sofia; Mandjukov, Petko
2009-01-01
Calibration procedure is an important part of instrumental analysis. Usually it is not the major uncertainty source in whole analytical procedure. However, improper calibration might cause a significant bias of the analytical results from the real (certified) value. Standard Gaussian linear regression is the most frequently used mathematical approach for estimation of calibration function parameters. In the present article are discussed some not quite popular, but highly recommended in certain cases methods for parameter estimation, such as: weighted regression, orthogonal regression, robust regression, bracketing calibration etc. Some useful approximations are also presented. Special attention is paid to the statistical criteria which to be used for selection of proper calibration model. Standard UV-VIS spectrometric procedure for determination of phosphates in water was used as a practical example. Several different approaches for estimation of the contribution of calibration to the general un-certainty of the analytical result are presented and compared
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Paul Robert Martin Werfette
2010-06-01
Full Text Available Analysis of quantitative structure - activity relationship (QSAR for a series of antimalarial compound artemisinin derivatives has been done using principal component regression. The descriptors for QSAR study were representation of electronic structure i.e. atomic net charges of the artemisinin skeleton calculated by AM1 semi-empirical method. The antimalarial activity of the compound was expressed in log 1/IC50 which is an experimental data. The main purpose of the principal component analysis approach is to transform a large data set of atomic net charges to simplify into a data set which known as latent variables. The best QSAR equation to analyze of log 1/IC50 can be obtained from the regression method as a linear function of several latent variables i.e. x1, x2, x3, x4 and x5. The best QSAR model is expressed in the following equation, (;; Keywords: QSAR, antimalarial, artemisinin, principal component regression
International Nuclear Information System (INIS)
Baser, Furkan; Demirhan, Haydar
2017-01-01
Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics. - Highlights: • A fuzzy regression functions with support vector machines approach is proposed. • The approach is robust against outlier observations and over-fitting problem. • Estimation accuracy of the model is superior to several existent alternatives. • A new solar radiation estimation model is proposed for the region of Turkey. • The model is useful under complex terrestrial and climatic conditions.
A multi-objective approach to improve SWAT model calibration in alpine catchments
Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele
2018-04-01
Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.
Corporate Social Responsibility and Financial Performance: A Two Least Regression Approach
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Alexander Olawumi Dabor
2017-12-01
Full Text Available The objective of this study is to investigate the casuality between corporate social responsibility and firm financial performance. The study employed two least square regression approaches. Fifty-two firms were selected using the scientific method. The findings revealed that corporate social responsibility and firm performance in manufacturing sector are mutually related at 5%. The study recommended that management of manufacturing companies in Nigeria should expend on CSR to boost profitability and corporate image.
Modelling the return distribution of salmon farming companies : a quantile regression approach
Jacobsen, Fredrik
2017-01-01
The salmon farming industry has gained increased attention from investors, portfolio managers, financial analysts and other stakeholders the recent years. Despite this development, very little is known about the risk and return of salmon farming company stocks, and especially how the relationship between risk and return varies under different market conditions, given the volatile nature of the salmon farming industry. We approach this problem by using quantile regression to examine the relati...
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J. L. Chau
2008-08-01
Full Text Available In recent years, more and more radar systems with multiple-receiver antennas are being used to study the atmospheric and ionospheric irregularities with either interferometric and/or imaging configurations. In such systems, one of the major challenges is to know the phase offsets between the different receiver channels. Such phases are intrinsic to the system and are due to different cable lengths, filters, attenuators, amplifiers, antenna impedance, etc. Moreover, such phases change as function of time, on different time scales, depending on the specific installation. In this work, we present three approaches using natural targets (radio stars, meteor-head and meteor trail echoes that allow either an absolute or relative phase calibration. In addition, we present the results of using an artificial source (radio beacon for a continuous calibration that complements the previous approaches. These approaches are robust and good alternatives to other approaches, e.g. self-calibration techniques using known data features, or for multiple-receiver configurations constantly changing their receiving elements. In order to show the good performance of the proposed phase calibration techniques, we present new radar imaging results of equatorial spread F (ESF irregularities. Finally we introduce a new way to represent range-time intensity (RTI maps color coded with the Doppler information. Such modified map allows the identification and interpretation of geophysical phenomena, previously hidden in conventional RTI maps, e.g. the time and altitude of occurrence of ESF irregularities pinching off from the bottomside and their respective Doppler velocity.
Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne
2016-04-01
Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.
Poisson regression approach for modeling fatal injury rates amongst Malaysian workers
International Nuclear Information System (INIS)
Kamarulzaman Ibrahim; Heng Khai Theng
2005-01-01
Many safety studies are based on the analysis carried out on injury surveillance data. The injury surveillance data gathered for the analysis include information on number of employees at risk of injury in each of several strata where the strata are defined in terms of a series of important predictor variables. Further insight into the relationship between fatal injury rates and predictor variables may be obtained by the poisson regression approach. Poisson regression is widely used in analyzing count data. In this study, poisson regression is used to model the relationship between fatal injury rates and predictor variables which are year (1995-2002), gender, recording system and industry type. Data for the analysis were obtained from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the data follow poisson distribution has been violated. After correction for the problem of over dispersion, the predictor variables that are found to be significant in the model are gender, system of recording, industry type, two interaction effects (interaction between recording system and industry type and between year and industry type). Introduction Regression analysis is one of the most popular
The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations.
Liu, Chunping; Laporte, Audrey; Ferguson, Brian S
2008-09-01
In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital - or nursing home - up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that, depending on the quantile estimated, the quantile regression approach may be a useful addition to the armamentarium of methods for estimating technical efficiency.
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Dimitrov BD
2015-04-01
Full Text Available Borislav D Dimitrov,1,2 Nicola Motterlini,2,† Tom Fahey2 1Academic Unit of Primary Care and Population Sciences, University of Southampton, Southampton, United Kingdom; 2HRB Centre for Primary Care Research, Department of General Medicine, Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland †Nicola Motterlini passed away on November 11, 2012 Objective: Estimating calibration performance of clinical prediction rules (CPRs in systematic reviews of validation studies is not possible when predicted values are neither published nor accessible or sufficient or no individual participant or patient data are available. Our aims were to describe a simplified approach for outcomes prediction and calibration assessment and evaluate its functionality and validity. Study design and methods: Methodological study of systematic reviews of validation studies of CPRs: a ABCD2 rule for prediction of 7 day stroke; and b CRB-65 rule for prediction of 30 day mortality. Predicted outcomes in a sample validation study were computed by CPR distribution patterns (“derivation model”. As confirmation, a logistic regression model (with derivation study coefficients was applied to CPR-based dummy variables in the validation study. Meta-analysis of validation studies provided pooled estimates of “predicted:observed” risk ratios (RRs, 95% confidence intervals (CIs, and indexes of heterogeneity (I2 on forest plots (fixed and random effects models, with and without adjustment of intercepts. The above approach was also applied to the CRB-65 rule. Results: Our simplified method, applied to ABCD2 rule in three risk strata (low, 0–3; intermediate, 4–5; high, 6–7 points, indicated that predictions are identical to those computed by univariate, CPR-based logistic regression model. Discrimination was good (c-statistics =0.61–0.82, however, calibration in some studies was low. In such cases with miscalibration, the under
Davies, Stephen R; Alamgir, Mahiuddin; Chan, Benjamin K H; Dang, Thao; Jones, Kai; Krishnaswami, Maya; Luo, Yawen; Mitchell, Peter S R; Moawad, Michael; Swan, Hilton; Tarrant, Greg J
2015-10-01
The purity determination of organic calibration standards using the traditional mass balance approach is described. Demonstrated examples highlight the potential for bias in each measurement and the need to implement an approach that provides a cross-check for each result, affording fit for purpose purity values in a timely and cost-effective manner. Chromatographic techniques such as gas chromatography with flame ionisation detection (GC-FID) and high-performance liquid chromatography with UV detection (HPLC-UV), combined with mass and NMR spectroscopy, provide a detailed impurity profile allowing an efficient conversion of chromatographic peak areas into relative mass fractions, generally avoiding the need to calibrate each impurity present. For samples analysed by GC-FID, a conservative measurement uncertainty budget is described, including a component to cover potential variations in the response of each unidentified impurity. An alternative approach is also detailed in which extensive purification eliminates the detector response factor issue, facilitating the certification of a super-pure calibration standard which can be used to quantify the main component in less-pure candidate materials. This latter approach is particularly useful when applying HPLC analysis with UV detection. Key to the success of this approach is the application of both qualitative and quantitative (1)H NMR spectroscopy.
RGB color calibration for quantitative image analysis: the "3D thin-plate spline" warping approach.
Menesatti, Paolo; Angelini, Claudio; Pallottino, Federico; Antonucci, Francesca; Aguzzi, Jacopo; Costa, Corrado
2012-01-01
In the last years the need to numerically define color by its coordinates in n-dimensional space has increased strongly. Colorimetric calibration is fundamental in food processing and other biological disciplines to quantitatively compare samples' color during workflow with many devices. Several software programmes are available to perform standardized colorimetric procedures, but they are often too imprecise for scientific purposes. In this study, we applied the Thin-Plate Spline interpolation algorithm to calibrate colours in sRGB space (the corresponding Matlab code is reported in the Appendix). This was compared with other two approaches. The first is based on a commercial calibration system (ProfileMaker) and the second on a Partial Least Square analysis. Moreover, to explore device variability and resolution two different cameras were adopted and for each sensor, three consecutive pictures were acquired under four different light conditions. According to our results, the Thin-Plate Spline approach reported a very high efficiency of calibration allowing the possibility to create a revolution in the in-field applicative context of colour quantification not only in food sciences, but also in other biological disciplines. These results are of great importance for scientific color evaluation when lighting conditions are not controlled. Moreover, it allows the use of low cost instruments while still returning scientifically sound quantitative data.
Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.
Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin
2015-02-01
To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.
A multi-scale relevance vector regression approach for daily urban water demand forecasting
Bai, Yun; Wang, Pu; Li, Chuan; Xie, Jingjing; Wang, Yin
2014-09-01
Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.
DEFF Research Database (Denmark)
Kiani, Alishir; Chwalibog, André; Nielsen, Mette O
2007-01-01
Late gestation energy expenditure (EE(gest)) originates from energy expenditure (EE) of development of conceptus (EE(conceptus)) and EE of homeorhetic adaptation of metabolism (EE(homeorhetic)). Even though EE(gest) is relatively easy to quantify, its partitioning is problematic. In the present...... study metabolizable energy (ME) intake ranges for twin-bearing ewes were 220-440, 350- 700, 350-900 kJ per metabolic body weight (W0.75) at week seven, five, two pre-partum respectively. Indirect calorimetry and a linear regression approach were used to quantify EE(gest) and then partition to EE......(conceptus) and EE(homeorhetic). Energy expenditure of basal metabolism of the non-gravid tissues (EE(bmng)), derived from the intercept of the linear regression equation of retained energy [kJ/W0.75] and ME intake [kJ/W(0.75)], was 298 [kJ/ W0.75]. Values of the intercepts of the regression equations at week seven...
DEFF Research Database (Denmark)
Heydorn, Kaj; Anglov, Thomas
2002-01-01
Methods recommended by the International Standardization Organisation and Eurachem are not satisfactory for the correct estimation of calibration uncertainty. A novel approach is introduced and tested on actual calibration data for the determination of Pb by ICP-AES. The improved calibration...
eSIP: A Novel Solution-Based Sectioned Image Property Approach for Microscope Calibration.
Directory of Open Access Journals (Sweden)
Malte Butzlaff
Full Text Available Fluorescence confocal microscopy represents one of the central tools in modern sciences. Correspondingly, a growing amount of research relies on the development of novel microscopic methods. During the last decade numerous microscopic approaches were developed for the investigation of various scientific questions. Thereby, the former qualitative imaging methods became replaced by advanced quantitative methods to gain more and more information from a given sample. However, modern microscope systems being as complex as they are, require very precise and appropriate calibration routines, in particular when quantitative measurements should be compared over longer time scales or between different setups. Multispectral beads with sub-resolution size are often used to describe the point spread function and thus the optical properties of the microscope. More recently, a fluorescent layer was utilized to describe the axial profile for each pixel, which allows a spatially resolved characterization. However, fabrication of a thin fluorescent layer with matching refractive index is technically not solved yet. Therefore, we propose a novel type of calibration concept for sectioned image property (SIP measurements which is based on fluorescent solution and makes the calibration concept available for a broader number of users. Compared to the previous approach, additional information can be obtained by application of this extended SIP chart approach, including penetration depth, detected number of photons, and illumination profile shape. Furthermore, due to the fit of the complete profile, our method is less susceptible to noise. Generally, the extended SIP approach represents a simple and highly reproducible method, allowing setup independent calibration and alignment procedures, which is mandatory for advanced quantitative microscopy.
Directory of Open Access Journals (Sweden)
Cuesta Eduardo
2015-09-01
Full Text Available This paper presents a multivariate regression predictive model of drift on the Coordinate Measuring Machine (CMM behaviour. Evaluation tests on a CMM with a multi-step gauge were carried out following an extended version of an ISO evaluation procedure with a periodicity of at least once a week and during more than five months. This test procedure consists in measuring the gauge for several range volumes, spatial locations, distances and repetitions. The procedure, environment conditions and even the gauge have been kept invariables, so a massive measurement dataset was collected over time under high repeatability conditions. A multivariate regression analysis has revealed the main parameters that could affect the CMM behaviour, and then detected a trend on the CMM performance drift. A performance model that considers both the size of the measured dimension and the elapsed time since the last CMM calibration has been developed. This model can predict the CMM performance and measurement reliability over time and also can estimate an optimized period between calibrations for a specific measurement length or accuracy level.
Vansteenkiste, Thomas; Tavakoli, Mohsen; Ntegeka, Victor; De Smedt, Florimond; Batelaan, Okke; Pereira, Fernando; Willems, Patrick
2014-11-01
calibration was tested by comparing the manual calibration approach with automatic calibrations of the VHM model based on different objective functions. The calibration approach did not significantly alter the model results for peak flow, but the low flow projections were again highly influenced. Model choice as well as calibration strategy hence have a critical impact on low flows, more than on peak flows. These results highlight the high uncertainty in low flow modelling, especially in a climate change context.
Shoff, Carla; Chen, Vivian Yi-Ju; Yang, Tse-Chuan
2014-01-01
Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilization in the US and found that most of the relationships between late or not prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study is to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employ an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this innovative framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and the results are changed after considering the spatially lagged effect of prenatal care utilization. The GWR-SL approach allows us to gain a place-specific understanding of prenatal care utilization in US counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., OLS and spatial lag models) and found that GWR-SL is the preferred modeling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilization across space, and determine whether and how the level of prenatal care utilization in neighboring counties matters. PMID:24893033
Approach for Self-Calibrating CO2 Measurements with Linear Membrane-Based Gas Sensors
Directory of Open Access Journals (Sweden)
Detlef Lazik
2016-11-01
Full Text Available Linear membrane-based gas sensors that can be advantageously applied for the measurement of a single gas component in large heterogeneous systems, e.g., for representative determination of CO2 in the subsurface, can be designed depending on the properties of the observation object. A resulting disadvantage is that the permeation-based sensor response depends on operating conditions, the individual site-adapted sensor geometry, the membrane material, and the target gas component. Therefore, calibration is needed, especially of the slope, which could change over several orders of magnitude. A calibration-free approach based on an internal gas standard is developed to overcome the multi-criterial slope dependency. This results in a normalization of sensor response and enables the sensor to assess the significance of measurement. The approach was proofed on the example of CO2 analysis in dry air with tubular PDMS membranes for various CO2 concentrations of an internal standard. Negligible temperature dependency was found within an 18 K range. The transformation behavior of the measurement signal and the influence of concentration variations of the internal standard on the measurement signal were shown. Offsets that were adjusted based on the stated theory for the given measurement conditions and material data from the literature were in agreement with the experimentally determined offsets. A measurement comparison with an NDIR reference sensor shows an unexpectedly low bias (<1% of the non-calibrated sensor response, and comparable statistical uncertainty.
Directory of Open Access Journals (Sweden)
Moath Kassim
2018-05-01
Full Text Available To maintain safety and reliability of reactors, redundant sensors are usually used to measure critical variables and estimate their averaged time-dependency. Nonhealthy sensors can badly influence the estimation result of the process variable. Since online condition monitoring was introduced, the online cross-calibration method has been widely used to detect any anomaly of sensor readings among the redundant group. The cross-calibration method has four main averaging techniques: simple averaging, band averaging, weighted averaging, and parity space averaging (PSA. PSA is used to weigh redundant signals based on their error bounds and their band consistency. Using the consistency weighting factor (C, PSA assigns more weight to consistent signals that have shared bands, based on how many bands they share, and gives inconsistent signals of very low weight. In this article, three approaches are introduced for improving the PSA technique: the first is to add another consistency factor, so called trend consistency (TC, to include a consideration of the preserving of any characteristic edge that reflects the behavior of equipment/component measured by the process parameter; the second approach proposes replacing the error bound/accuracy based weighting factor (Wa with a weighting factor based on the Euclidean distance (Wd, and the third approach proposes applying Wd,TC,andC, all together. Cold neutron source data sets of four redundant hydrogen pressure transmitters from a research reactor were used to perform the validation and verification. Results showed that the second and third modified approaches lead to reasonable improvement of the PSA technique. All approaches implemented in this study were similar in that they have the capability to (1 identify and isolate a drifted sensor that should undergo calibration, (2 identify a faulty sensor/s due to long and continuous missing data range, and (3 identify a healthy sensor. Keywords: Nuclear Reactors
A different approach to estimate nonlinear regression model using numerical methods
Mahaboob, B.; Venkateswarlu, B.; Mokeshrayalu, G.; Balasiddamuni, P.
2017-11-01
This research paper concerns with the computational methods namely the Gauss-Newton method, Gradient algorithm methods (Newton-Raphson method, Steepest Descent or Steepest Ascent algorithm method, the Method of Scoring, the Method of Quadratic Hill-Climbing) based on numerical analysis to estimate parameters of nonlinear regression model in a very different way. Principles of matrix calculus have been used to discuss the Gradient-Algorithm methods. Yonathan Bard [1] discussed a comparison of gradient methods for the solution of nonlinear parameter estimation problems. However this article discusses an analytical approach to the gradient algorithm methods in a different way. This paper describes a new iterative technique namely Gauss-Newton method which differs from the iterative technique proposed by Gorden K. Smyth [2]. Hans Georg Bock et.al [10] proposed numerical methods for parameter estimation in DAE’s (Differential algebraic equation). Isabel Reis Dos Santos et al [11], Introduced weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel. For large-scale non smooth convex minimization the Hager and Zhang (HZ) conjugate gradient Method and the modified HZ (MHZ) method were presented by Gonglin Yuan et al [12].
Novikov, I; Fund, N; Freedman, L S
2010-01-15
Different methods for the calculation of sample size for simple logistic regression (LR) with one normally distributed continuous covariate give different results. Sometimes the difference can be large. Furthermore, some methods require the user to specify the prevalence of cases when the covariate equals its population mean, rather than the more natural population prevalence. We focus on two commonly used methods and show through simulations that the power for a given sample size may differ substantially from the nominal value for one method, especially when the covariate effect is large, while the other method performs poorly if the user provides the population prevalence instead of the required parameter. We propose a modification of the method of Hsieh et al. that requires specification of the population prevalence and that employs Schouten's sample size formula for a t-test with unequal variances and group sizes. This approach appears to increase the accuracy of the sample size estimates for LR with one continuous covariate.
Heterogeneous effects of oil shocks on exchange rates: evidence from a quantile regression approach.
Su, Xianfang; Zhu, Huiming; You, Wanhai; Ren, Yinghua
2016-01-01
The determinants of exchange rates have attracted considerable attention among researchers over the past several decades. Most studies, however, ignore the possibility that the impact of oil shocks on exchange rates could vary across the exchange rate returns distribution. We employ a quantile regression approach to address this issue. Our results indicate that the effect of oil shocks on exchange rates is heterogeneous across quantiles. A large US depreciation or appreciation tends to heighten the effects of oil shocks on exchange rate returns. Positive oil demand shocks lead to appreciation pressures in oil-exporting countries and this result is robust across lower and upper return distributions. These results offer rich and useful information for investors and decision-makers.
International Nuclear Information System (INIS)
Kumar, Akansha; Tsvetkov, Pavel V.
2015-01-01
desired power peaking limits, desired effective and infinite neutron multiplication factors, high fast fission factor, high thermal efficiency in the conversion from thermal energy to electrical energy using the Brayton cycle, and high fuel burn-up. It is to be noted that we have kept the total mass of the fuel as constant. In this work, we present a module based (modular) approach to perform the optimization wherein, we have defined the following modules: single fuel pin cell, whole core, thermal–hydraulics, and energy conversion. In each of the modules we have defined a specific set of parameters and optimization objectives. The GA system (GAS), and RS together, play the role of optimizing each of the individual modules, and integrating the modules to determine the final nuclear reactor core. However, implementation of GA could lead to a local minimum or a non-unique set of parameters, those meet the specific optimization objectives. The GA code is built using Java, neutronic analysis using MCNP6, thermal–hydraulics calculations using Java, and regression analysis using R
A Gaussian process regression based hybrid approach for short-term wind speed prediction
International Nuclear Information System (INIS)
Zhang, Chi; Wei, Haikun; Zhao, Xin; Liu, Tianhong; Zhang, Kanjian
2016-01-01
Highlights: • A novel hybrid approach is proposed for short-term wind speed prediction. • This method combines the parametric AR model with the non-parametric GPR model. • The relative importance of different inputs is considered. • Different types of covariance functions are considered and combined. • It can provide both accurate point forecasts and satisfactory prediction intervals. - Abstract: This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.
Mumtaz, Ubaidullah; Ali, Yousaf; Petrillo, Antonella
2018-05-15
The increase in the environmental pollution is one of the most important topic in today's world. In this context, the industrial activities can pose a significant threat to the environment. To manage problems associate to industrial activities several methods, techniques and approaches have been developed. Green supply chain management (GSCM) is considered one of the most important "environmental management approach". In developing countries such as Pakistan the implementation of GSCM practices is still in its initial stages. Lack of knowledge about its effects on economic performance is the reason because of industries fear to implement these practices. The aim of this research is to perceive the effects of GSCM practices on organizational performance in Pakistan. In this research the GSCM practices considered are: internal practices, external practices, investment recovery and eco-design. While, the performance parameters considered are: environmental pollution, operational cost and organizational flexibility. A set of hypothesis propose the effect of each GSCM practice on the performance parameters. Factor analysis and linear regression are used to analyze the survey data of Pakistani industries, in order to authenticate these hypotheses. The findings of this research indicate a decrease in environmental pollution and operational cost with the implementation of GSCM practices, whereas organizational flexibility has not improved for Pakistani industries. These results aim to help managers regarding their decision of implementing GSCM practices in the industrial sector of Pakistan. Copyright © 2017 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Jo, Yunhee; Sanyal, Bhaskar; Chung, Namhyeok; Lee, Hyun-Gyu; Park, Yunji; Park, Hae-Jun; Kwon, Joong-Ho
2015-01-01
Photostimulated luminescence (PSL) has been employed as a fast screening method for various irradiated foods. In this study the potential use of PSL was evaluated to identify oranges irradiated with gamma ray, electron beam and X-ray (0–2 kGy) and stored under different conditions for 6 weeks. The effects of light conditions (natural light, artificial light, and dark) and storage temperatures (4 and 20 °C) on PSL photon counts (PCs) during post-irradiation periods were studied. Non-irradiated samples always showed negative values of PCs, while irradiated oranges exhibited intermediate results after first PSL measurements. However, the irradiated samples had much higher PCs. The PCs of all the samples declined as the storage time increased. Calibrated second PSL measurements showed PSL ratio <10 for the irradiated samples after 3 weeks of irradiation confirming their irradiation status in all the storage conditions. Calibrated PSL and sample storage in dark at 4 °C were found out to be most suitable approaches to identify irradiated oranges during storage. - Highlights: • Photostimulatedluminescence (PSL) was studied to identify irradiated orange for quarantine application. • PSL detection efficiency was compared amonggamma,electron, and X irradiation during shelf-life of oranges • PSL properties of samples were characterized by standard samples • Calibrated PSL gave a clear verdict on irradiation extending potential of PSL technique
Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration
Doherty, John E.; Hunt, Randall J.
2010-01-01
Highly parameterized groundwater models can create calibration difficulties. Regularized inversion-the combined use of large numbers of parameters with mathematical approaches for stable parameter estimation-is becoming a common approach to address these difficulties and enhance the transfer of information contained in field measurements to parameters used to model that system. Though commonly used in other industries, regularized inversion is somewhat imperfectly understood in the groundwater field. There is concern that this unfamiliarity can lead to underuse, and misuse, of the methodology. This document is constructed to facilitate the appropriate use of regularized inversion for calibrating highly parameterized groundwater models. The presentation is directed at an intermediate- to advanced-level modeler, and it focuses on the PEST software suite-a frequently used tool for highly parameterized model calibration and one that is widely supported by commercial graphical user interfaces. A brief overview of the regularized inversion approach is provided, and techniques for mathematical regularization offered by PEST are outlined, including Tikhonov, subspace, and hybrid schemes. Guidelines for applying regularized inversion techniques are presented after a logical progression of steps for building suitable PEST input. The discussion starts with use of pilot points as a parameterization device and processing/grouping observations to form multicomponent objective functions. A description of potential parameter solution methodologies and resources available through the PEST software and its supporting utility programs follows. Directing the parameter-estimation process through PEST control variables is then discussed, including guidance for monitoring and optimizing the performance of PEST. Comprehensive listings of PEST control variables, and of the roles performed by PEST utility support programs, are presented in the appendixes.
Explaining the heterogeneous scrapie surveillance figures across Europe: a meta-regression approach
Directory of Open Access Journals (Sweden)
Ru Giuseppe
2007-06-01
Full Text Available Abstract Background Two annual surveys, the abattoir and the fallen stock, monitor the presence of scrapie across Europe. A simple comparison between the prevalence estimates in different countries reveals that, in 2003, the abattoir survey appears to detect more scrapie in some countries. This is contrary to evidence suggesting the greater ability of the fallen stock survey to detect the disease. We applied meta-analysis techniques to study this apparent heterogeneity in the behaviour of the surveys across Europe. Furthermore, we conducted a meta-regression analysis to assess the effect of country-specific characteristics on the variability. We have chosen the odds ratios between the two surveys to inform the underlying relationship between them and to allow comparisons between the countries under the meta-regression framework. Baseline risks, those of the slaughtered populations across Europe, and country-specific covariates, available from the European Commission Report, were inputted in the model to explain the heterogeneity. Results Our results show the presence of significant heterogeneity in the odds ratios between countries and no reduction in the variability after adjustment for the different risks in the baseline populations. Three countries contributed the most to the overall heterogeneity: Germany, Ireland and The Netherlands. The inclusion of country-specific covariates did not, in general, reduce the variability except for one variable: the proportion of the total adult sheep population sampled as fallen stock by each country. A large residual heterogeneity remained in the model indicating the presence of substantial effect variability between countries. Conclusion The meta-analysis approach was useful to assess the level of heterogeneity in the implementation of the surveys and to explore the reasons for the variation between countries.
A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations
Directory of Open Access Journals (Sweden)
P. Koutrakis
2011-08-01
Full Text Available Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS. Subsequently, this method was used to predict ground daily PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.
Mendiguren, Gorka; Koch, Julian; Stisen, Simon
2017-11-01
Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds
Felton, A. J.; Smith, M. D.
2016-12-01
Heightened climatic variability due to atmospheric warming is forecast to increase the frequency and severity of climate extremes. In particular, changes to interannual variability in precipitation, characterized by increases in extreme wet and dry years, are likely to impact virtually all terrestrial ecosystem processes. However, to date experimental approaches have yet to explicitly test how ecosystem processes respond to multiple levels of climatic extremity, limiting our understanding of how ecosystems will respond to forecast increases in the magnitude of climate extremes. Here we report the results of a replicated regression experimental approach, in which we imposed 9 and 11 levels of growing season precipitation amount and extremity in mesic grassland during 2015 and 2016, respectively. Each level corresponded to a specific percentile of the long-term record, which produced a large gradient of soil moisture conditions that ranged from extreme wet to extreme dry. In both 2015 and 2016, asymptotic responses to water availability were observed for soil respiration. This asymmetry was driven in part by transitions between soil moisture versus temperature constraints on respiration as conditions became increasingly dry versus increasingly wet. In 2015, aboveground net primary production (ANPP) exhibited asymmetric responses to precipitation that largely mirrored those of soil respiration. In total, our results suggest that in this mesic ecosystem, these two carbon cycle processes were more sensitive to extreme drought than to extreme wet years. Future work will assess ANPP responses for 2016, soil nutrient supply and physiological responses of the dominant plant species. Future efforts are needed to compare our findings across a diverse array of ecosystem types, and in particular how the timing and magnitude of precipitation events may modify the response of ecosystem processes to increasing magnitudes of precipitation extremes.
A Vector Approach to Regression Analysis and Its Implications to Heavy-Duty Diesel Emissions
Energy Technology Data Exchange (ETDEWEB)
McAdams, H.T.
2001-02-14
An alternative approach is presented for the regression of response data on predictor variables that are not logically or physically separable. The methodology is demonstrated by its application to a data set of heavy-duty diesel emissions. Because of the covariance of fuel properties, it is found advantageous to redefine the predictor variables as vectors, in which the original fuel properties are components, rather than as scalars each involving only a single fuel property. The fuel property vectors are defined in such a way that they are mathematically independent and statistically uncorrelated. Because the available data set does not allow definitive separation of vehicle and fuel effects, and because test fuels used in several of the studies may be unrealistically contrived to break the association of fuel variables, the data set is not considered adequate for development of a full-fledged emission model. Nevertheless, the data clearly show that only a few basic patterns of fuel-property variation affect emissions and that the number of these patterns is considerably less than the number of variables initially thought to be involved. These basic patterns, referred to as ''eigenfuels,'' may reflect blending practice in accordance with their relative weighting in specific circumstances. The methodology is believed to be widely applicable in a variety of contexts. It promises an end to the threat of collinearity and the frustration of attempting, often unrealistically, to separate variables that are inseparable.
Model-free prediction and regression a transformation-based approach to inference
Politis, Dimitris N
2015-01-01
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, co...
Evans, Wiley; Mathis, Jeremy T.; Winsor, Peter; Statscewich, Hank; Whitledge, Terry E.
2013-01-01
northern Gulf of Alaska (GOA) shelf experiences carbonate system variability on seasonal and annual time scales, but little information exists to resolve higher frequency variability in this region. To resolve this variability using platforms-of-opportunity, we present multiple linear regression (MLR) models constructed from hydrographic data collected along the Northeast Pacific Global Ocean Ecosystems Dynamics (GLOBEC) Seward Line. The empirical algorithms predict dissolved inorganic carbon (DIC) and total alkalinity (TA) using observations of nitrate (NO3-), temperature, salinity and pressure from the surface to 500 m, with R2s > 0.97 and RMSE values of 11 µmol kg-1 for DIC and 9 µmol kg-1 for TA. We applied these relationships to high-resolution NO3- data sets collected during a novel 20 h glider flight and a GLOBEC mesoscale SeaSoar survey. Results from the glider flight demonstrated time/space along-isopycnal variability of aragonite saturations (Ωarag) associated with a dicothermal layer (a cold near-surface layer found in high latitude oceans) that rivaled changes seen vertically through the thermocline. The SeaSoar survey captured the uplift to aragonite saturation horizon (depth where Ωarag = 1) shoaled to a previously unseen depth in the northern GOA. This work is similar to recent studies aimed at predicting the carbonate system in continental margin settings, albeit demonstrates that a NO3--based approach can be applied to high-latitude data collected from platforms capable of high-frequency measurements.
Ulkhaq, M. M.; Widodo, A. K.; Yulianto, M. F. A.; Widhiyaningrum; Mustikasari, A.; Akshinta, P. Y.
2018-03-01
The implementation of renewable energy in this globalization era is inevitable since the non-renewable energy leads to climate change and global warming; hence, it does harm the environment and human life. However, in the developing countries, such as Indonesia, the implementation of the renewable energy sources does face technical and social problems. For the latter, renewable energy sources implementation is only effective if the public is aware of its benefits. This research tried to identify the determinants that influence consumers’ intention in adopting renewable energy sources. In addition, this research also tried to predict the consumers who are willing to apply the renewable energy sources in their houses using a logistic regression approach. A case study was conducted in Semarang, Indonesia. The result showed that only eight variables (from fifteen) that are significant statistically, i.e., educational background, employment status, income per month, average electricity cost per month, certainty about the efficiency of renewable energy project, relatives’ influence to adopt the renewable energy sources, energy tax deduction, and the condition of the price of the non-renewable energy sources. The finding of this study could be used as a basis for the government to set up a policy towards an implementation of the renewable energy sources.
An Ionospheric Index Model based on Linear Regression and Neural Network Approaches
Tshisaphungo, Mpho; McKinnell, Lee-Anne; Bosco Habarulema, John
2017-04-01
The ionosphere is well known to reflect radio wave signals in the high frequency (HF) band due to the present of electron and ions within the region. To optimise the use of long distance HF communications, it is important to understand the drivers of ionospheric storms and accurately predict the propagation conditions especially during disturbed days. This paper presents the development of an ionospheric storm-time index over the South African region for the application of HF communication users. The model will result into a valuable tool to measure the complex ionospheric behaviour in an operational space weather monitoring and forecasting environment. The development of an ionospheric storm-time index is based on a single ionosonde station data over Grahamstown (33.3°S,26.5°E), South Africa. Critical frequency of the F2 layer (foF2) measurements for a period 1996-2014 were considered for this study. The model was developed based on linear regression and neural network approaches. In this talk validation results for low, medium and high solar activity periods will be discussed to demonstrate model's performance.
Approach for Self-Calibrating CO₂ Measurements with Linear Membrane-Based Gas Sensors.
Lazik, Detlef; Sood, Pramit
2016-11-17
Linear membrane-based gas sensors that can be advantageously applied for the measurement of a single gas component in large heterogeneous systems, e.g., for representative determination of CO₂ in the subsurface, can be designed depending on the properties of the observation object. A resulting disadvantage is that the permeation-based sensor response depends on operating conditions, the individual site-adapted sensor geometry, the membrane material, and the target gas component. Therefore, calibration is needed, especially of the slope, which could change over several orders of magnitude. A calibration-free approach based on an internal gas standard is developed to overcome the multi-criterial slope dependency. This results in a normalization of sensor response and enables the sensor to assess the significance of measurement. The approach was proofed on the example of CO₂ analysis in dry air with tubular PDMS membranes for various CO₂ concentrations of an internal standard. Negligible temperature dependency was found within an 18 K range. The transformation behavior of the measurement signal and the influence of concentration variations of the internal standard on the measurement signal were shown. Offsets that were adjusted based on the stated theory for the given measurement conditions and material data from the literature were in agreement with the experimentally determined offsets. A measurement comparison with an NDIR reference sensor shows an unexpectedly low bias (sensor response, and comparable statistical uncertainty.
DEFF Research Database (Denmark)
De Sanctis, G.; Fischer, K.; Kohler, J.
2014-01-01
Fire risk models support decision making for engineering problems under the consistent consideration of the associated uncertainties. Empirical approaches can be used for cost-benefit studies when enough data about the decision problem are available. But often the empirical approaches...... a generic risk model that is calibrated to observed fire loss data. Generic risk models assess the risk of buildings based on specific risk indicators and support risk assessment at a portfolio level. After an introduction to the principles of generic risk assessment, the focus of the present paper...... are not detailed enough. Engineering risk models, on the other hand, may be detailed but typically involve assumptions that may result in a biased risk assessment and make a cost-benefit study problematic. In two related papers it is shown how engineering and data-driven modeling can be combined by developing...
Czech Academy of Sciences Publication Activity Database
Drabinová, Adéla; Martinková, Patrícia
2017-01-01
Roč. 54, č. 4 (2017), s. 498-517 ISSN 0022-0655 R&D Projects: GA ČR GJ15-15856Y Institutional support: RVO:67985807 Keywords : differential item functioning * non-linear regression * logistic regression * item response theory Subject RIV: AM - Education OBOR OECD: Statistics and probability Impact factor: 0.979, year: 2016
Statistical approach for selection of regression model during validation of bioanalytical method
Directory of Open Access Journals (Sweden)
Natalija Nakov
2014-06-01
Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.
Alkerwi, Ala'a; Vernier, Céderic; Sauvageot, Nicolas; Crichton, Georgina E; Elias, Merrill F
2015-01-01
Objectives This study aimed to examine the most important demographic and socioeconomic factors associated with diet quality, evaluated in terms of compliance with national dietary recommendations, selection of healthy and unhealthy food choices, energy density and food variety. We hypothesised that different demographic and socioeconomic factors may show disparate associations with diet quality. Study design A nationwide, cross-sectional, population-based study. Participants A total of 1352 apparently healthy and non-institutionalised subjects, aged 18–69 years, participated in the Observation of Cardiovascular Risk Factors in Luxembourg (ORISCAV-LUX) study in 2007–2008. The participants attended the nearest study centre after a telephone appointment, and were interviewed by trained research staff. Outcome measures Diet quality as measured by 5 dietary indicators, namely, recommendation compliance index (RCI), recommended foods score (RFS), non-recommended foods score (non-RFS), energy density score (EDS), and dietary diversity score (DDS). The novel Correlated Component Regression (CCR) technique was used to determine the importance and magnitude of the association of each socioeconomic factor with diet quality, in a global analytic approach. Results Increasing age, being male and living below the poverty threshold were predominant factors associated with eating a high energy density diet. Education level was an important factor associated with healthy and adequate food choices, whereas economic resources were predominant factors associated with food diversity and energy density. Conclusions Multiple demographic and socioeconomic circumstances were associated with different diet quality indicators. Efforts to improve diet quality for high-risk groups need an important public health focus. PMID:25967988
Rovlias, Aristedis; Theodoropoulos, Spyridon; Papoutsakis, Dimitrios
2015-01-01
Background: Chronic subdural hematoma (CSDH) is one of the most common clinical entities in daily neurosurgical practice which carries a most favorable prognosis. However, because of the advanced age and medical problems of patients, surgical therapy is frequently associated with various complications. This study evaluated the clinical features, radiological findings, and neurological outcome in a large series of patients with CSDH. Methods: A classification and regression tree (CART) technique was employed in the analysis of data from 986 patients who were operated at Asclepeion General Hospital of Athens from January 1986 to December 2011. Burr holes evacuation with closed system drainage has been the operative technique of first choice at our institution for 29 consecutive years. A total of 27 prognostic factors were examined to predict the outcome at 3-month postoperatively. Results: Our results indicated that neurological status on admission was the best predictor of outcome. With regard to the other data, age, brain atrophy, thickness and density of hematoma, subdural accumulation of air, and antiplatelet and anticoagulant therapy were found to correlate significantly with prognosis. The overall cross-validated predictive accuracy of CART model was 85.34%, with a cross-validated relative error of 0.326. Conclusions: Methodologically, CART technique is quite different from the more commonly used methods, with the primary benefit of illustrating the important prognostic variables as related to outcome. Since, the ideal therapy for the treatment of CSDH is still under debate, this technique may prove useful in developing new therapeutic strategies and approaches for patients with CSDH. PMID:26257985
SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach
International Nuclear Information System (INIS)
Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D
2014-01-01
Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme
SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach
Energy Technology Data Exchange (ETDEWEB)
Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D [UCLA, Los Angeles, CA (United States)
2014-06-01
Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme
A regression approach for Zircaloy-2 in-reactor creep constitutive equations
International Nuclear Information System (INIS)
Yung Liu, Y.; Bement, A.L.
1977-01-01
In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)
Jo, Yunhee; Sanyal, Bhaskar; Chung, Namhyeok; Lee, Hyun-Gyu; Park, Yunji; Park, Hae-Jun; Kwon, Joong-Ho
2015-06-01
Photostimulated luminescence (PSL) has been employed as a fast screening method for various irradiated foods. In this study the potential use of PSL was evaluated to identify oranges irradiated with gamma ray, electron beam and X-ray (0-2 kGy) and stored under different conditions for 6 weeks. The effects of light conditions (natural light, artificial light, and dark) and storage temperatures (4 and 20 °C) on PSL photon counts (PCs) during post-irradiation periods were studied. Non-irradiated samples always showed negative values of PCs, while irradiated oranges exhibited intermediate results after first PSL measurements. However, the irradiated samples had much higher PCs. The PCs of all the samples declined as the storage time increased. Calibrated second PSL measurements showed PSL ratio <10 for the irradiated samples after 3 weeks of irradiation confirming their irradiation status in all the storage conditions. Calibrated PSL and sample storage in dark at 4 °C were found out to be most suitable approaches to identify irradiated oranges during storage.
Calibrating a multi-model approach to defect production in high energy collision cascades
International Nuclear Information System (INIS)
Heinisch, H.L.; Singh, B.N.; Diaz de la Rubia, T.
1994-01-01
A multi-model approach to simulating defect production processes at the atomic scale is described that incorporates molecular dynamics (MD), binary collision approximation (BCA) calculations and stochastic annealing simulations. The central hypothesis is that the simple, fast computer codes capable of simulating large numbers of high energy cascades (e.g., BCA codes) can be made to yield the correct defect configurations when their parameters are calibrated using the results of the more physically realistic MD simulations. The calibration procedure is investigated using results of MD simulations of 25 keV cascades in copper. The configurations of point defects are extracted from the MD cascade simulations at the end of the collisional phase, thus providing information similar to that obtained with a binary collision model. The MD collisional phase defect configurations are used as input to the ALSOME annealing simulation code, and values of the ALSOME quenching parameters are determined that yield the best fit to the post-quenching defect configurations of the MD simulations. ((orig.))
Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah
2017-08-01
Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.
National Aeronautics and Space Administration — The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to...
HYBRID DATA APPROACH FOR SELECTING EFFECTIVE TEST CASES DURING THE REGRESSION TESTING
Mohan, M.; Shrimali, Tarun
2017-01-01
In the software industry, software testing becomes more important in the entire software development life cycle. Software testing is one of the fundamental components of software quality assurances. Software Testing Life Cycle (STLC)is a process involved in testing the complete software, which includes Regression Testing, Unit Testing, Smoke Testing, Integration Testing, Interface Testing, System Testing & etc. In the STLC of Regression testing, test case selection is one of the most importan...
Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa
2017-03-01
This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.
Oneill-Rood, Nora; Glover, Richard D.
1990-01-01
NASA's Dryden Flight Research Facility (Ames-Dryden), operates a diverse fleet of research aircraft which are heavily instrumented to provide both real time data for in-flight monitoring and recorded data for postflight analysis. Ames-Dryden's existing automated calibration (AUTOCAL) laboratory is a computerized facility which tests aircraft sensors to certify accuracy for anticipated harsh flight environments. Recently, a major AUTOCAL lab upgrade was initiated; the goal of this modernization is to enhance productivity and improve configuration management for both software and test data. The new system will have multiple testing stations employing distributed processing linked by a local area network to a centralized database. The baseline requirements for the new AUTOCAL lab and the design approach being taken for its mechanization are described.
An alternate approach to calibrating FAC-predictive models using inspection data (Log Book No. 398)
International Nuclear Information System (INIS)
Pulido, J.E.; Ksiazek, P.E.; Alecksick, R.M.
2004-01-01
Flow-Accelerated Corrosion (FAC) of piping and fittings in Nuclear Energy Plants can pose a threat to personnel safety, reduce plant availability, and result in undesirable challenges to plant safety systems. For these reasons, accurate predictions of FAC-induced wear rates are extremely valuable in that they allow action to be taken prior to component failure. The EPRI recommended method of predicting FAC wear rates for inspected as well as uninspected components allows for calibration of predictions through the use of wall thickness measurements obtained from UT inspections. This method uses a simple linear correction based on the median value of the ratio of measured to predicted thickness. An alternate approach is presented that takes local thermodynamic variations into account, thus resulting in an improved correlation with measured data. (author)
Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul
2015-11-04
Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.
Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W
2015-08-01
Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.
A regression approach for zircaloy-2 in-reactor creep constitutive equations
International Nuclear Information System (INIS)
Yung Liu, Y.; Bement, A.L.
1977-01-01
In this paper the methodology of multiple regressions as applied to zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. From data analysis and model development point of views, both the assumption of independence and prior committment to specific model forms are unacceptable. One would desire means which can not only estimate the required parameters directly from data but also provide basis for model selections, viz., one model against others. Basic understanding of the physics of deformation is important in choosing the forms of starting physical model equations, but the justifications must rely on their abilities in correlating the overall data. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) when there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets, (2) regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections
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.)
Shafiq, M. Najeeb
2013-01-01
Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15-year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…
Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...
Digital Repository Service at National Institute of Oceanography (India)
Tripathy, G.R.; Das, Anirban.
used methods, the Least Square Regression (LSR) and Inverse Modeling (IM), to determine the contributions of (i) solutes from different sources to global river water, and (ii) various rocks to a glacial till. The purpose of this exercise is to compare...
Replicating Experimental Impact Estimates Using a Regression Discontinuity Approach. NCEE 2012-4025
Gleason, Philip M.; Resch, Alexandra M.; Berk, Jillian A.
2012-01-01
This NCEE Technical Methods Paper compares the estimated impacts of an educational intervention using experimental and regression discontinuity (RD) study designs. The analysis used data from two large-scale randomized controlled trials--the Education Technology Evaluation and the Teach for America Study--to provide evidence on the performance of…
On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis
Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas
2011-01-01
The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…
Financial Aid and First-Year Collegiate GPA: A Regression Discontinuity Approach
Curs, Bradley R.; Harper, Casandra E.
2012-01-01
Using a regression discontinuity design, we investigate whether a merit-based financial aid program has a causal effect on the first-year grade point average of first-time out-of-state freshmen at the University of Oregon. Our results indicate that merit-based financial aid has a positive and significant effect on first-year collegiate grade point…
Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P
2015-01-01
This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of
Campbell, J. L.; Lee, M.; Jones, B. N.; Andrushenko, S. M.; Holmes, N. G.; Maxwell, J. A.; Taylor, S. M.
2009-04-01
The detection sensitivities of the Alpha Particle X-ray Spectrometer (APXS) instruments on the Mars Exploration Rovers for a wide range of elements were experimentally determined in 2002 using spectra of geochemical reference materials. A flight spare instrument was similarly calibrated, and the calibration exercise was then continued for this unit with an extended set of geochemical reference materials together with pure elements and simple chemical compounds. The flight spare instrument data are examined in detail here using a newly developed fundamental parameters approach which takes precise account of all the physics inherent in the two X-ray generation techniques involved, namely, X-ray fluorescence and particle-induced X-ray emission. The objectives are to characterize the instrument as fully as possible, to test this new approach, and to determine the accuracy of calibration for major, minor, and trace elements. For some of the lightest elements the resulting calibration exhibits a dependence upon the mineral assemblage of the geological reference material; explanations are suggested for these observations. The results will assist in designing the overall calibration approach for the APXS on the Mars Science Laboratory mission.
Simultaneous Calibration: A Joint Optimization Approach for Multiple Kinect and External Cameras.
Liao, Yajie; Sun, Ying; Li, Gongfa; Kong, Jianyi; Jiang, Guozhang; Jiang, Du; Cai, Haibin; Ju, Zhaojie; Yu, Hui; Liu, Honghai
2017-06-24
Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are targeted at joint calibration of multi-sensors (more than four devices), which normally is a practical issue in the real-time systems. In this paper, we propose a novel method and a corresponding workflow framework to simultaneously calibrate relative poses of a Kinect and three external cameras. By optimizing the final cost function and adding corresponding weights to the external cameras in different locations, an effective joint calibration of multiple devices is constructed. Furthermore, the method is tested in a practical platform, and experiment results show that the proposed joint calibration method can achieve a satisfactory performance in a project real-time system and its accuracy is higher than the manufacturer's calibration.
DEFF Research Database (Denmark)
Adhikari, Kabindra; Bou Kheir, Rania; Greve, Mette Balslev
2013-01-01
Information on the spatial variability of soil texture including soil clay content in a landscape is very important for agricultural and environmental use. Different prediction techniques are available to assess and map spatial variability of soil properties, but selecting the most suitable techn...... the prediction in OKst compared with that in OK, whereas RT showed the lowest performance of all (R2 = 0.52; RMSE = 0.52; and RPD = 1.17). We found RKrr to be an effective prediction method and recommend this method for any future soil mapping activities in Denmark....... technique at a given site has always been a major issue in all soil mapping applications. We studied the prediction performance of ordinary kriging (OK), stratified OK (OKst), regression trees (RT), and rule-based regression kriging (RKrr) for digital mapping of soil clay content at 30.4-m grid size using 6...
Agarwal, Parul; Sambamoorthi, Usha
2015-12-01
Depression is common among individuals with osteoarthritis and leads to increased healthcare burden. The objective of this study was to examine excess total healthcare expenditures associated with depression among individuals with osteoarthritis in the US. Adults with self-reported osteoarthritis (n = 1881) were identified using data from the 2010 Medical Expenditure Panel Survey (MEPS). Among those with osteoarthritis, chi-square tests and ordinary least square regressions (OLS) were used to examine differences in healthcare expenditures between those with and without depression. Post-regression linear decomposition technique was used to estimate the relative contribution of different constructs of the Anderson's behavioral model, i.e., predisposing, enabling, need, personal healthcare practices, and external environment factors, to the excess expenditures associated with depression among individuals with osteoarthritis. All analysis accounted for the complex survey design of MEPS. Depression coexisted among 20.6 % of adults with osteoarthritis. The average total healthcare expenditures were $13,684 among adults with depression compared to $9284 among those without depression. Multivariable OLS regression revealed that adults with depression had 38.8 % higher healthcare expenditures (p regression linear decomposition analysis indicated that 50 % of differences in expenditures among adults with and without depression can be explained by differences in need factors. Among individuals with coexisting osteoarthritis and depression, excess healthcare expenditures associated with depression were mainly due to comorbid anxiety, chronic conditions and poor health status. These expenditures may potentially be reduced by providing timely intervention for need factors or by providing care under a collaborative care model.
The effect of foreign aid on corruption: A quantile regression approach
Okada, Keisuke; Samreth, Sovannroeun
2011-01-01
This paper investigates the effect of foreign aid on corruption using a quantile regression method. Our estimation results illustrate that foreign aid generally lessens corruption and, in particular, its reduction effect is larger in countries with low levels of corruption. In addition, considering foreign aid by donors, our analysis indicates that while multilateral aid has a larger reduction impact on corruption, bilateral aid from the world’s leading donors, such as France, the United King...
International Nuclear Information System (INIS)
Fu, Shuai; Zhang, Liyan; Ye, Nan; Liu, Shenglan; Zhang, WeiZhong
2014-01-01
A monocular-vision-based coordinate measuring system (MVB-CMS) obtains the 3D coordinates of the probe tip center of a light pen by analyzing the monocular image of the target points on the light pen. The light pen calibration, including the target point calibration and the probe tip center calibration, is critical to guarantee the accuracy of the MVB-CMS. The currently used method resorts to special equipment to calibrate the feature points on the light pen in a separate offsite procedure and uses the system camera to calibrate the probe tip center onsite. Instead, a complete onsite light pen calibration method is proposed in this paper. It needs only several auxiliary target points with the same visual features of the light pen targets and two or more cone holes with known distance(s). The target point calibration and the probe tip center calibration are jointly implemented by simply taking two groups of images of the light pen with the camera of the system. The proposed method requires no extra equipment other than the system camera for the calibration, so it is easier to implement and flexible for use. It has been incorporated in a large field-of-view MVB-CMS, which uses active luminous infrared LEDs as the target points. Experimental results demonstrate the accuracy and effectiveness of the proposed method. (paper)
Lusiana, Evellin Dewi
2017-12-01
The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.
Yu, Wenbao; Park, Taesung
2014-01-01
It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.
Analysis of sparse data in logistic regression in medical research: A newer approach
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S Devika
2016-01-01
Full Text Available Background and Objective: In the analysis of dichotomous type response variable, logistic regression is usually used. However, the performance of logistic regression in the presence of sparse data is questionable. In such a situation, a common problem is the presence of high odds ratios (ORs with very wide 95% confidence interval (CI (OR: >999.999, 95% CI: 999.999. In this paper, we addressed this issue by using penalized logistic regression (PLR method. Materials and Methods: Data from case-control study on hyponatremia and hiccups conducted in Christian Medical College, Vellore, Tamil Nadu, India was used. The outcome variable was the presence/absence of hiccups and the main exposure variable was the status of hyponatremia. Simulation dataset was created with different sample sizes and with a different number of covariates. Results: A total of 23 cases and 50 controls were used for the analysis of ordinary and PLR methods. The main exposure variable hyponatremia was present in nine (39.13% of the cases and in four (8.0% of the controls. Of the 23 hiccup cases, all were males and among the controls, 46 (92.0% were males. Thus, the complete separation between gender and the disease group led into an infinite OR with 95% CI (OR: >999.999, 95% CI: 999.999 whereas there was a finite and consistent regression coefficient for gender (OR: 5.35; 95% CI: 0.42, 816.48 using PLR. After adjusting for all the confounding variables, hyponatremia entailed 7.9 (95% CI: 2.06, 38.86 times higher risk for the development of hiccups as was found using PLR whereas there was an overestimation of risk OR: 10.76 (95% CI: 2.17, 53.41 using the conventional method. Simulation experiment shows that the estimated coverage probability of this method is near the nominal level of 95% even for small sample sizes and for a large number of covariates. Conclusions: PLR is almost equal to the ordinary logistic regression when the sample size is large and is superior in small cell
Lukoschek, Vimoksalehi; Scott Keogh, J; Avise, John C
2012-01-01
Evolutionary and biogeographic studies increasingly rely on calibrated molecular clocks to date key events. Although there has been significant recent progress in development of the techniques used for molecular dating, many issues remain. In particular, controversies abound over the appropriate use and placement of fossils for calibrating molecular clocks. Several methods have been proposed for evaluating candidate fossils; however, few studies have compared the results obtained by different approaches. Moreover, no previous study has incorporated the effects of nucleotide saturation from different data types in the evaluation of candidate fossils. In order to address these issues, we compared three approaches for evaluating fossil calibrations: the single-fossil cross-validation method of Near, Meylan, and Shaffer (2005. Assessing concordance of fossil calibration points in molecular clock studies: an example using turtles. Am. Nat. 165:137-146), the empirical fossil coverage method of Marshall (2008. A simple method for bracketing absolute divergence times on molecular phylogenies using multiple fossil calibration points. Am. Nat. 171:726-742), and the Bayesian multicalibration method of Sanders and Lee (2007. Evaluating molecular clock calibrations using Bayesian analyses with soft and hard bounds. Biol. Lett. 3:275-279) and explicitly incorporate the effects of data type (nuclear vs. mitochondrial DNA) for identifying the most reliable or congruent fossil calibrations. We used advanced (Caenophidian) snakes as a case study; however, our results are applicable to any taxonomic group with multiple candidate fossils, provided appropriate taxon sampling and sufficient molecular sequence data are available. We found that data type strongly influenced which fossil calibrations were identified as outliers, regardless of which method was used. Despite the use of complex partitioned models of sequence evolution and multiple calibrations throughout the tree, saturation
Using the mean approach in pooling cross-section and time series data for regression modelling
International Nuclear Information System (INIS)
Nuamah, N.N.N.N.
1989-12-01
The mean approach is one of the methods for pooling cross section and time series data for mathematical-statistical modelling. Though a simple approach, its results are sometimes paradoxical in nature. However, researchers still continue using it for its simplicity. Here, the paper investigates the nature and source of such unwanted phenomena. (author). 7 refs
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Anke Hüls
2017-05-01
Full Text Available Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model and (ii to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate
Šarić, Željko; Xu, Xuecai; Duan, Li; Babić, Darko
2018-06-20
This study intended to investigate the interactions between accident rate and traffic signs in state roads located in Croatia, and accommodate the heterogeneity attributed to unobserved factors. The data from 130 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the heterogeneity, a panel quantile regression model was proposed, in which quantile regression model offers a more complete view and a highly comprehensive analysis of the relationship between accident rate and traffic signs, while the panel data model accommodates the heterogeneity attributed to unobserved factors. Results revealed that (1) low visibility of material damage (MD) and death or injured (DI) increased the accident rate; (2) the number of mandatory signs and the number of warning signs were more likely to reduce the accident rate; (3)average speed limit and the number of invalid traffic signs per km exhibited a high accident rate. To our knowledge, it's the first attempt to analyze the interactions between accident consequences and traffic signs by employing a panel quantile regression model; by involving the visibility, the present study demonstrates that the low visibility causes a relatively higher risk of MD and DI; It is noteworthy that average speed limit corresponds with accident rate positively; The number of mandatory signs and the number of warning signs are more likely to reduce the accident rate; The number of invalid traffic signs per km are significant for accident rate, thus regular maintenance should be kept for a safer roadway environment.
Allard, Alexandra; Takman, Johanna; Uddin, Gazi Salah; Ahmed, Ali
2018-02-01
We evaluate the N-shaped environmental Kuznets curve (EKC) using panel quantile regression analysis. We investigate the relationship between CO 2 emissions and GDP per capita for 74 countries over the period of 1994-2012. We include additional explanatory variables, such as renewable energy consumption, technological development, trade, and institutional quality. We find evidence for the N-shaped EKC in all income groups, except for the upper-middle-income countries. Heterogeneous characteristics are, however, observed over the N-shaped EKC. Finally, we find a negative relationship between renewable energy consumption and CO 2 emissions, which highlights the importance of promoting greener energy in order to combat global warming.
Cannon, Alex
2017-04-01
Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a
THE GENDER PAY GAP IN VIETNAM, 1993-2002: A QUANTILE REGRESSION APPROACH
Pham, Hung T; Reilly, Barry
2007-01-01
This paper uses mean and quantile regression analysis to investigate the gender pay gap for the wage employed in Vietnam over the period 1993 to 2002. It finds that the Doi moi reforms appear to have been associated with a sharp reduction in gender pay gap disparities for the wage employed. The average gender pay gap in this sector halved between 1993 and 2002 with most of the contraction evident by 1998. There has also been a narrowing in the gender pay gap at most selected points of the con...
The Gender Pay Gap In Vietnam, 1993-2002: A Quantile Regression Approach
Barry Reilly & T. Hung Pham
2006-01-01
This paper uses mean and quantile regression analysis to investigate the gender pay gap for the wage employed in Vietnam over the period 1993 to 2002. It finds that the Doi moi reforms have been associated with a sharp reduction in gender wage disparities for the wage employed. The average gender pay gap in this sector halved between 1993 and 2002 with most of the contraction evident by 1998. There has also been a contraction in the gender pay at most selected points of the conditional wage d...
Comparison of global optimization approaches for robust calibration of hydrologic model parameters
Jung, I. W.
2015-12-01
Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
Mutual-Coupling Based Phased-Array Calibration: A Robust and Versatile Approach
Bekers, D.J.; Dijk, R. van; Vliet, F.E. van
2013-01-01
The transmit and receive modules of a large phased array are often calibrated for amplitude and phase variations by an internal calibration network and an offline characterization of the complete array in an anechoic chamber. Such a solution is less obvious in view of current trends towards
Directory of Open Access Journals (Sweden)
Sutikno Sutikno
2010-08-01
Full Text Available One of the climate models used to predict the climatic conditions is Global Circulation Models (GCM. GCM is a computer-based model that consists of different equations. It uses numerical and deterministic equation which follows the physics rules. GCM is a main tool to predict climate and weather, also it uses as primary information source to review the climate change effect. Statistical Downscaling (SD technique is used to bridge the large-scale GCM with a small scale (the study area. GCM data is spatial and temporal data most likely to occur where the spatial correlation between different data on the grid in a single domain. Multicollinearity problems require the need for pre-processing of variable data X. Continuum Regression (CR and pre-processing with Principal Component Analysis (PCA methods is an alternative to SD modelling. CR is one method which was developed by Stone and Brooks (1990. This method is a generalization from Ordinary Least Square (OLS, Principal Component Regression (PCR and Partial Least Square method (PLS methods, used to overcome multicollinearity problems. Data processing for the station in Ambon, Pontianak, Losarang, Indramayu and Yuntinyuat show that the RMSEP values and R2 predict in the domain 8x8 and 12x12 by uses CR method produces results better than by PCR and PLS.
International Nuclear Information System (INIS)
Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.
2013-01-01
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability
Tests of Local Hadron Calibration Approaches in ATLAS Combined Beam Tests
International Nuclear Information System (INIS)
Grahn, Karl-Johan; Kiryunin, Andrey; Pospelov, Guennadi
2011-01-01
Three ATLAS calorimeters in the region of the forward crack at |η| 3.2 in the nominal ATLAS setup and a typical section of the two barrel calorimeters at |η| = 0.45 of ATLAS have been exposed to combined beam tests with single electrons and pions. Detailed shower shape studies of electrons and pions with comparisons to various Geant4 based simulations utilizing different physics lists are presented for the endcap beam test. The local hadron calibration approach as used in the full Atlas setup has been applied to the endcap beam test data. An extension of it using layer correlations has been tested with the barrel test beam data. Both methods utilize modular correction steps based on shower shape variables to correct for invisible energy inside the reconstructed clusters in the calorimeters (compensation) and for lost energy deposits outside of the reconstructed clusters (dead material and out-of-cluster deposits). Results for both methods and comparisons to Monte Carlo simulations are presented.
International Nuclear Information System (INIS)
Shankar, Ramesh; Hussey, Aaron; Davis, Eddie
2003-01-01
On-line monitoring of instrument channels provides increased information about the condition of monitored channels through accurate, more frequent evaluation of each cannel's performance over time. This type of performance monitoring is a methodology that offers an alternate approach to traditional time-directed calibration. EPRI's strategic role in on-line monitoring is to facilitate its implementation and cost-effective use in numerous applications at power plants. To this end, EPRI has sponsored an on-line monitoring implementation project at multiple nuclear plants specifically intended to install and use on-line monitoring technology. The selected on-line monitoring method is based on the Multivariate State Estimation Technique. The project has a planned three-year life; seven plants are participating in the project. The goal is to apply on-line monitoring to all types of power plant applications and document all aspects of the implementation process in a series of EPRI reports. These deliverables cover installation, modeling, optimization, and proven cost-benefit. This paper discusses the actual implementation of on-line monitoring to various nuclear plant instrument systems. Examples of detected instrument drift are provided. (author)
Regression models for categorical, count, and related variables an applied approach
Hoffmann, John P
2016-01-01
Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapte...
Soltanzadeh, Ahmad; Mohammadfam, Iraj; Moghimbeigi, Abbas; Ghiasvand, Reza
2016-03-01
Construction industry involves the highest risk of occupational accidents and bodily injuries, which range from mild to very severe. The aim of this cross-sectional study was to identify the factors associated with accident severity rate (ASR) in the largest Iranian construction companies based on data about 500 occupational accidents recorded from 2009 to 2013. We also gathered data on safety and health risk management and training systems. Data were analysed using Pearson's chi-squared coefficient and multiple regression analysis. Median ASR (and the interquartile range) was 107.50 (57.24- 381.25). Fourteen of the 24 studied factors stood out as most affecting construction accident severity (p<0.05). These findings can be applied in the design and implementation of a comprehensive safety and health risk management system to reduce ASR.
Flow modeling in a porous cylinder with regressing walls using semi analytical approach
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M Azimi
2016-10-01
Full Text Available In this paper, the mathematical modeling of the flow in a porous cylinder with a focus on applications to solid rocket motors is presented. As usual, the cylindrical propellant grain of a solid rocket motor is modeled as a long tube with one end closed at the headwall, while the other remains open. The cylindrical wall is assumed to be permeable so as to simulate the propellant burning and normal gas injection. At first, the problem description and formulation are considered. The Navier-Stokes equations for the viscous flow in a porous cylinder with regressing walls are reduced to a nonlinear ODE by using a similarity transformation in time and space. Application of Differential Transformation Method (DTM as an approximate analytical method has been successfully applied. Finally the results have been presented for various cases.
In search of a corrected prescription drug elasticity estimate: a meta-regression approach.
Gemmill, Marin C; Costa-Font, Joan; McGuire, Alistair
2007-06-01
An understanding of the relationship between cost sharing and drug consumption depends on consistent and unbiased price elasticity estimates. However, there is wide heterogeneity among studies, which constrains the applicability of elasticity estimates for empirical purposes and policy simulation. This paper attempts to provide a corrected measure of the drug price elasticity by employing meta-regression analysis (MRA). The results indicate that the elasticity estimates are significantly different from zero, and the corrected elasticity is -0.209 when the results are made robust to heteroskedasticity and clustering of observations. Elasticity values are higher when the study was published in an economic journal, when the study employed a greater number of observations, and when the study used aggregate data. Elasticity estimates are lower when the institutional setting was a tax-based health insurance system.
Casemix funding for a specialist paediatrics hospital: a hedonic regression approach.
Bridges, J F; Hanson, R M
2000-01-01
This paper inquires into the effects that Diagnosis Related Groups (DRGs) have had on the ability to explain patient-level costs in a specialist paediatrics hospital. Two hedonic models are estimated using 1996/97 New Children's Hospital (NCH) patient level cost data, one with and one without a casemix index (CMI). The results show that the inclusion of a casemix index as an explanatory variable leads to a better accounting of cost. The full hedonic model is then used to simulate a funding model for the 1997/98 NCH cost data. These costs are highly correlated with the actual costs reported for that year. In addition, univariate regression indicates that there has been inflation in costs in the order of 4.8% between the two years. In conclusion, hedonic analysis can provide valuable evidence for the design of funding models that account for casemix.
Mujasi, Paschal N; Asbu, Eyob Z; Puig-Junoy, Jaume
2016-07-08
Hospitals represent a significant proportion of health expenditures in Uganda, accounting for about 26 % of total health expenditure. Improving the technical efficiency of hospitals in Uganda can result in large savings which can be devoted to expand access to services and improve quality of care. This paper explores the technical efficiency of referral hospitals in Uganda during the 2012/2013 financial year. This was a cross sectional study using secondary data. Input and output data were obtained from the Uganda Ministry of Health annual health sector performance report for the period July 1, 2012 to June 30, 2013 for the 14 public sector regional referral and 4 large private not for profit hospitals. We assumed an output-oriented model with Variable Returns to Scale to estimate the efficiency score for each hospital using Data Envelopment Analysis (DEA) with STATA13. Using a Tobit model DEA, efficiency scores were regressed against selected institutional and contextual/environmental factors to estimate their impacts on efficiency. The average variable returns to scale (Pure) technical efficiency score was 91.4 % and the average scale efficiency score was 87.1 % while the average constant returns to scale technical efficiency score was 79.4 %. Technically inefficient hospitals could have become more efficient by increasing the outpatient department visits by 45,943; and inpatient days by 31,425 without changing the total number of inputs. Alternatively, they would achieve efficiency by for example transferring the excess 216 medical staff and 454 beds to other levels of the health system without changing the total number of outputs. Tobit regression indicates that significant factors in explaining hospital efficiency are: hospital size (p Uganda.
Identifying multiple outliers in linear regression: robust fit and clustering approach
International Nuclear Information System (INIS)
Robiah Adnan; Mohd Nor Mohamad; Halim Setan
2001-01-01
This research provides a clustering based approach for determining potential candidates for outliers. This is modification of the method proposed by Serbert et. al (1988). It is based on using the single linkage clustering algorithm to group the standardized predicted and residual values of data set fit by least trimmed of squares (LTS). (Author)
Energy Technology Data Exchange (ETDEWEB)
Pereyra A, P.; Lopez H, M. E. [Pontificia Universidad Catolica del Peru, Av. Universitaria 1801, San Miguel Lima 32 (Peru); Palacios F, D.; Sajo B, L. [Universidad Simon Bolivar, Laboratorio de Fisica Nuclear, Apartado 89000 Caracas (Venezuela, Bolivarian Republic of); Valdivia, P., E-mail: ppereyr@pucp.edu.pe [Universidad Nacional de Ingenieria, Av. Tupac Amaru s/n, Rimac, Lima 25 (Peru)
2016-10-15
Simulated and measured calibration of PADC detectors is given for cylindrical diffusion chambers employed in environmental radon measurements. The method is based on determining the minimum alpha energy (E{sub min}), average critical angle (<Θ{sub c}>), and fraction of {sup 218}Po atoms; the volume of the chamber (f{sub 1}), are compared to commercially available devices. Radon concentration for exposed detectors is obtained from induced track densities and the well-established calibration coefficient for NRPB monitor. Calibration coefficient of a PADC detector in a cylindrical diffusion chamber of any size is determined under the same chemical etching conditions and track analysis methodology. In this study the results of numerical examples and comparison between experimental calibration coefficients and simulation purpose made code. Results show that the developed method is applicable when uncertainties of 10% are acceptable. (Author)
New approach for calibration and interpretation of IRAD GAGE vibrating-wire stressmeters
International Nuclear Information System (INIS)
Mao, N.
1986-05-01
IRAD GAGE vibrating-wire stressmeters were installed in the Spent Fuel Facility at the Nevada Test Site to measure the change in in-situ stress during the Spent Fuel Test-Climax (SFT-C). This paper discusses the results of removing a cylindrical section of rock and gages as a unit through overcoring, and the subsequent post-test calibration of the stressmeters in the laboratory. The estimated in-situ stresses based on post test calibration data are quite consistent with those directly measured in nearby holes. The magnitude of stress change calculated from pre-test calibration data is generally much smaller than that estimated from post test calibration data. 11 refs., 5 figs., 2 tabs
International Nuclear Information System (INIS)
Pereyra A, P.; Lopez H, M. E.; Palacios F, D.; Sajo B, L.; Valdivia, P.
2016-10-01
Simulated and measured calibration of PADC detectors is given for cylindrical diffusion chambers employed in environmental radon measurements. The method is based on determining the minimum alpha energy (E min ), average critical angle (<Θ c >), and fraction of 218 Po atoms; the volume of the chamber (f 1 ), are compared to commercially available devices. Radon concentration for exposed detectors is obtained from induced track densities and the well-established calibration coefficient for NRPB monitor. Calibration coefficient of a PADC detector in a cylindrical diffusion chamber of any size is determined under the same chemical etching conditions and track analysis methodology. In this study the results of numerical examples and comparison between experimental calibration coefficients and simulation purpose made code. Results show that the developed method is applicable when uncertainties of 10% are acceptable. (Author)
Mazenq, Julie; Dubus, Jean-Christophe; Gaudart, Jean; Charpin, Denis; Viudes, Gilles; Noel, Guilhem
2017-11-01
Particulate matter, nitrogen dioxide (NO 2 ) and ozone are recognized as the three pollutants that most significantly affect human health. Asthma is a multifactorial disease. However, the place of residence has rarely been investigated. We compared the impact of air pollution, measured near patients' homes, on emergency department (ED) visits for asthma or trauma (controls) within the Provence-Alpes-Côte-d'Azur region. Variables were selected using classification and regression trees on asthmatic and control population, 3-99 years, visiting ED from January 1 to December 31, 2013. Then in a nested case control study, randomization was based on the day of ED visit and on defined age groups. Pollution, meteorological, pollens and viral data measured that day were linked to the patient's ZIP code. A total of 794,884 visits were reported including 6250 for asthma and 278,192 for trauma. Factors associated with an excess risk of emergency visit for asthma included short-term exposure to NO 2 , female gender, high viral load and a combination of low temperature and high humidity. Short-term exposures to high NO 2 concentrations, as assessed close to the homes of the patients, were significantly associated with asthma-related ED visits in children and adults. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Tetsuya Matsubayashi
Full Text Available Evidence collected in many parts of the world suggests that, compared to older students, students who are relatively younger at school entry tend to have worse academic performance and lower levels of income. This study examined how relative age in a grade affects suicide rates of adolescents and young adults between 15 and 25 years of age using data from Japan.We examined individual death records in the Vital Statistics of Japan from 1989 to 2010. In contrast to other countries, late entry to primary school is not allowed in Japan. We took advantage of the school entry cutoff date to implement a regression discontinuity (RD design, assuming that the timing of births around the school entry cutoff date was randomly determined and therefore that individuals who were born just before and after the cutoff date have similar baseline characteristics.We found that those who were born right before the school cutoff day and thus youngest in their cohort have higher mortality rates by suicide, compared to their peers who were born right after the cutoff date and thus older. We also found that those with relative age disadvantage tend to follow a different career path than those with relative age advantage, which may explain their higher suicide mortality rates.Relative age effects have broader consequences than was previously supposed. This study suggests that policy intervention that alleviates the relative age effect can be important.
Simultaneous Calibration: A Joint Optimization Approach for Multiple Kinect and External Cameras
Directory of Open Access Journals (Sweden)
Yajie Liao
2017-06-01
Full Text Available Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are targeted at joint calibration of multi-sensors (more than four devices, which normally is a practical issue in the real-time systems. In this paper, we propose a novel method and a corresponding workflow framework to simultaneously calibrate relative poses of a Kinect and three external cameras. By optimizing the final cost function and adding corresponding weights to the external cameras in different locations, an effective joint calibration of multiple devices is constructed. Furthermore, the method is tested in a practical platform, and experiment results show that the proposed joint calibration method can achieve a satisfactory performance in a project real-time system and its accuracy is higher than the manufacturer’s calibration.
A kernel regression approach to gene-gene interaction detection for case-control studies.
Larson, Nicholas B; Schaid, Daniel J
2013-11-01
Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.
Trombetti, Tomaso
This thesis presents an Experimental/Analytical approach to modeling and calibrating shaking tables for structural dynamic applications. This approach was successfully applied to the shaking table recently built in the structural laboratory of the Civil Engineering Department at Rice University. This shaking table is capable of reproducing model earthquake ground motions with a peak acceleration of 6 g's, a peak velocity of 40 inches per second, and a peak displacement of 3 inches, for a maximum payload of 1500 pounds. It has a frequency bandwidth of approximately 70 Hz and is designed to test structural specimens up to 1/5 scale. The rail/table system is mounted on a reaction mass of about 70,000 pounds consisting of three 12 ft x 12 ft x 1 ft reinforced concrete slabs, post-tensioned together and connected to the strong laboratory floor. The slip table is driven by a hydraulic actuator governed by a 407 MTS controller which employs a proportional-integral-derivative-feedforward-differential pressure algorithm to control the actuator displacement. Feedback signals are provided by two LVDT's (monitoring the slip table relative displacement and the servovalve main stage spool position) and by one differential pressure transducer (monitoring the actuator force). The dynamic actuator-foundation-specimen system is modeled and analyzed by combining linear control theory and linear structural dynamics. The analytical model developed accounts for the effects of actuator oil compressibility, oil leakage in the actuator, time delay in the response of the servovalve spool to a given electrical signal, foundation flexibility, and dynamic characteristics of multi-degree-of-freedom specimens. In order to study the actual dynamic behavior of the shaking table, the transfer function between target and actual table accelerations were identified using experimental results and spectral estimation techniques. The power spectral density of the system input and the cross power spectral
RGB Color Calibration for Quantitative Image Analysis: The “3D Thin-Plate Spline” Warping Approach
Menesatti, Paolo; Angelini, Claudio; Pallottino, Federico; Antonucci, Francesca; Aguzzi, Jacopo; Costa, Corrado
2012-01-01
In the last years the need to numerically define color by its coordinates in n-dimensional space has increased strongly. Colorimetric calibration is fundamental in food processing and other biological disciplines to quantitatively compare samples' color during workflow with many devices. Several software programmes are available to perform standardized colorimetric procedures, but they are often too imprecise for scientific purposes. In this study, we applied the Thin-Plate Spline interpolation algorithm to calibrate colours in sRGB space (the corresponding Matlab code is reported in the Appendix). This was compared with other two approaches. The first is based on a commercial calibration system (ProfileMaker) and the second on a Partial Least Square analysis. Moreover, to explore device variability and resolution two different cameras were adopted and for each sensor, three consecutive pictures were acquired under four different light conditions. According to our results, the Thin-Plate Spline approach reported a very high efficiency of calibration allowing the possibility to create a revolution in the in-field applicative context of colour quantification not only in food sciences, but also in other biological disciplines. These results are of great importance for scientific color evaluation when lighting conditions are not controlled. Moreover, it allows the use of low cost instruments while still returning scientifically sound quantitative data. PMID:22969337
RGB Color Calibration for Quantitative Image Analysis: The “3D Thin-Plate Spline” Warping Approach
Directory of Open Access Journals (Sweden)
Corrado Costa
2012-05-01
Full Text Available In the last years the need to numerically define color by its coordinates in n-dimensional space has increased strongly. Colorimetric calibration is fundamental in food processing and other biological disciplines to quantitatively compare samples’ color during workflow with many devices. Several software programmes are available to perform standardized colorimetric procedures, but they are often too imprecise for scientific purposes. In this study, we applied the Thin-Plate Spline interpolation algorithm to calibrate colours in sRGB space (the corresponding Matlab code is reported in the Appendix. This was compared with other two approaches. The first is based on a commercial calibration system (ProfileMaker and the second on a Partial Least Square analysis. Moreover, to explore device variability and resolution two different cameras were adopted and for each sensor, three consecutive pictures were acquired under four different light conditions. According to our results, the Thin-Plate Spline approach reported a very high efficiency of calibration allowing the possibility to create a revolution in the in-field applicative context of colour quantification not only in food sciences, but also in other biological disciplines. These results are of great importance for scientific color evaluation when lighting conditions are not controlled. Moreover, it allows the use of low cost instruments while still returning scientifically sound quantitative data.
International Nuclear Information System (INIS)
Zhang, Yue-Jun; Peng, Hua-Rong; Liu, Zhao; Tan, Weiping
2015-01-01
The transport sector appears a main energy consumer in China and plays a significant role in energy conservation. Improving energy efficiency proves an effective way to reduce energy consumption in transport sector, whereas its effectiveness may be affected by the rebound effect. This paper proposes a dynamic panel quantile regression model to estimate the direct energy rebound effect for road passenger transport in the whole country, eastern, central and western China, respectively, based on the data of 30 provinces from 2003 to 2012. The empirical results reveal that, first of all, the direct rebound effect does exist for road passenger transport and on the whole country, the short-term and long-term direct rebound effects are 25.53% and 26.56% on average, respectively. Second, the direct rebound effect for road passenger transport in central and eastern China tends to decrease, increase and then decrease again, whereas that in western China decreases and then increases, with the increasing passenger kilometers. Finally, when implementing energy efficiency policy in road passenger transport sector, the effectiveness of energy conservation in western China proves much better than that in central China overall, while the effectiveness in central China is relatively better than that in eastern China. - Highlights: • The direct rebound effect (RE) for road passenger transport in China is estimated. • The direct RE in the whole country, eastern, central, and western China is analyzed. • The short and long-term direct REs are 25.53% and 26.56% within the sample period. • Western China has better energy-saving performance than central and eastern China.
Consistency analysis of subspace identification methods based on a linear regression approach
DEFF Research Database (Denmark)
Knudsen, Torben
2001-01-01
In the literature results can be found which claim consistency for the subspace method under certain quite weak assumptions. Unfortunately, a new result gives a counter example showing inconsistency under these assumptions and then gives new more strict sufficient assumptions which however does n...... not include important model structures as e.g. Box-Jenkins. Based on a simple least squares approach this paper shows the possible inconsistency under the weak assumptions and develops only slightly stricter assumptions sufficient for consistency and which includes any model structure...
Gu, Huidong; Liu, Guowen; Wang, Jian; Aubry, Anne-Françoise; Arnold, Mark E
2014-09-16
A simple procedure for selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bioanalytical LC-MS/MS assays is reported. The correct weighting factor is determined by the relationship between the standard deviation of instrument responses (σ) and the concentrations (x). The weighting factor of 1, 1/x, or 1/x(2) should be selected if, over the entire concentration range, σ is a constant, σ(2) is proportional to x, or σ is proportional to x, respectively. For the first time, we demonstrated with detailed scientific reasoning, solid historical data, and convincing justification that 1/x(2) should always be used as the weighting factor for all bioanalytical LC-MS/MS assays. The impacts of using incorrect weighting factors on curve stability, data quality, and assay performance were thoroughly investigated. It was found that the most stable curve could be obtained when the correct weighting factor was used, whereas other curves using incorrect weighting factors were unstable. It was also found that there was a very insignificant impact on the concentrations reported with calibration curves using incorrect weighting factors as the concentrations were always reported with the passing curves which actually overlapped with or were very close to the curves using the correct weighting factor. However, the use of incorrect weighting factors did impact the assay performance significantly. Finally, the difference between the weighting factors of 1/x(2) and 1/y(2) was discussed. All of the findings can be generalized and applied into other quantitative analysis techniques using calibration curves with weighted least-squares regression algorithm.
International Nuclear Information System (INIS)
Chen, Kuilin; Yu, Jie
2014-01-01
Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations
Najera-Zuloaga, Josu; Lee, Dae-Jin; Arostegui, Inmaculada
2017-01-01
Health-related quality of life has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of health-related quality of life with patients and disease characteristics has become one of the primary aims of many health-related quality of life studies. Health-related quality of life scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data; however, the beta-binomial regression has been performed by means of two different approaches in the literature: (i) beta-binomial distribution with a logistic link; and (ii) hierarchical generalized linear models. None of the existing literature in the analysis of health-related quality of life survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of health-related quality of life outcomes in patients with Chronic Obstructive Pulmonary Disease, where the use of both approaches yields to contradictory results in terms of covariate effects significance and consequently the interpretation of the most relevant factors in health-related quality of life. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of health-related quality of life survey data for practitioners, providing an R package.
Directory of Open Access Journals (Sweden)
Jianhua Cheng
2015-02-01
Full Text Available To obtain the absolute position of a target is one of the basic topics for non-cooperated target tracking problems. In this paper, we present a simultaneously calibration method for an Inertial navigation system (INS/Global position system (GPS/Laser distance scanner (LDS integrated system based target positioning approach. The INS/GPS integrated system provides the attitude and position of observer, and LDS offers the distance between the observer and the target. The two most significant errors are taken into jointly consideration and analyzed: (1 the attitude measure error of INS/GPS; (2 the installation error between INS/GPS and LDS subsystems. Consequently, a INS/GPS/LDS based target positioning approach considering these two errors is proposed. In order to improve the performance of this approach, a novel calibration method is designed to simultaneously estimate and compensate these two main errors. Finally, simulations are conducted to access the performance of the proposed target positioning approach and the designed simultaneously calibration method.
Cheng, Jianhua; Chen, Daidai; Sun, Xiangyu; Wang, Tongda
2015-02-04
To obtain the absolute position of a target is one of the basic topics for non-cooperated target tracking problems. In this paper, we present a simultaneously calibration method for an Inertial navigation system (INS)/Global position system (GPS)/Laser distance scanner (LDS) integrated system based target positioning approach. The INS/GPS integrated system provides the attitude and position of observer, and LDS offers the distance between the observer and the target. The two most significant errors are taken into jointly consideration and analyzed: (1) the attitude measure error of INS/GPS; (2) the installation error between INS/GPS and LDS subsystems. Consequently, a INS/GPS/LDS based target positioning approach considering these two errors is proposed. In order to improve the performance of this approach, a novel calibration method is designed to simultaneously estimate and compensate these two main errors. Finally, simulations are conducted to access the performance of the proposed target positioning approach and the designed simultaneously calibration method.
Education-Based Gaps in eHealth: A Weighted Logistic Regression Approach.
Amo, Laura
2016-10-12
Persons with a college degree are more likely to engage in eHealth behaviors than persons without a college degree, compounding the health disadvantages of undereducated groups in the United States. However, the extent to which quality of recent eHealth experience reduces the education-based eHealth gap is unexplored. The goal of this study was to examine how eHealth information search experience moderates the relationship between college education and eHealth behaviors. Based on a nationally representative sample of adults who reported using the Internet to conduct the most recent health information search (n=1458), I evaluated eHealth search experience in relation to the likelihood of engaging in different eHealth behaviors. I examined whether Internet health information search experience reduces the eHealth behavior gaps among college-educated and noncollege-educated adults. Weighted logistic regression models were used to estimate the probability of different eHealth behaviors. College education was significantly positively related to the likelihood of 4 eHealth behaviors. In general, eHealth search experience was negatively associated with health care behaviors, health information-seeking behaviors, and user-generated or content sharing behaviors after accounting for other covariates. Whereas Internet health information search experience has narrowed the education gap in terms of likelihood of using email or Internet to communicate with a doctor or health care provider and likelihood of using a website to manage diet, weight, or health, it has widened the education gap in the instances of searching for health information for oneself, searching for health information for someone else, and downloading health information on a mobile device. The relationship between college education and eHealth behaviors is moderated by Internet health information search experience in different ways depending on the type of eHealth behavior. After controlling for college
Tomczyk, Aleksandra; Ewertowski, Marek; White, Piran; Kasprzak, Leszek
2016-04-01
The dual role of many Protected Natural Areas in providing benefits for both conservation and recreation poses challenges for management. Although recreation-based damage to ecosystems can occur very quickly, restoration can take many years. The protection of conservation interests at the same as providing for recreation requires decisions to be made about how to prioritise and direct management actions. Trails are commonly used to divert visitors from the most important areas of a site, but high visitor pressure can lead to increases in trail width and a concomitant increase in soil erosion. Here we use detailed field data on condition of recreational trails in Gorce National Park, Poland, as the basis for a regression tree analysis to determine the factors influencing trail deterioration, and link specific trail impacts with environmental, use related and managerial factors. We distinguished 12 types of trails, characterised by four levels of degradation: (1) trails with an acceptable level of degradation; (2) threatened trails; (3) damaged trails; and (4) heavily damaged trails. Damaged trails were the most vulnerable of all trails and should be prioritised for appropriate conservation and restoration. We also proposed five types of monitoring of recreational trail conditions: (1) rapid inventory of negative impacts; (2) monitoring visitor numbers and variation in type of use; (3) change-oriented monitoring focusing on sections of trail which were subjected to changes in type or level of use or subjected to extreme weather events; (4) monitoring of dynamics of trail conditions; and (5) full assessment of trail conditions, to be carried out every 10-15 years. The application of the proposed framework can enhance the ability of Park managers to prioritise their trail management activities, enhancing trail conditions and visitor safety, while minimising adverse impacts on the conservation value of the ecosystem. A.M.T. was supported by the Polish Ministry of
Ryu, Duchwan; Li, Erning; Mallick, Bani K.
2010-01-01
" 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.
Model independent approach to the single photoelectron calibration of photomultiplier tubes
Energy Technology Data Exchange (ETDEWEB)
Saldanha, R.; Grandi, L.; Guardincerri, Y.; Wester, T.
2017-08-01
The accurate calibration of photomultiplier tubes is critical in a wide variety of applications in which it is necessary to know the absolute number of detected photons or precisely determine the resolution of the signal. Conventional calibration methods rely on fitting the photomultiplier response to a low intensity light source with analytical approximations to the single photoelectron distribution, often leading to biased estimates due to the inability to accurately model the full distribution, especially at low charge values. In this paper we present a simple statistical method to extract the relevant single photoelectron calibration parameters without making any assumptions about the underlying single photoelectron distribution. We illustrate the use of this method through the calibration of a Hamamatsu R11410 photomultiplier tube and study the accuracy and precision of the method using Monte Carlo simulations. The method is found to have significantly reduced bias compared to conventional methods and works under a wide range of light intensities, making it suitable for simultaneously calibrating large arrays of photomultiplier tubes.
Jumadi, Nur Anida; Beng, Gan Kok; Ali, Mohd Alauddin Mohd; Zahedi, Edmond; Morsin, Marlia
2017-09-01
The implementation of surface-based Monte Carlo simulation technique for oxygen saturation (SaO2) calibration curve estimation is demonstrated in this paper. Generally, the calibration curve is estimated either from the empirical study using animals as the subject of experiment or is derived from mathematical equations. However, the determination of calibration curve using animal is time consuming and requires expertise to conduct the experiment. Alternatively, an optical simulation technique has been used widely in the biomedical optics field due to its capability to exhibit the real tissue behavior. The mathematical relationship between optical density (OD) and optical density ratios (ODR) associated with SaO2 during systole and diastole is used as the basis of obtaining the theoretical calibration curve. The optical properties correspond to systolic and diastolic behaviors were applied to the tissue model to mimic the optical properties of the tissues. Based on the absorbed ray flux at detectors, the OD and ODR were successfully calculated. The simulation results of optical density ratio occurred at every 20 % interval of SaO2 is presented with maximum error of 2.17 % when comparing it with previous numerical simulation technique (MC model). The findings reveal the potential of the proposed method to be used for extended calibration curve study using other wavelength pair.
Semiparametric approach for non-monotone missing covariates in a parametric regression model
Sinha, Samiran
2014-02-26
Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semiparametric method for handling non-monotone patterns of missing data. The proposed method relies on the assumption that the missingness mechanism of a variable does not depend on the missing variable itself but may depend on the other missing variables. This mechanism is somewhat less general than the completely non-ignorable mechanism but is sometimes more flexible than the missing at random mechanism where the missingness mechansim is allowed to depend only on the completely observed variables. The proposed approach is robust to misspecification of the distribution of the missing covariates, and the proposed mechanism helps to nullify (or reduce) the problems due to non-identifiability that result from the non-ignorable missingness mechanism. The asymptotic properties of the proposed estimator are derived. Finite sample performance is assessed through simulation studies. Finally, for the purpose of illustration we analyze an endometrial cancer dataset and a hip fracture dataset.
Regressive Prediction Approach to Vertical Handover in Fourth Generation Wireless Networks
Directory of Open Access Journals (Sweden)
Abubakar M. Miyim
2014-11-01
Full Text Available The over increasing demand for deployment of wireless access networks has made wireless mobile devices to face so many challenges in choosing the best suitable network from a set of available access networks. Some of the weighty issues in 4G wireless networks are fastness and seamlessness in handover process. This paper therefore, proposes a handover technique based on movement prediction in wireless mobile (WiMAX and LTE-A environment. The technique enables the system to predict signal quality between the UE and Radio Base Stations (RBS/Access Points (APs in two different networks. Prediction is achieved by employing the Markov Decision Process Model (MDPM where the movement of the UE is dynamically estimated and averaged to keep track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The performances of various handover approaches influenced by different metrics (mobility velocities were evaluated. The results presented demonstrate good accuracy the proposed method was able to achieve in predicting the next signal level by reducing the total handover latency.
The New Approach to Camera Calibration – GCPs or TLS Data?
Directory of Open Access Journals (Sweden)
J. Markiewicz
2016-06-01
Full Text Available Camera calibration is one of the basic photogrammetric tasks responsible for the quality of processed products. The majority of calibration is performed with a specially designed test field or during the self-calibration process. The research presented in this paper aims to answer the question of whether it is necessary to use control points designed in the standard way for determination of camera interior orientation parameters. Data from close-range laser scanning can be used as an alternative. The experiments shown in this work demonstrate the potential of laser measurements, since the number of points that may be involved in the calculation is much larger than that of commonly used ground control points. The problem which still exists is the correct and automatic identification of object details in the image, taken with a tested camera, as well as in the data set registered with the laser scanner.
The Public-Private Sector Wage Gap in Zambia in the 1990s: A Quantile Regression Approach
DEFF Research Database (Denmark)
Nielsen, Helena Skyt; Rosholm, Michael
2001-01-01
of economic transition, because items as privatization and deregulation were on the political agenda. The focus is placed on the public-private sector wage gap, and the results show that this gap was relatively favorable for the low-skilled and less favorable for the high-skilled. This picture was further......We investigate the determinants of wages in Zambia and based on the quantile regression approach, we analyze how their effects differ at different points in the wage distribution and over time. We use three cross-sections of Zambian household data from the early nineties, which was a period...
Multi-objective calibration of a reservoir model: aggregation and non-dominated sorting approaches
Huang, Y.
2012-12-01
Numerical reservoir models can be helpful tools for water resource management. These models are generally calibrated against historical measurement data made in reservoirs. In this study, two methods are proposed for the multi-objective calibration of such models: aggregation and non-dominated sorting methods. Both methods use a hybrid genetic algorithm as an optimization engine and are different in fitness assignment. In the aggregation method, a weighted sum of scaled simulation errors is designed as an overall objective function to measure the fitness of solutions (i.e. parameter values). The contribution of this study to the aggregation method is the correlation analysis and its implication to the choice of weight factors. In the non-dominated sorting method, a novel method based on non-dominated sorting and the method of minimal distance is used to calculate the dummy fitness of solutions. The proposed methods are illustrated using a water quality model that was set up to simulate the water quality of Pepacton Reservoir, which is located to the north of New York City and is used for water supply of city. The study also compares the aggregation and the non-dominated sorting methods. The purpose of this comparison is not to evaluate the pros and cons between the two methods but to determine whether the parameter values, objective function values (simulation errors) and simulated results obtained are significantly different with each other. The final results (objective function values) from the two methods are good compromise between all objective functions, and none of these results are the worst for any objective function. The calibrated model provides an overall good performance and the simulated results with the calibrated parameter values match the observed data better than the un-calibrated parameters, which supports and justifies the use of multi-objective calibration. The results achieved in this study can be very useful for the calibration of water
Directory of Open Access Journals (Sweden)
Defeng Wu
2016-08-01
Full Text Available A robot-based three-dimensional (3D measurement system is presented. In the presented system, a structured light vision sensor is mounted on the arm of an industrial robot. Measurement accuracy is one of the most important aspects of any 3D measurement system. To improve the measuring accuracy of the structured light vision sensor, a novel sensor calibration approach is proposed to improve the calibration accuracy. The approach is based on a number of fixed concentric circles manufactured in a calibration target. The concentric circle is employed to determine the real projected centres of the circles. Then, a calibration point generation procedure is used with the help of the calibrated robot. When enough calibration points are ready, the radial alignment constraint (RAC method is adopted to calibrate the camera model. A multilayer perceptron neural network (MLPNN is then employed to identify the calibration residuals after the application of the RAC method. Therefore, the hybrid pinhole model and the MLPNN are used to represent the real camera model. Using a standard ball to validate the effectiveness of the presented technique, the experimental results demonstrate that the proposed novel calibration approach can achieve a highly accurate model of the structured light vision sensor.
A new approach for interexaminer reliability data analysis on dental caries calibration
Directory of Open Access Journals (Sweden)
Andréa Videira Assaf
2007-12-01
Full Text Available Objectives: a to evaluate the interexaminer reliability in caries detection considering different diagnostic thresholds and b to indicate, by using Kappa statistics, the best way of measuring interexaminer agreement during the calibration process in dental caries surveys. Methods: Eleven dentists participated in the initial training, which was divided into theoretical discussions and practical activities, and calibration exercises, performed at baseline, 3 and 6 months after the initial training. For the examinations of 6-7-year-old schoolchildren, the World Health Organization (WHO recommendations were followed and different diagnostic thresholds were used: WHO (decayed/missing/filled teeth - DMFT index and WHO + IL (initial lesion diagnostic thresholds. The interexaminer reliability was calculated by Kappa statistics, according to WHO and WHO+IL thresholds considering: a the entire dentition; b upper/lower jaws; c sextants; d each tooth individually. Results: Interexaminer reliability was high for both diagnostic thresholds; nevertheless, it decreased in all calibration sections when considering teeth individually. Conclusion: The interexaminer reliability was possible during the period of 6 months, under both caries diagnosis thresholds. However, great disagreement was observed for posterior teeth, especially using the WHO+IL criteria. Analysis considering dental elements individually was the best way of detecting interexaminer disagreement during the calibration sections.
A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies
Directory of Open Access Journals (Sweden)
Jingyuan Zhao
2012-01-01
Full Text Available We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by using L1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD penalty (Fan and Li, 2001 and Jeffrey’s Prior penalty (Firth, 1993, a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008. The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005 and the LASSO-patternsearch algorithm (Shi et al. 2007.
Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.
2015-12-01
Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be
Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach.
Stollenwerk, Björn; Welchowski, Thomas; Vogl, Matthias; Stock, Stephanie
2016-04-01
Despite the increasing availability of routine data, no analysis method has yet been presented for cost-of-illness (COI) studies based on massive data. We aim, first, to present such a method and, second, to assess the relevance of the associated gain in numerical efficiency. We propose a prevalence-based, top-down regression approach consisting of five steps: aggregating the data; fitting a generalized additive model (GAM); predicting costs via the fitted GAM; comparing predicted costs between prevalent and non-prevalent subjects; and quantifying the stochastic uncertainty via error propagation. To demonstrate the method, it was applied to aggregated data in the context of chronic lung disease to German sickness funds data (from 1999), covering over 7.3 million insured. To assess the gain in numerical efficiency, the computational time of the innovative approach has been compared with corresponding GAMs applied to simulated individual-level data. Furthermore, the probability of model failure was modeled via logistic regression. Applying the innovative method was reasonably fast (19 min). In contrast, regarding patient-level data, computational time increased disproportionately by sample size. Furthermore, using patient-level data was accompanied by a substantial risk of model failure (about 80 % for 6 million subjects). The gain in computational efficiency of the innovative COI method seems to be of practical relevance. Furthermore, it may yield more precise cost estimates.
Sumner, Anne E; Luercio, Marcella F; Frempong, Barbara A; Ricks, Madia; Sen, Sabyasachi; Kushner, Harvey; Tulloch-Reid, Marshall K
2009-02-01
The disposition index, the product of the insulin sensitivity index (S(I)) and the acute insulin response to glucose, is linked in African Americans to chromosome 11q. This link was determined with S(I) calculated with the nonlinear regression approach to the minimal model and data from the reduced-sample insulin-modified frequently-sampled intravenous glucose tolerance test (Reduced-Sample-IM-FSIGT). However, the application of the nonlinear regression approach to calculate S(I) using data from the Reduced-Sample-IM-FSIGT has been challenged as being not only inaccurate but also having a high failure rate in insulin-resistant subjects. Our goal was to determine the accuracy and failure rate of the Reduced-Sample-IM-FSIGT using the nonlinear regression approach to the minimal model. With S(I) from the Full-Sample-IM-FSIGT considered the standard and using the nonlinear regression approach to the minimal model, we compared the agreement between S(I) from the Full- and Reduced-Sample-IM-FSIGT protocols. One hundred African Americans (body mass index, 31.3 +/- 7.6 kg/m(2) [mean +/- SD]; range, 19.0-56.9 kg/m(2)) had FSIGTs. Glucose (0.3 g/kg) was given at baseline. Insulin was infused from 20 to 25 minutes (total insulin dose, 0.02 U/kg). For the Full-Sample-IM-FSIGT, S(I) was calculated based on the glucose and insulin samples taken at -1, 1, 2, 3, 4, 5, 6, 7, 8,10, 12, 14, 16, 19, 22, 23, 24, 25, 27, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, and 180 minutes. For the Reduced-Sample-FSIGT, S(I) was calculated based on the time points that appear in bold. Agreement was determined by Spearman correlation, concordance, and the Bland-Altman method. In addition, for both protocols, the population was divided into tertiles of S(I). Insulin resistance was defined by the lowest tertile of S(I) from the Full-Sample-IM-FSIGT. The distribution of subjects across tertiles was compared by rank order and kappa statistic. We found that the rate of failure of resolution of S(I) by
International Nuclear Information System (INIS)
Pradels, Gregory; Touboul, Pierre
2003-01-01
The MICROSCOPE mission is a space experiment of fundamental physics which aims to test the equality between the gravitational and inertial mass with a 10 -15 accuracy. Considering these scientific objectives, very weak accelerations have to be controlled and measured in orbit. By modelling the expected acceleration signals applied to the MICROSCOPE instrument in orbit, the developed analytic model of the mission measurement shows the requirements for instrument calibration. Because of on-ground perturbations, the instrument cannot be calibrated in the laboratory and an in-orbit procedure has to be defined. The proposed approach exploits the drag-free system of the satellite and is an important element of the future data analysis of the MICROSCOPE space experiment
浅野, 美代子; マーコ, ユー K.W.
2007-01-01
This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...
Development of self-calibration techniques for on-wafer and fixtured measurements: a novel approach
Pradell i Cara, Lluís; Purroy Martín, Francesc; Cáceres, M.
1992-01-01
Network Analyzer self-calibration techniques - TRL, LMR, TAR- are developed, implemented and compared in several transmission media. A novel LMR (Line-Match-Reflect) technique based on known LINE and REFLECT Standards, is proposed and compared to conventional LMR (based on known LINE and MATCH Standards) and other techniques (TRL, TAR). They are applied to on-wafer S-parameter measurement as well as to coaxial, waveguide and microstrip media. Experimental results up to 40 GHz are presented. ...
An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes
Energy Technology Data Exchange (ETDEWEB)
Lewis, Allison, E-mail: lewis.allison10@gmail.com [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Smith, Ralph [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Williams, Brian [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Figueroa, Victor [Sandia National Laboratories, Albuquerque, NM 87185 (United States)
2016-11-01
For many simulation models, it can be prohibitively expensive or physically infeasible to obtain a complete set of experimental data to calibrate model parameters. In such cases, one can alternatively employ validated higher-fidelity codes to generate simulated data, which can be used to calibrate the lower-fidelity code. In this paper, we employ an information-theoretic framework to determine the reduction in parameter uncertainty that is obtained by evaluating the high-fidelity code at a specific set of design conditions. These conditions are chosen sequentially, based on the amount of information that they contribute to the low-fidelity model parameters. The goal is to employ Bayesian experimental design techniques to minimize the number of high-fidelity code evaluations required to accurately calibrate the low-fidelity model. We illustrate the performance of this framework using heat and diffusion examples, a 1-D kinetic neutron diffusion equation, and a particle transport model, and include initial results from the integration of the high-fidelity thermal-hydraulics code Hydra-TH with a low-fidelity exponential model for the friction correlation factor.
Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu
2018-02-01
A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.
Fatekurohman, Mohamat; Nurmala, Nita; Anggraeni, Dian
2018-04-01
Lungs are the most important organ, in the case of respiratory system. Problems related to disorder of the lungs are various, i.e. pneumonia, emphysema, tuberculosis and lung cancer. Comparing all those problems, lung cancer is the most harmful. Considering about that, the aim of this research applies survival analysis and factors affecting the endurance of the lung cancer patient using comparison of exact, Efron and Breslow parameter approach method on hazard ratio and stratified cox regression model. The data applied are based on the medical records of lung cancer patients in Jember Paru-paru hospital on 2016, east java, Indonesia. The factors affecting the endurance of the lung cancer patients can be classified into several criteria, i.e. sex, age, hemoglobin, leukocytes, erythrocytes, sedimentation rate of blood, therapy status, general condition, body weight. The result shows that exact method of stratified cox regression model is better than other. On the other hand, the endurance of the patients is affected by their age and the general conditions.
Directory of Open Access Journals (Sweden)
Zhao Hongbo
2009-09-01
Full Text Available Abstract Background It is quite common that the genetic architecture of complex traits involves many genes and their interactions. Therefore, dealing with multiple unlinked genomic regions simultaneously is desirable. Results In this paper we develop a regression-based approach to assess the interactions of haplotypes that belong to different unlinked regions, and we use score statistics to test the null hypothesis of non-genetic association. Additionally, multiple marker combinations at each unlinked region are considered. The multiple tests are settled via the minP approach. The P value of the "best" multi-region multi-marker configuration is corrected via Monte-Carlo simulations. Through simulation studies, we assess the performance of the proposed approach and demonstrate its validity and power in testing for haplotype interaction association. Conclusion Our simulations showed that, for binary trait without covariates, our proposed methods prove to be equal and even more powerful than htr and hapcc which are part of the FAMHAP program. Additionally, our model can be applied to a wider variety of traits and allow adjustment for other covariates. To test the validity, our methods are applied to analyze the association between four unlinked candidate genes and pig meat quality.
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
A calibration approach to glandular tissue composition estimation in digital mammography
International Nuclear Information System (INIS)
Kaufhold, J.; Thomas, J.A.; Eberhard, J.W.; Galbo, C.E.; Trotter, D.E. Gonzalez
2002-01-01
The healthy breast is almost entirely composed of a mixture of fatty, epithelial, and stromal tissues which can be grouped into two distinctly attenuating tissue types: fatty and glandular. Further, the amount of glandular tissue is linked to breast cancer risk, so an objective quantitative analysis of glandular tissue can aid in risk estimation. Highnam and Brady have measured glandular tissue composition objectively. However, they argue that their work should only be used for 'relative' tissue measurements unless a careful calibration has been performed. In this work, we perform such a 'careful calibration' on a digital mammography system and use it to estimate breast tissue composition of patient breasts. We imaged 0%, 50%, and 100% glandular-equivalent phantoms of varying thicknesses for a number of clinically relevant x-ray techniques on a digital mammography system. From these images, we extracted mean signal and noise levels and computed calibration curves that can be used for quantitative tissue composition estimation. In this way, we calculate the percent glandular composition of a patient breast on a pixelwise basis. This tissue composition estimation method was applied to 23 digital mammograms. We estimated the quantitative impact of different error sources on the estimates of tissue composition. These error sources include compressed breast height estimation error, residual scattered radiation, quantum noise, and beam hardening. Errors in the compressed breast height estimate contribute the most error in tissue composition--on the order of ±7% for a 4 cm compressed breast height. The spatially varying scattered radiation will contribute quantitatively less error overall, but may be significant in regions near the skinline. It is calculated that for a 4 cm compressed breast height, a residual scatter signal error is mitigated by approximately sixfold in the composition estimate. The error in composition due to the quantum noise, which is the limiting
Planktonic Foraminifera Proxies Calibration Off the NW Iberian Margin: Nutrients Approach
Salgueiro, E.; Castro, C. G.; Zuniga, D.; Martin, P. A.; Groeneveld, J.; de la Granda, F.; Villaceiros-Robineau, N.; Alonso-Perez, F.; Alberto, A.; Rodrigues, T.; Rufino, M. M.; Abrantes, F. F. G.; Voelker, A. H. L.
2014-12-01
Planktonic foraminifera (PF) shells preserved in marine sediments are a useful tool to reconstruct productivity conditions at different geological timescales. However, the accuracy of these paleoreconstructions depends on the data set and calibration quality. Several calibration works have been defining and improving the use of proxies for productivity and nutrient cycling parameters. Our contribution is centred on a multi-proxy calibration at a regional coastal upwelling system. To minimize the existing uncertainties affecting the use of trace elements and C stable isotopes as productivity proxy in the high productivity upwelling areas, we investigate the content and distribution of Ba/Ca and δ13C in the water column, its transference into the planktonic foraminifera shells, and, how the living planktonic foraminifera Ba/Ca and δ13C signal is related to the same planktonic foraminiferal species preserved in the sediment record. This study is based on a large data set from two stations (RAIA - 75m water depth, and CALIBERIA - 350m water depth) located off the NW Iberian margin (41.5-42.5ºN; 9-10ºW), and includes: i) two year monthly water column data (temperature, salinity, nutrients, chlorophyll a, Ba/Ca, and δ13C-DIC); ii) seasonal Ba/Ca, δ13C in several living PF species at both stations; and iii) Ba/Ca and δ13C in several PF species from a large set of core-top sediment samples in the study region. Additionally, total organic carbon and total alkenones were also measured in the sediment. Our results showed the link between productivity proxies in the surface sediment foraminifera assemblage and the processes regulating the actual phytoplankton dynamics in an upwelling area. The understanding of this relationship has special relevance since it gives fundamental information related to the past oceanic biogeochemistry and/or climate and improves the prevision of future changes against possible climate variability due to anthropogenic forcing.
Asadzadeh, M.; Maclean, A.; Tolson, B. A.; Burn, D. H.
2009-05-01
Hydrologic model calibration aims to find a set of parameters that adequately simulates observations of watershed behavior, such as streamflow, or a state variable, such as snow water equivalent (SWE). There are different metrics for evaluating calibration effectiveness that involve quantifying prediction errors, such as the Nash-Sutcliffe (NS) coefficient and bias evaluated for the entire calibration period, on a seasonal basis, for low flows, or for high flows. Many of these metrics are conflicting such that the set of parameters that maximizes the high flow NS differs from the set of parameters that maximizes the low flow NS. Conflicting objectives are very likely when different calibration objectives are based on different fluxes and/or state variables (e.g., NS based on streamflow versus SWE). One of the most popular ways to balance different metrics is to aggregate them based on their importance and find the set of parameters that optimizes a weighted sum of the efficiency metrics. Comparing alternative hydrologic models (e.g., assessing model improvement when a process or more detail is added to the model) based on the aggregated objective might be misleading since it represents one point on the tradeoff of desired error metrics. To derive a more comprehensive model comparison, we solved a bi-objective calibration problem to estimate the tradeoff between two error metrics for each model. Although this approach is computationally more expensive than the aggregation approach, it results in a better understanding of the effectiveness of selected models at each level of every error metric and therefore provides a better rationale for judging relative model quality. The two alternative models used in this study are two MESH hydrologic models (version 1.2) of the Wolf Creek Research basin that differ in their watershed spatial discretization (a single Grouped Response Unit, GRU, versus multiple GRUs). The MESH model, currently under development by Environment
CSIR Research Space (South Africa)
Pretorius, W
2014-01-01
Full Text Available the relationship more accurately in terms of MSE, RMSE and MAE, than a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict p...
Koh, T S; Wu, X Y; Cheong, L H; Lim, C C T
2004-12-01
The assessment of tissue perfusion by dynamic contrast-enhanced (DCE) imaging involves a deconvolution process. For analysis of DCE imaging data, we implemented a regression approach to select appropriate regularization parameters for deconvolution using the standard and generalized singular value decomposition methods. Monte Carlo simulation experiments were carried out to study the performance and to compare with other existing methods used for deconvolution analysis of DCE imaging data. The present approach is found to be robust and reliable at the levels of noise commonly encountered in DCE imaging, and for different models of the underlying tissue vasculature. The advantages of the present method, as compared with previous methods, include its efficiency of computation, ability to achieve adequate regularization to reproduce less noisy solutions, and that it does not require prior knowledge of the noise condition. The proposed method is applied on actual patient study cases with brain tumors and ischemic stroke, to illustrate its applicability as a clinical tool for diagnosis and assessment of treatment response.
Directory of Open Access Journals (Sweden)
M. Adduci
2014-06-01
Full Text Available Human detection and tracking has been a prominent research area for several scientists around the globe. State of the art algorithms have been implemented, refined and accelerated to significantly improve the detection rate and eliminate false positives. While 2D approaches are well investigated, 3D human detection and tracking is still an unexplored research field. In both 2D/3D cases, introducing a multi camera system could vastly expand the accuracy and confidence of the tracking process. Within this work, a quality evaluation is performed on a multi RGB-D camera indoor tracking system for examining how camera calibration and pose can affect the quality of human tracks in the scene, independently from the detection and tracking approach used. After performing a calibration step on every Kinect sensor, state of the art single camera pose estimators were evaluated for checking how good the quality of the poses is estimated using planar objects such as an ordinate chessboard. With this information, a bundle block adjustment and ICP were performed for verifying the accuracy of the single pose estimators in a multi camera configuration system. Results have shown that single camera estimators provide high accuracy results of less than half a pixel forcing the bundle to converge after very few iterations. In relation to ICP, relative information between cloud pairs is more or less preserved giving a low score of fitting between concatenated pairs. Finally, sensor calibration proved to be an essential step for achieving maximum accuracy in the generated point clouds, and therefore in the accuracy of the produced 3D trajectories, from each sensor.
Baydaroğlu, Özlem; Koçak, Kasım; Duran, Kemal
2018-06-01
Prediction of water amount that will enter the reservoirs in the following month is of vital importance especially for semi-arid countries like Turkey. Climate projections emphasize that water scarcity will be one of the serious problems in the future. This study presents a methodology for predicting river flow for the subsequent month based on the time series of observed monthly river flow with hybrid models of support vector regression (SVR). Monthly river flow over the period 1940-2012 observed for the Kızılırmak River in Turkey has been used for training the method, which then has been applied for predictions over a period of 3 years. SVR is a specific implementation of support vector machines (SVMs), which transforms the observed input data time series into a high-dimensional feature space (input matrix) by way of a kernel function and performs a linear regression in this space. SVR requires a special input matrix. The input matrix was produced by wavelet transforms (WT), singular spectrum analysis (SSA), and a chaotic approach (CA) applied to the input time series. WT convolutes the original time series into a series of wavelets, and SSA decomposes the time series into a trend, an oscillatory and a noise component by singular value decomposition. CA uses a phase space formed by trajectories, which represent the dynamics producing the time series. These three methods for producing the input matrix for the SVR proved successful, while the SVR-WT combination resulted in the highest coefficient of determination and the lowest mean absolute error.
Directory of Open Access Journals (Sweden)
Gentile Antonio
2012-09-01
Full Text Available Abstract Background HPV infection is a worldwide problem strictly linked to the development of cervical cancer. Persistence of the infection is one of the main factors responsible for the invasive progression and women diagnosed with intraepithelial squamous lesions are referred for further assessment and surgical treatments which are prone to complications. Despite this, there are several reports on the spontaneous regression of the infection. This study was carried out to evaluate the effectiveness of a long term polyhexamethylene biguanide (PHMB-based local treatment in improving the viral clearance, reducing the time exposure to the infection and avoiding the complications associated with the invasive treatments currently available. Method 100 women diagnosed with HPV infection were randomly assigned to receive six months of treatment with a PHMB-based gynecological solution (Monogin®, Lo.Li. Pharma, Rome - Italy or to remain untreated for the same period of time. Results A greater number of patients, who received the treatment were cleared of the infection at the two time points of the study (three and six months compared to that of the control group. A significant difference in the regression rate (90% Monogin group vs 70% control group was observed at the end of the study highlighting the time-dependent ability of PHMB to interact with the infection progression. Conclusions The topic treatment with PHMB is a preliminary safe and promising approach for patients with detected HPV infection increasing the chance of clearance and avoiding the use of invasive treatments when not strictly necessary. Trial registration ClinicalTrials.gov Identifier NCT01571141
Mohammad Al Alfy, Ibrahim
2018-01-01
A set of three pads was constructed from primary materials (sand, gravel and cement) to calibrate the gamma-gamma density tool. A simple equation was devised to convert the qualitative cps values to quantitative g/cc values. The neutron-neutron porosity tool measures the qualitative cps porosity values. A direct equation was derived to calculate the porosity percentage from the cps porosity values. Cement-bond log illustrates the cement quantities, which surround well pipes. This log needs a difficult process due to the existence of various parameters, such as: drilling well diameter as well as internal diameter, thickness and type of well pipes. An equation was invented to calculate the cement percentage at standard conditions. This equation can be modified according to varying conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.
A simple approach for EPID dosimetric calibration to overcome the effect of image-lag and ghosting
International Nuclear Information System (INIS)
Alshanqity, Mukhtar; Duane, Simon; Nisbet, Andrew
2012-01-01
EPID dosimetry has known drawbacks. The main issue is that a measurable residual signal is observed after the end of irradiation for prolonged periods of time, thus making measurement difficult. We present a detailed analysis of EPID response and suggest a simple, yet accurate approach for calibration that avoids the complexity of incorporating ghosting and image-lag using the maximum integrated signal instead of the total integrated signal. This approach is linear with dose and independent of dose rate. - Highlights: ► Image-lag and ghosting effects dosimetric accuracy. ► Image-lag and ghosting result in the reduction of total integrated signal for low doses. ► Residual signal is the most significant result for the image-lag and ghosting effects. ► Image-lag and ghosting can result in under-dosing of up to 2.5%.
Error-in-variables models in calibration
Lira, I.; Grientschnig, D.
2017-12-01
In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration process and from the possible inaccuracy of the calibration model. However, the premise that the stimuli are completely known is never strictly fulfilled and in some instances it may be grossly inadequate. Then, error-in-variables (EIV) regression models have to be employed. In metrology, these models have been approached mostly from the frequentist perspective. In contrast, not much guidance is available on their Bayesian analysis. In this paper, we first present a brief summary of the conventional statistical techniques that have been developed to deal with EIV models in calibration. We then proceed to discuss the alternative Bayesian framework under some simplifying assumptions. Through a detailed example about the calibration of an instrument for measuring flow rates, we provide advice on how the user of the calibration function should employ the latter framework for inferring the stimulus acting on the calibrated device when, in use, a certain response is measured.
International Nuclear Information System (INIS)
Zhang, Zibin; Cai, Wenxin; Feng, Xiangzhao
2017-01-01
China is the largest electricity consumption country after it has passed the United States in 2011. Residential electricity consumption in China grew by 381.35% (12.85% per annum) between 2000 and 2013. In order to deal with rapid growth in residential electricity consumption, an increasing block pricing policy was introduced for residential electricity consumers in China on July 1st, 2012. Using difference-in-differences models with a fuzzy regression discontinuity design, we estimate a causal effect of price on electricity consumption for urban households during the introduction of increasing block pricing policy in Guangdong province of China. We find that consumers do not respond to a smaller (approximately 8%) increase in marginal price. However, consumers do respond to a larger increase in marginal price. An approximately 40% increase in marginal price induces an approximately 35% decrease in electricity use (284 kW h per month). Our results suggest that although the increasing block pricing could affect the behavior of households with higher electricity use, there is only a limit potential to overall energy conservation. - Highlights: • Estimate electricity consumption changes in response to the IBP in China. • Employ quasi-experimental approach and micro household level data in China. • Households do not respond to a smaller increase in marginal price. • 40% increase in marginal price induces a 35% decrease in electricity use.
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-25
This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.
Nistal-Nuño, Beatriz
2017-03-31
In Chile, a new law introduced in March 2012 lowered the blood alcohol concentration (BAC) limit for impaired drivers from 0.1% to 0.08% and the BAC limit for driving under the influence of alcohol from 0.05% to 0.03%, but its effectiveness remains uncertain. The goal of this investigation was to evaluate the effects of this enactment on road traffic injuries and fatalities in Chile. A retrospective cohort study. Data were analyzed using a descriptive and a Generalized Linear Models approach, type of Poisson regression, to analyze deaths and injuries in a series of additive Log-Linear Models accounting for the effects of law implementation, month influence, a linear time trend and population exposure. A review of national databases in Chile was conducted from 2003 to 2014 to evaluate the monthly rates of traffic fatalities and injuries associated to alcohol and in total. It was observed a decrease by 28.1 percent in the monthly rate of traffic fatalities related to alcohol as compared to before the law (Plaw (Plaw implemented in 2012 in Chile. Chile experienced a significant reduction in alcohol-related traffic fatalities and injuries, being a successful public health intervention.
Directory of Open Access Journals (Sweden)
Yuehjen E. Shao
2013-01-01
Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.
Directory of Open Access Journals (Sweden)
Siti Choirun Nisak
2016-06-01
Full Text Available Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS assumes the variance-covariance matrix has a constant error ~(, but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR approach is used to accommodate the weakness of the OLS. SUR assumption is ~(, for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS. Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1(1,12,36,(0,(1-SUR with determination coefficient generated with the average of 57.726%.
A NOVEL APPROACH TO CAMERA CALIBRATION METHOD FOR SMART PHONES UNDER ROAD ENVIRONMENT
Directory of Open Access Journals (Sweden)
B. Lee
2016-06-01
Full Text Available Monocular vision-based lane departure warning system has been increasingly used in advanced driver assistance systems (ADAS. By the use of the lane mark detection and identification, we proposed an automatic and efficient camera calibration method for smart phones. At first, we can detect the lane marker feature in a perspective space and calculate edges of lane markers in image sequences. Second, because of the width of lane marker and road lane is fixed under the standard structural road environment, we can automatically build a transformation matrix between perspective space and 3D space and get a local map in vehicle coordinate system. In order to verify the validity of this method, we installed a smart phone in the ‘Tuzhi’ self-driving car of Wuhan University and recorded more than 100km image data on the road in Wuhan. According to the result, we can calculate the positions of lane markers which are accurate enough for the self-driving car to run smoothly on the road.
a Novel Approach to Camera Calibration Method for Smart Phones Under Road Environment
Lee, Bijun; Zhou, Jian; Ye, Maosheng; Guo, Yuan
2016-06-01
Monocular vision-based lane departure warning system has been increasingly used in advanced driver assistance systems (ADAS). By the use of the lane mark detection and identification, we proposed an automatic and efficient camera calibration method for smart phones. At first, we can detect the lane marker feature in a perspective space and calculate edges of lane markers in image sequences. Second, because of the width of lane marker and road lane is fixed under the standard structural road environment, we can automatically build a transformation matrix between perspective space and 3D space and get a local map in vehicle coordinate system. In order to verify the validity of this method, we installed a smart phone in the `Tuzhi' self-driving car of Wuhan University and recorded more than 100km image data on the road in Wuhan. According to the result, we can calculate the positions of lane markers which are accurate enough for the self-driving car to run smoothly on the road.
Directory of Open Access Journals (Sweden)
N. M. Velpuri
2012-01-01
Full Text Available Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of inter- and intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellite-driven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE of 0.80 during the validation period (2004–2009. Model error estimates were within 10% of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30% of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1–2 m. The lake level fluctuated in the range up to 4 m between the years 1998–2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated
Velpuri, N.M.; Senay, G.B.; Asante, K.O.
2012-01-01
Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of interand intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellitedriven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE) of 0.80 during the validation period (2004-2009). Model error estimates were within 10% of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30% of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1-2m. The lake level fluctuated in the range up to 4m between the years 1998-2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated satellite-driven water balance
Möltgen, C-V; Herdling, T; Reich, G
2013-11-01
This study demonstrates an approach, using science-based calibration (SBC), for direct coating thickness determination on heart-shaped tablets in real-time. Near-Infrared (NIR) spectra were collected during four full industrial pan coating operations. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film up to a film thickness of 28 μm. The application of SBC permits the calibration of the NIR spectral data without using costly determined reference values. This is due to the fact that SBC combines classical methods to estimate the coating signal and statistical methods for the noise estimation. The approach enabled the use of NIR for the measurement of the film thickness increase from around 8 to 28 μm of four independent batches in real-time. The developed model provided a spectroscopic limit of detection for the coating thickness of 0.64 ± 0.03 μm root-mean square (RMS). In the commonly used statistical methods for calibration, such as Partial Least Squares (PLS), sufficiently varying reference values are needed for calibration. For thin non-functional coatings this is a challenge because the quality of the model depends on the accuracy of the selected calibration standards. The obvious and simple approach of SBC eliminates many of the problems associated with the conventional statistical methods and offers an alternative for multivariate calibration. Copyright © 2013 Elsevier B.V. All rights reserved.
Calibration with Absolute Shrinkage
DEFF Research Database (Denmark)
Øjelund, Henrik; Madsen, Henrik; Thyregod, Poul
2001-01-01
In this paper, penalized regression using the L-1 norm on the estimated parameters is proposed for chemometric je calibration. The algorithm is of the lasso type, introduced by Tibshirani in 1996 as a linear regression method with bound on the absolute length of the parameters, but a modification...
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Kumar, V.; Melet, A.; Meyssignac, B.; Ganachaud, A.; Kessler, W. S.; Singh, A.; Aucan, J.
2018-02-01
Rising sea levels are a critical concern in small island nations. The problem is especially serious in the western south Pacific, where the total sea level rise over the last 60 years has been up to 3 times the global average. In this study, we aim at reconstructing sea levels at selected sites in the region (Suva, Lautoka—Fiji, and Nouméa—New Caledonia) as a multilinear regression (MLR) of atmospheric and oceanic variables. We focus on sea level variability at interannual-to-interdecadal time scales, and trend over the 1988-2014 period. Local sea levels are first expressed as a sum of steric and mass changes. Then a dynamical approach is used based on wind stress curl as a proxy for the thermosteric component, as wind stress curl anomalies can modulate the thermocline depth and resultant sea levels via Rossby wave propagation. Statistically significant predictors among wind stress curl, halosteric sea level, zonal/meridional wind stress components, and sea surface temperature are used to construct a MLR model simulating local sea levels. Although we are focusing on the local scale, the global mean sea level needs to be adjusted for. Our reconstructions provide insights on key drivers of sea level variability at the selected sites, showing that while local dynamics and the global signal modulate sea level to a given extent, most of the variance is driven by regional factors. On average, the MLR model is able to reproduce 82% of the variance in island sea level, and could be used to derive local sea level projections via downscaling of climate models.
Li, L.; Yang, C.
2017-12-01
Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP
Directory of Open Access Journals (Sweden)
Horst Entorf
2015-07-01
Full Text Available Two alternative hypotheses – referred to as opportunity- and stigma-based behavior – suggest that the magnitude of the link between unemployment and crime also depends on preexisting local crime levels. In order to analyze conjectured nonlinearities between both variables, we use quantile regressions applied to German district panel data. While both conventional OLS and quantile regressions confirm the positive link between unemployment and crime for property crimes, results for assault differ with respect to the method of estimation. Whereas conventional mean regressions do not show any significant effect (which would confirm the usual result found for violent crimes in the literature, quantile regression reveals that size and importance of the relationship are conditional on the crime rate. The partial effect is significantly positive for moderately low and median quantiles of local assault rates.
DEFF Research Database (Denmark)
Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.
2016-01-01
the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new...... approach, called d-ECC, is applied to wind forecasts from the high resolution ensemble system COSMO-DE-EPS run operationally at the German weather service. Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and in a product...
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Moyer, Douglas; Hirsch, Robert M.; Hyer, Kenneth
2012-01-01
Nutrient and sediment fluxes and changes in fluxes over time are key indicators that water resource managers can use to assess the progress being made in improving the structure and function of the Chesapeake Bay ecosystem. The U.S. Geological Survey collects annual nutrient (nitrogen and phosphorus) and sediment flux data and computes trends that describe the extent to which water-quality conditions are changing within the major Chesapeake Bay tributaries. Two regression-based approaches were compared for estimating annual nutrient and sediment fluxes and for characterizing how these annual fluxes are changing over time. The two regression models compared are the traditionally used ESTIMATOR and the newly developed Weighted Regression on Time, Discharge, and Season (WRTDS). The model comparison focused on answering three questions: (1) What are the differences between the functional form and construction of each model? (2) Which model produces estimates of flux with the greatest accuracy and least amount of bias? (3) How different would the historical estimates of annual flux be if WRTDS had been used instead of ESTIMATOR? One additional point of comparison between the two models is how each model determines trends in annual flux once the year-to-year variations in discharge have been determined. All comparisons were made using total nitrogen, nitrate, total phosphorus, orthophosphorus, and suspended-sediment concentration data collected at the nine U.S. Geological Survey River Input Monitoring stations located on the Susquehanna, Potomac, James, Rappahannock, Appomattox, Pamunkey, Mattaponi, Patuxent, and Choptank Rivers in the Chesapeake Bay watershed. Two model characteristics that uniquely distinguish ESTIMATOR and WRTDS are the fundamental model form and the determination of model coefficients. ESTIMATOR and WRTDS both predict water-quality constituent concentration by developing a linear relation between the natural logarithm of observed constituent
Hallquist, Michael N; Hwang, Kai; Luna, Beatriz
2013-11-15
Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10-.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass-regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability. Copyright © 2013 Elsevier Inc. All rights reserved.
A quasi-dense matching approach and its calibration application with Internet photos.
Wan, Yanli; Miao, Zhenjiang; Wu, Q M Jonathan; Wang, Xifu; Tang, Zhen; Wang, Zhifei
2015-03-01
This paper proposes a quasi-dense matching approach to the automatic acquisition of camera parameters, which is required for recovering 3-D information from 2-D images. An affine transformation-based optimization model and a new matching cost function are used to acquire quasi-dense correspondences with high accuracy in each pair of views. These correspondences can be effectively detected and tracked at the sub-pixel level in multiviews with our neighboring view selection strategy. A two-layer iteration algorithm is proposed to optimize 3-D quasi-dense points and camera parameters. In the inner layer, different optimization strategies based on local photometric consistency and a global objective function are employed to optimize the 3-D quasi-dense points and camera parameters, respectively. In the outer layer, quasi-dense correspondences are resampled to guide a new estimation and optimization process of the camera parameters. We demonstrate the effectiveness of our algorithm with several experiments.
International Nuclear Information System (INIS)
Mayr, Nina A.; Wang, Jian Z.; Lo, Simon S.; Zhang Dongqing; Grecula, John C.; Lu Lanchun; Montebello, Joseph F.; Fowler, Jeffrey M.; Yuh, William T.C.
2010-01-01
Purpose: To assess individual volumetric tumor regression pattern in cervical cancer during therapy using serial four-dimensional MRI and to define the regression parameters' prognostic value validated with local control and survival correlation. Methods and Materials: One hundred and fifteen patients with Stage IB 2 -IVA cervical cancer treated with radiation therapy (RT) underwent serial MRI before (MRI 1) and during RT, at 2-2.5 weeks (MRI 2, at 20-25 Gy), and at 4-5 weeks (MRI 3, at 40-50 Gy). Eighty patients had a fourth MRI 1-2 months post-RT. Mean follow-up was 5.3 years. Tumor volume was measured by MRI-based three-dimensional volumetry, and plotted as dose(time)/volume regression curves. Volume regression parameters were correlated with local control, disease-specific, and overall survival. Results: Residual tumor volume, slope, and area under the regression curve correlated significantly with local control and survival. Residual volumes ≥20% at 40-50 Gy were independently associated with inferior 5-year local control (53% vs. 97%, p <0.001) and disease-specific survival rates (50% vs. 72%, p = 0.009) than smaller volumes. Patients with post-RT residual volumes ≥10% had 0% local control and 17% disease-specific survival, compared with 91% and 72% for <10% volume (p <0.001). Conclusion: Using more accurate four-dimensional volumetric regression analysis, tumor response can now be directly translated into individual patients' outcome for clinical application. Our results define two temporal thresholds critically influencing local control and survival. In patients with ≥20% residual volume at 40-50 Gy and ≥10% post-RT, the risk for local failure and death are so high that aggressive intervention may be warranted.
Nouri, N. M.; Mostafapour, K.; Kamran, M.
2018-02-01
In a closed water-tunnel circuit, the multi-component strain gauge force and moment sensor (also known as balance) are generally used to measure hydrodynamic forces and moments acting on scaled models. These balances are periodically calibrated by static loading. Their performance and accuracy depend significantly on the rig and the method of calibration. In this research, a new calibration rig was designed and constructed to calibrate multi-component internal strain gauge balances. The calibration rig has six degrees of freedom and six different component-loading structures that can be applied separately and synchronously. The system was designed based on the applicability of formal experimental design techniques, using gravity for balance loading and balance positioning and alignment relative to gravity. To evaluate the calibration rig, a six-component internal balance developed by Iran University of Science and Technology was calibrated using response surface methodology. According to the results, calibration rig met all design criteria. This rig provides the means by which various methods of formal experimental design techniques can be implemented. The simplicity of the rig saves time and money in the design of experiments and in balance calibration while simultaneously increasing the accuracy of these activities.
Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo
2009-01-01
We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...
DEFF Research Database (Denmark)
Hansen, Aslak Hedemann
2008-01-01
The development in the consumption of fruit and vegetables in the period 1999-2004 in Denmark was investigated using quantile regression and two previously overlooked problems were identified. First, the change in the ten percent quantile samples decreased. This could have been caused by changes ...
Van Der Meer, D.; Hoekstra, P. J.; Van Donkelaar, M.; Bralten, J.; Oosterlaan, J.; Heslenfeld, D.; Faraone, S. V.; Franke, B.; Buitelaar, J. K.; Hartman, C. A.
2017-01-01
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression
Maggin, Daniel M.; Swaminathan, Hariharan; Rogers, Helen J.; O'Keeffe, Breda V.; Sugai, George; Horner, Robert H.
2011-01-01
A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of…
van der Meer, D.; Hoekstra, P. J.; van Donkelaar, Marjolein M. J.; Bralten, Janita; Oosterlaan, J; Heslenfeld, Dirk J.; Faraone, S. V.; Franke, B.; Buitelaar, J. K.; Hartman, C. A.
2017-01-01
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression
Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...
International Nuclear Information System (INIS)
Yousufzai, M.A.K; Aansari, M.R.K.; Quamar, J.; Iqbal, J.; Hussain, M.A.
2010-01-01
This communication presents the development of a comprehensive characterization of ozone layer depletion (OLD) phenomenon as a physical process in the form of mathematical models that comprise the usual regression, multiple or polynomial regression and stochastic strategy. The relevance of these models has been illuminated using predicted values of different parameters under a changing environment. The information obtained from such analysis can be employed to alter the possible factors and variables to achieve optimum performance. This kind of analysis initiates a study towards formulating the phenomenon of OLD as a physical process with special reference to the stratospheric region of Pakistan. The data presented here establishes that the Auto regressive (AR) nature of modeling OLD as a physical process is an appropriate scenario rather than using usual regression. The data reported in literature suggest quantitatively the OLD is occurring in our region. For this purpose we have modeled this phenomenon using the data recorded at the Geophysical Centre Quetta during the period 1960-1999. The predictions made by this analysis are useful for public, private and other relevant organizations. (author)
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
Madonna, Erica; Ginsbourger, David; Martius, Olivia
2018-05-01
In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Heddam, Salim
2014-11-01
The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).
Yu, Wenbao; Park, Taesung
2014-01-01
Motivation It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. Results We propose an AUC-based approach u...
Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Kammoun, Abla; Al-Naffouri, Tareq Y.
2016-01-01
This paper proposes a new approach to find the regularization parameter for linear least-squares discrete ill-posed problems. In the proposed approach, an artificial perturbation matrix with a bounded norm is forced into the discrete ill-posed model
Suliman, Mohamed Abdalla Elhag
2016-12-19
This paper proposes a new approach to find the regularization parameter for linear least-squares discrete ill-posed problems. In the proposed approach, an artificial perturbation matrix with a bounded norm is forced into the discrete ill-posed model matrix. This perturbation is introduced to enhance the singular-value (SV) structure of the matrix and hence to provide a better solution. The proposed approach is derived to select the regularization parameter in a way that minimizes the mean-squared error (MSE) of the estimator. Numerical results demonstrate that the proposed approach outperforms a set of benchmark methods in most cases when applied to different scenarios of discrete ill-posed problems. Jointly, the proposed approach enjoys the lowest run-time and offers the highest level of robustness amongst all the tested methods.
Owusu-Edusei, Kwame; Gift, Thomas L; Leichliter, Jami S; Romaguera, Raul A
2018-02-01
The number of categorical sexually transmitted disease (STD) clinics is declining in the United States. Federally qualified health centers (FQHCs) have the potential to supplement the needed sexually transmitted infection (STI) services. In this study, we describe the spatial distribution of FQHC sites and determine if reported county-level nonviral STI morbidity were associated with having FQHC(s) using spatial regression techniques. We extracted map data from the Health Resources and Services Administration data warehouse on FQHCs (ie, geocoded health care service delivery [HCSD] sites) and extracted county-level data on the reported rates of chlamydia, gonorrhea and, primary and secondary (P&S) syphilis (2008-2012) from surveillance data. A 3-equation seemingly unrelated regression estimation procedure (with a spatial regression specification that controlled for county-level multiyear (2008-2012) demographic and socioeconomic factors) was used to determine the association between reported county-level STI morbidity and HCSD sites. Counties with HCSD sites had higher STI, poverty, unemployment, and violent crime rates than counties with no HCSD sites (P < 0.05). The number of HCSD sites was associated (P < 0.01) with increases in the temporally smoothed rates of chlamydia, gonorrhea, and P&S syphilis, but there was no significant association between the number of HCSD per 100,000 population and reported STI rates. There is a positive association between STI morbidity and the number of HCSD sites; however, this association does not exist when adjusting by population size. Further work may determine the extent to which HCSD sites can meet unmet needs for safety net STI services.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David
2017-10-01
Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Tania Dehesh
2015-01-01
Full Text Available Background. Univariate meta-analysis (UM procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC, common correlation (CC, estimated correlation (EC, and multivariate multilevel correlation (MMC on the estimation bias, mean square error (MSE, and 95% probability coverage of the confidence interval (CI in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.
Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi
2015-01-01
Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.
Tripathi, Subarna; Guenter, Brian
2016-01-01
We present a novel, automatic eye gaze tracking scheme inspired by smooth pursuit eye motion while playing mobile games or watching virtual reality contents. Our algorithm continuously calibrates an eye tracking system for a head mounted display. This eliminates the need for an explicit calibration step and automatically compensates for small movements of the headset with respect to the head. The algorithm finds correspondences between corneal motion and screen space motion, and uses these to...
Payande, Abolfazl; Tabesh, Hamed; Shakeri, Mohammad Taghi; Saki, Azadeh; Safarian, Mohammad
2013-01-14
Growth charts are widely used to assess children's growth status and can provide a trajectory of growth during early important months of life. The objectives of this study are going to construct growth charts and normal values of weight-for-age for children aged 0 to 5 years using a powerful and applicable methodology. The results compare with the World Health Organization (WHO) references and semi-parametric LMS method of Cole and Green. A total of 70737 apparently healthy boys and girls aged 0 to 5 years were recruited in July 2004 for 20 days from those attending community clinics for routine health checks as a part of a national survey. Anthropometric measurements were done by trained health staff using WHO methodology. The nonparametric quantile regression method obtained by local constant kernel estimation of conditional quantiles curves using for estimation of curves and normal values. The weight-for-age growth curves for boys and girls aged from 0 to 5 years were derived utilizing a population of children living in the northeast of Iran. The results were similar to the ones obtained by the semi-parametric LMS method in the same data. Among all age groups from 0 to 5 years, the median values of children's weight living in the northeast of Iran were lower than the corresponding values in WHO reference data. The weight curves of boys were higher than those of girls in all age groups. The differences between growth patterns of children living in the northeast of Iran versus international ones necessitate using local and regional growth charts. International normal values may not properly recognize the populations at risk for growth problems in Iranian children. Quantile regression (QR) as a flexible method which doesn't require restricted assumptions, proposed for estimation reference curves and normal values.
Calibration factor or calibration coefficient?
International Nuclear Information System (INIS)
Meghzifene, A.; Shortt, K.R.
2002-01-01
Full text: The IAEA/WHO network of SSDLs was set up in order to establish links between SSDL members and the international measurement system. At the end of 2001, there were 73 network members in 63 Member States. The SSDL network members provide calibration services to end-users at the national or regional level. The results of the calibrations are summarized in a document called calibration report or calibration certificate. The IAEA has been using the term calibration certificate and will continue using the same terminology. The most important information in a calibration certificate is a list of calibration factors and their related uncertainties that apply to the calibrated instrument for the well-defined irradiation and ambient conditions. The IAEA has recently decided to change the term calibration factor to calibration coefficient, to be fully in line with ISO [ISO 31-0], which recommends the use of the term coefficient when it links two quantities A and B (equation 1) that have different dimensions. The term factor should only be used for k when it is used to link the terms A and B that have the same dimensions A=k.B. However, in a typical calibration, an ion chamber is calibrated in terms of a physical quantity such as air kerma, dose to water, ambient dose equivalent, etc. If the chamber is calibrated together with its electrometer, then the calibration refers to the physical quantity to be measured per electrometer unit reading. In this case, the terms referred have different dimensions. The adoption by the Agency of the term coefficient to express the results of calibrations is consistent with the 'International vocabulary of basic and general terms in metrology' prepared jointly by the BIPM, IEC, ISO, OIML and other organizations. The BIPM has changed from factor to coefficient. The authors believe that this is more than just a matter of semantics and recommend that the SSDL network members adopt this change in terminology. (author)
Wilson, Asa B; Kerr, Bernard J; Bastian, Nathaniel D; Fulton, Lawrence V
2012-01-01
From 1980 to 1999, rural designated hospitals closed at a disproportionally high rate. In response to this emergent threat to healthcare access in rural settings, the Balanced Budget Act of 1997 made provisions for the creation of a new rural hospital--the critical access hospital (CAH). The conversion to CAH and the associated cost-based reimbursement scheme significantly slowed the closure rate of rural hospitals. This work investigates which methods can ensure the long-term viability of small hospitals. This article uses a two-step design to focus on a hypothesized relationship between technical efficiency of CAHs and a recently developed set of financial monitors for these entities. The goal is to identify the financial performance measures associated with efficiency. The first step uses data envelopment analysis (DEA) to differentiate efficient from inefficient facilities within a data set of 183 CAHs. Determining DEA efficiency is an a priori categorization of hospitals in the data set as efficient or inefficient. In the second step, DEA efficiency is the categorical dependent variable (efficient = 0, inefficient = 1) in the subsequent binary logistic regression (LR) model. A set of six financial monitors selected from the array of 20 measures were the LR independent variables. We use a binary LR to test the null hypothesis that recently developed CAH financial indicators had no predictive value for categorizing a CAH as efficient or inefficient, (i.e., there is no relationship between DEA efficiency and fiscal performance).
Mohebbi, Mohammadreza; Wolfe, Rory; Forbes, Andrew
2014-01-01
This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference. PMID:24413702
Directory of Open Access Journals (Sweden)
Shelley M. ALEXANDER
2009-02-01
Full Text Available We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS-based approaches: logistic regression and Akaike’s Information Criterion (AIC, Multiple Criteria Evaluation (MCE, and Bayesian Analysis (specifically Dempster-Shafer theory. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy, the failure to predict a species where it occurred (omission error and the prediction of presence where there was absence (commission error. Our overall accuracy showed the logistic regression approach was the most accurate (74.51%. The multiple criteria evaluation was intermediate (39.22%, while the Dempster-Shafer (D-S theory model was the poorest (29.90%. However, omission and commission error tell us a different story: logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans[Current Zoology 55(1: 28 – 40, 2009].
Le, Laetitia Minh Maï; Kégl, Balázs; Gramfort, Alexandre; Marini, Camille; Nguyen, David; Cherti, Mehdi; Tfaili, Sana; Tfayli, Ali; Baillet-Guffroy, Arlette; Prognon, Patrice; Chaminade, Pierre; Caudron, Eric
2018-07-01
The use of monoclonal antibodies (mAbs) constitutes one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors. These antibodies are prescribed by the physician and prepared by hospital pharmacists. An analytical control enables the quality of the preparations to be ensured. The aim of this study was to explore the development of a rapid analytical method for quality control. The method used four mAbs (Infliximab, Bevacizumab, Rituximab and Ramucirumab) at various concentrations and was based on recording Raman data and coupling them to a traditional chemometric and machine learning approach for data analysis. Compared to conventional linear approach, prediction errors are reduced with a data-driven approach using statistical machine learning methods. In the latter, preprocessing and predictive models are jointly optimized. An additional original aspect of the work involved on submitting the problem to a collaborative data challenge platform called Rapid Analytics and Model Prototyping (RAMP). This allowed using solutions from about 300 data scientists in collaborative work. Using machine learning, the prediction of the four mAbs samples was considerably improved. The best predictive model showed a combined error of 2.4% versus 14.6% using linear approach. The concentration and classification errors were 5.8% and 0.7%, only three spectra were misclassified over the 429 spectra of the test set. This large improvement obtained with machine learning techniques was uniform for all molecules but maximal for Bevacizumab with an 88.3% reduction on combined errors (2.1% versus 17.9%). Copyright © 2018 Elsevier B.V. All rights reserved.
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Directory of Open Access Journals (Sweden)
Gwo-Fong Lin
2016-01-01
Full Text Available This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs model, the self-organizing map (SOM is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively. Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively. Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively. These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model. In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process. The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression model because of its accuracy and robustness.
Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan
2014-02-01
In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.
Influence of rainfall observation network on model calibration and application
Directory of Open Access Journals (Sweden)
A. Bárdossy
2008-01-01
Full Text Available The objective in this study is to investigate the influence of the spatial resolution of the rainfall input on the model calibration and application. The analysis is carried out by varying the distribution of the raingauge network. A meso-scale catchment located in southwest Germany has been selected for this study. First, the semi-distributed HBV model is calibrated with the precipitation interpolated from the available observed rainfall of the different raingauge networks. An automatic calibration method based on the combinatorial optimization algorithm simulated annealing is applied. The performance of the hydrological model is analyzed as a function of the raingauge density. Secondly, the calibrated model is validated using interpolated precipitation from the same raingauge density used for the calibration as well as interpolated precipitation based on networks of reduced and increased raingauge density. Lastly, the effect of missing rainfall data is investigated by using a multiple linear regression approach for filling in the missing measurements. The model, calibrated with the complete set of observed data, is then run in the validation period using the above described precipitation field. The simulated hydrographs obtained in the above described three sets of experiments are analyzed through the comparisons of the computed Nash-Sutcliffe coefficient and several goodness-of-fit indexes. The results show that the model using different raingauge networks might need re-calibration of the model parameters, specifically model calibrated on relatively sparse precipitation information might perform well on dense precipitation information while model calibrated on dense precipitation information fails on sparse precipitation information. Also, the model calibrated with the complete set of observed precipitation and run with incomplete observed data associated with the data estimated using multiple linear regressions, at the locations treated as
International Nuclear Information System (INIS)
Gaudio, P; Gelfusa, M; Lupelli, I; Murari, A; Vega, J
2014-01-01
A new approach to determine the power law expressions for the threshold between the H and L mode of confinement is presented. The method is based on two powerful machine learning tools for classification: neural networks and support vector machines. Using as inputs clear examples of the systems on either side of the transition, the machine learning tools learn the input–output mapping corresponding to the equations of the boundary separating the confinement regimes. Systematic tests with synthetic data show that the machine learning tools provide results competitive with traditional statistical regression and more robust against random noise and systematic errors. The developed tools have then been applied to the multi-machine International Tokamak Physics Activity International Global Threshold Database of validated ITER-like Tokamak discharges. The machine learning tools converge on the same scaling law parameters obtained with non-linear regression. On the other hand, the developed tools allow a reduction of 50% of the uncertainty in the extrapolations to ITER. Therefore the proposed approach can effectively complement traditional regression since its application poses much less stringent requirements on the experimental data, to be used to determine the scaling laws, because they do not require examples exactly at the moment of the transition. (paper)
Observation models in radiocarbon calibration
International Nuclear Information System (INIS)
Jones, M.D.; Nicholls, G.K.
2001-01-01
The observation model underlying any calibration process dictates the precise mathematical details of the calibration calculations. Accordingly it is important that an appropriate observation model is used. Here this is illustrated with reference to the use of reservoir offsets where the standard calibration approach is based on a different model to that which the practitioners clearly believe is being applied. This sort of error can give rise to significantly erroneous calibration results. (author). 12 refs., 1 fig
Liu, Danping; Yeung, Edwina H; McLain, Alexander C; Xie, Yunlong; Buck Louis, Germaine M; Sundaram, Rajeshwari
2017-09-01
Imperfect follow-up in longitudinal studies commonly leads to missing outcome data that can potentially bias the inference when the missingness is nonignorable; that is, the propensity of missingness depends on missing values in the data. In the Upstate KIDS Study, we seek to determine if the missingness of child development outcomes is nonignorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism. To correct for nonignorable missingness, the shared random effects model (SREM) jointly models the outcome and the missing mechanism. However, the computational complexity and lack of software packages has limited its practical applications. This paper proposes a novel two-step approach to handle nonignorable missing outcomes in generalized linear mixed models. We first analyse the missing mechanism with a generalized linear mixed model and predict values of the random effects; then, the outcome model is fitted adjusting for the predicted random effects to account for heterogeneity in the missingness propensity. Extensive simulation studies suggest that the proposed method is a reliable approximation to SREM, with a much faster computation. The nonignorability of missing data in the Upstate KIDS Study is estimated to be mild to moderate, and the analyses using the two-step approach or SREM are similar to the model assuming ignorable missingness. The two-step approach is a computationally straightforward method that can be conducted as sensitivity analyses in longitudinal studies to examine violations to the ignorable missingness assumption and the implications relative to health outcomes. © 2017 John Wiley & Sons Ltd.
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Directory of Open Access Journals (Sweden)
Carolina Barrientos
2016-07-01
Full Text Available The absolute radiometric calibration of a satellite sensor is the critical factor that ensures the usefulness of the acquired data for quantitative applications on remote sensing. This work presents the results of the first cross-calibration of the sensor on board the Sistema Satelital de Observación de la Tierra (SSOT Chilean satellite or Air Force Satellite FASat-C. RapidEye-MSI was chosen as the reference sensor, and a simultaneous Nadir Overpass Approach (SNO was applied. The biases caused by differences in the spectral responses of both instruments were compensated through an adjustment factor derived from EO-1 Hyperion data. Through this method, the variations affecting the radiometric response of New AstroSat Optical Modular Instrument (NAOMI-1, have been corrected based on collections over the Frenchman Flat calibration site. The results of a preliminary evaluation of the pre-flight and updated coefficients have shown a significant improvement in the accuracy of at-sensor radiances and TOA reflectances: an average agreement of 2.63% (RMSE was achieved for the multispectral bands of both instruments. This research will provide a basis for the continuity of calibration and validation tasks of future Chilean space missions.
Sattler, Tine; Sekulic, Damir; Spasic, Miodrag; Osmankac, Nedzad; Vicente João, Paulo; Dervisevic, Edvin; Hadzic, Vedran
2016-01-01
Previous investigations noted potential importance of isokinetic strength in rapid muscular performances, such as jumping. This study aimed to identify the influence of isokinetic-knee-strength on specific jumping performance in volleyball. The secondary aim of the study was to evaluate reliability and validity of the two volleyball-specific jumping tests. The sample comprised 67 female (21.96±3.79 years; 68.26±8.52 kg; 174.43±6.85 cm) and 99 male (23.62±5.27 years; 84.83±10.37 kg; 189.01±7.21 cm) high- volleyball players who competed in 1st and 2nd National Division. Subjects were randomly divided into validation (N.=55 and 33 for males and females, respectively) and cross-validation subsamples (N.=54 and 34 for males and females, respectively). Set of predictors included isokinetic tests, to evaluate the eccentric and concentric strength capacities of the knee extensors, and flexors for dominant and non-dominant leg. The main outcome measure for the isokinetic testing was peak torque (PT) which was later normalized for body mass and expressed as PT/Kg. Block-jump and spike-jump performances were measured over three trials, and observed as criteria. Forward stepwise multiple regressions were calculated for validation subsamples and then cross-validated. Cross validation included correlations between and t-test differences between observed and predicted scores; and Bland Altman graphics. Jumping tests were found to be reliable (spike jump: ICC of 0.79 and 0.86; block-jump: ICC of 0.86 and 0.90; for males and females, respectively), and their validity was confirmed by significant t-test differences between 1st vs. 2nd division players. Isokinetic variables were found to be significant predictors of jumping performance in females, but not among males. In females, the isokinetic-knee measures were shown to be stronger and more valid predictors of the block-jump (42% and 64% of the explained variance for validation and cross-validation subsample, respectively
Directory of Open Access Journals (Sweden)
Zhi-Sai Ma
2017-01-01
Full Text Available Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA. A kernel ridge regression functional series TARMA (FS-TARMA recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.
Cárdenas, V; Cordobés, M; Blanco, M; Alcalà, M
2015-10-10
The pharmaceutical industry is under stringent regulations on quality control of their products because is critical for both, productive process and consumer safety. According to the framework of "process analytical technology" (PAT), a complete understanding of the process and a stepwise monitoring of manufacturing are required. Near infrared spectroscopy (NIRS) combined with chemometrics have lately performed efficient, useful and robust for pharmaceutical analysis. One crucial step in developing effective NIRS-based methodologies is selecting an appropriate calibration set to construct models affording accurate predictions. In this work, we developed calibration models for a pharmaceutical formulation during its three manufacturing stages: blending, compaction and coating. A novel methodology is proposed for selecting the calibration set -"process spectrum"-, into which physical changes in the samples at each stage are algebraically incorporated. Also, we established a "model space" defined by Hotelling's T(2) and Q-residuals statistics for outlier identification - inside/outside the defined space - in order to select objectively the factors to be used in calibration set construction. The results obtained confirm the efficacy of the proposed methodology for stepwise pharmaceutical quality control, and the relevance of the study as a guideline for the implementation of this easy and fast methodology in the pharma industry. Copyright © 2015 Elsevier B.V. All rights reserved.
Khoram, Nafiseh; Zayane, Chadia; Djellouli, Rabia; Laleg-Kirati, Taous-Meriem
2016-01-01
We have designed an iterative numerical technique, called the TNM-CKF algorithm, for calibrating the mathematical model that describes the single-event related brain response when fMRI measurements are given. The method appears to be highly accurate and effective in reconstructing the BOLD signal even when the measurements are tainted with high noise level (as high as 30%).
Calibration Under Uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Swiler, Laura Painton; Trucano, Timothy Guy
2005-03-01
This report is a white paper summarizing the literature and different approaches to the problem of calibrating computer model parameters in the face of model uncertainty. Model calibration is often formulated as finding the parameters that minimize the squared difference between the model-computed data (the predicted data) and the actual experimental data. This approach does not allow for explicit treatment of uncertainty or error in the model itself: the model is considered the %22true%22 deterministic representation of reality. While this approach does have utility, it is far from an accurate mathematical treatment of the true model calibration problem in which both the computed data and experimental data have error bars. This year, we examined methods to perform calibration accounting for the error in both the computer model and the data, as well as improving our understanding of its meaning for model predictability. We call this approach Calibration under Uncertainty (CUU). This talk presents our current thinking on CUU. We outline some current approaches in the literature, and discuss the Bayesian approach to CUU in detail.
Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza
2018-03-01
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.
Shrivastava, Prashant Kumar; Pandey, Arun Kumar
2018-06-01
Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.
Westenbroek, Stephen M.; Doherty, John; Walker, John F.; Kelson, Victor A.; Hunt, Randall J.; Cera, Timothy B.
2012-01-01
The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST (Parameter ESTimation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.
Gross, Samuel M; Tibshirani, Robert
2015-04-01
We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with these type of data is "sparse multiple canonical correlation analysis" (sparse mCCA). All of the current sparse mCCA techniques are biconvex and thus have no guarantees about reaching a global optimum. We propose a method for performing sparse supervised canonical correlation analysis (sparse sCCA), a specific case of sparse mCCA when one of the datasets is a vector. Our proposal for sparse sCCA is convex and thus does not face the same difficulties as the other methods. We derive efficient algorithms for this problem that can be implemented with off the shelf solvers, and illustrate their use on simulated and real data. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Huang, Yongtai
2014-03-01
Numerical water quality models are developed to predict contaminant fate and transport in receiving waters such as reservoirs and lakes. They can be helpful tools for water resource management. The objective of this study is to calibrate a water quality model which was set up to simulate the water quality conditions of Pepacton Reservoir, Downsville, New York, USA, using an aggregation hybrid genetic algorithm (AHGA) and a non-dominated sorting hybrid genetic algorithm (NSHGA). Both AHGA and NSHGA use a hybrid genetic algorithm (HGA) as optimization engines but are different in fitness assignment. In the AHGA, a weighted sum of scaled simulation errors is designed as an overall objective function to measure the fitness of solutions (i.e., parameter values). In the NSHGA, a method based on non-dominated sorting and Euclidean distances is proposed to calculate the dummy fitness of solutions. In addition, this study also compares the AHGA and the NSHGA. The purpose of this comparison is to determine whether the objective function values (i.e., simulation errors) and simulated results obtained by the AHGA and the NSHGA are significantly different from each other. The results show that the objective function values from the two HGAs are good compromises between all objective functions, and the calibrated model results match the observed data reasonably well and are comparable to other studies, supporting and justifying the use of multi-objective calibration.
Zhang, Hua; Zeng, Luan
2017-11-01
Binocular stereoscopic vision can be used for space-based space targets near observation. In order to solve the problem that the traditional binocular vision system cannot work normally after interference, an online calibration method of binocular stereo measuring camera with self-reference is proposed. The method uses an auxiliary optical imaging device to insert the image of the standard reference object into the edge of the main optical path and image with the target on the same focal plane, which is equivalent to a standard reference in the binocular imaging optical system; When the position of the system and the imaging device parameters are disturbed, the image of the standard reference will change accordingly in the imaging plane, and the position of the standard reference object does not change. The camera's external parameters can be re-calibrated by the visual relationship of the standard reference object. The experimental results show that the maximum mean square error of the same object can be reduced from the original 72.88mm to 1.65mm when the right camera is deflected by 0.4 degrees and the left camera is high and low with 0.2° rotation. This method can realize the online calibration of binocular stereoscopic vision measurement system, which can effectively improve the anti - jamming ability of the system.
Khoram, Nafiseh
2016-01-21
Background The calibration of the hemodynamic model that describes changes in blood flow and blood oxygenation during brain activation is a crucial step for successfully monitoring and possibly predicting brain activity. This in turn has the potential to provide diagnosis and treatment of brain diseases in early stages. New Method We propose an efficient numerical procedure for calibrating the hemodynamic model using some fMRI measurements. The proposed solution methodology is a regularized iterative method equipped with a Kalman filtering-type procedure. The Newton component of the proposed method addresses the nonlinear aspect of the problem. The regularization feature is used to ensure the stability of the algorithm. The Kalman filter procedure is incorporated here to address the noise in the data. Results Numerical results obtained with synthetic data as well as with real fMRI measurements are presented to illustrate the accuracy, robustness to the noise, and the cost-effectiveness of the proposed method. Comparison with Existing Method(s) We present numerical results that clearly demonstrate that the proposed method outperforms the Cubature Kalman Filter (CKF), one of the most prominent existing numerical methods. Conclusion We have designed an iterative numerical technique, called the TNM-CKF algorithm, for calibrating the mathematical model that describes the single-event related brain response when fMRI measurements are given. The method appears to be highly accurate and effective in reconstructing the BOLD signal even when the measurements are tainted with high noise level (as high as 30%).
Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun
2013-10-01
Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
International Nuclear Information System (INIS)
Auban, O.; Malet, J.; Brun, P.; Brinster, J.; Quillico, J. J.; Studer, E.
2003-01-01
. This work concerns measurement of steam-air-helium mixtures of any composition distributed in pressurized vessels. The developed systems provide concentration point measurements at typically a few tens of different locations in the facility vessels. This paper will give an overview of the three different measurement systems and will address the main difficulties encountered to develop these systems such as those related to the sample pressure reduction or to the presence of water vapor. The paper focuses on different attempts and approaches to generate the calibration gas mixtures containing water vapor and calibrate the QMS. Even if the calibration procedures still have to be improved, some recently acquired data are here to prove the promising achievements of the technique
Xu, Q.; Hou, Z.; Maltamo, M.; Tokola, T.
2015-12-01
Diameter distributions of trees are important indicators of current forest stand structure and future dynamics. A new method was proposed in the study to combine the diameter distributions derived from the area-based approach (ABA) and the diameter distribution derived from the individual tree detection (ITD) in order to obtain more accurate forest stand attributes. Since dominant trees can be reliably detected and measured by the Lidar data via the ITD, the focus of the study is to retrieve the suppressed trees (trees that were missed by the ITD) from the ABA. Replacement and histogram matching were respectively employed at the plot level to retrieve the suppressed trees. Cut point was detected from the ITD-derived diameter distribution for each sample plot to distinguish dominant trees from the suppressed trees. The results showed that calibrated diameter distributions were more accurate in terms of error index and the entire growing stock estimates. Compared with the best performer between the ABA and the ITD, calibrated diameter distributions decreased the relative RMSE of the estimated entire growing stock, saw log and pulpwood fractions by 2.81%, 3.05% and 7.73% points respectively. Calibration improved the estimation of pulpwood fraction significantly, resulting in a negligible bias of the estimated entire growing stock.
Directory of Open Access Journals (Sweden)
Varga Csaba
2012-10-01
Full Text Available Abstract Background Identifying risk factors for Salmonella Enteritidis (SE infections in Ontario will assist public health authorities to design effective control and prevention programs to reduce the burden of SE infections. Our research objective was to identify risk factors for acquiring SE infections with various phage types (PT in Ontario, Canada. We hypothesized that certain PTs (e.g., PT8 and PT13a have specific risk factors for infection. Methods Our study included endemic SE cases with various PTs whose isolates were submitted to the Public Health Laboratory-Toronto from January 20th to August 12th, 2011. Cases were interviewed using a standardized questionnaire that included questions pertaining to demographics, travel history, clinical symptoms, contact with animals, and food exposures. A multinomial logistic regression method using the Generalized Linear Latent and Mixed Model procedure and a case-case study design were used to identify risk factors for acquiring SE infections with various PTs in Ontario, Canada. In the multinomial logistic regression model, the outcome variable had three categories representing human infections caused by SE PT8, PT13a, and all other SE PTs (i.e., non-PT8/non-PT13a as a referent category to which the other two categories were compared. Results In the multivariable model, SE PT8 was positively associated with contact with dogs (OR=2.17, 95% CI 1.01-4.68 and negatively associated with pepper consumption (OR=0.35, 95% CI 0.13-0.94, after adjusting for age categories and gender, and using exposure periods and health regions as random effects to account for clustering. Conclusions Our study findings offer interesting hypotheses about the role of phage type-specific risk factors. Multinomial logistic regression analysis and the case-case study approach are novel methodologies to evaluate associations among SE infections with different PTs and various risk factors.
Directory of Open Access Journals (Sweden)
Javis Anyangwe Nwaboh
2014-01-01
Full Text Available We employed a comparison method to determine the optical path length of gas cells which can be used in spectroscopic setup based on laser absorption spectroscopy or FTIR. The method is based on absorption spectroscopy itself. A reference gas cell, whose length is a priori known and desirably traceable to the international system of units (SI, and a gas mixture are used to calibrate the path length of a cell under test. By comparing spectra derived from pressure-dependent measurements on the two cells, the path length of the gas cell under test is determined. The method relies neither on the knowledge of the gas concentration nor on the line strength parameter of the probed transition which is very rarely traceable to the SI and of which the uncertainty is often relatively large. The method is flexible such that any infrared light source and infrared active molecule with isolated lines can be used. We elaborate on the method, substantiate the method by reporting results of this calibration procedure applied to multipass and single pass gas cells of lengths from 0.38 m to 21 m, and compare this to other methods. The relative combined uncertainty of the path length results determined using the comparison method was found to be in the ±0.4% range.
Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien
2017-07-24
Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.
Comparison of different calibration methods suited for calibration problems with many variables
DEFF Research Database (Denmark)
Holst, Helle
1992-01-01
This paper describes and compares different kinds of statistical methods proposed in the literature as suited for solving calibration problems with many variables. These are: principal component regression, partial least-squares, and ridge regression. The statistical techniques themselves do...
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directory of Open Access Journals (Sweden)
Marco Passafiume
2017-03-01
Full Text Available Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.
Passafiume, Marco; Maddio, Stefano; Cidronali, Alessandro
2017-03-29
Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.
A Step-wise Approach to the Determination of the Lower Limit of Analysis of the Calibration line
DEFF Research Database (Denmark)
Andersen, Jens Enevold Thaulov
2008-01-01
. In addition, a minimum STDEV of measurement was expressed in terms of STDEV’s on slope and on intercept and the calculation was performed by using a modified version of the law of propagation of errors (LPE). The method was validated on experiments of gas chromatography (GC), liquid chromatography (LC......), electrochemistry, flow-injection analysis (FIA), atomic emission spectrometry (AES), flame atomic absorption spectrometry (FAAS) and inductively-coupled-plasma mass-spectrometry (ICP-MS). A novel definition of the ULA was proposed as the concentration where the response started to deviate significantly from......A method is presented of stipulating the extension of the calibration line in analytical chemistry. It is based on a combination of a chemometric data interpretation and a calculation of the minimum standard deviation (STDEV) of experiments. From a depiction of the STDEV of a straight line vs...
Ito, Shin-ichi; Yoshie, Naoki; Okunishi, Takeshi; Ono, Tsuneo; Okazaki, Yuji; Kuwata, Akira; Hashioka, Taketo; Rose, Kenneth A.; Megrey, Bernard A.; Kishi, Michio J.; Nakamachi, Miwa; Shimizu, Yugo; Kakehi, Shigeho; Saito, Hiroaki; Takahashi, Kazutaka; Tadokoro, Kazuaki; Kusaka, Akira; Kasai, Hiromi
2010-10-01
The Oyashio region in the western North Pacific supports high biological productivity and has been well monitored. We applied the NEMURO (North Pacific Ecosystem Model for Understanding Regional Oceanography) model to simulate the nutrients, phytoplankton, and zooplankton dynamics. Determination of parameters values is very important, yet ad hoc calibration methods are often used. We used the automatic calibration software PEST (model-independent Parameter ESTimation), which has been used previously with NEMURO but in a system without ontogenetic vertical migration of the large zooplankton functional group. Determining the performance of PEST with vertical migration, and obtaining a set of realistic parameter values for the Oyashio, will likely be useful in future applications of NEMURO. Five identical twin simulation experiments were performed with the one-box version of NEMURO. The experiments differed in whether monthly snapshot or averaged state variables were used, in whether state variables were model functional groups or were aggregated (total phytoplankton, small plus large zooplankton), and in whether vertical migration of large zooplankton was included or not. We then applied NEMURO to monthly climatological field data covering 1 year for the Oyashio, and compared model fits and parameter values between PEST-determined estimates and values used in previous applications to the Oyashio region that relied on ad hoc calibration. We substituted the PEST and ad hoc calibrated parameter values into a 3-D version of NEMURO for the western North Pacific, and compared the two sets of spatial maps of chlorophyll- a with satellite-derived data. The identical twin experiments demonstrated that PEST could recover the known model parameter values when vertical migration was included, and that over-fitting can occur as a result of slight differences in the values of the state variables. PEST recovered known parameter values when using monthly snapshots of aggregated
Inertial Sensor Self-Calibration in a Visually-Aided Navigation Approach for a Micro-AUV
Directory of Open Access Journals (Sweden)
Francisco Bonin-Font
2015-01-01
Full Text Available This paper presents a new solution for underwater observation, image recording, mapping and 3D reconstruction in shallow waters. The platform, designed as a research and testing tool, is based on a small underwater robot equipped with a MEMS-based IMU, two stereo cameras and a pressure sensor. The data given by the sensors are fused, adjusted and corrected in a multiplicative error state Kalman filter (MESKF, which returns a single vector with the pose and twist of the vehicle and the biases of the inertial sensors (the accelerometer and the gyroscope. The inclusion of these biases in the state vector permits their self-calibration and stabilization, improving the estimates of the robot orientation. Experiments in controlled underwater scenarios and in the sea have demonstrated a satisfactory performance and the capacity of the vehicle to operate in real environments and in real time.
Directory of Open Access Journals (Sweden)
Lauren S. Wakschlag
2009-06-01
Full Text Available Maternal smoking during pregnancy is a major public health problem that has been associated with numerous short- and long-term adverse health outcomes in offspring. However, characterizing smoking exposure during pregnancy precisely has been rather difficult: self-reported measures of smoking often suffer from recall bias, deliberate misreporting, and selective non-disclosure, while single bioassay measures of nicotine metabolites only reflect recent smoking history and cannot capture the fluctuating and complex patterns of varying exposure of the fetus. Recently, Dukic et al. [1] have proposed a statistical method for combining information from both sources in order to increase the precision of the exposure measurement and power to detect more subtle effects of smoking. In this paper, we extend the Dukic et al. [1] method to incorporate individual variation of the metabolic parameters (such as clearance rates into the calibration model of smoking exposure during pregnancy. We apply the new method to the Family Health and Development Project (FHDP, a small convenience sample of 96 predominantly working-class white pregnant women oversampled for smoking. We find that, on average, misreporters smoke 7.5 cigarettes more than what they report to smoke, with about one third underreporting by 1.5, one third under-reporting by about 6.5, and one third underreporting by 8.5 cigarettes. Partly due to the limited demographic heterogeneity in the FHDP sample, the results are similar to those obtained by the deterministic calibration model, whose adjustments were slightly lower (by 0.5 cigarettes on average. The new results are also, as expected, less sensitive to assumed values of cotinine half-life.
Thevissen, P W; Fieuws, S; Willems, G
2010-01-01
Dental age estimation methods based on the radiologically detected third molar developmental stages are implemented in forensic age assessments to discriminate between juveniles and adults considering the judgment of young unaccompanied asylum seekers. Accurate and unbiased age estimates combined with appropriate quantified uncertainties are the required properties for accurate forensic reporting. In this study, a subset of 910 individuals uniformly distributed in age between 16 and 22 years was selected from an existing dataset collected by Gunst et al. containing 2,513 panoramic radiographs with known third molar developmental stages of Belgian Caucasian men and women. This subset was randomly split in a training set to develop a classical regression analysis and a Bayesian model for the multivariate distribution of the third molar developmental stages conditional on age and in a test set to assess the performance of both models. The aim of this study was to verify if the Bayesian approach differentiates the age of maturity more precisely and removes the bias, which disadvantages the systematically overestimated young individuals. The Bayesian model offers the discrimination of subjects being older than 18 years more appropriate and produces more meaningful prediction intervals but does not strongly outperform the classical approaches.
International Nuclear Information System (INIS)
Mack, D.A.
1976-08-01
Procedures for the calibration of different types of laboratory equipment are described. Provisions for maintaining the integrity of reference and working standards traceable back to a national standard are discussed. Methods of validation and certification methods are included. An appendix lists available publications and services of national standardizing agencies
International Nuclear Information System (INIS)
Zheng, Xiujuan; Fang, Huajing
2015-01-01
The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking the degradation of lithium-ion batteries. It is important to predict the capacity of a lithium-ion battery for future cycles to assess its health condition and remaining useful life (RUL). In this paper, a novel method is developed using unscented Kalman filter (UKF) with relevance vector regression (RVR) and applied to RUL and short-term capacity prediction of batteries. A RVR model is employed as a nonlinear time-series prediction model to predict the UKF future residuals which otherwise remain zero during the prediction period. Taking the prediction step into account, the predictive value through the RVR method and the latest real residual value constitute the future evolution of the residuals with a time-varying weighting scheme. Next, the future residuals are utilized by UKF to recursively estimate the battery parameters for predicting RUL and short-term capacity. Finally, the performance of the proposed method is validated and compared to other predictors with the experimental data. According to the experimental and analysis results, the proposed approach has high reliability and prediction accuracy, which can be applied to battery monitoring and prognostics, as well as generalized to other prognostic applications. - Highlights: • An integrated method is proposed for RUL prediction as well as short-term capacity prediction. • Relevance vector regression model is employed as a nonlinear time-series prediction model. • Unscented Kalman filter is used to recursively update the states for battery model parameters during the prediction. • A time-varying weighting scheme is utilized to improve the accuracy of the RUL prediction. • The proposed method demonstrates high reliability and prediction accuracy.
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
International Nuclear Information System (INIS)
Toman, Blaza; Nelson, Michael A.; Lippa, Katrice A.
2016-01-01
Chemical purity assessment using quantitative "1H-nuclear magnetic resonance spectroscopy is a method based on ratio references of mass and signal intensity of the analyte species to that of chemical standards of known purity. As such, it is an example of a calculation using a known measurement equation with multiple inputs. Though multiple samples are often analyzed during purity evaluations in order to assess measurement repeatability, the uncertainty evaluation must also account for contributions from inputs to the measurement equation. Furthermore, there may be other uncertainty components inherent in the experimental design, such as independent implementation of multiple calibration standards. As such, the uncertainty evaluation is not purely bottom up (based on the measurement equation) or top down (based on the experimental design), but inherently contains elements of both. This hybrid form of uncertainty analysis is readily implemented with Bayesian statistical analysis. In this article we describe this type of analysis in detail and illustrate it using data from an evaluation of chemical purity and its uncertainty for a folic acid material. (authors)
Directory of Open Access Journals (Sweden)
M. Gålfalk
2018-03-01
Full Text Available The calibration and validation of remote sensing land cover products are highly dependent on accurate field reference data, which are costly and practically challenging to collect. We describe an optical method for collection of field reference data that is a fast, cost-efficient, and robust alternative to field surveys and UAV imaging. A lightweight, waterproof, remote-controlled RGB camera (GoPro HERO4 Silver, GoPro Inc. was used to take wide-angle images from 3.1 to 4.5 m in altitude using an extendable monopod, as well as representative near-ground (< 1 m images to identify spectral and structural features that correspond to various land covers in present lighting conditions. A semi-automatic classification was made based on six surface types (graminoids, water, shrubs, dry moss, wet moss, and rock. The method enables collection of detailed field reference data, which is critical in many remote sensing applications, such as satellite-based wetland mapping. The method uses common non-expensive equipment, does not require special skills or training, and is facilitated by a step-by-step manual that is included in the Supplement. Over time a global ground cover database can be built that can be used as reference data for studies of non-forested wetlands from satellites such as Sentinel 1 and 2 (10 m pixel size.
Gålfalk, Magnus; Karlson, Martin; Crill, Patrick; Bousquet, Philippe; Bastviken, David
2018-03-01
The calibration and validation of remote sensing land cover products are highly dependent on accurate field reference data, which are costly and practically challenging to collect. We describe an optical method for collection of field reference data that is a fast, cost-efficient, and robust alternative to field surveys and UAV imaging. A lightweight, waterproof, remote-controlled RGB camera (GoPro HERO4 Silver, GoPro Inc.) was used to take wide-angle images from 3.1 to 4.5 m in altitude using an extendable monopod, as well as representative near-ground (wetland mapping. The method uses common non-expensive equipment, does not require special skills or training, and is facilitated by a step-by-step manual that is included in the Supplement. Over time a global ground cover database can be built that can be used as reference data for studies of non-forested wetlands from satellites such as Sentinel 1 and 2 (10 m pixel size).
Pan, Haitao; Yuan, Ying; Xia, Jielai
2017-11-01
A biosimilar refers to a follow-on biologic intended to be approved for marketing based on biosimilarity to an existing patented biological product (i.e., the reference product). To develop a biosimilar product, it is essential to demonstrate biosimilarity between the follow-on biologic and the reference product, typically through two-arm randomization trials. We propose a Bayesian adaptive design for trials to evaluate biosimilar products. To take advantage of the abundant historical data on the efficacy of the reference product that is typically available at the time a biosimilar product is developed, we propose the calibrated power prior, which allows our design to adaptively borrow information from the historical data according to the congruence between the historical data and the new data collected from the current trial. We propose a new measure, the Bayesian biosimilarity index, to measure the similarity between the biosimilar and the reference product. During the trial, we evaluate the Bayesian biosimilarity index in a group sequential fashion based on the accumulating interim data, and stop the trial early once there is enough information to conclude or reject the similarity. Extensive simulation studies show that the proposed design has higher power than traditional designs. We applied the proposed design to a biosimilar trial for treating rheumatoid arthritis.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Calibration and Data Analysis of the MC-130 Air Balance
Booth, Dennis; Ulbrich, N.
2012-01-01
Design, calibration, calibration analysis, and intended use of the MC-130 air balance are discussed. The MC-130 balance is an 8.0 inch diameter force balance that has two separate internal air flow systems and one external bellows system. The manual calibration of the balance consisted of a total of 1854 data points with both unpressurized and pressurized air flowing through the balance. A subset of 1160 data points was chosen for the calibration data analysis. The regression analysis of the subset was performed using two fundamentally different analysis approaches. First, the data analysis was performed using a recently developed extension of the Iterative Method. This approach fits gage outputs as a function of both applied balance loads and bellows pressures while still allowing the application of the iteration scheme that is used with the Iterative Method. Then, for comparison, the axial force was also analyzed using the Non-Iterative Method. This alternate approach directly fits loads as a function of measured gage outputs and bellows pressures and does not require a load iteration. The regression models used by both the extended Iterative and Non-Iterative Method were constructed such that they met a set of widely accepted statistical quality requirements. These requirements lead to reliable regression models and prevent overfitting of data because they ensure that no hidden near-linear dependencies between regression model terms exist and that only statistically significant terms are included. Finally, a comparison of the axial force residuals was performed. Overall, axial force estimates obtained from both methods show excellent agreement as the differences of the standard deviation of the axial force residuals are on the order of 0.001 % of the axial force capacity.
Sarkar, Arnab; Karki, Vijay; Aggarwal, Suresh K.; Maurya, Gulab S.; Kumar, Rohit; Rai, Awadhesh K.; Mao, Xianglei; Russo, Richard E.
2015-06-01
Laser induced breakdown spectroscopy (LIBS) was applied for elemental characterization of high alloy steel using partial least squares regression (PLSR) with an objective to evaluate the analytical performance of this multivariate approach. The optimization of the number of principle components for minimizing error in PLSR algorithm was investigated. The effect of different pre-treatment procedures on the raw spectral data before PLSR analysis was evaluated based on several statistical (standard error of prediction, percentage relative error of prediction etc.) parameters. The pre-treatment with "NORM" parameter gave the optimum statistical results. The analytical performance of PLSR model improved by increasing the number of laser pulses accumulated per spectrum as well as by truncating the spectrum to appropriate wavelength region. It was found that the statistical benefit of truncating the spectrum can also be accomplished by increasing the number of laser pulses per accumulation without spectral truncation. The constituents (Co and Mo) present in hundreds of ppm were determined with relative precision of 4-9% (2σ), whereas the major constituents Cr and Ni (present at a few percent levels) were determined with a relative precision of ~ 2%(2σ).
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.
2018-06-01
The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.
Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration
Directory of Open Access Journals (Sweden)
Haitao Chang
2016-06-01
Full Text Available One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20–200 µg/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity.
Directory of Open Access Journals (Sweden)
Frederick Schauer
2017-09-01
Full Text Available Objective to study the notion and essence of legal judgments calibration the possibilities of using it in the lawenforcement activity to explore the expenses and advantages of using it. Methods dialectic approach to the cognition of social phenomena which enables to analyze them in historical development and functioning in the context of the integrity of objective and subjective factors it determined the choice of the following research methods formallegal comparative legal sociological methods of cognitive psychology and philosophy. Results In ordinary life people who assess other peoplersaquos judgments typically take into account the other judgments of those they are assessing in order to calibrate the judgment presently being assessed. The restaurant and hotel rating website TripAdvisor is exemplary because it facilitates calibration by providing access to a raterrsaquos previous ratings. Such information allows a user to see whether a particular rating comes from a rater who is enthusiastic about every place she patronizes or instead from someone who is incessantly hard to please. And even when less systematized as in assessing a letter of recommendation or college transcript calibration by recourse to the decisional history of those whose judgments are being assessed is ubiquitous. Yet despite the ubiquity and utility of such calibration the legal system seems perversely to reject it. Appellate courts do not openly adjust their standard of review based on the previous judgments of the judge whose decision they are reviewing nor do judges in reviewing legislative or administrative decisions magistrates in evaluating search warrant representations or jurors in assessing witness perception. In most legal domains calibration by reference to the prior decisions of the reviewee is invisible either because it does not exist or because reviewing bodies are unwilling to admit using what they in fact know and employ. Scientific novelty for the first
Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation
Energy Technology Data Exchange (ETDEWEB)
and Ben Polly, Joseph Robertson [National Renewable Energy Lab. (NREL), Golden, CO (United States); Polly, Ben [National Renewable Energy Lab. (NREL), Golden, CO (United States); Collis, Jon [Colorado School of Mines, Golden, CO (United States)
2013-09-01
This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define "explicit" input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.
Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation
Energy Technology Data Exchange (ETDEWEB)
Robertson, J.; Polly, B.; Collis, J.
2013-09-01
This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define 'explicit' input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
Function approximation with polynomial regression slines
International Nuclear Information System (INIS)
Urbanski, P.
1996-01-01
Principles of the polynomial regression splines as well as algorithms and programs for their computation are presented. The programs prepared using software package MATLAB are generally intended for approximation of the X-ray spectra and can be applied in the multivariate calibration of radiometric gauges. (author)
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Energy Technology Data Exchange (ETDEWEB)
Narlesky, Joshua Edward [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Kelly, Elizabeth J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-09-10
This report documents the new PG calibration regression equation. These calibration equations incorporate new data that have become available since revision 1 of “A Calibration to Predict the Concentrations of Impurities in Plutonium Oxide by Prompt Gamma Analysis” was issued [3] The calibration equations are based on a weighted least squares (WLS) approach for the regression. The WLS method gives each data point its proper amount of influence over the parameter estimates. This gives two big advantages, more precise parameter estimates and better and more defensible estimates of uncertainties. The WLS approach makes sense both statistically and experimentally because the variances increase with concentration, and there are physical reasons that the higher measurements are less reliable and should be less influential. The new magnesium calibration includes a correction for sodium and separate calibration equation for items with and without chlorine. These additional calibration equations allow for better predictions and smaller uncertainties for sodium in materials with and without chlorine. Chlorine and sodium have separate equations for RICH materials. Again, these equations give better predictions and smaller uncertainties chlorine and sodium for RICH materials.
Travelling gradient thermocouple calibration
International Nuclear Information System (INIS)
Broomfield, G.H.
1975-01-01
A short discussion of the origins of the thermocouple EMF is used to re-introduce the idea that the Peltier and Thompson effects are indistinguishable from one another. Thermocouples may be viewed as devices which generate an EMF at junctions or as integrators of EMF's developed in thermal gradients. The thermal gradient view is considered the more appropriate, because of its better accord with theory and behaviour, the correct approach to calibration, and investigation of service effects is immediately obvious. Inhomogeneities arise in thermocouples during manufacture and in service. The results of travelling gradient measurements are used to show that such effects are revealed with a resolution which depends on the length of the gradient although they may be masked during simple immersion calibration. Proposed tests on thermocouples irradiated in a nuclear reactor are discussed
Yang, Xiaolan; Hu, Xiaolei; Xu, Bangtian; Wang, Xin; Qin, Jialin; He, Chenxiong; Xie, Yanling; Li, Yuanli; Liu, Lin; Liao, Fei
2014-06-17
A fluorometric titration approach was proposed for the calibration of the quantity of monoclonal antibody (mcAb) via the quench of fluorescence of tryptophan residues. It applied to purified mcAbs recognizing tryptophan-deficient epitopes, haptens nonfluorescent at 340 nm under the excitation at 280 nm, or fluorescent haptens bearing excitation valleys nearby 280 nm and excitation peaks nearby 340 nm to serve as Förster-resonance-energy-transfer (FRET) acceptors of tryptophan. Titration probes were epitopes/haptens themselves or conjugates of nonfluorescent haptens or tryptophan-deficient epitopes with FRET acceptors of tryptophan. Under the excitation at 280 nm, titration curves were recorded as fluorescence specific for the FRET acceptors or for mcAbs at 340 nm. To quantify the binding site of a mcAb, a universal model considering both static and dynamic quench by either type of probes was proposed for fitting to the titration curve. This was easy for fitting to fluorescence specific for the FRET acceptors but encountered nonconvergence for fitting to fluorescence of mcAbs at 340 nm. As a solution, (a) the maximum of the absolute values of first-order derivatives of a titration curve as fluorescence at 340 nm was estimated from the best-fit model for a probe level of zero, and (b) molar quantity of the binding site of the mcAb was estimated via consecutive fitting to the same titration curve by utilizing such a maximum as an approximate of the slope for linear response of fluorescence at 340 nm to quantities of the mcAb. This fluorometric titration approach was proved effective with one mcAb for six-histidine and another for penicillin G.
Brix, Kevin V; DeForest, David K; Tear, Lucinda; Grosell, Martin; Adams, William J
2017-05-02
Biotic Ligand Models (BLMs) for metals are widely applied in ecological risk assessments and in the development of regulatory water quality guidelines in Europe, and in 2007 the United States Environmental Protection Agency (USEPA) recommended BLM-based water quality criteria (WQC) for Cu in freshwater. However, to-date, few states have adopted BLM-based Cu criteria into their water quality standards on a state-wide basis, which appears to be due to the perception that the BLM is too complicated or requires too many input variables. Using the mechanistic BLM framework to first identify key water chemistry parameters that influence Cu bioavailability, namely dissolved organic carbon (DOC), pH, and hardness, we developed Cu criteria using the same basic methodology used by the USEPA to derive hardness-based criteria but with the addition of DOC and pH. As an initial proof of concept, we developed stepwise multiple linear regression (MLR) models for species that have been tested over wide ranges of DOC, pH, and hardness conditions. These models predicted acute Cu toxicity values that were within a factor of ±2 in 77% to 97% of tests (5 species had adequate data) and chronic Cu toxicity values that were within a factor of ±2 in 92% of tests (1 species had adequate data). This level of accuracy is comparable to the BLM. Following USEPA guidelines for WQC development, the species data were then combined to develop a linear model with pooled slopes for each independent parameter (i.e., DOC, pH, and hardness) and species-specific intercepts using Analysis of Covariance. The pooled MLR and BLM models predicted species-specific toxicity with similar precision; adjusted R 2 and R 2 values ranged from 0.56 to 0.86 and 0.66-0.85, respectively. Graphical exploration of relationships between predicted and observed toxicity, residuals and observed toxicity, and residuals and concentrations of key input parameters revealed many similarities and a few key distinctions between the
Cabrera-León, A; Lopez-Villaverde, V; Rueda, M; Moya-Garrido, M N
2015-11-01
How does the estimated prevalence of infertility among 30- to 49-year-old women vary when using different approaches to its measurement? The prevalence of women with difficulties in conceiving differed widely according to the measurement approach adopted. Establishing the true magnitude of infertility as a public health problem is challenging, given that it is not categorized as a disability or chronic condition and may be largely unreported. The time required to conceive is an increasingly frequent concern among couples of reproductive age. Population-based studies do not consider multiple approaches to infertility measurement in the same sample. A face-to-face cross-sectional population-based survey of 443 women aged between 30 and 49 years residing in Huelva, southern Spain, was carried out. The sample size estimation was based on an assumed prevalence of infertility of 19%, a sampling error of ±4.84 percentage points, a design effect of 1.8 and a 95% confidence level. The information was collected in 2011. Self-reported information was gathered on socio-demographic data, pregnancy history, time required to become pregnant and perception of difficulties in becoming pregnant. Eight approaches to the estimation of infertility prevalence were considered: diagnosed infertility, subjective infertility, 1-year infertility, primary infertility, secondary infertility and subfertility based on the time taken to conceive (6, 12 or 24 months). Calibration estimators (indirect estimation techniques) were used to extrapolate the infertility prevalences to the whole of Spain. The response rate was 61.05%. Among 30- to 49-year-old Spanish women, 1.26% had a clinical diagnosis of infertility, 17.58% did not achieve pregnancy in 1 year (1-year infertility), 8.22% perceived difficulties in procreation (subjective infertility), 6.12% had not succeeded in having biological children (primary infertility) and 11.33% had not been able to have another biological child (secondary
Executive compensation: a calibration approach
Ivilina Popova; Joseph G. Haubrich
1998-01-01
We use a version of the Grossman and Hart principal-agent model with 10 actions and 10 states to produce quantitative predictions for executive compensation. Performance incentives derived from the model are compared with the performance incentives of 350 firms chosen from a survey by Michael Jensen and Kevin Murphy. The results suggest both that the model does a reasonable job of explaining the data and that actual incentives are close to the optimal incentives predicted by theory.
Non-linear calibration models for near infrared spectroscopy
DEFF Research Database (Denmark)
Ni, Wangdong; Nørgaard, Lars; Mørup, Morten
2014-01-01
by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear...... models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS......-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration...
Walsh, Colin G; Sharman, Kavya; Hripcsak, George
2017-12-01
Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration
Hallquist, Michael N.; Hwang, Kai; Luna, Beatriz
2013-01-01
Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent...
Directory of Open Access Journals (Sweden)
Paulino José García Nieto
2016-05-01
Full Text Available Remaining useful life (RUL estimation is considered as one of the most central points in the prognostics and health management (PHM. The present paper describes a nonlinear hybrid ABC–MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS, which have been successfully adopted for regression problems, with the artificial bee colony (ABC technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC–MARS-based model from the remaining measured parameters (input variables for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC–MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Burr, T.; Croft, S.; Krieger, T.; Martin, K.; Norman, C.; Walsh, S.
2016-01-01
One example of top-down uncertainty quantification (UQ) involves comparing two or more measurements on each of multiple items. One example of bottom-up UQ expresses a measurement result as a function of one or more input variables that have associated errors, such as a measured count rate, which individually (or collectively) can be evaluated for impact on the uncertainty in the resulting measured value. In practice, it is often found that top-down UQ exhibits larger error variances than bottom-up UQ, because some error sources are present in the fielded assay methods used in top-down UQ that are not present (or not recognized) in the assay studies used in bottom-up UQ. One would like better consistency between the two approaches in order to claim understanding of the measurement process. The purpose of this paper is to refine bottom-up uncertainty estimation by using calibration information so that if there are no unknown error sources, the refined bottom-up uncertainty estimate will agree with the top-down uncertainty estimate to within a specified tolerance. Then, in practice, if the top-down uncertainty estimate is larger than the refined bottom-up uncertainty estimate by more than the specified tolerance, there must be omitted sources of error beyond those predicted from calibration uncertainty. The paper develops a refined bottom-up uncertainty approach for four cases of simple linear calibration: (1) inverse regression with negligible error in predictors, (2) inverse regression with non-negligible error in predictors, (3) classical regression followed by inversion with negligible error in predictors, and (4) classical regression followed by inversion with non-negligible errors in predictors. Our illustrations are of general interest, but are drawn from our experience with nuclear material assay by non-destructive assay. The main example we use is gamma spectroscopy that applies the enrichment meter principle. Previous papers that ignore error in predictors
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Testing for marginal linear effects in quantile regression
Wang, Huixia Judy
2017-10-23
The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.
Testing for marginal linear effects in quantile regression
Wang, Huixia Judy; McKeague, Ian W.; Qian, Min
2017-01-01
The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
O'Dwyer, Jean; Morris Downes, Margaret; Adley, Catherine C
2016-02-01
This study analyses the relationship between meteorological phenomena and outbreaks of waterborne-transmitted vero cytotoxin-producing Escherichia coli (VTEC) in the Republic of Ireland over an 8-year period (2005-2012). Data pertaining to the notification of waterborne VTEC outbreaks were extracted from the Computerised Infectious Disease Reporting system, which is administered through the national Health Protection Surveillance Centre as part of the Health Service Executive. Rainfall and temperature data were obtained from the national meteorological office and categorised as cumulative rainfall, heavy rainfall events in the previous 7 days, and mean temperature. Regression analysis was performed using logistic regression (LR) analysis. The LR model was significant (p < 0.001), with all independent variables: cumulative rainfall, heavy rainfall and mean temperature making a statistically significant contribution to the model. The study has found that rainfall, particularly heavy rainfall in the preceding 7 days of an outbreak, is a strong statistical indicator of a waterborne outbreak and that temperature also impacts waterborne VTEC outbreak occurrence.
Model Calibration in Option Pricing
Directory of Open Access Journals (Sweden)
Andre Loerx
2012-04-01
Full Text Available We consider calibration problems for models of pricing derivatives which occur in mathematical finance. We discuss various approaches such as using stochastic differential equations or partial differential equations for the modeling process. We discuss the development in the past literature and give an outlook into modern approaches of modelling. Furthermore, we address important numerical issues in the valuation of options and likewise the calibration of these models. This leads to interesting problems in optimization, where, e.g., the use of adjoint equations or the choice of the parametrization for the model parameters play an important role.
Immune Algorithm Complex Method for Transducer Calibration
Directory of Open Access Journals (Sweden)
YU Jiangming
2014-08-01
Full Text Available As a key link in engineering test tasks, the transducer calibration has significant influence on accuracy and reliability of test results. Because of unknown and complex nonlinear characteristics, conventional method can’t achieve satisfactory accuracy. An Immune algorithm complex modeling approach is proposed, and the simulated studies on the calibration of third multiple output transducers is made respectively by use of the developed complex modeling. The simulated and experimental results show that the Immune algorithm complex modeling approach can improve significantly calibration precision comparison with traditional calibration methods.
DEFF Research Database (Denmark)
Henneberg, Morten; Jørgensen, Bent; Eriksen, René Lynge
2016-01-01
In this paper, we present an oil condition and wear debris evaluation method for ship thruster gears using T2 statistics to form control charts from a multi-sensor platform. The proposed method takes into account the different ambient conditions by multiple linear regression on the mean value...... only quasi-stationary data are included in phase I of the T2 statistics. Data from two thruster gears onboard two different ships are presented and analyzed, and the selection of the phase I data size is discussed. A graphic overview for quick localization of T2 signaling is also demonstrated using...... spider plots. Finally, progression and trending of the T2 statistics are investigated using orthogonal polynomials for a fix-sized data window....
Energy Technology Data Exchange (ETDEWEB)
Nicoulaud-Gouin, V.; Giacalone, M.; Gonze, M.A. [Institut de Radioprotection et de Surete Nucleaire-PRP-ENV/SERIS/LM2E (France); Martin-Garin, A.; Garcia-Sanchez, L. [IRSN-PRP-ENV/SERIS/L2BT (France)
2014-07-01
Calibration of transfer models according to observation data is a challenge, especially if parameters uncertainty is required, and if competing models should be decided between them. Generally two main calibration methods are used: The frequentist approach in which the unknown parameter of interest is supposed fixed and its estimation is based on the data only. In this category, least squared method has many restrictions in nonlinear models and competing models need to be nested in order to be compared. The bayesian inference in which the unknown parameter of interest is supposed random and its estimation is based on the data and on prior information. Compared to frequentist method, it provides probability density functions and therefore pointwise estimation with credible intervals. However, in practical cases, Bayesian inference is a complex problem of numerical integration, which explains its low use in operational modeling including radioecology. This study aims to illustrate the interest and feasibility of Bayesian approach in radioecology particularly in the case of ordinary differential equations with non-constant coefficients models, which cover most radiological risk assessment models, notably those implemented in the Symbiose platform (Gonze et al, 2010). Markov Chain Monte Carlo (MCMC) method (Metropolis et al., 1953) was used because the posterior expectations are intractable integrals. The invariant distribution of the parameters was performed by the metropolis-Hasting algorithm (Hastings, 1970). GNU-MCSim software (Bois and Maszle, 2011) a bayesian hierarchical framework, was used to deal with nonlinear differential models. Two case studies including this type of model were investigated: An Equilibrium Kinetic sorption model (EK) (e.g. van Genuchten et al, 1974), with experimental data concerning {sup 137}Cs and {sup 85}Sr sorption and desorption in different soils studied in stirred flow-through reactors. This model, generalizing the K{sub d} approach
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Solid laboratory calibration of a nonimaging spectroradiometer.
Schaepman, M E; Dangel, S
2000-07-20
Field-based nonimaging spectroradiometers are often used in vicarious calibration experiments for airborne or spaceborne imaging spectrometers. The calibration uncertainties associated with these ground measurements contribute substantially to the overall modeling error in radiance- or reflectance-based vicarious calibration experiments. Because of limitations in the radiometric stability of compact field spectroradiometers, vicarious calibration experiments are based primarily on reflectance measurements rather than on radiance measurements. To characterize the overall uncertainty of radiance-based approaches and assess the sources of uncertainty, we carried out a full laboratory calibration. This laboratory calibration of a nonimaging spectroradiometer is based on a measurement plan targeted at achieving a calibration. The individual calibration steps include characterization of the signal-to-noise ratio, the noise equivalent signal, the dark current, the wavelength calibration, the spectral sampling interval, the nonlinearity, directional and positional effects, the spectral scattering, the field of view, the polarization, the size-of-source effects, and the temperature dependence of a particular instrument. The traceability of the radiance calibration is established to a secondary National Institute of Standards and Technology calibration standard by use of a 95% confidence interval and results in an uncertainty of less than ?7.1% for all spectroradiometer bands.
International Nuclear Information System (INIS)
Nuamah, N.N.N.N.
1990-12-01
The paradoxical nature of results of the mean approach in pooling cross-section and time series data has been identified to be caused by the presence in the normal equations of phenomena such as autocovariances, multicollinear covariances, drift covariances and drift multicollinear covariances. This paper considers the problem of autocorrelation and suggests ways of solving it. (author). 4 refs
Directory of Open Access Journals (Sweden)
Chau-Kuang Chen
2015-02-01
Full Text Available Data from the Center for Disease Control (CDC has shown that the obesity rate doubled among adults within the past two decades. This upsurge was the result of changes in human behavior and environment. Partial least squares (PLS regression and support vector machine (SVM models were conducted to determine the relationship between U.S. county-level adult obesity rate and multiple risk factors. The outcome variable was the adult obesity rate. The 23 risk factors were categorized into four domains of the social ecological model including biological/behavioral factor, socioeconomic status, food environment, and physical environment. Of the 23 risk factors related to adult obesity, the top eight significant risk factors with high normalized importance were identified including physical inactivity, natural amenity, percent of households receiving SNAP benefits, and percent of all restaurants being fast food. The study results were consistent with those in the literature. The study showed that adult obesity rate was influenced by biological/behavioral factor, socioeconomic status, food environment, and physical environment embedded in the social ecological theory. By analyzing multiple risk factors of obesity in the communities, may lead to the proposal of more comprehensive and integrated policies and intervention programs to solve the population-based problem.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael; Smargiassi, Audrey
2014-09-01
Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data.
Caravaggi, Paolo; Leardini, Alberto; Giacomozzi, Claudia
2016-10-03
Plantar load can be considered as a measure of the foot ability to transmit forces at the foot/ground, or foot/footwear interface during ambulatory activities via the lower limb kinematic chain. While morphological and functional measures have been shown to be correlated with plantar load, no exhaustive data are currently available on the possible relationships between range of motion of foot joints and plantar load regional parameters. Joints' kinematics from a validated multi-segmental foot model were recorded together with plantar pressure parameters in 21 normal-arched healthy subjects during three barefoot walking trials. Plantar pressure maps were divided into six anatomically-based regions of interest associated to corresponding foot segments. A stepwise multiple regression analysis was performed to determine the relationships between pressure-based parameters, joints range of motion and normalized walking speed (speed/subject height). Sagittal- and frontal-plane joint motion were those most correlated to plantar load. Foot joints' range of motion and normalized walking speed explained between 6% and 43% of the model variance (adjusted R 2 ) for pressure-based parameters. In general, those joints' presenting lower mobility during stance were associated to lower vertical force at forefoot and to larger mean and peak pressure at hindfoot and forefoot. Normalized walking speed was always positively correlated to mean and peak pressure at hindfoot and forefoot. While a large variance in plantar pressure data is still not accounted for by the present models, this study provides statistical corroboration of the close relationship between joint mobility and plantar pressure during stance in the normal healthy foot. Copyright © 2016 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Waller, W.C.; Cram, M.E.; Hall, J.E.
1975-01-01
For any measurement to have meaning, it must be related to generally accepted standard units by a valid and specified system of comparison. To calibrate well-logging tools, sensing systems are designed which produce consistent and repeatable indications over the range for which the tool was intended. The basics of calibration theory, procedures, and calibration record presentations are reviewed. Calibrations for induction, electrical, radioactivity, and sonic logging tools will be discussed. The authors' intent is to provide an understanding of the sources of errors, of the way errors are minimized in the calibration process, and of the significance of changes in recorded calibration data
Reactor operations, inspection and maintenance. PNGS Calibration Program
International Nuclear Information System (INIS)
Lopez, E.
1997-01-01
The PNGS Calibration Program is being implemented as a response to various concerns identified in recent PEER evaluations and AECB audits. Identified areas of concern were the approach to instrument calibration of Special Safety Systems (SSS). The implementation of a calibration program is a significant improvement in operating practices. A systematic and comprehensive approach to calibration of instrumentation will improve the quality of operation of the plant with a positive contribution to PNGS safety of operation and economic objectives. This paper describes the strategy to implement the proposed calibration program and describes its calibration data requirements. (DM)
NIST display colorimeter calibration facility
Brown, Steven W.; Ohno, Yoshihiro
2003-07-01
A facility has been developed at the National Institute of Standards and Technology (NIST) to provide calibration services for color-measuring instruments to address the need for improving and certifying the measurement uncertainties of this type of instrument. While NIST has active programs in photometry, flat panel display metrology, and color and appearance measurements, these are the first services offered by NIST tailored to color-measuring instruments for displays. An overview of the facility, the calibration approach, and associated uncertainties are presented. Details of a new tunable colorimetric source and the development of new transfer standard instruments are discussed.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Energy Technology Data Exchange (ETDEWEB)
John Schabron; Joseph Rovani; Mark Sanderson
2008-02-29
outputs of mercury generators are compared to one another using a nesting procedure which allows direct comparison of one generator with another and eliminates analyzer variability effects. The qualification portion of the EPA interim traceability protocol requires the vendors to define generator performance as affected by variables such as pressure, temperature, line voltage, and shipping. WRI is focusing efforts to determine actual generator performance related to the variables defined in the qualification portion of the interim protocol. The protocol will then be further revised by EPA based on what can actually be achieved with the generators. Another focus of the study is to evaluate approaches for field verification of generator performance. Upcoming work includes evaluation of oxidized mercury calibration generators, for which a separate protocol will be prepared by EPA. In addition, the variability of the spectrometers/analyzers under various environmental conditions needs to be defined and understood better. A main objective of the current work is to provide data on the performance and capabilities of elemental mercury generator/calibration systems for the development of realistic NIST traceability protocols for mercury vapor standards for continuous emission CEM calibration. This work is providing a direct contribution to the enablement of continuous emissions monitoring at coal-fired power plants in conformance with the CAMR. EPA Specification 12 states that mercury CEMs must be calibrated with NIST-traceable standards (Federal Register 2005). The initial draft of an elemental mercury generator traceability protocol was circulated by EPA in May 2007 for comment, and an interim protocol was issued in August 2007 (EPA 2007). Initially it was assumed that the calibration and implementation of mercury CEMs would be relatively simple, and implementation would follow the implementation of the Clean Air Interstate Rule (CAIR) SO{sub 2} and NO{sub x} monitoring, and
Synthesis Polarimetry Calibration
Moellenbrock, George
2017-10-01
Synthesis instrumental polarization calibration fundamentals for both linear (ALMA) and circular (EVLA) feed bases are reviewed, with special attention to the calibration heuristics supported in CASA. Practical problems affecting modern instruments are also discussed.
Calibration pipeline for VIR data
Carraro, F.; Fonte, S.; Coradini, A.; Filacchione, G.; de Sanctis, M. C.; Ammannito, E.; Capria, M. T.; Cartacci, M.; Noschese, R.; Tosi, F.; Capaccioni, F.
2011-10-01
During the second quarter of 2011 VIR-MS (VIS and IR Mapping Spectrometer) [1] aboard Dawn mission [2] has approached Vesta in order to start a long period of acquisitions that will end at the beginning of 2012. Data acquired by each instrument always require a calibration process in order to remove all the instrument effects that could affect the scientific evaluations and analysis. VIR-MS instrument team has realized a calibration pipeline which has the goal of producing calibrated (1b level) data starting from the raw (1a level) ones. The other goal of the tool has been the check of the goodness of acquired data by means of the evaluation of a series of minimum requisites of each data file, such as the percentage of the saturated pixels, the presence of spikes or the mean S/N ratio of each qube.
International Nuclear Information System (INIS)
Berger, C.D.; Gupton, E.D.; Lane, B.H.; Miller, J.H.; Nichols, S.W.
1982-08-01
The ORNL Calibrations Facility is operated by the Instrumentation Group of the Industrial Safety and Applied Health Physics Division. Its primary purpose is to maintain radiation calibration standards for calibration of ORNL health physics instruments and personnel dosimeters. This report includes a discussion of the radioactive sources and ancillary equipment in use and a step-by-step procedure for calibration of those survey instruments and personnel dosimeters in routine use at ORNL
Sheykhizadeh, Saheleh; Naseri, Abdolhossein
2018-04-01
Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.
Directory of Open Access Journals (Sweden)
Yu-Kang Tu
2011-04-01
Full Text Available Due to a problem of identification, how to estimate the distinct effects of age, time period and cohort has been a controversial issue in the analysis of trends in health outcomes in epidemiology. In this study, we propose a novel approach, partial least squares (PLS analysis, to separate the effects of age, period, and cohort. Our example for illustration is taken from the Glasgow Alumni cohort. A total of 15,322 students (11,755 men and 3,567 women received medical screening at the Glasgow University between 1948 and 1968. The aim is to investigate the secular trends in blood pressure over 1925 and 1950 while taking into account the year of examination and age at examination. We excluded students born before 1925 or aged over 25 years at examination and those with missing values in confounders from the analyses, resulting in 12,546 and 12,516 students for analysis of systolic and diastolic blood pressure, respectively. PLS analysis shows that both systolic and diastolic blood pressure increased with students' age, and students born later had on average lower blood pressure (SBP: -0.17 mmHg/per year [95% confidence intervals: -0.19 to -0.15] for men and -0.25 [-0.28 to -0.22] for women; DBP: -0.14 [-0.15 to -0.13] for men; -0.09 [-0.11 to -0.07] for women. PLS also shows a decreasing trend in blood pressure over the examination period. As identification is not a problem for PLS, it provides a flexible modelling strategy for age-period-cohort analysis. More emphasis is then required to clarify the substantive and conceptual issues surrounding the definitions and interpretations of age, period and cohort effects.
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Wiandt, T. J.
2008-06-01
The Hart Scientific Division of the Fluke Corporation operates two accredited standard platinum resistance thermometer (SPRT) calibration facilities, one at the Hart Scientific factory in Utah, USA, and the other at a service facility in Norwich, UK. The US facility is accredited through National Voluntary Laboratory Accreditation Program (NVLAP), and the UK facility is accredited through UKAS. Both provide SPRT calibrations using similar equipment and procedures, and at similar levels of uncertainty. These uncertainties are among the lowest available commercially. To achieve and maintain low uncertainties, it is required that the calibration procedures be thorough and optimized. However, to minimize customer downtime, it is also important that the instruments be calibrated in a timely manner and returned to the customer. Consequently, subjecting the instrument to repeated calibrations or extensive repeated measurements is not a viable approach. Additionally, these laboratories provide SPRT calibration services involving a wide variety of SPRT designs. These designs behave differently, yet predictably, when subjected to calibration measurements. To this end, an evaluation strategy involving both statistical process control and internal consistency measures is utilized to provide confidence in both the instrument calibration and the calibration process. This article describes the calibration facilities, procedure, uncertainty analysis, and internal quality assurance measures employed in the calibration of SPRTs. Data will be reviewed and generalities will be presented. Finally, challenges and considerations for future improvements will be discussed.
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Calibration of Nanopositioning Stages
Directory of Open Access Journals (Sweden)
Ning Tan
2015-12-01
Full Text Available Accuracy is one of the most important criteria for the performance evaluation of micro- and nanorobots or systems. Nanopositioning stages are used to achieve the high positioning resolution and accuracy for a wide and growing scope of applications. However, their positioning accuracy and repeatability are not well known and difficult to guarantee, which induces many drawbacks for many applications. For example, in the mechanical characterisation of biological samples, it is difficult to perform several cycles in a repeatable way so as not to induce negative influences on the study. It also prevents one from controlling accurately a tool with respect to a sample without adding additional sensors for closed loop control. This paper aims at quantifying the positioning repeatability and accuracy based on the ISO 9283:1998 standard, and analyzing factors influencing positioning accuracy onto a case study of 1-DoF (Degree-of-Freedom nanopositioning stage. The influence of thermal drift is notably quantified. Performances improvement of the nanopositioning stage are then investigated through robot calibration (i.e., open-loop approach. Two models (static and adaptive models are proposed to compensate for both geometric errors and thermal drift. Validation experiments are conducted over a long period (several days showing that the accuracy of the stage is improved from typical micrometer range to 400 nm using the static model and even down to 100 nm using the adaptive model. In addition, we extend the 1-DoF calibration to multi-DoF with a case study of a 2-DoF nanopositioning robot. Results demonstrate that the model efficiently improved the 2D accuracy from 1400 nm to 200 nm.
40 CFR 92.119 - Hydrocarbon analyzer calibration.
2010-07-01
... concentrations. (5) Perform a linear least square regression on the data generated. Use an equation of the form y... periodic calibration: (a) Initial and periodic optimization of detector response. Prior to introduction... to find the linear chart deflection (z) for each calibration gas concentration (y). (7) Determine the...
Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers
Walker, Eric L.; Starnes, B. Alden; Birch, Jeffery B.; Mays, James E.
2010-01-01
This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.
2016-05-01
This study evaluated the accuracy of approach volumes and free flow approach speeds collected by the Wavetronix : SmartSensor Advance sensor for the Signal Performance Metrics system of the Utah Department of Transportation (UDOT), : using the field ...
Calibration of Flick standards
International Nuclear Information System (INIS)
Thalmann, Ruedi; Spiller, Jürg; Küng, Alain; Jusko, Otto
2012-01-01
Flick standards or magnification standards are widely used for an efficient and functional calibration of the sensitivity of form measuring instruments. The results of a recent measurement comparison have shown to be partially unsatisfactory and revealed problems related to the calibration of these standards. In this paper the influence factors for the calibration of Flick standards using roundness measurement instruments are discussed in detail, in particular the bandwidth of the measurement chain, residual form errors of the device under test, profile distortions due to the diameter of the probing element and questions related to the definition of the measurand. The different contributions are estimated using simulations and are experimentally verified. Also alternative methods to calibrate Flick standards are investigated. Finally the practical limitations of Flick standard calibration are shown and the usability of Flick standards both to calibrate the sensitivity of roundness instruments and to check the filter function of such instruments is analysed. (paper)
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Pathological assessment of liver fibrosis regression
Directory of Open Access Journals (Sweden)
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
RF impedance measurement calibration
International Nuclear Information System (INIS)
Matthews, P.J.; Song, J.J.
1993-01-01
The intent of this note is not to explain all of the available calibration methods in detail. Instead, we will focus on the calibration methods of interest for RF impedance coupling measurements and attempt to explain: (1). The standards and measurements necessary for the various calibration techniques. (2). The advantages and disadvantages of each technique. (3). The mathematical manipulations that need to be applied to the measured standards and devices. (4). An outline of the steps needed for writing a calibration routine that operated from a remote computer. For further details of the various techniques presented in this note, the reader should consult the references
International Nuclear Information System (INIS)
Young, T J; Lodeiro, M J; Gower, M R L; Sassi, M B
2013-01-01
This paper describes a systematic method for calibrating polyimide recoated fibre Bragg grating (FBG) optical fibres and the associated models used to measure temperature and relative humidity (RH) from 20 to 70 °C and 20% RH to 80% RH. The method was validated by comparing known values of temperature and RH with calculated values from two FBG sensors with different thicknesses of polyimide recoat. Results show good agreement, with a standard deviation error of 0.5 °C and 4.8% RH for temperature and humidity respectively. Drift in the measured wavelength was observed for both thicknesses of polyimide coating under the combined effect of elevated temperature and high humidity. This drift was reversed after a reduction in the humidity. Additional results are provided on the use of embedded polyimide recoated FBG optical fibres for measuring moisture ingress within polymers and composites. (paper)
Calibration biases in logical reasoning tasks
Directory of Open Access Journals (Sweden)
Guillermo Macbeth
2013-08-01
Full Text Available The aim of this contribution is to present an experimental study about calibration in deductive reasoning tasks. Calibration is defi ned as the empirical convergence or divergence between the objective and the subjective success. The underconfi dence bias is understood as the dominance of the former over the latter. The hypothesis of this study states that the form of the propositions presented in the experiment is critical for calibration phenomena. Affi rmative and negative propositions are distinguished in their cognitive processing. Results suggests that monotonous compound propositions are prone to underconfi dence. An heuristic approach to this phenomenon is proposed. The activation of a monotony heuristic would produce an illusion of simplicity that generates the calibration bias. These evidence is analysed in the context of the metacognitive modeling of calibration phenomena.
On-line monitoring for calibration reduction
International Nuclear Information System (INIS)
Hoffmann, M.
2005-09-01
On-Line Monitoring evaluates instrument channel performance by assessing its consistency with other plant indications. Elimination or reduction of unnecessary field calibrations can reduce associated labour costs, reduce personnel radiation exposure, and reduce the potential for calibration errors. On-line calibration monitoring is an important technique to implement a state-based maintenance approach and reduce unnecessary field calibrations. In this report we will look at how the concept is currently applied in the industry and what the arising needs are as it becomes more commonplace. We will also look at the PEANO System, a tool developed by the Halden Project to perform signal validation and on-line calibration monitoring. Some issues will be identified that are being addressed in the further development of these tools to better serve the future needs of the industry in this area. An outline for how to improve these points and which aspects should be taken into account is described in detail. (Author)
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Modeling of the Monthly Rainfall-Runoff Process Through Regressions
Directory of Open Access Journals (Sweden)
Campos-Aranda Daniel Francisco
2014-10-01
Full Text Available To solve the problems associated with the assessment of water resources of a river, the modeling of the rainfall-runoff process (RRP allows the deduction of runoff missing data and to extend its record, since generally the information available on precipitation is larger. It also enables the estimation of inputs to reservoirs, when their building led to the suppression of the gauging station. The simplest mathematical model that can be set for the RRP is the linear regression or curve on a monthly basis. Such a model is described in detail and is calibrated with the simultaneous record of monthly rainfall and runoff in Ballesmi hydrometric station, which covers 35 years. Since the runoff of this station has an important contribution from the spring discharge, the record is corrected first by removing that contribution. In order to do this a procedure was developed based either on the monthly average regional runoff coefficients or on nearby and similar watershed; in this case the Tancuilín gauging station was used. Both stations belong to the Partial Hydrologic Region No. 26 (Lower Rio Panuco and are located within the state of San Luis Potosi, México. The study performed indicates that the monthly regression model, due to its conceptual approach, faithfully reproduces monthly average runoff volumes and achieves an excellent approximation in relation to the dispersion, proved by calculation of the means and standard deviations.
CryoSat SIRAL Calibration and Performance
Fornari, Marco; Scagliola, Michele; Tagliani, Nicolas; Parrinello, Tommaso
2013-04-01
The main payload of CryoSat is a Ku band pulse-width limited radar altimeter, called SIRAL (Synthetic interferometric radar altimeter), that transmits pulses at a high pulse repetition frequency thus making the received echoes phase coherent and suitable for azimuth processing. This allows to reach an along track resolution of about 250 meters which is a significant improvement over traditional pulse-width limited altimeters. Due to the fact that SIRAL is a phase coherent pulse-width limited radar altimeter, a proper calibration approach has been developed, including both an internal and external calibration. The internal calibration monitors the instrument impulse response and the transfer function, like traditional altimeters. In addition to that, the interferometer requires a special calibration developed ad hoc for SIRAL. The external calibration is performed with the use of a ground transponder, located in Svalbard, which receives SIRAL signal and sends the echo back to the satellite. Internal calibration data are processed on ground by the CryoSat Instrument Processing Facility (IPF1) and then applied to the science data. By April 2013, almost 3 years of calibration data will be available, which will be shown in this poster. The external calibration (transponder) data are processed and analyzed independently from the operational chain. The use of an external transponder has been very useful to determine instrument performance and for the tuning of the on-ground processor. This poster presents the transponder results in terms of range noise and datation error.
Photometric calibration of the COMBO-17 survey with the Softassign Procrustes Matching method
Sheikhbahaee, Z.; Nakajima, R.; Erben, T.; Schneider, P.; Hildebrandt, H.; Becker, A. C.
2017-11-01
Accurate photometric calibration of optical data is crucial for photometric redshift estimation. We present the Softassign Procrustes Matching (SPM) method to improve the colour calibration upon the commonly used Stellar Locus Regression (SLR) method for the COMBO-17 survey. Our colour calibration approach can be categorised as a point-set matching method, which is frequently used in medical imaging and pattern recognition. We attain a photometric redshift precision Δz/(1 + zs) of better than 2 per cent. Our method is based on aligning the stellar locus of the uncalibrated stars to that of a spectroscopic sample of the Sloan Digital Sky Survey standard stars. We achieve our goal by finding a correspondence matrix between the two point-sets and applying the matrix to estimate the appropriate translations in multidimensional colour space. The SPM method is able to find the translation between two point-sets, despite the existence of noise and incompleteness of the common structures in the sets, as long as there is a distinct structure in at least one of the colour-colour pairs. We demonstrate the precision of our colour calibration method with a mock catalogue. The SPM colour calibration code is publicly available at https://neuronphysics@bitbucket.org/neuronphysics/spm.git.
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Calibration of moisture monitors
International Nuclear Information System (INIS)
Gutierrez, R.L.
1979-02-01
A method for calibrating an aluminum oxide hygrometer against an optical chilled mirror dew-point hygrometer has been established. A theoretical cross-point line of dew points from both hygrometers and a maximum moisture content of 10 ppM/sub v/ are used to define an area for calibrating the sensor probes of the aluminum oxide hygrometer
DEFF Research Database (Denmark)
Yordanova, Ginka; Vesth, Allan
The report describes site calibration measurements carried out on a site in Denmark. The measurements are carried out in accordance to Ref. [1]. The site calibration is carried out before a power performance measurement on a given turbine to clarify the influence from the terrain on the ratio...
Topics in Statistical Calibration
2014-03-27
Natural cubic spline speed di st 110 B.2 The calibrate function The most basic calibration problem, the one often encountered in more advanced ...0040-1706, 1537-2723. A. M. Mood, F. A. Graybill, and D. C. Boes. Introduction to the Theory of Statistics. McGraw-Hill, Auckland , U.A, 1974. ISBN
Calibration of higher eigenmodes of cantilevers
International Nuclear Information System (INIS)
Labuda, Aleksander; Kocun, Marta; Walsh, Tim; Meinhold, Jieh; Proksch, Tania; Meinhold, Waiman; Anderson, Caleb; Proksch, Roger; Lysy, Martin
2016-01-01
A method is presented for calibrating the higher eigenmodes (resonant modes) of atomic force microscopy cantilevers that can be performed prior to any tip-sample interaction. The method leverages recent efforts in accurately calibrating the first eigenmode by providing the higher-mode stiffness as a ratio to the first mode stiffness. A one-time calibration routine must be performed for every cantilever type to determine a power-law relationship between stiffness and frequency, which is then stored for future use on similar cantilevers. Then, future calibrations only require a measurement of the ratio of resonant frequencies and the stiffness of the first mode. This method is verified through stiffness measurements using three independent approaches: interferometric measurement, AC approach-curve calibration, and finite element analysis simulation. Power-law values for calibrating higher-mode stiffnesses are reported for several cantilever models. Once the higher-mode stiffnesses are known, the amplitude of each mode can also be calibrated from the thermal spectrum by application of the equipartition theorem.
Calibration of higher eigenmodes of cantilevers
Energy Technology Data Exchange (ETDEWEB)
Labuda, Aleksander; Kocun, Marta; Walsh, Tim; Meinhold, Jieh; Proksch, Tania; Meinhold, Waiman; Anderson, Caleb; Proksch, Roger [Asylum Research, an Oxford Instruments Company, Santa Barbara, California 93117 (United States); Lysy, Martin [Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1 (Canada)
2016-07-15
A method is presented for calibrating the higher eigenmodes (resonant modes) of atomic force microscopy cantilevers that can be performed prior to any tip-sample interaction. The method leverages recent efforts in accurately calibrating the first eigenmode by providing the higher-mode stiffness as a ratio to the first mode stiffness. A one-time calibration routine must be performed for every cantilever type to determine a power-law relationship between stiffness and frequency, which is then stored for future use on similar cantilevers. Then, future calibrations only require a measurement of the ratio of resonant frequencies and the stiffness of the first mode. This method is verified through stiffness measurements using three independent approaches: interferometric measurement, AC approach-curve calibration, and finite element analysis simulation. Power-law values for calibrating higher-mode stiffnesses are reported for several cantilever models. Once the higher-mode stiffnesses are known, the amplitude of each mode can also be calibrated from the thermal spectrum by application of the equipartition theorem.
Sandia WIPP calibration traceability
Energy Technology Data Exchange (ETDEWEB)
Schuhen, M.D. [Sandia National Labs., Albuquerque, NM (United States); Dean, T.A. [RE/SPEC, Inc., Albuquerque, NM (United States)
1996-05-01
This report summarizes the work performed to establish calibration traceability for the instrumentation used by Sandia National Laboratories at the Waste Isolation Pilot Plant (WIPP) during testing from 1980-1985. Identifying the calibration traceability is an important part of establishing a pedigree for the data and is part of the qualification of existing data. In general, the requirement states that the calibration of Measuring and Test equipment must have a valid relationship to nationally recognized standards or the basis for the calibration must be documented. Sandia recognized that just establishing calibration traceability would not necessarily mean that all QA requirements were met during the certification of test instrumentation. To address this concern, the assessment was expanded to include various activities.
Energy Technology Data Exchange (ETDEWEB)
Baudis, Laura; Froborg, Francis; Tarka, Michael; Bruch, Tobias; Ferella, Alfredo [Physik-Institut, Universitaet Zuerich (Switzerland); Collaboration: GERDA-Collaboration
2012-07-01
A system with three identical custom made units is used for the energy calibration of the GERDA Ge diodes. To perform a calibration the {sup 228}Th sources are lowered from the parking positions at the top of the cryostat. Their positions are measured by two independent modules. One, the incremental encoder, counts the holes in the perforated steel band holding the sources, the other measures the drive shaft's angular position even if not powered. The system can be controlled remotely by a Labview program. The calibration data is analyzed by an iterative calibration algorithm determining the calibration functions for different energy reconstruction algorithms and the resolution of several peaks in the {sup 228}Th spectrum is determined. A Monte Carlo simulation using the GERDA simulation software MAGE has been performed to determine the background induced by the sources in the parking positions.
Sandia WIPP calibration traceability
International Nuclear Information System (INIS)
Schuhen, M.D.; Dean, T.A.
1996-05-01
This report summarizes the work performed to establish calibration traceability for the instrumentation used by Sandia National Laboratories at the Waste Isolation Pilot Plant (WIPP) during testing from 1980-1985. Identifying the calibration traceability is an important part of establishing a pedigree for the data and is part of the qualification of existing data. In general, the requirement states that the calibration of Measuring and Test equipment must have a valid relationship to nationally recognized standards or the basis for the calibration must be documented. Sandia recognized that just establishing calibration traceability would not necessarily mean that all QA requirements were met during the certification of test instrumentation. To address this concern, the assessment was expanded to include various activities
Feature-based automatic color calibration for networked camera system
Yamamoto, Shoji; Taki, Keisuke; Tsumura, Norimichi; Nakaguchi, Toshiya; Miyake, Yoichi
2011-01-01
In this paper, we have developed a feature-based automatic color calibration by using an area-based detection and adaptive nonlinear regression method. Simple color matching of chartless is achieved by using the characteristic of overlapping image area with each camera. Accurate detection of common object is achieved by the area-based detection that combines MSER with SIFT. Adaptive color calibration by using the color of detected object is calculated by nonlinear regression method. This method can indicate the contribution of object's color for color calibration, and automatic selection notification for user is performed by this function. Experimental result show that the accuracy of the calibration improves gradually. It is clear that this method can endure practical use of multi-camera color calibration if an enough sample is obtained.
Estimation of age in forensic medicine using multivariate approach to image analysis
DEFF Research Database (Denmark)
Kucheryavskiy, Sergey V.; Belyaev, Ivan; Fominykh, Sergey
2009-01-01
approach based on statistical analysis of grey-level co-occurrence matrix, fractal analysis, wavelet transformation and Angle Measure Technique. Projection on latent structures regression was chosen for calibration and prediction. The method has been applied to 70 male and 63 female individuals aged from...... 21 to 93 and results were compared with traditional approach. Some important questions and problems have been raised....
International Nuclear Information System (INIS)
Shin, Ho Cheol; Park, Moon Ghu; You, Skin
2006-01-01
Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto associative kernel regression (AAKR) by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
Meng, Lingsen; Zhang, Ailin; Yagi, Yuji
2016-01-01
The 2015 Mw 7.8 Nepal-Gorkha earthquake with casualties of over 9000 people was the most devastating disaster to strike Nepal since the 1934 Nepal-Bihar earthquake. Its rupture process was imaged by teleseismic back projections (BP) of seismograms recorded by three, large regional networks in Australia, North America, and Europe. The source images of all three arrays reveal a unilateral eastward rupture; however, the propagation directions and speeds differ significantly between the arrays. To understand the spatial uncertainties of the BP analyses, we analyze four moderate size aftershocks recorded by all three arrays exactly as had been conducted for the main shock. The apparent source locations inferred from BPs are systematically biased from the catalog locations, as a result of a slowness error caused by three-dimensional Earth structures. We introduce a physics-based slowness correction that successfully mitigates the source location discrepancies among the arrays. Our calibrated BPs are found to be mutually consistent and reveal a unilateral rupture propagating eastward at a speed of 2.7 km/s, localized in a relatively narrow and deep swath along the downdip edge of the locked Himalayan thrust zone. We find that the 2015 Gorkha earthquake was a localized rupture that failed to break the entire Himalayan décollement to the surface, which can be regarded as an intermediate event during the interseismic period of larger Himalayan ruptures that break the whole seismogenic zone width. Thus, our physics-based slowness correction is an important technical improvement of BP, mitigating spatial uncertainties and improving the robustness of single and multiarray studies.
Regression of environmental noise in LIGO data
International Nuclear Information System (INIS)
Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G
2015-01-01
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)
Compact radiometric microwave calibrator
International Nuclear Information System (INIS)
Fixsen, D. J.; Wollack, E. J.; Kogut, A.; Limon, M.; Mirel, P.; Singal, J.; Fixsen, S. M.
2006-01-01
The calibration methods for the ARCADE II instrument are described and the accuracy estimated. The Steelcast coated aluminum cones which comprise the calibrator have a low reflection while maintaining 94% of the absorber volume within 5 mK of the base temperature (modeled). The calibrator demonstrates an absorber with the active part less than one wavelength thick and only marginally larger than the mouth of the largest horn and yet black (less than -40 dB or 0.01% reflection) over five octaves in frequency
DEFF Research Database (Denmark)
Fernandez Garcia, Sergio; Villanueva, Héctor
This report presents the result of the lidar to lidar calibration performed for ground-based lidar. Calibration is here understood as the establishment of a relation between the reference lidar wind speed measurements with measurement uncertainties provided by measurement standard and corresponding...... lidar wind speed indications with associated measurement uncertainties. The lidar calibration concerns the 10 minute mean wind speed measurements. The comparison of the lidar measurements of the wind direction with that from the reference lidar measurements are given for information only....
Theory of net analyte signal vectors in inverse regression
DEFF Research Database (Denmark)
Bro, R.; Andersen, Charlotte Møller
2003-01-01
The. net analyte signal and the net analyte signal vector are useful measures in building and optimizing multivariate calibration models. In this paper a theory for their use in inverse regression is developed. The theory of net analyte signal was originally derived from classical least squares...
Application of calibrations to hyperspectral images of food grains: example for wheat falling number
Directory of Open Access Journals (Sweden)
Nicola Caporaso
2017-04-01
Full Text Available The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN method, which is a batch analysis. Hyperspectral imaging (HSI can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR region (~900–2500 nm and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel; ii calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 s; RMSEP ~ 63 s compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA was also applied to segregate wheat kernels into low (250 s HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.
There is No Quantum Regression Theorem
International Nuclear Information System (INIS)
Ford, G.W.; OConnell, R.F.
1996-01-01
The Onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium. It is here asserted that this hypothesis fails in the quantum case. This is shown first by explicit calculation for the example of quantum Brownian motion of an oscillator and then in general from the fluctuation-dissipation theorem. It is asserted that the correct generalization of the Onsager hypothesis is the fluctuation-dissipation theorem. copyright 1996 The American Physical Society
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.; Carroll, R.J.; Wand, M.P.
2010-01-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Federal Laboratory Consortium — This facility is for low altitude subsonic altimeter system calibrations of air vehicles. Mission is a direct support of the AFFTC mission. Postflight data merge is...
U.S. Environmental Protection Agency — an UV calibration curve for SRHA quantitation. This dataset is associated with the following publication: Chang, X., and D. Bouchard. Surfactant-Wrapped Multiwalled...
International Nuclear Information System (INIS)
Zhang Bingyun; Li Xiaonan; Zhu Kejun; Zhang Jiawen; Gong Mingyu
2003-01-01
We constructed BES (Beijing Spectrometer) online calibration system to ensure the coherence of readout electronic channels due to huge data volume in high energy physics experiment. This paper describes the structure of hardware and software, and its characteristic and function
International Nuclear Information System (INIS)
Ahlers, C.F.; Liu, H.H.
2001-01-01
The purpose of this Analysis/Model Report (AMR) is to document the Calibrated Properties Model that provides calibrated parameter sets for unsaturated zone (UZ) flow and transport process models for the Yucca Mountain Site Characterization Project (YMP). This work was performed in accordance with the AMR Development Plan for U0035 Calibrated Properties Model REV00 (CRWMS M and O 1999c). These calibrated property sets include matrix and fracture parameters for the UZ Flow and Transport Model (UZ Model), drift seepage models, drift-scale and mountain-scale coupled-processes models, and Total System Performance Assessment (TSPA) models as well as Performance Assessment (PA) and other participating national laboratories and government agencies. These process models provide the necessary framework to test conceptual hypotheses of flow and transport at different scales and predict flow and transport behavior under a variety of climatic and thermal-loading conditions
International Nuclear Information System (INIS)
Ahlers, C.; Liu, H.
2000-01-01
The purpose of this Analysis/Model Report (AMR) is to document the Calibrated Properties Model that provides calibrated parameter sets for unsaturated zone (UZ) flow and transport process models for the Yucca Mountain Site Characterization Project (YMP). This work was performed in accordance with the ''AMR Development Plan for U0035 Calibrated Properties Model REV00. These calibrated property sets include matrix and fracture parameters for the UZ Flow and Transport Model (UZ Model), drift seepage models, drift-scale and mountain-scale coupled-processes models, and Total System Performance Assessment (TSPA) models as well as Performance Assessment (PA) and other participating national laboratories and government agencies. These process models provide the necessary framework to test conceptual hypotheses of flow and transport at different scales and predict flow and transport behavior under a variety of climatic and thermal-loading conditions
Directory of Open Access Journals (Sweden)
Patterson E.
2010-06-01
Full Text Available The results are presented using the procedure outlined by the Standardisation Project for Optical Techniques of Strain measurement to calibrate a digital image correlation system. The process involves comparing the experimental data obtained with the optical measurement system to the theoretical values for a specially designed specimen. The standard states the criteria which must be met in order to achieve successful calibration, in addition to quantifying the measurement uncertainty in the system. The system was evaluated at three different displacement load levels, generating strain ranges from 289 µstrain to 2110 µstrain. At the 289 µstrain range, the calibration uncertainty was found to be 14.1 µstrain, and at the 2110 µstrain range it was found to be 28.9 µstrain. This calibration procedure was performed without painting a speckle pattern on the surface of the metal. Instead, the specimen surface was prepared using different grades of grit paper to produce the desired texture.
Directory of Open Access Journals (Sweden)
Pozhitkov Alexander E
2010-07-01
Full Text Available Abstract Background Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (BMC Bioinformatics, 2005, 6, Art. No. S11 Suppl. 2. reported usage of a Full Moon BioSystems slide for calibration. Inspired by the Shi et al. work, we have calibrated microarray scanners in our previous research. We were puzzled however, that most of the signal intensities from a biological sample fell below the sensitivity threshold level determined by the calibration slide. This conundrum led us to re-investigate the quality of calibration provided by the Full Moon BioSystems slide as well as the accuracy of the analysis performed by Shi et al. Methods Signal intensities were recorded on three different microarray scanners at various photomultiplier gain levels using the same calibration slide from Full Moon BioSystems. Data analysis was conducted on raw signal intensities without normalization or transformation of any kind. Weighted least-squares method was used to fit the data. Results We found that initial analysis performed by Shi et al. did not take into account autofluorescence of the Full Moon BioSystems slide, which led to a grossly distorted microarray scanner response. Our analysis revealed that a power-law function, which is explicitly accounting for the slide autofluorescence, perfectly described a relationship between signal intensities and fluorophore quantities. Conclusions Microarray scanners respond in a much less distorted fashion than was reported by Shi et al. Full Moon BioSystems calibration slides are inadequate for performing calibration. We recommend against using these slides.
Calibration of thermoluminiscent materials
International Nuclear Information System (INIS)
Bos, A.J.J.
1989-07-01
In this report the relation between exposure and absorbed radiation dose in various materials is represented, on the base of recent data. With the help of this a calibration procedure for thermoluminescent materials, adapted to the IRI radiation standard is still the exposure in rontgen. In switching to the air kerma standard the calibration procedure will have to be adapted. (author). 6 refs.; 4 tabs
Energy Technology Data Exchange (ETDEWEB)
Courtney, M.
2013-01-15
Nacelle mounted, forward looking wind lidars are beginning to be used to provide reference wind speed measurements for the power performance testing of wind turbines. In such applications, a formal calibration procedure with a corresponding uncertainty assessment will be necessary. This report presents four concepts for performing such a nacelle lidar calibration. Of the four methods, two are found to be immediately relevant and are pursued in some detail. The first of these is a line of sight calibration method in which both lines of sight (for a two beam lidar) are individually calibrated by accurately aligning the beam to pass close to a reference wind speed sensor. A testing procedure is presented, reporting requirements outlined and the uncertainty of the method analysed. It is seen that the main limitation of the line of sight calibration method is the time required to obtain a representative distribution of radial wind speeds. An alternative method is to place the nacelle lidar on the ground and incline the beams upwards to bisect a mast equipped with reference instrumentation at a known height and range. This method will be easier and faster to implement and execute but the beam inclination introduces extra uncertainties. A procedure for conducting such a calibration is presented and initial indications of the uncertainties given. A discussion of the merits and weaknesses of the two methods is given together with some proposals for the next important steps to be taken in this work. (Author)
Model Calibration in Watershed Hydrology
Yilmaz, Koray K.; Vrugt, Jasper A.; Gupta, Hoshin V.; Sorooshian, Soroosh
2009-01-01
Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must, therefore, be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. This Chapter reviews the current state-of-the-art of model calibration in watershed hydrology with special emphasis on our own contributions in the last few decades. We discuss the historical background that has led to current perspectives, and review different approaches for manual and automatic single- and multi-objective parameter estimation. In particular, we highlight the recent developments in the calibration of distributed hydrologic models using parameter dimensionality reduction sampling, parameter regularization and parallel computing.
Temperature corrected-calibration of GRACE's accelerometer
Encarnacao, J.; Save, H.; Siemes, C.; Doornbos, E.; Tapley, B. D.
2017-12-01
Since April 2011, the thermal control of the accelerometers on board the GRACE satellites has been turned off. The time series of along-track bias clearly show a drastic change in the behaviour of this parameter, while the calibration model has remained unchanged throughout the entire mission lifetime. In an effort to improve the quality of the gravity field models produced at CSR in future mission-long re-processing of GRACE data, we quantify the added value of different calibration strategies. In one approach, the temperature effects that distort the raw accelerometer measurements collected without thermal control are corrected considering the housekeeping temperature readings. In this way, one single calibration strategy can be consistently applied during the whole mission lifetime, since it is valid to thermal the conditions before and after April 2011. Finally, we illustrate that the resulting calibrated accelerations are suitable for neutral thermospheric density studies.
Advanced Method of the Elastomagnetic Sensors Calibration
Directory of Open Access Journals (Sweden)
Mikulas Prascak
2004-01-01
Full Text Available Elastomagnetic method (EM method is a highly sensitive non-contact evaluation method for measuring tensile and compressive stress in steel. The latest development of measuring devices and EM sensors has shown that the thermomagnetic phenomenon has a stron influence on th accuracy during the EM sensor calibration. To eliminate the influence of this effect a two dimensional regression method is presented.
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.
Option price calibration from Renyi entropy
International Nuclear Information System (INIS)
Brody, Dorje C.; Buckley, Ian R.C.; Constantinou, Irene C.
2007-01-01
The calibration of the risk-neutral density function for the future asset price, based on the maximisation of the entropy measure of Renyi, is proposed. Whilst the conventional approach based on the use of logarithmic entropy measure fails to produce the observed power-law distribution when calibrated against option prices, the approach outlined here is shown to produce the desired form of the distribution. Procedures for the maximisation of the Renyi entropy under constraints are outlined in detail, and a number of interesting properties of the resulting power-law distributions are also derived. The result is applied to efficiently evaluate prices of path-independent derivatives
Directory of Open Access Journals (Sweden)
I. V. Geogdzhayev
2018-01-01
Full Text Available The unique position of the Deep Space Climate Observatory (DSCOVR Earth Polychromatic Imaging Camera (EPIC at the Lagrange 1 point makes an important addition to the data from currently operating low Earth orbit observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar-orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1 km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443, 551, 680 and 780 nm. MODIS and EPIC measurements made between June 2015 and 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts s−1 and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 and 764 nm, assuming that there is a small difference between moon reflectances separated by ∼ 10 nm in wavelength and provided the calibration factors of the red (680 nm and NIR (780 nm are known from comparison between EPIC and MODIS.
Geogdzhayev, Igor V.; Marshak, Alexander
2018-01-01
The unique position of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange 1 point makes an important addition to the data from currently operating low Earth orbit observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar-orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS) is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443, 551, 680 and 780 nm. MODIS and EPIC measurements made between June 2015 and 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts/sec and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 and 764 nm), assuming that there is a small difference between moon reflectances separated by approx.10 nm in wavelength provided the calibration factors of the red (680 nm) and near-IR (780 nm) are known from comparison between EPIC and MODIS.
Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
POLCAL - POLARIMETRIC RADAR CALIBRATION
Vanzyl, J.
1994-01-01
Calibration of polarimetric radar systems is a field of research in which great progress has been made over the last few years. POLCAL (Polarimetric Radar Calibration) is a software tool intended to assist in the calibration of Synthetic Aperture Radar (SAR) systems. In particular, POLCAL calibrates Stokes matrix format data produced as the standard product by the NASA/Jet Propulsion Laboratory (JPL) airborne imaging synthetic aperture radar (AIRSAR). POLCAL was designed to be used in conjunction with data collected by the NASA/JPL AIRSAR system. AIRSAR is a multifrequency (6 cm, 24 cm, and 68 cm wavelength), fully polarimetric SAR system which produces 12 x 12 km imagery at 10 m resolution. AIRSTAR was designed as a testbed for NASA's Spaceborne Imaging Radar program. While the images produced after 1991 are thought to be calibrated (phase calibrated, cross-talk removed, channel imbalance removed, and absolutely calibrated), POLCAL can and should still be used to check the accuracy of the calibration and to correct it if necessary. Version 4.0 of POLCAL is an upgrade of POLCAL version 2.0 released to AIRSAR investigators in June, 1990. New options in version 4.0 include automatic absolute calibration of 89/90 data, distributed target analysis, calibration of nearby scenes with calibration parameters from a scene with corner reflectors, altitude or roll angle corrections, and calibration of errors introduced by known topography. Many sources of error can lead to false conclusions about the nature of scatterers on the surface. Errors in the phase relationship between polarization channels result in incorrect synthesis of polarization states. Cross-talk, caused by imperfections in the radar antenna itself, can also lead to error. POLCAL reduces cross-talk and corrects phase calibration without the use of ground calibration equipment. Removing the antenna patterns during SAR processing also forms a very important part of the calibration of SAR data. Errors in the
Vibration transducer calibration techniques
Brinkley, D. J.
1980-09-01
Techniques for the calibration of vibration transducers used in the Aeronautical Quality Assurance Directorate of the British Ministry of Defence are presented. Following a review of the types of measurements necessary in the calibration of vibration transducers, the performance requirements of vibration transducers, which can be used to measure acceleration, velocity or vibration amplitude, are discussed, with particular attention given to the piezoelectric accelerometer. Techniques for the accurate measurement of sinusoidal vibration amplitude in reference-grade transducers are then considered, including the use of a position sensitive photocell and the use of a Michelson laser interferometer. Means of comparing the output of working-grade accelerometers with that of previously calibrated reference-grade devices are then outlined, with attention given to a method employing a capacitance bridge technique and a method to be used at temperatures between -50 and 200 C. Automatic calibration procedures developed to speed up the calibration process are outlined, and future possible extensions of system software are indicated.
Fouad, Geoffrey; Skupin, André; Hope, Allen
2016-04-01
The flow duration curve (FDC) is one of the most widely used tools to quantify streamflow. Its percentile flows are often required for water resource applications, but these values must be predicted for ungauged basins with insufficient or no streamflow data. Regional regression is a commonly used approach for predicting percentile flows that involves identifying hydrologic regions and calibrating regression models to each region. The independent variables used to describe the physiographic and climatic setting of the basins are a critical component of regional regression, yet few studies have investigated their effect on resulting predictions. In this study, the complexity of the independent variables needed for regional regression is investigated. Different levels of variable complexity are applied for a regional regression consisting of 918 basins in the US. Both the hydrologic regions and regression models are determined according to the different sets of variables, and the accuracy of resulting predictions is assessed. The different sets of variables include (1) a simple set of three variables strongly tied to the FDC (mean annual precipitation, potential evapotranspiration, and baseflow index), (2) a traditional set of variables describing the average physiographic and climatic conditions of the basins, and (3) a more complex set of variables extending the traditional variables to include statistics describing the distribution of physiographic data and temporal components of climatic data. The latter set of variables is not typically used in regional regression, and is evaluated for its potential to predict percentile flows. The simplest set of only three variables performed similarly to the other more complex sets of variables. Traditional variables used to describe climate, topography, and soil offered little more to the predictions, and the experimental set of variables describing the distribution of basin data in more detail did not improve predictions
Satellite rainfall retrieval by logistic regression
Chiu, Long S.
1986-01-01
The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
Online examiner calibration across specialties.
Sturman, Nancy; Wong, Wai Yee; Turner, Jane; Allan, Chris
2017-09-26
Integrating undergraduate medical curricula horizontally across clinical medical specialties may be a more patient-centred and learner-centred approach than rotating students through specialty-specific teaching and assessment, but requires some interspecialty calibration of examiner judgements. Our aim was to evaluate the acceptability and feasibility of an online pilot of interdisciplinary examiner calibration. Fair clinical assessment is important to both medical students and clinical teachers METHODS: Clinical teachers were invited to rate video-recorded student objective structured clinical examination (OSCE) performances and join subsequent online discussions using the university's learning management system. Post-project survey free-text and Likert-scale participant responses were analysed to evaluate the acceptability of the pilot and to identify recommendations for improvement. Although 68 clinicians were recruited to participate, and there were 1599 hits on recordings and discussion threads, only 25 clinical teachers rated at least one student performance, and 18 posted at least one comment. Participants, including rural doctors, appeared to value the opportunity for interdisciplinary rating calibration and discussion. Although the asynchronous online format had advantages, especially for rural doctors, participants reported considerable IT challenges. Our findings suggest that fair clinical assessment is important to both medical students and clinical teachers. Interspecialty discussions about assessment may have the potential to enrich intraspecialty perspectives, enhance interspecialty engagement and collaboration, and improve the quality of clinical teacher assessment. Better alignment of university and hospital systems, a face to face component and other modifications may have enhanced clinician engagement with this project. Findings suggest that specialty assessment cultures and content expertise may not be barriers to pursuing more integrated
Calibration of groundwater vulnerability mapping using the generalized reduced gradient method.
Elçi, Alper
2017-12-01
Groundwater vulnerability assessment studies are essential in water resources management. Overlay-and-index methods such as DRASTIC are widely used for mapping of groundwater vulnerability, however, these methods mainly suffer from a subjective selection of model parameters. The objective of this study is to introduce a calibration procedure that results in a more accurate assessment of groundwater vulnerability. The improvement of the assessment is formulated as a parameter optimization problem using an objective function that is based on the correlation between actual groundwater contamination and vulnerability index values. The non-linear optimization problem is solved with the generalized-reduced-gradient (GRG) method, which is numerical algorithm based optimization method. To demonstrate the applicability of the procedure, a vulnerability map for the Tahtali stream basin is calibrated using nitrate concentration data. The calibration procedure is easy to implement and aims the maximization of correlation between observed pollutant concentrations and groundwater vulnerability index values. The influence of each vulnerability parameter in the calculation of the vulnerability index is assessed by performing a single-parameter sensitivity analysis. Results of the sensitivity analysis show that all factors are effective on the final vulnerability index. Calibration of the vulnerability map improves the correlation between index values and measured nitrate concentrations by 19%. The regression coefficient increases from 0.280 to 0.485. It is evident that the spatial distribution and the proportions of vulnerability class areas are significantly altered with the calibration process. Although the applicability of the calibration method is demonstrated on the DRASTIC model, the applicability of the approach is not specific to a certain model and can also be easily applied to other overlay-and-index methods. Copyright © 2017 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Bird, A.J.; Barlow, E.J.; Tikkanen, T.; Bazzano, A.; Del Santo, M.; Ubertini, P.; Blondel, C.; Laurent, P.; Lebrun, F.; Di Cocco, G.; Malaguti, E.; Gabriele, M.; La Rosa, G.; Segreto, A.; Quadrini, E.; Volkmer, R.
2003-01-01
We present an overview of results obtained from IBIS ground calibrations. The spectral and spatial characteristics of the detector planes and surrounding passive materials have been determined through a series of calibration campaigns. Measurements of pixel gain, energy resolution, detection uniformity, efficiency and imaging capability are presented. The key results obtained from the ground calibration have been: - optimization of the instrument tunable parameters, - determination of energy linearity for all detection modes, - determination of energy resolution as a function of energy through the range 20 keV - 3 MeV, - demonstration of imaging capability in each mode, - measurement of intrinsic detector non-uniformity and understanding of the effects of passive materials surrounding the detector plane, and - discovery (and closure) of various leakage paths through the passive shielding system
Gamma counter calibration system
International Nuclear Information System (INIS)
1977-01-01
A method and apparatus are described for the calibration of a gamma radiation measurement instrument to be used over any of a number of different absolute energy ranges. The method includes the steps of adjusting the overall signal gain associated with pulses which are derived from detected gamma rays, until the instrument is calibrated for a particular absolute energy range; then storing parameter settings corresponding to the adjusted overall signal gain, and repeating the process for other desired absolute energy ranges. The stored settings can be subsequently retrieved and reapplied so that test measurements can be made using a selected one of the absolute energy ranges. Means are provided for adjusting the overall signal gain and a specific technique is disclosed for making coarse, then fine adjustments to the signal gain, for rapid convergence of the required calibration settings. (C.F.)
Laser Calibration of the ATLAS Tile Calorimeter
Di Gregorio, Giulia; The ATLAS collaboration
2017-01-01
High performance stability of the ATLAS Tile Calorimeter is achieved with a set of calibration procedures. One step of the calibration procedure is based on measurements of the response stability to laser excitation of the PMTs that are used to readout the calorimeter cells. A facility to study in lab the PMT stability response is operating in the PISA-INFN laboratories since 2015. Goals of the tests in lab are to study the time evolution of the PMT response to reproduce and to understand the origin of the response drifts seen with the PMT mounted on the Tile calorimeter in its normal operating during LHC run I and run II. A new statistical approach was developed to measure drift of the absolute gain. This approach was applied to both the ATLAS laser calibration data and to data collected in the Pisa local laboratory. The preliminary results from these two studies are shown.
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Individual dosimetry and calibration
International Nuclear Information System (INIS)
Hoefert, M.; Nielsen, M.
1996-01-01
In 1995 both the Individual Dosimetry and Calibration Sections worked under the condition of a status quo and concentrated fully on the routine part of their work. Nevertheless, the machine for printing the bar code which will be glued onto the film holder and hence identify the people when entering into high radiation areas was put into operation and most of the holders were equipped with the new identification. As far as the Calibration Section is concerned the project of the new source control system that is realized by the Technical Support Section was somewhat accelerated
Radiation Calibration Measurements
International Nuclear Information System (INIS)
Omondi, C.
2017-01-01
KEBS Radiation Dosimetry mandate are: Custodian of Kenya Standards on Ionizing radiation, Ensure traceability to International System (SI ) and Calibration radiation equipment. RAF 8/040 on Radioisotope applications for troubleshooting and optimizing industrial process established Radiotracer Laboratory objective is to introduce and implement radiotracer technique for problem solving of industrial challenges. Gamma ray scanning technique applied is to Locate blockages, Locate liquid in vapor lines, Locate areas of lost refractory or lining in a pipe and Measure flowing densities. Equipment used for diagnostic and radiation protection must be calibrated to ensure Accuracy and Traceability
DEFF Research Database (Denmark)
Gómez Arranz, Paula; Courtney, Michael
This report describes the tests carried out on a scanning lidar at the DTU Test Station for large wind turbines, Høvsøre. The tests were divided in two parts. In the first part, the purpose was to obtain wind speed calibrations at two heights against two cup anemometers mounted on a mast. Additio......This report describes the tests carried out on a scanning lidar at the DTU Test Station for large wind turbines, Høvsøre. The tests were divided in two parts. In the first part, the purpose was to obtain wind speed calibrations at two heights against two cup anemometers mounted on a mast...
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Cumulative sum quality control for calibrated breast density measurements
International Nuclear Information System (INIS)
Heine, John J.; Cao Ke; Beam, Craig
2009-01-01
Purpose: Breast density is a significant breast cancer risk factor. Although various methods are used to estimate breast density, there is no standard measurement for this important factor. The authors are developing a breast density standardization method for use in full field digital mammography (FFDM). The approach calibrates for interpatient acquisition technique differences. The calibration produces a normalized breast density pixel value scale. The method relies on first generating a baseline (BL) calibration dataset, which required extensive phantom imaging. Standardizing prospective mammograms with calibration data generated in the past could introduce unanticipated error in the standardized output if the calibration dataset is no longer valid. Methods: Sample points from the BL calibration dataset were imaged approximately biweekly over an extended timeframe. These serial samples were used to evaluate the BL dataset reproducibility and quantify the serial calibration accuracy. The cumulative sum (Cusum) quality control method was used to evaluate the serial sampling. Results: There is considerable drift in the serial sample points from the BL calibration dataset that is x-ray beam dependent. Systematic deviation from the BL dataset caused significant calibration errors. This system drift was not captured with routine system quality control measures. Cusum analysis indicated that the drift is a sign of system wear and eventual x-ray tube failure. Conclusions: The BL calibration dataset must be monitored and periodically updated, when necessary, to account for sustained system variations to maintain the calibration accuracy.
Cumulative sum quality control for calibrated breast density measurements
Energy Technology Data Exchange (ETDEWEB)
Heine, John J.; Cao Ke; Beam, Craig [Cancer Prevention and Control Division, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612 (United States); Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor St., Chicago, Illinois 60612 (United States)
2009-12-15
Purpose: Breast density is a significant breast cancer risk factor. Although various methods are used to estimate breast density, there is no standard measurement for this important factor. The authors are developing a breast density standardization method for use in full field digital mammography (FFDM). The approach calibrates for interpatient acquisition technique differences. The calibration produces a normalized breast density pixel value scale. The method relies on first generating a baseline (BL) calibration dataset, which required extensive phantom imaging. Standardizing prospective mammograms with calibration data generated in the past could introduce unanticipated error in the standardized output if the calibration dataset is no longer valid. Methods: Sample points from the BL calibration dataset were imaged approximately biweekly over an extended timeframe. These serial samples were used to evaluate the BL dataset reproducibility and quantify the serial calibration accuracy. The cumulative sum (Cusum) quality control method was used to evaluate the serial sampling. Results: There is considerable drift in the serial sample points from the BL calibration dataset that is x-ray beam dependent. Systematic deviation from the BL dataset caused significant calibration errors. This system drift was not captured with routine system quality control measures. Cusum analysis indicated that the drift is a sign of system wear and eventual x-ray tube failure. Conclusions: The BL calibration dataset must be monitored and periodically updated, when necessary, to account for sustained system variations to maintain the calibration accuracy.
Multivariate calibration applied to the quantitative analysis of infrared spectra
Energy Technology Data Exchange (ETDEWEB)
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
Finite Algorithms for Robust Linear Regression
DEFF Research Database (Denmark)
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Calibration of the Herschel SPIRE Fourier Transform Spectrometer
Swinyard, Bruce; Polehampton, E. T.; Hopwood, R.; Valtchanov, I.; Lu, N.; Fulton, T.; Benielli, D.; Imhof, P.; Marchili, N.; Baluteau, J.- P.; Bendo, G. J.; Ferlet, M.; Griffin, Matthew Jason; Lim, T. L.; Makiwa, G.
2014-01-01
The Herschel Spectral and Photometric REceiver (SPIRE) instrument consists of an imaging photometric camera and an imaging Fourier Transform Spectrometer (FTS), both operating over a frequency range of ∼450–1550 GHz. In this paper, we briefly review the FTS design, operation, and data reduction, and describe in detail the approach taken to relative calibration (removal of instrument signatures) and absolute calibration against standard astronomical sources. The calibration scheme assumes a sp...
Actuator-Assisted Calibration of Freehand 3D Ultrasound System.
Koo, Terry K; Silvia, Nathaniel
2018-01-01
Freehand three-dimensional (3D) ultrasound has been used independently of other technologies to analyze complex geometries or registered with other imaging modalities to aid surgical and radiotherapy planning. A fundamental requirement for all freehand 3D ultrasound systems is probe calibration. The purpose of this study was to develop an actuator-assisted approach to facilitate freehand 3D ultrasound calibration using point-based phantoms. We modified the mathematical formulation of the calibration problem to eliminate the need of imaging the point targets at different viewing angles and developed an actuator-assisted approach/setup to facilitate quick and consistent collection of point targets spanning the entire image field of view. The actuator-assisted approach was applied to a commonly used cross wire phantom as well as two custom-made point-based phantoms (original and modified), each containing 7 collinear point targets, and compared the results with the traditional freehand cross wire phantom calibration in terms of calibration reproducibility, point reconstruction precision, point reconstruction accuracy, distance reconstruction accuracy, and data acquisition time. Results demonstrated that the actuator-assisted single cross wire phantom calibration significantly improved the calibration reproducibility and offered similar point reconstruction precision, point reconstruction accuracy, distance reconstruction accuracy, and data acquisition time with respect to the freehand cross wire phantom calibration. On the other hand, the actuator-assisted modified "collinear point target" phantom calibration offered similar precision and accuracy when compared to the freehand cross wire phantom calibration, but it reduced the data acquisition time by 57%. It appears that both actuator-assisted cross wire phantom and modified collinear point target phantom calibration approaches are viable options for freehand 3D ultrasound calibration.
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
In situ neutron moisture meter calibration in lateritic soils
International Nuclear Information System (INIS)
Ruprecht, J.K.; Schofield, N.J.
1990-01-01
An in situ calibration procedure for complex lateritic soils of the jarrah forest of Western Australia is described. The calibration is based on non-destructive sampling of each access tube and on a regression of change in water content on change in neutron count ratio at 'wet' and 'dry' times of the year. Calibration equations with adequate precision were produced. However, there were high residual errors in the calibration equations which were due to a number of factors including soil water variability, the presence of a duricrust layer, soil sampling of gravelly soils and the variability of the cement slurry annulus surrounding each access tube. The calibration equations derived did not compare well with those from other studies in south-west Western Australia, but there was reasonable agreement with the general equations obtained by the Institute of Hydrology, U.K. 15 refs., 6 figs., 2 tabs
Retinal vascular calibres are significantly associated with cardiovascular risk factors
DEFF Research Database (Denmark)
von Hanno, T.; Bertelsen, G.; Sjølie, Anne K.
2014-01-01
. Association between retinal vessel calibre and the cardiovascular risk factors was assessed by multivariable linear and logistic regression analyses. Results: Retinal arteriolar calibre was independently associated with age, blood pressure, HbA1c and smoking in women and men, and with HDL cholesterol in men......Purpose: To describe the association between retinal vascular calibres and cardiovascular risk factors. Methods: Population-based cross-sectional study including 6353 participants of the TromsO Eye Study in Norway aged 38-87years. Retinal arteriolar calibre (central retinal artery equivalent...... cardiovascular risk factors were independently associated with retinal vascular calibre, with stronger effect of HDL cholesterol and BMI in men than in women. Blood pressure and smoking contributed most to the explained variance....
Calibration Lessons Learned from Hyperion Experience
Casement, S.; Ho, K.; Sandor-Leahy, S.; Biggar, S.; Czapla-Myers, J.; McCorkel, J.; Thome, K.
2009-12-01
improved methodologies during sensor development and additional capabilities of for on-orbit calibration systems that will enable reduction of the error terms. A key lesson learned was the approach taken to combine the various calibration results of Hyperion which differed by more than uncertainties in the methods.
CERN. Geneva
2015-01-01
My talk will be covering my work as a whole over the course of the semester. The focus will be on using energy flow calibration in ECAL to check the precision of the corrections made by the light monitoring system used to account for transparency loss within ECAL crystals due to radiation damage over time.
Calibration bench of flowmeters
International Nuclear Information System (INIS)
Bremond, J.; Da Costa, D.; Calvet, A.; Vieuxmaire, C.
1966-01-01
This equipment is devoted to the comparison of signals from two turbines installed in the Cabri experimental loop. The signal is compared to the standard turbine. The characteristics and the performance of the calibration bench are presented. (A.L.B.)
Calibration of farmer dosemeters
International Nuclear Information System (INIS)
Ahmad, S.S.; Anwar, K.; Arshed, W.; Mubarak, M.A.; Orfi, S.D.
1984-08-01
The Farmer Dosemeters of Atomic Energy Medical Centre (AEMC) Jamshoro were calibrated in the Secondary Standard Dosimetry Laboratory (SSDL) at PINSTECH, using the NPL Secondary Standard Therapy level X-ray exposure meter. The results are presented in this report. (authors)
Physiotherapy ultrasound calibrations
International Nuclear Information System (INIS)
Gledhill, M.
1996-01-01
Calibration of physiotherapy ultrasound equipment has long been a problem. Numerous surveys around the world over the past 20 years have all found that only a low percentage of the units tested had an output within 30% of that indicatd. In New Zealand, a survey carried out by the NRL in 1985 found that only 24% had an output, at the maximum setting, within + or - 20% of that indicated. The present performance Standard for new equipment (NZS 3200.2.5:1992) requires that the measured output should not deviate from that indicated by more than + or - 30 %. This may be tightened to + or - 20% in the next few years. Any calibration is only as good as the calibration equipment. Some force balances can be tested with small weights to simulate the force exerted by an ultrasound beam, but with others this is not possible. For such balances, testing may only be feasible with a calibrated source which could be used like a transfer standard. (author). 4 refs., 3 figs
NVLAP calibration laboratory program
Energy Technology Data Exchange (ETDEWEB)
Cigler, J.L.
1993-12-31
This paper presents an overview of the progress up to April 1993 in the development of the Calibration Laboratories Accreditation Program within the framework of the National Voluntary Laboratory Accreditation Program (NVLAP) at the National Institute of Standards and Technology (NIST).
NVLAP calibration laboratory program
International Nuclear Information System (INIS)
Cigler, J.L.
1993-01-01
This paper presents an overview of the progress up to April 1993 in the development of the Calibration Laboratories Accreditation Program within the framework of the National Voluntary Laboratory Accreditation Program (NVLAP) at the National Institute of Standards and Technology (NIST)
International Nuclear Information System (INIS)
Rosauer, P.J.; Flaherty, J.J.
1981-01-01
This invention is in the field of gamma ray inspection devices for tubular products and the like employing an improved calibrating block which prevents the sensing system from being overloaded when no tubular product is present, and also provides the operator with a means for visually detecting the presence of wall thicknesses which are less than a required minimum. (author)
PLEIADES ABSOLUTE CALIBRATION : INFLIGHT CALIBRATION SITES AND METHODOLOGY
Directory of Open Access Journals (Sweden)
S. Lachérade
2012-07-01
Full Text Available In-flight calibration of space sensors once in orbit is a decisive step to be able to fulfil the mission objectives. This article presents the methods of the in-flight absolute calibration processed during the commissioning phase. Four In-flight calibration methods are used: absolute calibration, cross-calibration with reference sensors such as PARASOL or MERIS, multi-temporal monitoring and inter-bands calibration. These algorithms are based on acquisitions over natural targets such as African deserts, Antarctic sites, La Crau (Automatic calibration station and Oceans (Calibration over molecular scattering or also new extra-terrestrial sites such as the Moon and selected stars. After an overview of the instrument and a description of the calibration sites, it is pointed out how each method is able to address one or several aspects of the calibration. We focus on how these methods complete each other in their operational use, and how they help building a coherent set of information that addresses all aspects of in-orbit calibration. Finally, we present the perspectives that the high level of agility of PLEIADES offers for the improvement of its calibration and a better characterization of the calibration sites.
Energy Technology Data Exchange (ETDEWEB)
John F. Schabron; Joseph F. Rovani; Susan S. Sorini
2007-03-31
The Clean Air Mercury Rule (CAMR) which was published in the Federal Register on May 18, 2005, requires that calibration of mercury continuous emissions monitors (CEMs) be performed with NIST-traceable standards. Western Research Institute (WRI) is working closely with the Electric Power Research Institute (EPRI), the National Institute of Standards and Technology (NIST), and the Environmental Protection Agency (EPA) to facilitate the development of the experimental criteria for a NIST traceability protocol for dynamic elemental mercury vapor generators. The traceability protocol will be written by EPA. Traceability will be based on the actual analysis of the output of each calibration unit at several concentration levels ranging from about 2-40 ug/m{sup 3}, and this analysis will be directly traceable to analyses by NIST using isotope dilution inductively coupled plasma/mass spectrometry (ID ICP/MS) through a chain of analyses linking the calibration unit in the power plant to the NIST ID ICP/MS. Prior to this project, NIST did not provide a recommended mercury vapor pressure equation or list mercury vapor pressure in its vapor pressure database. The NIST Physical and Chemical Properties Division in Boulder, Colorado was subcontracted under this project to study the issue in detail and to recommend a mercury vapor pressure equation that the vendors of mercury vapor pressure calibration units can use to calculate the elemental mercury vapor concentration in an equilibrium chamber at a particular temperature. As part of this study, a preliminary evaluation of calibration units from five vendors was made. The work was performed by NIST in Gaithersburg, MD and Joe Rovani from WRI who traveled to NIST as a Visiting Scientist.
Directory of Open Access Journals (Sweden)
L. F. C. Rezende
Full Text Available Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2 were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR, and data mining techniques as the Classification And Regression Tree (CART and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Field calibration of cup anemometers
DEFF Research Database (Denmark)
Schmidt Paulsen, Uwe; Mortensen, Niels Gylling; Hansen, Jens Carsten
2007-01-01
A field calibration method and results are described along with the experience gained with the method. The cup anemometers to be calibrated are mounted in a row on a 10-m high rig and calibrated in the free wind against a reference cup anemometer. The method has been reported [1] to improve...... the statistical bias on the data relative to calibrations carried out in a wind tunnel. The methodology is sufficiently accurate for calibration of cup anemometers used for wind resource assessments and provides a simple, reliable and cost-effective solution to cup anemometer calibration, especially suited...
Interferometric detection of single gold nanoparticles calibrated against TEM size distributions
DEFF Research Database (Denmark)
Zhang, Lixue; Christensen, Sune; Bendix, Pól Martin
2015-01-01
Single nanoparticle analysis: An interferometric optical approach calibrates sizes of gold nanoparticles (AuNPs) from the interference intensities by calibrating their interferometric signals against the corresponding transmission electron microscopy measurements. This method is used to investigate...