The current and future use of ridge regression for prediction in quantitative genetics

OpenAIRE

Vlaming, Ronald; Groenen, Patrick

2015-01-01

textabstractIn recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to...

Comparison of some biased estimation methods (including ordinary subset regression) in the linear model

Science.gov (United States)

Sidik, S. M.

1975-01-01

Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.

IMPROVING CORRELATION FUNCTION FITTING WITH RIDGE REGRESSION: APPLICATION TO CROSS-CORRELATION RECONSTRUCTION

International Nuclear Information System (INIS)

Matthews, Daniel J.; Newman, Jeffrey A.

2012-01-01

Cross-correlation techniques provide a promising avenue for calibrating photometric redshifts and determining redshift distributions using spectroscopy which is systematically incomplete (e.g., current deep spectroscopic surveys fail to obtain secure redshifts for 30%-50% or more of the galaxies targeted). In this paper, we improve on the redshift distribution reconstruction methods from our previous work by incorporating full covariance information into our correlation function fits. Correlation function measurements are strongly covariant between angular or spatial bins, and accounting for this in fitting can yield substantial reduction in errors. However, frequently the covariance matrices used in these calculations are determined from a relatively small set (dozens rather than hundreds) of subsamples or mock catalogs, resulting in noisy covariance matrices whose inversion is ill-conditioned and numerically unstable. We present here a method of conditioning the covariance matrix known as ridge regression which results in a more well behaved inversion than other techniques common in large-scale structure studies. We demonstrate that ridge regression significantly improves the determination of correlation function parameters. We then apply these improved techniques to the problem of reconstructing redshift distributions. By incorporating full covariance information, applying ridge regression, and changing the weighting of fields in obtaining average correlation functions, we obtain reductions in the mean redshift distribution reconstruction error of as much as ∼40% compared to previous methods. We provide a description of POWERFIT, an IDL code for performing power-law fits to correlation functions with ridge regression conditioning that we are making publicly available.

The Collinearity Free and Bias Reduced Regression Estimation Project: The Theory of Normalization Ridge Regression. Report No. 2.

Science.gov (United States)

Bulcock, J. W.; And Others

Multicollinearity refers to the presence of highly intercorrelated independent variables in structural equation models, that is, models estimated by using techniques such as least squares regression and maximum likelihood. There is a problem of multicollinearity in both the natural and social sciences where theory formulation and estimation is in…

Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation

Directory of Open Access Journals (Sweden)

Tian Jie

2004-01-01

Full Text Available It is important to estimate the ridge distance accurately, an intrinsic texture property of a fingerprint image. Up to now, only several articles have touched directly upon ridge distance estimation. Little has been published providing detailed evaluation of methods for ridge distance estimation, in particular, the traditional spectral analysis method applied in the frequency field. In this paper, a novel method on nonoverlap blocks, called the statistical method, is presented to estimate the ridge distance. Direct estimation ratio (DER and estimation accuracy (EA are defined and used as parameters along with time consumption (TC to evaluate performance of these two methods for ridge distance estimation. Based on comparison of performances of these two methods, a third hybrid method is developed to combine the merits of both methods. Experimental results indicate that DER is 44.7%, 63.8%, and 80.6%; EA is 84%, 93%, and 91%; and TC is , , and seconds, with the spectral analysis method, statistical method, and hybrid method, respectively.

a comparative study of some robust ridge and liu estimators

African Journals Online (AJOL)

Dr A.B.Ahmed

estimation techniques such as Ridge and Liu Estimators are preferable to Ordinary Least Square. On the other hand, when outliers exist in the data, robust estimators like M, MM, LTS and S. Estimators, are preferred. To handle these two problems jointly, the study combines the Ridge and Liu Estimators with Robust.

Some improved classification-based ridge parameter of Hoerl and ...

African Journals Online (AJOL)

Some improved classification-based ridge parameter of Hoerl and Kennard estimation techniques. ... This assumption is often violated and Ridge Regression estimator introduced by [2]has been identified to be more efficient than ordinary least square (OLS) in handling it. However, it requires a ridge parameter, K, of which ...

Model Selection in Kernel Ridge Regression

DEFF Research Database (Denmark)

Exterkate, Peter

Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L. Grown under Three Water Regimes

Directory of Open Access Journals (Sweden)

Javier Hernandez

2015-02-01

Full Text Available Plant breeding based on grain yield (GY is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes of wheat (Triticum aestivum L. through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed, mild water stress (MWS; one irrigation event around booting and full irrigation (FI with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2 increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

DRREP: deep ridge regressed epitope predictor.

Science.gov (United States)

Sher, Gene; Zhi, Degui; Zhang, Shaojie

2017-10-03

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

A generalization of voxel-wise procedures for highdimensional statistical inference using ridge regression

DEFF Research Database (Denmark)

Sjöstrand, Karl; Cardenas, Valerie A.; Larsen, Rasmus

2008-01-01

regression to address this issue, allowing for a gradual introduction of correlation information into the model. We make the connections between ridge regression and voxel-wise procedures explicit and discuss relations to other statistical methods. Results are given on an in-vivo data set of deformation......Whole-brain morphometry denotes a group of methods with the aim of relating clinical and cognitive measurements to regions of the brain. Typically, such methods require the statistical analysis of a data set with many variables (voxels and exogenous variables) paired with few observations (subjects...

Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis

Science.gov (United States)

Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae

The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

Science.gov (United States)

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

Science.gov (United States)

Deng, Yifan; Zeng, Huidan; Jiang, Yejia; Chen, Guorong; Chen, Jianding; Sun, Luyi

2018-03-01

It is of great significance to design glasses with satisfactory mechanical properties predictively through modeling. Among various modeling methods, data-driven modeling is such a reliable approach that can dramatically shorten research duration, cut research cost and accelerate the development of glass materials. In this work, the ridge regression (RR) analysis was used to construct regression models for predicting the compositional dependence of CaO-Al2O3-SiO2 glass elastic moduli (Shear, Bulk, and Young’s moduli) and hardness based on the ternary diagram of the compositions. The property prediction over a large glass composition space was accomplished with known experimental data of various compositions in the literature, and the simulated results are in good agreement with the measured ones. This regression model can serve as a facile and effective tool for studying the relationship between the compositions and the property, enabling high-efficient design of glasses to meet the requirements for specific elasticity and hardness.

Local Prediction Models on Mid-Atlantic Ridge MORB by Principal Component Regression

Science.gov (United States)

Ling, X.; Snow, J. E.; Chin, W.

2017-12-01

The isotopic compositions of the daughter isotopes of long-lived radioactive systems (Sr, Nd, Hf and Pb ) can be used to map the scale and history of mantle heterogeneities beneath mid-ocean ridges. Our goal is to relate the multidimensional structure in the existing isotopic dataset with an underlying physical reality of mantle sources. The numerical technique of Principal Component Analysis is useful to reduce the linear dependence of the data to a minimum set of orthogonal eigenvectors encapsulating the information contained (cf Agranier et al 2005). The dataset used for this study covers almost all the MORBs along mid-Atlantic Ridge (MAR), from 54oS to 77oN and 8.8oW to -46.7oW, including replicating the dataset of Agranier et al., 2005 published plus 53 basalt samples dredged and analyzed since then (data from PetDB). The principal components PC1 and PC2 account for 61.56% and 29.21%, respectively, of the total isotope ratios variability. The samples with similar compositions to HIMU and EM and DM are identified to better understand the PCs. PC1 and PC2 are accountable for HIMU and EM whereas PC2 has limited control over the DM source. PC3 is more strongly controlled by the depleted mantle source than PC2. What this means is that all three principal components have a high degree of significance relevant to the established mantle sources. We also tested the relationship between mantle heterogeneity and sample locality. K-means clustering algorithm is a type of unsupervised learning to find groups in the data based on feature similarity. The PC factor scores of each sample are clustered into three groups. Cluster one and three are alternating on the north and south MAR. Cluster two exhibits on 45.18oN to 0.79oN and -27.9oW to -30.40oW alternating with cluster one. The ridge has been preliminarily divided into 16 sections considering both the clusters and ridge segments. The principal component regression models the section based on 6 isotope ratios and PCs. The

Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration.

Science.gov (United States)

Jović, Ozren; Smrečki, Neven; Popović, Zora

2016-04-01

A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for poil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEPoil (R(2)>0.99). Copyright © 2015 Elsevier B.V. All rights reserved.

Independent contrasts and PGLS regression estimators are equivalent.

Science.gov (United States)

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Partial correlation matrix estimation using ridge penalty followed by thresholding and re-estimation.

Science.gov (United States)

Ha, Min Jin; Sun, Wei

2014-09-01

Motivated by the problem of construction of gene co-expression network, we propose a statistical framework for estimating high-dimensional partial correlation matrix by a three-step approach. We first obtain a penalized estimate of a partial correlation matrix using ridge penalty. Next we select the non-zero entries of the partial correlation matrix by hypothesis testing. Finally we re-estimate the partial correlation coefficients at these non-zero entries. In the second step, the null distribution of the test statistics derived from penalized partial correlation estimates has not been established. We address this challenge by estimating the null distribution from the empirical distribution of the test statistics of all the penalized partial correlation estimates. Extensive simulation studies demonstrate the good performance of our method. Application on a yeast cell cycle gene expression data shows that our method delivers better predictions of the protein-protein interactions than the Graphic Lasso. © 2014, The International Biometric Society.

Principal component regression for crop yield estimation

CERN Document Server

Suryanarayana, T M V

2016-01-01

This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...

A logistic regression estimating function for spatial Gibbs point processes

DEFF Research Database (Denmark)

Baddeley, Adrian; Coeurjolly, Jean-François; Rubak, Ege

We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related to the p......We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related...

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2011-01-01

In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator

Investigating the effects of climate variations on bacillary dysentery incidence in northeast China using ridge regression and hierarchical cluster analysis

Directory of Open Access Journals (Sweden)

Guo Junqiao

2008-09-01

Full Text Available Abstract Background The effects of climate variations on bacillary dysentery incidence have gained more recent concern. However, the multi-collinearity among meteorological factors affects the accuracy of correlation with bacillary dysentery incidence. Methods As a remedy, a modified method to combine ridge regression and hierarchical cluster analysis was proposed for investigating the effects of climate variations on bacillary dysentery incidence in northeast China. Results All weather indicators, temperatures, precipitation, evaporation and relative humidity have shown positive correlation with the monthly incidence of bacillary dysentery, while air pressure had a negative correlation with the incidence. Ridge regression and hierarchical cluster analysis showed that during 1987–1996, relative humidity, temperatures and air pressure affected the transmission of the bacillary dysentery. During this period, all meteorological factors were divided into three categories. Relative humidity and precipitation belonged to one class, temperature indexes and evaporation belonged to another class, and air pressure was the third class. Conclusion Meteorological factors have affected the transmission of bacillary dysentery in northeast China. Bacillary dysentery prevention and control would benefit from by giving more consideration to local climate variations.

Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

Directory of Open Access Journals (Sweden)

Weibo Zhao

2017-12-01

Full Text Available Power generation industry is the key industry of carbon dioxide (CO2 emission in China. Assessing its future CO2 emissions is of great significance to the formulation and implementation of energy saving and emission reduction policies. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT, the influencing factors analysis model of CO2 emission of power generation industry is established. The ridge regression (RR method is used to estimate the historical data. In addition, a wavelet neural network (WNN prediction model based on Cuckoo Search algorithm optimized by Gauss (GCS is put forward to predict the factors in the STIRPAT model. Then, the predicted values are substituted into the regression model, and the CO2 emission estimation values of the power generation industry in China are obtained. It’s concluded that population, per capita Gross Domestic Product (GDP, standard coal consumption and thermal power specific gravity are the key factors affecting the CO2 emission from the power generation industry. Besides, the GCS-WNN prediction model has higher prediction accuracy, comparing with other models. Moreover, with the development of science and technology in the future, the CO2 emission growth in the power generation industry will gradually slow down according to the prediction results.

Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

Science.gov (United States)

Gegiuc, Alexandru; Similä, Markku; Karvonen, Juha; Lensu, Mikko; Mäkynen, Marko; Vainio, Jouni

2018-01-01

For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

Science.gov (United States)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2009-01-01

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2010-01-01

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

Regression Equations for Birth Weight Estimation using ...

African Journals Online (AJOL)

In this study, Birth Weight has been estimated from anthropometric measurements of hand and foot. Linear regression equations were formed from each of the measured variables. These simple equations can be used to estimate Birth Weight of new born babies, in order to identify those with low birth weight and referred to ...

Sparse reduced-rank regression with covariance estimation

KAUST Repository

Chen, Lisha

2014-12-08

Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

Sparse reduced-rank regression with covariance estimation

KAUST Repository

Chen, Lisha; Huang, Jianhua Z.

2014-01-01

Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

Regression and Sparse Regression Methods for Viscosity Estimation of Acid Milk From it’s Sls Features

DEFF Research Database (Denmark)

Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann

2012-01-01

Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...

Estimating the exceedance probability of rain rate by logistic regression

Science.gov (United States)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Robust median estimator in logisitc regression

Czech Academy of Sciences Publication Activity Database

Hobza, T.; Pardo, L.; Vajda, Igor

2008-01-01

Roč. 138, č. 12 (2008), s. 3822-3840 ISSN 0378-3758 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MPO FI - IM3/136; GA MŠk(CZ) MTM 2006-06872 Institutional research plan: CEZ:AV0Z10750506 Keywords : Logistic regression * Median * Robustness * Consistency and asymptotic normality * Morgenthaler * Bianco and Yohai * Croux and Hasellbroeck Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.679, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/vajda-robust%20median%20estimator%20in%20logistic%20regression.pdf

Estimating nonlinear selection gradients using quadratic regression coefficients: double or nothing?

Science.gov (United States)

Stinchcombe, John R; Agrawal, Aneil F; Hohenlohe, Paul A; Arnold, Stevan J; Blows, Mark W

2008-09-01

The use of regression analysis has been instrumental in allowing evolutionary biologists to estimate the strength and mode of natural selection. Although directional and correlational selection gradients are equal to their corresponding regression coefficients, quadratic regression coefficients must be doubled to estimate stabilizing/disruptive selection gradients. Based on a sample of 33 papers published in Evolution between 2002 and 2007, at least 78% of papers have not doubled quadratic regression coefficients, leading to an appreciable underestimate of the strength of stabilizing and disruptive selection. Proper treatment of quadratic regression coefficients is necessary for estimation of fitness surfaces and contour plots, canonical analysis of the gamma matrix, and modeling the evolution of populations on an adaptive landscape.

Estimation of genetic effects in the presence of multicollinearity in multibreed beef cattle evaluation.

Science.gov (United States)

Roso, V M; Schenkel, F S; Miller, S P; Schaeffer, L R

2005-08-01

Breed additive, dominance, and epistatic loss effects are of concern in the genetic evaluation of a multibreed population. Multiple regression equations used for fitting these effects may show a high degree of multicollinearity among predictor variables. Typically, when strong linear relationships exist, the regression coefficients have large SE and are sensitive to changes in the data file and to the addition or deletion of variables in the model. Generalized ridge regression methods were applied to obtain stable estimates of direct and maternal breed additive, dominance, and epistatic loss effects in the presence of multicollinearity among predictor variables. Preweaning weight gains of beef calves in Ontario, Canada, from 1986 to 1999 were analyzed. The genetic model included fixed direct and maternal breed additive, dominance, and epistatic loss effects, fixed environmental effects of age of the calf, contemporary group, and age of the dam x sex of the calf, random additive direct and maternal genetic effects, and random maternal permanent environment effect. The degree and the nature of the multicollinearity were identified and ridge regression methods were used as an alternative to ordinary least squares (LS). Ridge parameters were obtained using two different objective methods: 1) generalized ridge estimator of Hoerl and Kennard (R1); and 2) bootstrap in combination with cross-validation (R2). Both ridge regression methods outperformed the LS estimator with respect to mean squared error of predictions (MSEP) and variance inflation factors (VIF) computed over 100 bootstrap samples. The MSEP of R1 and R2 were similar, and they were 3% less than the MSEP of LS. The average VIF of LS, R1, and R2 were equal to 26.81, 6.10, and 4.18, respectively. Ridge regression methods were particularly effective in decreasing the multicollinearity involving predictor variables of breed additive effects. Because of a high degree of confounding between estimates of maternal

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

Science.gov (United States)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

Science.gov (United States)

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

Partial Correlation Matrix Estimation using Ridge Penalty Followed by Thresholding and Reestimation

Science.gov (United States)

2014-01-01

Summary Motivated by the problem of construction gene co-expression network, we propose a statistical framework for estimating high-dimensional partial correlation matrix by a three-step approach. We first obtain a penalized estimate of a partial correlation matrix using ridge penalty. Next we select the non-zero entries of the partial correlation matrix by hypothesis testing. Finally we reestimate the partial correlation coefficients at these non-zero entries. In the second step, the null distribution of the test statistics derived from penalized partial correlation estimates has not been established. We address this challenge by estimating the null distribution from the empirical distribution of the test statistics of all the penalized partial correlation estimates. Extensive simulation studies demonstrate the good performance of our method. Application on a yeast cell cycle gene expression data shows that our method delivers better predictions of the protein-protein interactions than the Graphic Lasso. PMID:24845967

Dynamic travel time estimation using regression trees.

Science.gov (United States)

2008-10-01

This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...

Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.

Science.gov (United States)

Bulcock, J. W.

The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…

A SAS-macro for estimation of the cumulative incidence using Poisson regression

DEFF Research Database (Denmark)

Waltoft, Berit Lindum

2009-01-01

the hazard rates, and the hazard rates are often estimated by the Cox regression. This procedure may not be suitable for large studies due to limited computer resources. Instead one uses Poisson regression, which approximates the Cox regression. Rosthøj et al. presented a SAS-macro for the estimation...... of the cumulative incidences based on the Cox regression. I present the functional form of the probabilities and variances when using piecewise constant hazard rates and a SAS-macro for the estimation using Poisson regression. The use of the macro is demonstrated through examples and compared to the macro presented...

A flexible fuzzy regression algorithm for forecasting oil consumption estimation

International Nuclear Information System (INIS)

Azadeh, A.; Khakestani, M.; Saberi, M.

2009-01-01

Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.

Tightness of M-estimators for multiple linear regression in time series

DEFF Research Database (Denmark)

Johansen, Søren; Nielsen, Bent

We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

Energy Technology Data Exchange (ETDEWEB)

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu [R& D, Safety Science Research, Kao Corporation, Tochigi (Japan); Yoshinari, Kouichi [Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka (Japan); Honda, Hiroshi, E-mail: honda.hiroshi@kao.co.jp [R& D, Safety Science Research, Kao Corporation, Tochigi (Japan)

2017-03-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

International Nuclear Information System (INIS)

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-01-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic

Thickness of Knox Group overburden on Central Chestnut Ridge, Oak Ridge Reservation

International Nuclear Information System (INIS)

Staub, W.P.; Hopkins, R.A.

1984-05-01

The thickness of residual soil overlying the Knox Group along Central Chestnut Ridge was estimated by a conventional seismic refraction survey. The purpose of this survey was to identify sites on the Department of Energy's Oak Ridge Reservation where ample overburden exists above the water table for the shallow land burial of low-level radioactive waste. The results of the survey suggest that the upper slopes of the higher ridges in the area have a minimum of 16 to 26 m (52 to 85 ft) of overburden and that the crests of these ridges may have more than 30 m (100 ft). Therefore, it is unlikely that sound bedrock would be encountered during trench excavation [maximum of 10 m (32 ft)] along Central Chestnut Ridge. Also, the relatively low seismic wave velocities measured in the overburden suggest that the water table is generally deep. On the basis of these preliminary results, Central Chestnut Ridge appears to be suitable for further site characterization for the shallow land burial of low-level radioactive waste. 3 references, 5 figures, 1 table

On the estimation and testing of predictive panel regressions

NARCIS (Netherlands)

Karabiyik, H.; Westerlund, Joakim; Narayan, Paresh

2016-01-01

Hjalmarsson (2010) considers an OLS-based estimator of predictive panel regressions that is argued to be mixed normal under very general conditions. In a recent paper, Westerlund et al. (2016) show that while consistent, the estimator is generally not mixed normal, which invalidates standard normal

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

Directory of Open Access Journals (Sweden)

Jeffrey B. Endelman

2011-11-01

Full Text Available Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR, which is equivalent to best linear unbiased prediction (BLUP when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identification of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was significantly higher than RR for wheat ( L. grain yield but equivalent for several maize ( L. traits.

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

Science.gov (United States)

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

Science.gov (United States)

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Parameter Estimation for Improving Association Indicators in Binary Logistic Regression

Directory of Open Access Journals (Sweden)

Mahdi Bashiri

2012-02-01

Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.

Small sample GEE estimation of regression parameters for longitudinal data.

Science.gov (United States)

Paul, Sudhir; Zhang, Xuemao

2014-09-28

Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used. Copyright © 2014 John Wiley & Sons, Ltd.

Early cost estimating for road construction projects using multiple regression techniques

Directory of Open Access Journals (Sweden)

Ibrahim Mahamid

2011-12-01

Full Text Available The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% and ±50%.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

Science.gov (United States)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Estimating monotonic rates from biological data using local linear regression.

Science.gov (United States)

Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

2017-03-01

Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

Science.gov (United States)

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.

Robust best linear estimation for regression analysis using surrogate and instrumental variables.

Science.gov (United States)

Wang, C Y

2012-04-01

We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

Directory of Open Access Journals (Sweden)

Maria Karlsson

2014-05-01

Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions.

Science.gov (United States)

Rativa, Diego; Fernandes, Bruno J T; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care.

Simultaneous Estimation of Regression Functions for Marine Corps Technical Training Specialties.

Science.gov (United States)

Dunbar, Stephen B.; And Others

This paper considers the application of Bayesian techniques for simultaneous estimation to the specification of regression weights for selection tests used in various technical training courses in the Marine Corps. Results of a method for m-group regression developed by Molenaar and Lewis (1979) suggest that common weights for training courses…

Robust estimation for homoscedastic regression in the secondary analysis of case-control data

KAUST Repository

Wei, Jiawei; Carroll, Raymond J.; Mü ller, Ursula U.; Keilegom, Ingrid Van; Chatterjee, Nilanjan

2012-01-01

Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

Robust estimation for homoscedastic regression in the secondary analysis of case-control data

KAUST Repository

Wei, Jiawei

2012-12-04

Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

Inverse estimation of multiple muscle activations based on linear logistic regression.

Science.gov (United States)

Sekiya, Masashi; Tsuji, Toshiaki

2017-07-01

This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.

Optimized support vector regression for drilling rate of penetration estimation

Science.gov (United States)

Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa

2015-12-01

In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.

Two biased estimation techniques in linear regression: Application to aircraft

Science.gov (United States)

Klein, Vladislav

1988-01-01

Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.

Generalized allometric regression to estimate biomass of Populus in short-rotation coppice

Energy Technology Data Exchange (ETDEWEB)

Ben Brahim, Mohammed; Gavaland, Andre; Cabanettes, Alain [INRA Centre de Toulouse, Castanet-Tolosane Cedex (France). Unite Agroforesterie et Foret Paysanne

2000-07-01

Data from four different stands were combined to establish a single generalized allometric equation to estimate above-ground biomass of individual Populus trees grown on short-rotation coppice. The generalized model was performed using diameter at breast height, the mean diameter and the mean height of each site as dependent variables and then compared with the stand-specific regressions using F-test. Results showed that this single regression estimates tree biomass well at each stand and does not introduce bias with increasing diameter.

Variational Ridging in Sea Ice Models

Science.gov (United States)

Roberts, A.; Hunke, E. C.; Lipscomb, W. H.; Maslowski, W.; Kamal, S.

2017-12-01

This work presents the results of a new development to make basin-scale sea ice models aware of the shape, porosity and extent of individual ridges within the pack. We have derived an analytic solution for the Euler-Lagrange equation of individual ridges that accounts for non-conservative forces, and therefore the compressive strength of individual ridges. Because a region of the pack is simply a collection of paths of individual ridges, we are able to solve the Euler-Lagrange equation for a large-scale sea ice field also, and therefore the compressive strength of a region of the pack that explicitly accounts for the macro-porosity of ridged debris. We make a number of assumptions that have simplified the problem, such as treating sea ice as a granular material in ridges, and assuming that bending moments associated with ridging are perturbations around an isostatic state. Regardless of these simplifications, the ridge model is remarkably predictive of macro-porosity and ridge shape, and, because our equations are analytic, they do not require costly computations to solve the Euler-Lagrange equation of ridges on the large scale. The new ridge model is therefore applicable to large-scale sea ice models. We present results from this theoretical development, as well as plans to apply it to the Regional Arctic System Model and a community sea ice code. Most importantly, the new ridging model is particularly useful for pinpointing gaps in our observational record of sea ice ridges, and points to the need for improved measurements of the evolution of porosity of deformed ice in the Arctic and Antarctic. Such knowledge is not only useful for improving models, but also for improving estimates of sea ice volume derived from altimetric measurements of sea ice freeboard.

Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

Science.gov (United States)

Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

2018-06-01

Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

Science.gov (United States)

Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

2018-03-01

Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

Regression estimators for generic health-related quality of life and quality-adjusted life years.

Science.gov (United States)

Basu, Anirban; Manca, Andrea

2012-01-01

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models.

Science.gov (United States)

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-03-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

Variability of footprint ridge density and its use in estimation of sex in forensic examinations.

Science.gov (United States)

Krishan, Kewal; Kanchan, Tanuj; Pathania, Annu; Sharma, Ruchika; DiMaggio, John A

2015-10-01

The present study deals with a comparatively new biometric parameter of footprints called footprint ridge density. The study attempts to evaluate sex-dependent variations in ridge density in different areas of the footprint and its usefulness in discriminating sex in the young adult population of north India. The sample for the study consisted of 160 young adults (121 females) from north India. The left and right footprints were taken from each subject according to the standard procedures. The footprints were analysed using a 5 mm × 5 mm square and the ridge density was calculated in four different well-defined areas of the footprints. These were: F1 - the great toe on its proximal and medial side; F2 - the medial ball of the footprint, below the triradius (the triradius is a Y-shaped group of ridges on finger balls, palms and soles which forms the basis of ridge counting in identification); F3 - the lateral ball of the footprint, towards the most lateral part; and F4 - the heel in its central part where the maximum breadth at heel is cut by a perpendicular line drawn from the most posterior point on heel. This value represents the number of ridges in a 25 mm(2) area and reflects the ridge density value. Ridge densities analysed on different areas of footprints were compared with each other using the Friedman test for related samples. The total footprint ridge density was calculated as the sum of the ridge density in the four areas of footprints included in the study (F1 + F2 + F3 + F4). The results show that the mean footprint ridge density was higher in females than males in all the designated areas of the footprints. The sex differences in footprint ridge density were observed to be statistically significant in the analysed areas of the footprint, except for the heel region of the left footprint. The total footprint ridge density was also observed to be significantly higher among females than males. A statistically significant correlation

Extraction of lead and ridge characteristics from SAR images of sea ice

Science.gov (United States)

Vesecky, John F.; Smith, Martha P.; Samadani, Ramin

1990-01-01

Image-processing techniques for extracting the characteristics of lead and pressure ridge features in SAR images of sea ice are reported. The methods are applied to a SAR image of the Beaufort Sea collected from the Seasat satellite on October 3, 1978. Estimates of lead and ridge statistics are made, e.g., lead and ridge density (number of lead or ridge pixels per unit area of image) and the distribution of lead area and orientation as well as ridge length and orientation. The information derived is useful in both ice science and polar operations for such applications as albedo and heat and momentum transfer estimates, as well as ship routing and offshore engineering.

Application of the wavelet ridges method for the estimation of the decay ratio in Boiling Water Reactors; Atomos para el desarrollo de Mexico

Energy Technology Data Exchange (ETDEWEB)

Prieto G, A.; Espinosa P, G. [UAM-I, 09340 Mexico D.F. (Mexico)

2008-07-01

A wavelet ridges application is proposed as a simple method to determine the evolution of the linear stability parameters of a BWR NPP using neutronic noise signals. The wavelets ridges are used to track the instantaneous frequencies contained in a signal and to estimate the Decay Ratio (DR). The first step of the method consists of de noising the analyzed signals by Discrete Wavelet Transform (DWT) to reduce the interference of high-frequency noise and concentrate the analysis in the band where crucial frequencies are presented. Next, is computation of the wavelet ridges by Continuous Wavelet Transform (CWT) to obtain the modulus maxima from the normalized scalogram of the signal. In general, associations with these wavelets ridges can be used to compute instantaneous frequency contained in the signal and the DR evolution with the measurement. To study the performance of the wavelet ridges method, by computing the evolution of the linear stability parameters, both simulated and real neutronic signals were considered. The simulated signal is used to validate methodically and to study some features of the wavelet ridges method. To demonstrate the method applicability a real neutronic signal from the instability event in Laguna Verde was analyzed. The investigations show that most of the local energies of the signal are concentrated in the wavelet ridges and DR variations of the signals were observed along the measurements. (Author)

Estimation of past sea-level variations based on ground-penetrating radar mapping of beach-ridges - preliminary results from Feddet, Faxe Bay, eastern Denmark

DEFF Research Database (Denmark)

Hede, Mikkel Ulfeldt; Nielsen, Lars; Clemmensen, Lars B

2011-01-01

Estimates of past sea-level variations based on different methods and techniques have been presented in a range of studies, including interpretation of beach ridge characteristics. In Denmark, Holocene beach ridge plains have been formed during the last c. 7700 years, a period characterised by both...... isostatic uplift and changes in eustatic sea-level, and therefore represent an archive of past relative sea-level variations. Here, we present preliminary results from investigation of beach ridges from Feddet, a small peninsula located in Faxe Bay (Baltic Sea) in the eastern part of Denmark. Feddet has...... been chosen as a key-locality in this project, as it is located relatively close to the current 0-isobase of isostatic rebound. GPR reflection data have been acquired with shielded 250 MHz Sensors & software antennae along a number of profile lines across beach ridge and swale structures of the Feddet...

Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Directory of Open Access Journals (Sweden)

Qiutong Jin

2016-06-01

Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

Online and Batch Supervised Background Estimation via L1 Regression

KAUST Repository

Dutta, Aritra

2017-11-23

We propose a surprisingly simple model for supervised video background estimation. Our model is based on $\\\\ell_1$ regression. As existing methods for $\\\\ell_1$ regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.

Online and Batch Supervised Background Estimation via L1 Regression

KAUST Repository

Dutta, Aritra; Richtarik, Peter

2017-01-01

We propose a surprisingly simple model for supervised video background estimation. Our model is based on $\\ell_1$ regression. As existing methods for $\\ell_1$ regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.

On the robust nonparametric regression estimation for a functional regressor

OpenAIRE

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

2009-01-01

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

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

Directory of Open Access Journals (Sweden)

Yoonseok Shin

2015-01-01

Full Text Available Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

Science.gov (United States)

Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

2009-11-01

Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.

Oak Ridge Reservation environmental report for 1991

International Nuclear Information System (INIS)

Mucke, P.C.

1992-10-01

The Oak Ridge Reservation Environmental Report for 1991 is the 21st in a series that began in 1971. The report documents the annual results of a comprehensive program to estimate the impact of the US Department of Energy (DOE) Oak Ridge operations upon human health and the environment. The report is organized into ten sections that address various aspects of effluent monitoring, environmental surveillance, dose assessment, waste management, and quality assurance. A compliance summary gives a synopsis of the status of each facility relative to applicable state and federal regulations. Data are included for the following: Oak Ridge Y-12 Plant; Oak Ridge National Laboratory (ORNL); and Oak Ridge K-25 Site. Effluent monitoring and environmental surveillance programs are intended to serve as effective indicators of contaminant releases and ambient contaminant concentrations that have the potential to result in adverse impacts to human health and the environment

Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation

DEFF Research Database (Denmark)

Alan, Sule; Honore, Bo E.; Hu, Luojia

2014-01-01

This paper constructs estimators for panel data regression models with individual speci…fic heterogeneity and two–sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re–censored or re–truncated residuals. While these moment...

Nonparametric Regression Estimation for Multivariate Null Recurrent Processes

Directory of Open Access Journals (Sweden)

Biqing Cai

2015-04-01

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

Performance of a New Restricted Biased Estimator in Logistic Regression

Directory of Open Access Journals (Sweden)

Yasin ASAR

2017-12-01

Full Text Available It is known that the variance of the maximum likelihood estimator (MLE inflates when the explanatory variables are correlated. This situation is called the multicollinearity problem. As a result, the estimations of the model may not be trustful. Therefore, this paper introduces a new restricted estimator (RLTE that may be applied to get rid of the multicollinearity when the parameters lie in some linear subspace  in logistic regression. The mean squared errors (MSE and the matrix mean squared errors (MMSE of the estimators considered in this paper are given. A Monte Carlo experiment is designed to evaluate the performances of the proposed estimator, the restricted MLE (RMLE, MLE and Liu-type estimator (LTE. The criterion of performance is chosen to be MSE. Moreover, a real data example is presented. According to the results, proposed estimator has better performance than MLE, RMLE and LTE.

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

KAUST Repository

Dai, Wenlin; Tong, Tiejun; Zhu, Lixing

2017-01-01

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

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

KAUST Repository

Dai, Wenlin

2017-09-01

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

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

Science.gov (United States)

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Multi-model ensemble estimation of volume transport through the straits of the East/Japan Sea

Science.gov (United States)

Han, Sooyeon; Hirose, Naoki; Usui, Norihisa; Miyazawa, Yasumasa

2016-01-01

The volume transports measured at the Korea/Tsushima, Tsugaru, and Soya/La Perouse Straits remain quantitatively inconsistent. However, data assimilation models at least provide a self-consistent budget despite subtle differences among the models. This study examined the seasonal variation of the volume transport using the multiple linear regression and ridge regression of multi-model ensemble (MME) methods to estimate more accurately transport at these straits by using four different data assimilation models. The MME outperformed all of the single models by reducing uncertainties, especially the multicollinearity problem with the ridge regression. However, the regression constants turned out to be inconsistent with each other if the MME was applied separately for each strait. The MME for a connected system was thus performed to find common constants for these straits. The estimation of this MME was found to be similar to the MME result of sea level difference (SLD). The estimated mean transport (2.43 Sv) was smaller than the measurement data at the Korea/Tsushima Strait, but the calibrated transport of the Tsugaru Strait (1.63 Sv) was larger than the observed data. The MME results of transport and SLD also suggested that the standard deviation (STD) of the Korea/Tsushima Strait is larger than the STD of the observation, whereas the estimated results were almost identical to that observed for the Tsugaru and Soya/La Perouse Straits. The similarity between MME results enhances the reliability of the present MME estimation.

A different approach to estimate nonlinear regression model using numerical methods

Science.gov (United States)

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].

Circulation in the region of the Reykjanes Ridge in June-July 2015

Science.gov (United States)

Tillys, Petit; Herle, Mercier; Virginie, Thierry

2017-04-01

The Reykjanes Ridge is a major topographic feature of the North-Atlantic Ocean lying south of Iceland that strongly influences the pathways of the upper and lower limbs of the meridional overturning cell. The circulation in the vicinity of the Reykjanes Ridge is anticyclonic and characterized by a southwestward flow (the East Reykjanes Ridge Current, ERRC) along the eastern flank and a northeastward flow (the Irminger Current, IC) along the western flank. Even if it is admitted that the ERRC feeds the IC through a cross-ridge flow, details and magnitude of this circulation remain unclear. In this study, the circulation in the region of the Reykjanes Ridge was investigated based on ADCP and CTDO2 measurements carried out from the R/V Thalassa during the RREX cruise, which provided a snapshot of the water mass distribution and circulation during summer 2015. One hydrographic section followed the top of the Reykjanes Ridge between Iceland and 50˚ N and three other sections were carried out perpendicularly to the ridge at 62˚ N, 58.5˚ N and 56˚ N. Geostrophic transports were estimated by combining ADCP and hydrographic data. Those observations were used to provide an estimate of the circulation around the Ridge and to discuss the meridional evolutions of the ERRC and IC transports along the Ridge and their connection to the cross-Ridge flows. The section along the top of the Reykjanes Ridge allowed us to describe the cross ridge exchanges. A westward flow crossed the Ridge between Iceland and 53˚ N. Its top to bottom integrated transport was estimated at 17.7 Sv. Two main passages were identified for the westward crossing. A first passage is located near 57˚ N (Bight Fracture Zone, BFZ) in agreement with previous studies. More surprisingly, a second passage is located near 59˚ N. The top-to-bottom transports of those two main flows were estimated at 6.5 and 8 Sv respectively. The IC and ERRC top-to-bottom integrated transports were maximum at 58.5˚ N and

A comparison of regression algorithms for wind speed forecasting at Alexander Bay

CSIR Research Space (South Africa)

Botha, Nicolene

2016-12-01

Full Text Available to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting...

Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods

Directory of Open Access Journals (Sweden)

Dick Apronti

2016-12-01

Full Text Available Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost-effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies.

A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

Science.gov (United States)

Huang, C.; Townshend, J.R.G.

2003-01-01

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

Some Improved Classification-Based Ridge Parameter Of Hoerl And ...

African Journals Online (AJOL)

Of Hoerl And Kennard Estimation Techniques. 1Adewale F. Lukmanand 1Kayode Ayinde. 1 Department of Statistics, ... ordinary least square (OLS) in handling it. However, it requires a ridge parameter, K, of which many have ... handle the problem of multicollinearity. They suggested the addition of ridge parameter K to the ...

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.)

Asymptotic normality of kernel estimator of $\\psi$-regression function for functional ergodic data

OpenAIRE

Laksaci ALI; Benziadi Fatima; Gheriballak Abdelkader

2016-01-01

In this paper we consider the problem of the estimation of the $\\psi$-regression function when the covariates take values in an infinite dimensional space. Our main aim is to establish, under a stationary ergodic process assumption, the asymptotic normality of this estimate.

Estimating integrated variance in the presence of microstructure noise using linear regression

Science.gov (United States)

Holý, Vladimír

2017-07-01

Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

Oak Ridge Health Studies phase 1 report, Volume 1: Oak Ridge Phase 1 overview

International Nuclear Information System (INIS)

Yarbrough, M.I.; Van Cleave, M.L.; Turri, P.; Daniel, J.

1993-09-01

In July 1991, the State of Tennessee initiated the Health Studies Agreement with the United States Department of Energy to carry out independent studies of possible adverse health effects in people living in the vicinity of the Oak Ridge Reservation. The health studies focus on those effects that could have resulted or could result from exposures to chemicals and radioactivity released at the Reservation since 1942. The major focus of the first phase was to complete a Dose Reconstruction Feasibility Study. This study was designed to find out if enough data exist about chemical and radionuclide releases from the Oak Ridge Reservation to conduct a second phase. The second phase will lead to estimates of the actual amounts or the ''doses'' of various contaminants received by people as a result of off-site releases. Once the doses of various contaminants have been estimated, scientists and physicians will be better able to evaluate whether adverse health effects could have resulted from the releases

Oak Ridge Health Studies phase 1 report, Volume 1: Oak Ridge Phase 1 overview

Energy Technology Data Exchange (ETDEWEB)

Yarbrough, M.I.; Van Cleave, M.L.; Turri, P.; Daniel, J.

1993-09-01

In July 1991, the State of Tennessee initiated the Health Studies Agreement with the United States Department of Energy to carry out independent studies of possible adverse health effects in people living in the vicinity of the Oak Ridge Reservation. The health studies focus on those effects that could have resulted or could result from exposures to chemicals and radioactivity released at the Reservation since 1942. The major focus of the first phase was to complete a Dose Reconstruction Feasibility Study. This study was designed to find out if enough data exist about chemical and radionuclide releases from the Oak Ridge Reservation to conduct a second phase. The second phase will lead to estimates of the actual amounts or the ``doses`` of various contaminants received by people as a result of off-site releases. Once the doses of various contaminants have been estimated, scientists and physicians will be better able to evaluate whether adverse health effects could have resulted from the releases.

Genomic breeding value estimation using nonparametric additive regression models

Directory of Open Access Journals (Sweden)

Solberg Trygve

2009-01-01

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

ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS

Directory of Open Access Journals (Sweden)

muhammad zahid rashid

2011-04-01

Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR,  moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes

Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution

Directory of Open Access Journals (Sweden)

Petrovic Nemanja

2007-01-01

Full Text Available We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video super-resolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework. The ranking formulation is a variant of classification and regression formulation, in which the ordering present in class labels namely, the shift between patches is explicitly taken into account. Finally, we demonstrate the applicability of our approach on superresolving synthetically generated images with global subpixel shifts and enhancing real video frames by accounting for both local integer and subpixel shifts.

The importance of the chosen technique to estimate diffuse solar radiation by means of regression

Energy Technology Data Exchange (ETDEWEB)

Arslan, Talha; Altyn Yavuz, Arzu [Department of Statistics. Science and Literature Faculty. Eskisehir Osmangazi University (Turkey)], email: mtarslan@ogu.edu.tr, email: aaltin@ogu.edu.tr; Acikkalp, Emin [Department of Mechanical and Manufacturing Engineering. Engineering Faculty. Bilecik University (Turkey)], email: acikkalp@gmail.com

2011-07-01

The Ordinary Least Squares (OLS) method is one of the most frequently used for estimation of diffuse solar radiation. The data set must provide certain assumptions for the OLS method to work. The most important is that the regression equation offered by OLS error terms must fit within the normal distribution. Utilizing an alternative robust estimator to get parameter estimations is highly effective in solving problems where there is a lack of normal distribution due to the presence of outliers or some other factor. The purpose of this study is to investigate the value of the chosen technique for the estimation of diffuse radiation. This study described alternative robust methods frequently used in applications and compared them with the OLS method. Making a comparison of the data set analysis of the OLS and that of the M Regression (Huber, Andrews and Tukey) techniques, it was study found that robust regression techniques are preferable to OLS because of the smoother explanation values.

Outlier Detection in Regression Using an Iterated One-Step Approximation to the Huber-Skip Estimator

DEFF Research Database (Denmark)

Johansen, Søren; Nielsen, Bent

2013-01-01

In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber-skip estima......In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber...... that the normalized estimation errors are tight and are close to a linear function of the kernel, thus providing a stochastic expansion of the estimators, which is the same as for the Huber-skip. This implies that the iterated estimator is a close approximation of the Huber-skip...

Estimating HIES Data through Ratio and Regression Methods for Different Sampling Designs

Directory of Open Access Journals (Sweden)

Faqir Muhammad

2007-01-01

Full Text Available In this study, comparison has been made for different sampling designs, using the HIES data of North West Frontier Province (NWFP for 2001-02 and 1998-99 collected from the Federal Bureau of Statistics, Statistical Division, Government of Pakistan, Islamabad. The performance of the estimators has also been considered using bootstrap and Jacknife. A two-stage stratified random sample design is adopted by HIES. In the first stage, enumeration blocks and villages are treated as the first stage Primary Sampling Units (PSU. The sample PSU’s are selected with probability proportional to size. Secondary Sampling Units (SSU i.e., households are selected by systematic sampling with a random start. They have used a single study variable. We have compared the HIES technique with some other designs, which are: Stratified Simple Random Sampling. Stratified Systematic Sampling. Stratified Ranked Set Sampling. Stratified Two Phase Sampling. Ratio and Regression methods were applied with two study variables, which are: Income (y and Household sizes (x. Jacknife and Bootstrap are used for variance replication. Simple Random Sampling with sample size (462 to 561 gave moderate variances both by Jacknife and Bootstrap. By applying Systematic Sampling, we received moderate variance with sample size (467. In Jacknife with Systematic Sampling, we obtained variance of regression estimator greater than that of ratio estimator for a sample size (467 to 631. At a sample size (952 variance of ratio estimator gets greater than that of regression estimator. The most efficient design comes out to be Ranked set sampling compared with other designs. The Ranked set sampling with jackknife and bootstrap, gives minimum variance even with the smallest sample size (467. Two Phase sampling gave poor performance. Multi-stage sampling applied by HIES gave large variances especially if used with a single study variable.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

Science.gov (United States)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

Directory of Open Access Journals (Sweden)

Akpona Okujeni

2014-07-01

Full Text Available Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR, kernel ridge regression (KRR, artificial neural networks (NN, random forest regression (RFR and partial least squares regression (PLSR. Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN or limited (RFR and PLSR performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.

Identification performance of evidential value estimation for ridge-based biometrics

NARCIS (Netherlands)

Kotzerke, Johannes; Hao, Hao; Davis, Stephen A.; Hayes, Robert; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Horadam, K.J.

2016-01-01

Law enforcement agencies around the world use ridge-based biometrics, especially fingerprints, to fight crime. Fingermarks that are left at a crime scene and identified as potentially having evidential value (EV) in a court of law are recorded for further forensic analysis. Here, we test our

Optical dating of dune ridges on Rømø

DEFF Research Database (Denmark)

Madsen, Anni Tindahl; Murray, A. S.; Andersen, Thorbjørn Joest

2007-01-01

The application of optically stimulated luminescence (OSL) to the dating of recent aeolian sand ridges on Rømø, an island off the southwest coast of Denmark, is tested. These sand ridges began to form approximately 300 years ago, and estimates of the ages are available from historical records....... Samples for OSL dating were taken ~0.5 m below the crests of four different dune ridges; at least five samples were recovered from each ridge to test the internal consistency of the ages. Additional samples were recovered from the low lying areas in the swales and from the scattered dune formations......-defined building phases separated by inactive periods and the first major ridge formed ~235 years ago. This study demonstrates that optical dating can be successfully applied to these young aeolian sand deposits, and we conclude that OSL dating is a powerful chronological tool in studies of coastal change....

An evaluation of regression methods to estimate nutritional condition of canvasbacks and other water birds

Science.gov (United States)

Sparling, D.W.; Barzen, J.A.; Lovvorn, J.R.; Serie, J.R.

1992-01-01

Regression equations that use mensural data to estimate body condition have been developed for several water birds. These equations often have been based on data that represent different sexes, age classes, or seasons, without being adequately tested for intergroup differences. We used proximate carcass analysis of 538 adult and juvenile canvasbacks (Aythya valisineria ) collected during fall migration, winter, and spring migrations in 1975-76 and 1982-85 to test regression methods for estimating body condition.

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Directory of Open Access Journals (Sweden)

Jibo Yue

2018-01-01

Full Text Available Above-ground biomass (AGB provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index and eight statistical regression techniques: artificial neural network (ANN, multivariable linear regression (MLR, decision-tree regression (DT, boosted binary regression tree (BBRT, partial least squares regression (PLSR, random forest regression (RF, support vector machine regression (SVM, and principal component regression (PCR, which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise. Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE = 0.18; thus, PLSR is best suited for works requiring high

Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

Science.gov (United States)

Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

2017-11-01

Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.

Reduced Rank Regression

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...

Learning a peptide-protein binding affinity predictor with kernel ridge regression

Science.gov (United States)

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting

Ridge Width Correlations between Inked Prints and Powdered Latent Fingerprints.

Science.gov (United States)

De Alcaraz-Fossoul, Josep; Barrot-Feixat, Carme; Zapico, Sara C; Mancenido, Michelle; Broatch, Jennifer; Roberts, Katherine A; Carreras-Marin, Clara; Tasker, Jack

2017-10-03

A methodology to estimate the time of latent fingerprint deposition would be of great value to law enforcement and courts. It has been observed that ridge topography changes as latent prints age, including the widths of ridges that could be measured as a function of time. Crime suspects are commonly identified using fingerprint databases that contain reference inked tenprints (flat and rolled impressions). These can be of interest in aging studies as they provide baseline information relating to the original (nonaged) ridges' widths. In practice, the age of latent fingerprints could be estimated following a comparison process between the evidentiary aged print and the corresponding reference inked print. The present article explores possible correlations between inked and fresh latent fingerprints deposited on different substrates and visualized with TiO 2 . The results indicate that the ridge width of flat inked prints is most similar to fresh latent fingerprints , and these should be used as the comparison standard for future aging studies. © 2017 American Academy of Forensic Sciences.

A subagging regression method for estimating the qualitative and quantitative state of groundwater

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young

2017-08-01

A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.

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

Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression

Science.gov (United States)

Islamiyati, A.; Fatmawati; Chamidah, N.

2018-03-01

The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.

Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models.

Science.gov (United States)

Salmerón, Diego; Cano, Juan A; Chirlaque, María D

2015-08-30

In cohort studies, binary outcomes are very often analyzed by logistic regression. However, it is well known that when the goal is to estimate a risk ratio, the logistic regression is inappropriate if the outcome is common. In these cases, a log-binomial regression model is preferable. On the other hand, the estimation of the regression coefficients of the log-binomial model is difficult owing to the constraints that must be imposed on these coefficients. Bayesian methods allow a straightforward approach for log-binomial regression models and produce smaller mean squared errors in the estimation of risk ratios than the frequentist methods, and the posterior inferences can be obtained using the software WinBUGS. However, Markov chain Monte Carlo methods implemented in WinBUGS can lead to large Monte Carlo errors in the approximations to the posterior inferences because they produce correlated simulations, and the accuracy of the approximations are inversely related to this correlation. To reduce correlation and to improve accuracy, we propose a reparameterization based on a Poisson model and a sampling algorithm coded in R. Copyright © 2015 John Wiley & Sons, Ltd.

Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques

Directory of Open Access Journals (Sweden)

Basheerulla Shaik

2013-01-01

value than those of MLR and SVM techniques. Rm2= metrics and ridge regression analysis indicated that the proposed four-variable model MATS5e, RDF080u, T(O⋯O, and MATS5m as correlating descriptors is the best for estimating the anti-HIV activity (log 1/C present set of compounds.

Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

Directory of Open Access Journals (Sweden)

Hongjian Wang

2014-01-01

Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.

Mass estimation of loose parts in nuclear power plant based on multiple regression

International Nuclear Information System (INIS)

He, Yuanfeng; Cao, Yanlong; Yang, Jiangxin; Gan, Chunbiao

2012-01-01

According to the application of the Hilbert–Huang transform to the non-stationary signal and the relation between the mass of loose parts in nuclear power plant and corresponding frequency content, a new method for loose part mass estimation based on the marginal Hilbert–Huang spectrum (MHS) and multiple regression is proposed in this paper. The frequency spectrum of a loose part in a nuclear power plant can be expressed by the MHS. The multiple regression model that is constructed by the MHS feature of the impact signals for mass estimation is used to predict the unknown masses of a loose part. A simulated experiment verified that the method is feasible and the errors of the results are acceptable. (paper)

Semi-parametric estimation of random effects in a logistic regression model using conditional inference

DEFF Research Database (Denmark)

Petersen, Jørgen Holm

2016-01-01

This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...

Adding a Parameter Increases the Variance of an Estimated Regression Function

Science.gov (United States)

Withers, Christopher S.; Nadarajah, Saralees

2011-01-01

The linear regression model is one of the most popular models in statistics. It is also one of the simplest models in statistics. It has received applications in almost every area of science, engineering and medicine. In this article, the authors show that adding a predictor to a linear model increases the variance of the estimated regression…

Detailed analysis of a RCRA landfill for the United Nuclear Corporation Disposal Site at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1991-04-01

The purpose of this detailed analysis is to provide a preliminary compilation of data, information, and estimated costs associated with a RCRA landfill alternative for UNC Disposal Site. This is in response to Environmental Protection Agency (EPA) comment No. 6 from their review of a open-quotes Feasibility Study for the United Nuclear Corporation Disposal Site at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee.close quotes

On the degrees of freedom of reduced-rank estimators in multivariate regression.

Science.gov (United States)

Mukherjee, A; Chen, K; Wang, N; Zhu, J

We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than the sample size. The derived analytical form facilitates the investigation of theoretical properties and provides new insights into the empirical behaviour of the degrees of freedom. In particular, we examine the differences and connections between the proposed estimator and a commonly-used naive estimator. The use of the proposed estimator leads to efficient and accurate prediction risk estimation and model selection, as demonstrated by simulation studies and a data example.

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks

International Nuclear Information System (INIS)

Elliott Campbell, J.; Moen, Jeremie C.; Ney, Richard A.; Schnoor, Jerald L.

2008-01-01

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively. - Large differences in estimates of soil organic carbon stocks and annual changes in stocks for Wisconsin forestlands indicate a need for validation from forthcoming forest surveys

Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

Directory of Open Access Journals (Sweden)

Menon Carlo

2011-09-01

Full Text Available Abstract Background Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG signals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Results It was shown that mean adjusted coefficient of determination (Ra2 values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean Ra2 values between 64% to 74% for different models. Conclusions Model estimation accuracy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estimation accuracy. Among the models compared, ordinary least squares linear regression model (OLS was shown to have high isometric torque estimation accuracy combined with very short training times.

Development of flood regressions and climate change scenarios to explore estimates of future peak flows

Science.gov (United States)

Burns, Douglas A.; Smith, Martyn J.; Freehafer, Douglas A.

2015-12-31

A new Web-based application, titled “Application of Flood Regressions and Climate Change Scenarios To Explore Estimates of Future Peak Flows”, has been developed by the U.S. Geological Survey, in cooperation with the New York State Department of Transportation, that allows a user to apply a set of regression equations to estimate the magnitude of future floods for any stream or river in New York State (exclusive of Long Island) and the Lake Champlain Basin in Vermont. The regression equations that are the basis of the current application were developed in previous investigations by the U.S. Geological Survey (USGS) and are described at the USGS StreamStats Web sites for New York (http://water.usgs.gov/osw/streamstats/new_york.html) and Vermont (http://water.usgs.gov/osw/streamstats/Vermont.html). These regression equations include several fixed landscape metrics that quantify aspects of watershed geomorphology, basin size, and land cover as well as a climate variable—either annual precipitation or annual runoff.

Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods

Directory of Open Access Journals (Sweden)

Yi-Ming Kuo

2011-06-01

Full Text Available Fine airborne particulate matter (PM2.5 has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS, the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME method. The resulting epistemic framework can assimilate knowledge bases including: (a empirical-based spatial trends of PM concentration based on landuse regression, (b the spatio-temporal dependence among PM observation information, and (c site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan from 2005–2007.

Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods.

Science.gov (United States)

Yu, Hwa-Lung; Wang, Chih-Hsih; Liu, Ming-Che; Kuo, Yi-Ming

2011-06-01

Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005-2007.

Estimating Gestational Age With Sonography: Regression-Derived Formula Versus the Fetal Biometric Average.

Science.gov (United States)

Cawyer, Chase R; Anderson, Sarah B; Szychowski, Jeff M; Neely, Cherry; Owen, John

2018-03-01

To compare the accuracy of a new regression-derived formula developed from the National Fetal Growth Studies data to the common alternative method that uses the average of the gestational ages (GAs) calculated for each fetal biometric measurement (biparietal diameter, head circumference, abdominal circumference, and femur length). This retrospective cross-sectional study identified nonanomalous singleton pregnancies that had a crown-rump length plus at least 1 additional sonographic examination with complete fetal biometric measurements. With the use of the crown-rump length to establish the referent estimated date of delivery, each method's (National Institute of Child Health and Human Development regression versus Hadlock average [Radiology 1984; 152:497-501]), error at every examination was computed. Error, defined as the difference between the crown-rump length-derived GA and each method's predicted GA (weeks), was compared in 3 GA intervals: 1 (14 weeks-20 weeks 6 days), 2 (21 weeks-28 weeks 6 days), and 3 (≥29 weeks). In addition, the proportion of each method's examinations that had errors outside prespecified (±) day ranges was computed by using odds ratios. A total of 16,904 sonograms were identified. The overall and prespecified GA range subset mean errors were significantly smaller for the regression compared to the average (P < .01), and the regression had significantly lower odds of observing examinations outside the specified range of error in GA intervals 2 (odds ratio, 1.15; 95% confidence interval, 1.01-1.31) and 3 (odds ratio, 1.24; 95% confidence interval, 1.17-1.32) than the average method. In a contemporary unselected population of women dated by a crown-rump length-derived GA, the National Institute of Child Health and Human Development regression formula produced fewer estimates outside a prespecified margin of error than the commonly used Hadlock average; the differences were most pronounced for GA estimates at 29 weeks and later. �

Oak Ridge Dose Reconstruction Project Summary Report; Reports of the Oak Ridge Dose Reconstruction, Vol. 7

International Nuclear Information System (INIS)

Widner, Thomas E.; email = twidner@jajoneses.com

1999-01-01

In the early 1990s, concern about the Oak Ridge Reservation's past releases of contaminants to the environment prompted Tennessee's public health officials to pursue an in-depth study of potential off-site health effects at Oak Ridge. This study, the Oak Ridge dose reconstruction, was supported by an agreement between the U.S. Department of Energy (DOE) and the State of Tennessee, and was overseen by a 12-member panel of individuals appointed by Tennessee's Commissioner of Health. The panel requested that the principal investigator for the project prepare the following report, ''Oak Ridge Dose Reconstruction Project Summary Report,'' to serve the following purposes: (1) summarize in a single, less technical report, the methods and results of the various investigations that comprised the Phase II of the dose reconstruction; (2) describe the systematic searching of classified and unclassified historical records that was a vital component of the project; and (3) summarize the less detailed, screening-level assessments that were performed to evaluate the potential health significance of a number of materials, such a uranium, whose priority did not require a complete dose reconstruction effort. This report describes each major step of the dose reconstruction study: (1) the review of thousands of historical records to obtain information relating to past operations at each facility; (2) estimation of the quantity and timing of releases of radioiodines from X-10, of mercury from Y-12, of PCB's from all facilities, and of cesium-137 and other radionuclides from White Oak Creek; (3) evaluation of the routes taken by these contaminants through the environment to nearby populations; and (4) estimation of doses and health risks to exposed groups. Calculations found the highest excess cancer risks for a female born in 1952 who drank goat milk; the highest non-cancer health risk was for children in a farm family exposed to PCBs in and near East Fork Poplar Creek. More detailed

Volcanism and hydrothermalism on a hotspot-influenced ridge: Comparing Reykjanes Peninsula and Reykjanes Ridge, Iceland

Science.gov (United States)

Pałgan, Dominik; Devey, Colin W.; Yeo, Isobel A.

2017-12-01

Current estimates indicate that the number of high-temperature vents (one of the primary pathways for the heat extraction from the Earth's mantle) - at least 1 per 100 km of axial length - scales with spreading rate and should scale with crustal thickness. But up to present, shallow ridge axes underlain by thick crust show anomalously low incidences of high-temperature activity. Here we compare the Reykjanes Ridge, an abnormally shallow ridge with thick crust and only one high-temperature vent known over 900 km axial length, to the adjacent subaerial Reykjanes Peninsula (RP), which is characterized by high-temperature geothermal sites confined to four volcanic systems transected by fissure swarms with young (Holocene) volcanic activity, multiple faults, cracks and fissures, and continuous seismic activity. New high-resolution bathymetry (gridded at 60 m) of the Reykjanes Ridge between 62°30‧N and 63°30‧N shows seven Axial Volcanic Ridges (AVR) that, based on their morphology, geometry and tectonic regime, are analogues for the volcanic systems and fissure swarms on land. We investigate in detail the volcano-tectonic features of all mapped AVRs and show that they do not fit with the previously suggested 4-stage evolution model for AVR construction. Instead, we suggest that AVR morphology reflects the robust or weak melt supply to the system and two (or more) eruption mechanisms may co-exist on one AVR (in contrast to 4-stage evolution model). Our interpretations indicate that, unlike on the Reykjanes Peninsula, faults on and around AVRs do not cluster in orientation domains but all are subparallel to the overall strike of AVRs (orthogonal to spreading direction). High abundance of seamounts shows that the region centered at 62°47‧N and 25°04‧W (between AVR-5 and -6) is volcanically robust while the highest fault density implies that AVR-1 and southern part of AVR-6 rather undergo period of melt starvation. Based on our observations and interpretations we

Oil and gas pipeline construction cost analysis and developing regression models for cost estimation

Science.gov (United States)

Thaduri, Ravi Kiran

In this study, cost data for 180 pipelines and 136 compressor stations have been analyzed. On the basis of the distribution analysis, regression models have been developed. Material, Labor, ROW and miscellaneous costs make up the total cost of a pipeline construction. The pipelines are analyzed based on different pipeline lengths, diameter, location, pipeline volume and year of completion. In a pipeline construction, labor costs dominate the total costs with a share of about 40%. Multiple non-linear regression models are developed to estimate the component costs of pipelines for various cross-sectional areas, lengths and locations. The Compressor stations are analyzed based on the capacity, year of completion and location. Unlike the pipeline costs, material costs dominate the total costs in the construction of compressor station, with an average share of about 50.6%. Land costs have very little influence on the total costs. Similar regression models are developed to estimate the component costs of compressor station for various capacities and locations.

Replicating Experimental Impact Estimates Using a Regression Discontinuity Approach. NCEE 2012-4025

Science.gov (United States)

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…

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

Science.gov (United States)

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

2012-12-01

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

Relation of whole blood carboxyhemoglobin concentration to ambient carbon monoxide exposure estimated using regression.

Science.gov (United States)

Rudra, Carole B; Williams, Michelle A; Sheppard, Lianne; Koenig, Jane Q; Schiff, Melissa A; Frederick, Ihunnaya O; Dills, Russell

2010-04-15

Exposure to carbon monoxide (CO) and other ambient air pollutants is associated with adverse pregnancy outcomes. While there are several methods of estimating CO exposure, few have been evaluated against exposure biomarkers. The authors examined the relation between estimated CO exposure and blood carboxyhemoglobin concentration in 708 pregnant western Washington State women (1996-2004). Carboxyhemoglobin was measured in whole blood drawn around 13 weeks' gestation. CO exposure during the month of blood draw was estimated using a regression model containing predictor terms for year, month, street and population densities, and distance to the nearest major road. Year and month were the strongest predictors. Carboxyhemoglobin level was correlated with estimated CO exposure (rho = 0.22, 95% confidence interval (CI): 0.15, 0.29). After adjustment for covariates, each 10% increase in estimated exposure was associated with a 1.12% increase in median carboxyhemoglobin level (95% CI: 0.54, 1.69). This association remained after exclusion of 286 women who reported smoking or being exposed to secondhand smoke (rho = 0.24). In this subgroup, the median carboxyhemoglobin concentration increased 1.29% (95% CI: 0.67, 1.91) for each 10% increase in CO exposure. Monthly estimated CO exposure was moderately correlated with an exposure biomarker. These results support the validity of this regression model for estimating ambient CO exposures in this population and geographic setting.

Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression.

Science.gov (United States)

Zhen, Xiantong; Zhang, Heye; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

2017-02-01

Cardiac four-chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically developed for one single ventricle, i.e., left ventricle (LV), or bi-ventricles; they can not be directly used for four chamber volume estimation due to the great combinatorial variability and highly complex anatomical interdependency of the four chambers. In this paper, we propose a new, general framework for direct and simultaneous four chamber volume estimation. We have addressed two key issues, i.e., cardiac image representation and simultaneous four chamber volume estimation, which enables accurate and efficient four-chamber volume estimation. We generate compact and discriminative image representations by supervised descriptor learning (SDL) which can remove irrelevant information and extract discriminative features. We propose direct and simultaneous four-chamber volume estimation by the multioutput sparse latent regression (MSLR), which enables jointly modeling nonlinear input-output relationships and capturing four-chamber interdependence. The proposed method is highly generalized, independent of imaging modalities, which provides a general regression framework that can be extensively used for clinical data prediction to achieve automated diagnosis. Experiments on both MR and CT images show that our method achieves high performance with a correlation coefficient of up to 0.921 with ground truth obtained manually by human experts, which is clinically significant and enables more accurate, convenient and comprehensive assessment of cardiac functions. Copyright © 2016 Elsevier

Regression to fuzziness method for estimation of remaining useful life in power plant components

Science.gov (United States)

Alamaniotis, Miltiadis; Grelle, Austin; Tsoukalas, Lefteri H.

2014-10-01

Mitigation of severe accidents in power plants requires the reliable operation of all systems and the on-time replacement of mechanical components. Therefore, the continuous surveillance of power systems is a crucial concern for the overall safety, cost control, and on-time maintenance of a power plant. In this paper a methodology called regression to fuzziness is presented that estimates the remaining useful life (RUL) of power plant components. The RUL is defined as the difference between the time that a measurement was taken and the estimated failure time of that component. The methodology aims to compensate for a potential lack of historical data by modeling an expert's operational experience and expertise applied to the system. It initially identifies critical degradation parameters and their associated value range. Once completed, the operator's experience is modeled through fuzzy sets which span the entire parameter range. This model is then synergistically used with linear regression and a component's failure point to estimate the RUL. The proposed methodology is tested on estimating the RUL of a turbine (the basic electrical generating component of a power plant) in three different cases. Results demonstrate the benefits of the methodology for components for which operational data is not readily available and emphasize the significance of the selection of fuzzy sets and the effect of knowledge representation on the predicted output. To verify the effectiveness of the methodology, it was benchmarked against the data-based simple linear regression model used for predictions which was shown to perform equal or worse than the presented methodology. Furthermore, methodology comparison highlighted the improvement in estimation offered by the adoption of appropriate of fuzzy sets for parameter representation.

The thermal structure of a wind-driven Reynolds ridge

Energy Technology Data Exchange (ETDEWEB)

Phongikaroon, Supathorn; Peter Judd, K.; Smith, Geoffrey B.; Handler, Robert A. [Remote Sensing Division, Naval Research Laboratory, 20375, Washington, DC (United States)

2004-08-01

In this study, we investigate the nature of a Reynolds ridge formed by wind shear. We have simultaneously imaged the water surface, with a deposit of a monolayer of the surfactant, oleyl alcohol, subject to different wind shears, by using a high-resolution infrared (IR) detector and a high-speed (HS) digital camera. The results reveal that the regions around the wind-driven Reynolds ridge, which have subtle manifestations in visual imagery, possess surprisingly complex hydrodynamical and thermal structures when observed in the infrared. The IR measurements reveal a warm, clean region upstream of the ridge, which is composed of the so called fishscale structures observed in earlier investigations. The region downstream of the ridge is composed of colder fluid which forms two counter-rotating cells. A region of intermediate temperature, which we call the mixing (wake) region, forms immediately downstream of the ridge near the channel centerline. By measuring the velocity of the advected fishscales, we have determined a surface drift speed of about 2% of the wind speed. The spanwise length-scale of the structures has also been used to estimate the wind shear. In addition, a comparison of IR and visual imagery shows that the thermal field is a very sensitive indicator of the exact position of the ridge itself. (orig.)

Estimation of Geographically Weighted Regression Case Study on Wet Land Paddy Productivities in Tulungagung Regency

Directory of Open Access Journals (Sweden)

Danang Ariyanto

2017-11-01

Full Text Available Regression is a method connected independent variable and dependent variable with estimation parameter as an output. Principal problem in this method is its application in spatial data. Geographically Weighted Regression (GWR method used to solve the problem. GWR  is a regression technique that extends the traditional regression framework by allowing the estimation of local rather than global parameters. In other words, GWR runs a regression for each location, instead of a sole regression for the entire study area. The purpose of this research is to analyze the factors influencing wet land paddy productivities in Tulungagung Regency. The methods used in this research is  GWR using cross validation  bandwidth and weighted by adaptive Gaussian kernel fungtion.This research using  4 variables which are presumed affecting the wet land paddy productivities such as:  the rate of rainfall(X1, the average cost of fertilizer per hectare(X2, the average cost of pestisides per hectare(X3 and Allocation of subsidized NPK fertilizer of food crops sub-sector(X4. Based on the result, X1, X2, X3 and X4  has a different effect on each Distric. So, to improve the productivity of wet land paddy in Tulungagung Regency required a special policy based on the GWR model in each distric.

Estimation of adjusted rate differences using additive negative binomial regression.

Science.gov (United States)

Donoghoe, Mark W; Marschner, Ian C

2016-08-15

Rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios. When the data are event counts observed during an exposure period, adjusted rate differences may be estimated using an identity-link Poisson generalised linear model, also known as additive Poisson regression. A problem with this approach is that the assumption of equality of mean and variance rarely holds in real data, which often show overdispersion. An additive negative binomial model is the natural alternative to account for this; however, standard model-fitting methods are often unable to cope with the constrained parameter space arising from the non-negativity restrictions of the additive model. In this paper, we propose a novel solution to this problem using a variant of the expectation-conditional maximisation-either algorithm. Our method provides a reliable way to fit an additive negative binomial regression model and also permits flexible generalisations using semi-parametric regression functions. We illustrate the method using a placebo-controlled clinical trial of fenofibrate treatment in patients with type II diabetes, where the outcome is the number of laser therapy courses administered to treat diabetic retinopathy. An R package is available that implements the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A comparison on parameter-estimation methods in multiple regression analysis with existence of multicollinearity among independent variables

Directory of Open Access Journals (Sweden)

Hukharnsusatrue, A.

2005-11-01

Full Text Available The objective of this research is to compare multiple regression coefficients estimating methods with existence of multicollinearity among independent variables. The estimation methods are Ordinary Least Squares method (OLS, Restricted Least Squares method (RLS, Restricted Ridge Regression method (RRR and Restricted Liu method (RL when restrictions are true and restrictions are not true. The study used the Monte Carlo Simulation method. The experiment was repeated 1,000 times under each situation. The analyzed results of the data are demonstrated as follows. CASE 1: The restrictions are true. In all cases, RRR and RL methods have a smaller Average Mean Square Error (AMSE than OLS and RLS method, respectively. RRR method provides the smallest AMSE when the level of correlations is high and also provides the smallest AMSE for all level of correlations and all sample sizes when standard deviation is equal to 5. However, RL method provides the smallest AMSE when the level of correlations is low and middle, except in the case of standard deviation equal to 3, small sample sizes, RRR method provides the smallest AMSE.The AMSE varies with, most to least, respectively, level of correlations, standard deviation and number of independent variables but inversely with to sample size.CASE 2: The restrictions are not true.In all cases, RRR method provides the smallest AMSE, except in the case of standard deviation equal to 1 and error of restrictions equal to 5%, OLS method provides the smallest AMSE when the level of correlations is low or median and there is a large sample size, but the small sample sizes, RL method provides the smallest AMSE. In addition, when error of restrictions is increased, OLS method provides the smallest AMSE for all level, of correlations and all sample sizes, except when the level of correlations is high and sample sizes small. Moreover, the case OLS method provides the smallest AMSE, the most RLS method has a smaller AMSE than

Volume estimate of radium-contaminated soil in a section of Barrows Field Park, Glen Ridge, New Jersey, November--December 1989

International Nuclear Information System (INIS)

Robinet, M.J.; Mosho, G.D.

1990-04-01

The objective of this project was to estimate the in-place volume of radium-contaminated soil in an area of Barrows Field Park, Glen Ridge, New Jersey. The information was necessary to determine whether or not there was sufficient soil with the proper radium concentration to test a new method of soil decontamination. The steps used by Argonne National Laboratory personnel to obtain the required data for estimating the volume of contaminated soil was to measure the contamination-depth profile at 118 locations in a 60 ft times 150 ft area in the park, plot the contours of depths to the specified concentration, and measure the area of the closed depth contours. 6 refs., 23 figs., 3 tabs

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1994-03-01

This document outlines the activities necessary to conduct a Remedial Investigation (RI) of the Chestnut Ridge Security Pits (CRSP) at the Oak Ridge Y-12 Plant. The CRSP, also designated Chestnut Ridge Operable Unit (OU) 1, is one of four OUs along Chestnut Ridge on the Oak Ridge Reservation (ORR). The purpose of the RI is to collect data to (1) evaluate the nature and extent of known and suspected contaminants, (2) support an Ecological Risk Assessment (ERA) and a Human Health Risk Assessment (HHRA), (3) support the feasibility study in the development and analysis of remedial alternatives, and (4) ultimately, develop a Record of Decision (ROD) for the site. This chapter summarizes the regulatory background of environmental investigation on the ORR and the approach currently being followed and provides an overview of the RI to be conducted at the CRSP. Subsequent chapters provide details on site history, sampling activities, procedures and methods, quality assurance (QA), health and safety, and waste management related to the RI

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

1994-03-01

This document outlines the activities necessary to conduct a Remedial Investigation (RI) of the Chestnut Ridge Security Pits (CRSP) at the Oak Ridge Y-12 Plant. The CRSP, also designated Chestnut Ridge Operable Unit (OU) 1, is one of four OUs along Chestnut Ridge on the Oak Ridge Reservation (ORR). The purpose of the RI is to collect data to (1) evaluate the nature and extent of known and suspected contaminants, (2) support an Ecological Risk Assessment (ERA) and a Human Health Risk Assessment (HHRA), (3) support the feasibility study in the development and analysis of remedial alternatives, and (4) ultimately, develop a Record of Decision (ROD) for the site. This chapter summarizes the regulatory background of environmental investigation on the ORR and the approach currently being followed and provides an overview of the RI to be conducted at the CRSP. Subsequent chapters provide details on site history, sampling activities, procedures and methods, quality assurance (QA), health and safety, and waste management related to the RI.

An aerial radiological survey of the Oak Ridge Reservation, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

Maurer, R.J.

1993-04-01

An aerial radiological survey of the Oak Ridge Reservation (ORR) and surrounding area in Oak Ridge, Tennessee, was conducted during the period March 30 to April 14,1992. The purpose of the survey was to measure and document the terrestrial radiological environment of the Oak Ridge Reservation for use in environmental management programs and emergency response planning. The aerial survey was flown at an altitude of 150 feet (46 meters) along a series of parallel lines 250 feet (76 meters) apart and included X-10 (Oak Ridge National Laboratory), K-25 (former Gaseous Diffusion Plant), Y-12 (Weapons Production Plant), the Freels Bend Area and Oak Ridge Institute for Science and Education, the East Fork Poplar Creek (100-year floodplain extending from K-25 to Y-12), Elza Gate (former uranium ore storage site located in the city of Oak Ridge), Parcel A, the Clinch River (river banks extending from Melton Hill Dam to the city of Kingston), and the CSX Railroad Tracks (extending from Y-12 to the city of Oak Ridge). The survey encompassed approximately 55 square miles (1 41 square kilometers) of the Oak Ridge Reservation and surrounding area

Large biases in regression-based constituent flux estimates: causes and diagnostic tools

Science.gov (United States)

Hirsch, Robert M.

2014-01-01

It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

Science.gov (United States)

Meaney, Christopher; Moineddin, Rahim

2014-01-24

In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

Support vector regression methodology for estimating global solar radiation in Algeria

Science.gov (United States)

Guermoui, Mawloud; Rabehi, Abdelaziz; Gairaa, Kacem; Benkaciali, Said

2018-01-01

Accurate estimation of Daily Global Solar Radiation (DGSR) has been a major goal for solar energy applications. In this paper we show the possibility of developing a simple model based on the Support Vector Regression (SVM-R), which could be used to estimate DGSR on the horizontal surface in Algeria based only on sunshine ratio as input. The SVM model has been developed and tested using a data set recorded over three years (2005-2007). The data was collected at the Applied Research Unit for Renewable Energies (URAER) in Ghardaïa city. The data collected between 2005-2006 are used to train the model while the 2007 data are used to test the performance of the selected model. The measured and the estimated values of DGSR were compared during the testing phase statistically using the Root Mean Square Error (RMSE), Relative Square Error (rRMSE), and correlation coefficient (r2), which amount to 1.59(MJ/m2), 8.46 and 97,4%, respectively. The obtained results show that the SVM-R is highly qualified for DGSR estimation using only sunshine ratio.

Dual Regression

OpenAIRE

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...

Boosted beta regression.

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.

Oak Ridge Dose Reconstruction Project Summary Report; Reports of the Oak Ridge Dose Reconstruction, Vol. 7

Energy Technology Data Exchange (ETDEWEB)

Thomas E. Widner; et. al.

1999-07-01

In the early 1990s, concern about the Oak Ridge Reservation's past releases of contaminants to the environment prompted Tennessee's public health officials to pursue an in-depth study of potential off-site health effects at Oak Ridge. This study, the Oak Ridge dose reconstruction, was supported by an agreement between the U.S. Department of Energy (DOE) and the State of Tennessee, and was overseen by a 12-member panel of individuals appointed by Tennessee's Commissioner of Health. The panel requested that the principal investigator for the project prepare the following report, ''Oak Ridge Dose Reconstruction Project Summary Report,'' to serve the following purposes: (1) summarize in a single, less technical report, the methods and results of the various investigations that comprised the Phase II of the dose reconstruction; (2) describe the systematic searching of classified and unclassified historical records that was a vital component of the project; and (3) summarize the less detailed, screening-level assessments that were performed to evaluate the potential health significance of a number of materials, such a uranium, whose priority did not require a complete dose reconstruction effort. This report describes each major step of the dose reconstruction study: (1) the review of thousands of historical records to obtain information relating to past operations at each facility; (2) estimation of the quantity and timing of releases of radioiodines from X-10, of mercury from Y-12, of PCB's from all facilities, and of cesium-137 and other radionuclides from White Oak Creek; (3) evaluation of the routes taken by these contaminants through the environment to nearby populations; and (4) estimation of doses and health risks to exposed groups. Calculations found the highest excess cancer risks for a female born in 1952 who drank goat milk; the highest non-cancer health risk was for children in a farm family exposed to PCBs in and near

Minimax Regression Quantiles

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....

Contaminated scrap metal management on the Oak Ridge Reservation

International Nuclear Information System (INIS)

Hayden, H.W.; Stephenson, M.J.; Bailey, J.K.; Weir, J.R.; Gilbert, W.C.

1993-01-01

Large quantities of scrap metal are accumulating at the various Department of Energy (DOE) installations across the country as a result of ongoing DOE programs and missions in concert with present day waste management practices. DOE Oak Ridge alone is presently storing around 500,000 tons of scrap metal. The local generation rate, currently estimated at 1,400 tons/yr, is expected to increase sharply over the next couple of years as numerous environmental restoration and decommissioning programs gain momentum. Projections show that 775,000 tons of scrap metal could be generated at the K-25 Site over the next ten years. The Y-12 Plant and Oak Ridge National Laboratory (ORNL) have similar potentials. The history of scrap metal management at Oak Ridge and future challenges and opportunities are discussed

Estimation of genetic parameters related to eggshell strength using random regression models.

Science.gov (United States)

Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

2015-01-01

This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

Science.gov (United States)

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.

Science.gov (United States)

Luque-Fernandez, Miguel Angel; Belot, Aurélien; Quaresma, Manuela; Maringe, Camille; Coleman, Michel P; Rachet, Bernard

2016-10-01

In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion.

In search of a corrected prescription drug elasticity estimate: a meta-regression approach.

Science.gov (United States)

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.

The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

DEFF Research Database (Denmark)

Brunori, Paolo; Hufe, Paul; Mahler, Daniel Gerszon

2017-01-01

the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make...... the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions....

Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

Directory of Open Access Journals (Sweden)

Changwei Ma

2015-01-01

Full Text Available Discrete Fourier transform- (DFT- based maximum likelihood (ML algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB, which is dependent on the number of DFT points. However, its mean square error (MSE performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR, least squares SVR (LS-SVR can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.

Real estate value prediction using multivariate regression models

Science.gov (United States)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

Science.gov (United States)

Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

2018-05-01

Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

An aerial radiological survey of the White Oak Creek Floodplain, Oak Ridge Reservation, Oak Ridge, Tennessee: Date of survey: September-October 1986

International Nuclear Information System (INIS)

Fritzsche, A.E.

1987-06-01

An aerial radiological survey was conducted over the White Oak Creek Floodplain of the Oak Ridge Reservation during the period 30 September through 3 October 1986. The survey was performed at the request of the United States Department of Energy (DOE), Oak Ridge Operations Office, by EG and G Energy Measurements, Inc. (EG and G/EM), a contractor of the DOE. The survey results will be utilized in support of the Remedial Action Program being conducted at the site by Martin Marietta Energy Systems, Inc., operator of the Oak Ridge National Laboratory (ORNL). A flight line spacing of 37 meters (120 feet) and a survey altitude of 46 meters (150 feet) yielded the maximum data density and sensitivity achievable by the aerial system, which was greater than that achieved from prior surveys of the entire Oak Ridge Reservation. Isopleth maps of Cs-137, Co-60, Ti-208 implied concentrations, and exposure rates provided an estimate of the location and magnitude of the man-made activity. These maps, overlaid on a current photograph of the area, combine to yield a view of the radiological condition of the White Oak Creek Floodplain. 5 refs., 40 figs., 3 tabs

The limiting behavior of the estimated parameters in a misspecified random field regression model

DEFF Research Database (Denmark)

Dahl, Christian Møller; Qin, Yu

This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection of n...

Estimation of Stature from Foot Dimensions and Stature among South Indian Medical Students Using Regression Models

Directory of Open Access Journals (Sweden)

Rajesh D. R

2015-01-01

Full Text Available Background: At times fragments of soft tissues are found disposed off in the open, in ditches at the crime scene and the same are brought to forensic experts for the purpose of identification and such type of cases pose a real challenge. Objectives: This study was aimed at developing a methodology which could help in personal identification by studying the relation between foot dimensions and stature among south subjects using regression models. Material and Methods: Stature and foot length of 100 subjects (age range 18-22 years were measured. Linear regression equations for stature estimation were calculated. Result: The correlation coefficients between stature and foot lengths were found to be positive and statistically significant. Height = 98.159 + 3.746 × FLRT (r = 0.821 and Height = 91.242 + 3.284 × FLRT (r = 0.837 are the regression formulas from foot lengths for males and females respectively. Conclusion: The regression equation derived in the study can be used reliably for estimation of stature in a diverse population group thus would be of immense value in the field of personal identification especially from mutilated bodies or fragmentary remains.

Skeletal height estimation from regression analysis of sternal lengths in a Northwest Indian population of Chandigarh region: a postmortem study.

Science.gov (United States)

Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H

2011-03-20

Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the

Estimation of monthly solar exposure on horizontal surface by Angstrom-type regression equation

International Nuclear Information System (INIS)

Ravanshid, S.H.

1981-01-01

To obtain solar flux intensity, solar radiation measuring instruments are the best. In the absence of instrumental data there are other meteorological measurements which are related to solar energy and also it is possible to use empirical relationships to estimate solar flux intensit. One of these empirical relationships to estimate monthly averages of total solar radiation on a horizontal surface is the modified angstrom-type regression equation which has been employed in this report in order to estimate the solar flux intensity on a horizontal surface for Tehran. By comparing the results of this equation with four years measured valued by Tehran's meteorological weather station the values of meteorological constants (a,b) in the equation were obtained for Tehran. (author)

Estimation of error components in a multi-error linear regression model, with an application to track fitting

International Nuclear Information System (INIS)

Fruehwirth, R.

1993-01-01

We present an estimation procedure of the error components in a linear regression model with multiple independent stochastic error contributions. After solving the general problem we apply the results to the estimation of the actual trajectory in track fitting with multiple scattering. (orig.)

A comparison of the performances of an artificial neural network and a regression model for GFR estimation.

Science.gov (United States)

Liu, Xun; Li, Ning-shan; Lv, Lin-sheng; Huang, Jian-hua; Tang, Hua; Chen, Jin-xia; Ma, Hui-juan; Wu, Xiao-ming; Lou, Tan-qi

2013-12-01

Accurate estimation of glomerular filtration rate (GFR) is important in clinical practice. Current models derived from regression are limited by the imprecision of GFR estimates. We hypothesized that an artificial neural network (ANN) might improve the precision of GFR estimates. A study of diagnostic test accuracy. 1,230 patients with chronic kidney disease were enrolled, including the development cohort (n=581), internal validation cohort (n=278), and external validation cohort (n=371). Estimated GFR (eGFR) using a new ANN model and a new regression model using age, sex, and standardized serum creatinine level derived in the development and internal validation cohort, and the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) 2009 creatinine equation. Measured GFR (mGFR). GFR was measured using a diethylenetriaminepentaacetic acid renal dynamic imaging method. Serum creatinine was measured with an enzymatic method traceable to isotope-dilution mass spectrometry. In the external validation cohort, mean mGFR was 49±27 (SD) mL/min/1.73 m2 and biases (median difference between mGFR and eGFR) for the CKD-EPI, new regression, and new ANN models were 0.4, 1.5, and -0.5 mL/min/1.73 m2, respectively (P30% from mGFR) were 50.9%, 77.4%, and 78.7%, respectively (Psource of systematic bias in comparisons of new models to CKD-EPI, and both the derivation and validation cohorts consisted of a group of patients who were referred to the same institution. An ANN model using 3 variables did not perform better than a new regression model. Whether ANN can improve GFR estimation using more variables requires further investigation. Copyright © 2013 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

Science.gov (United States)

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Estimation of pyrethroid pesticide intake using regression modeling of food groups based on composite dietary samples

Data.gov (United States)

U.S. Environmental Protection Agency — Population-based estimates of pesticide intake are needed to characterize exposure for particular demographic groups based on their dietary behaviors. Regression...

Prediction of facial height, width, and ratio from thumbprints ridge count and its possible applications

Directory of Open Access Journals (Sweden)

Lawan Hassan Adamu

2017-01-01

Full Text Available The fingerprints and face recognition are two biometric processes that comprise methods for uniquely recognizing humans based on certain number of intrinsic physical or behavioral traits. The objectives of the study were to predict the facial height (FH, facial width, and ratios from thumbprints ridge count and its possible applications. This was a cross-sectional study. A total of 457 participants were recruited. A fingerprint live scanner was used to capture the plain thumbprint. The facial photograph was captured using a digital camera. Pearson's correlation analysis was used for the relationship between thumbprint ridge density and facial linear dimensions. Step-wise linear multiple regression analysis was used to predict facial distances from thumbprint ridge density. The result showed that in males the right ulnar ridge count correlates negatively with lower facial width (LFW, upper facial width/upper FH (UFW/UFH, lower FH/FH (LFH/FH, and positively with UFH and UFW/LFW. The right and left proximal ridge counts correlate with LFW and UFH, respectively. In males, the right ulnar ridge count predicts LFW, UFW/LFW, UFW/UFH, and LFH/FH. Special upper face height I, LFW, height of lower third of the face, UFW/LFW was predicted by right radial ridge counts. LFH, height of lower third of the face, and LFH/FH were predicted from left ulnar ridge count whereas left proximal ridge count predicted LFW. In females only, the special upper face height I was predicted by right ulnar ridge count. In conclusion, thumbprint ridge counts can be used to predict FH, width, ratios among Hausa population. The possible application of fingerprints in facial characterization for used in human biology, paleodemography, and forensic science was demonstrated.

A regressive methodology for estimating missing data in rainfall daily time series

Science.gov (United States)

Barca, E.; Passarella, G.

2009-04-01

The "presence" of gaps in environmental data time series represents a very common, but extremely critical problem, since it can produce biased results (Rubin, 1976). Missing data plagues almost all surveys. The problem is how to deal with missing data once it has been deemed impossible to recover the actual missing values. Apart from the amount of missing data, another issue which plays an important role in the choice of any recovery approach is the evaluation of "missingness" mechanisms. When data missing is conditioned by some other variable observed in the data set (Schafer, 1997) the mechanism is called MAR (Missing at Random). Otherwise, when the missingness mechanism depends on the actual value of the missing data, it is called NCAR (Not Missing at Random). This last is the most difficult condition to model. In the last decade interest arose in the estimation of missing data by using regression (single imputation). More recently multiple imputation has become also available, which returns a distribution of estimated values (Scheffer, 2002). In this paper an automatic methodology for estimating missing data is presented. In practice, given a gauging station affected by missing data (target station), the methodology checks the randomness of the missing data and classifies the "similarity" between the target station and the other gauging stations spread over the study area. Among different methods useful for defining the similarity degree, whose effectiveness strongly depends on the data distribution, the Spearman correlation coefficient was chosen. Once defined the similarity matrix, a suitable, nonparametric, univariate, and regressive method was applied in order to estimate missing data in the target station: the Theil method (Theil, 1950). Even though the methodology revealed to be rather reliable an improvement of the missing data estimation can be achieved by a generalization. A first possible improvement consists in extending the univariate technique to

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

Science.gov (United States)

Kim, Yoonsang; Emery, Sherry

2013-01-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

Science.gov (United States)

Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

2013-08-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

SNR Estimation in Linear Systems with Gaussian Matrices

KAUST Repository

Suliman, Mohamed Abdalla Elhag; Alrashdi, Ayed; Ballal, Tarig; Al-Naffouri, Tareq Y.

2017-01-01

This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.

SNR Estimation in Linear Systems with Gaussian Matrices

KAUST Repository

Suliman, Mohamed Abdalla Elhag

2017-09-27

This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.

Environmental Monitoring Plan for the Oak Ridge Reservation, 2012

Energy Technology Data Exchange (ETDEWEB)

Thompson, Sharon D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2012-10-01

The purpose of Oak Ridge Reservation (ORR) environmental surveillance is to characterize radiological and nonradiological conditions of the off-site environs and estimate public doses related to these conditions, confirm estimations of public dose based on effluent monitoring data, and, where appropriate, provide supplemental data to support compliance monitoring for applicable environmental regulations. This environmental monitoring plan (EMP) is intended to document the rationale, frequency, parameters, and analytical methods for the ORR environmental surveillance program and provides information on ORR site characteristics, environmental pathways, dose assessment methods, and quality management. ORR-wide environmental monitoring activities include a variety of media including air, surface water, vegetation, biota, and wildlife. In addition to these activities, site-specific effluent, groundwater, and best management monitoring programs are conducted at the Oak Ridge National Laboratory (ORNL), the Y-12 National Security Complex (Y-12), and the East Tennessee Technology Park (ETTP). This is revision 5.

A Seemingly Unrelated Poisson Regression Model

OpenAIRE

King, Gary

1989-01-01

This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

Using a Regression Method for Estimating Performance in a Rapid Serial Visual Presentation Target-Detection Task

Science.gov (United States)

2017-12-01

Fig. 2 Simulation method; the process for one iteration of the simulation . It was repeated 250 times per combination of HR and FAR. Analysis was...distribution is unlimited. 8 Fig. 2 Simulation method; the process for one iteration of the simulation . It was repeated 250 times per combination of HR...stimuli. Simulations show that this regression method results in an unbiased and accurate estimate of target detection performance. The regression

Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model.

Science.gov (United States)

Sun, Jianguo; Feng, Yanqin; Zhao, Hui

2015-01-01

Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.

Project management plan for the gunite and associated tanks treatability studies project at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1995-12-01

This plan for the Gunite and Associated Tanks (GAAT) Treatability Studies Project satisfies the requirements of the program management plan for the Oak Ridge National Laboratory (ORNL) Environmental Restoration (ER) Program as established in the Program Management Plan for the Martin Marietta Energy Systems, Inc., Oak Ridge National Laboratory Site Environmental Restoration Program. This plan is a subtier of several other ER documents designed to satisfy the US Department of Energy (DOE) Order 4700.1 requirement for major systems acquisitions. This project management plan identifies the major activities of the GAAT Treatability Studies Project; establishes performance criteria; discusses the roles and responsibilities of the organizations that will perform the work; and summarizes the work breakdown structure, schedule, milestones, and cost estimate for the project

Evaluation of Regression and Neuro_Fuzzy Models in Estimating Saturated Hydraulic Conductivity

Directory of Open Access Journals (Sweden)

J. Behmanesh

2015-06-01

Full Text Available Study of soil hydraulic properties such as saturated and unsaturated hydraulic conductivity is required in the environmental investigations. Despite numerous research, measuring saturated hydraulic conductivity using by direct methods are still costly, time consuming and professional. Therefore estimating saturated hydraulic conductivity using rapid and low cost methods such as pedo-transfer functions with acceptable accuracy was developed. The purpose of this research was to compare and evaluate 11 pedo-transfer functions and Adaptive Neuro-Fuzzy Inference System (ANFIS to estimate saturated hydraulic conductivity of soil. In this direct, saturated hydraulic conductivity and physical properties in 40 points of Urmia were calculated. The soil excavated was used in the lab to determine its easily accessible parameters. The results showed that among existing models, Aimrun et al model had the best estimation for soil saturated hydraulic conductivity. For mentioned model, the Root Mean Square Error and Mean Absolute Error parameters were 0.174 and 0.028 m/day respectively. The results of the present research, emphasises the importance of effective porosity application as an important accessible parameter in accuracy of pedo-transfer functions. sand and silt percent, bulk density and soil particle density were selected to apply in 561 ANFIS models. In training phase of best ANFIS model, the R2 and RMSE were calculated 1 and 1.2×10-7 respectively. These amounts in the test phase were 0.98 and 0.0006 respectively. Comparison of regression and ANFIS models showed that the ANFIS model had better results than regression functions. Also Nuro-Fuzzy Inference System had capability to estimatae with high accuracy in various soil textures.

Global Models of Ridge-Push Force, Geoid, and Lithospheric Strength of Oceanic plates

Science.gov (United States)

Mahatsente, Rezene

2017-12-01

An understanding of the transmission of ridge-push related stresses in the interior of oceanic plates is important because ridge-push force is one of the principal forces driving plate motion. Here, I assess the transmission of ridge-push related stresses in oceanic plates by comparing the magnitude of the ridge-push force to the integrated strength of oceanic plates. The strength is determined based on plate cooling and rheological models. The strength analysis includes low-temperature plasticity (LTP) in the upper mantle and assumes a range of possible tectonic conditions and rheology in the plates. The ridge-push force has been derived from the thermal state of oceanic lithosphere, seafloor depth and crustal age data. The results of modeling show that the transmission of ridge-push related stresses in oceanic plates mainly depends on rheology and predominant tectonic conditions. If a lithosphere has dry rheology, the estimated strength is higher than the ridge-push force at all ages for compressional tectonics and at old ages (>75 Ma) for extension. Therefore, under such conditions, oceanic plates may not respond to ridge-push force by intraplate deformation. Instead, the plates may transmit the ridge-push related stress in their interior. For a wet rheology, however, the strength of young lithosphere (stress may dissipate in the interior of oceanic plates and diffuses by intraplate deformation. The state of stress within a plate depends on the balance of far-field and intraplate forces.

A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction.

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.

Department of Energy Air Emissions Annual Report Oak Ridge Reservation, Oak Ridge, Tennessee 40 Code of Federal Regulations (CFR) 61, Subpart H Calendar Year 2016

Energy Technology Data Exchange (ETDEWEB)

Martin, Richard [Oak Ridge Y-12 Plant (Y-12), Oak Ridge, TN (United States)

2017-06-30

As defined in the preamble of the final rule, the entire DOE facility on the Oak Ridge Reservation (ORR) must meet the 10 mrem/yr ED standard.1 In other words, the combined ED from all radiological air emission sources from Y-12 National Security Complex (Y-12 Complex), Oak Ridge National Laboratory (ORNL), East Tennessee Technology Park (ETTP), Oak Ridge Institute for Science and Education (ORISE) and any other DOE operation on the reservation must meet the 10 mrem/yr standard. Compliance with the standard is demonstrated through emission sampling, monitoring, calculations and radiation dose modeling in accordance with approved EPA methodologies and procedures. DOE estimates the ED to many individuals or receptor points in the vicinity of ORR, but it is the dose to the maximally exposed individual (MEI) that determines compliance with the standard.

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

KAUST Repository

Carroll, Raymond J.

2011-03-01

In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.

Flexible regression models for estimating postmortem interval (PMI) in forensic medicine.

Science.gov (United States)

Muñoz Barús, José Ignacio; Febrero-Bande, Manuel; Cadarso-Suárez, Carmen

2008-10-30

Correct determination of time of death is an important goal in forensic medicine. Numerous methods have been described for estimating postmortem interval (PMI), but most are imprecise, poorly reproducible and/or have not been validated with real data. In recent years, however, some progress in PMI estimation has been made, notably through the use of new biochemical methods for quantifying relevant indicator compounds in the vitreous humour. The best, but unverified, results have been obtained with [K+] and hypoxanthine [Hx], using simple linear regression (LR) models. The main aim of this paper is to offer more flexible alternatives to LR, such as generalized additive models (GAMs) and support vector machines (SVMs) in order to obtain improved PMI estimates. The present study, based on detailed analysis of [K+] and [Hx] in more than 200 vitreous humour samples from subjects with known PMI, compared classical LR methodology with GAM and SVM methodologies. Both proved better than LR for estimation of PMI. SVM showed somewhat greater precision than GAM, but GAM offers a readily interpretable graphical output, facilitating understanding of findings by legal professionals; there are thus arguments for using both types of models. R code for these methods is available from the authors, permitting accurate prediction of PMI from vitreous humour [K+], [Hx] and [U], with confidence intervals and graphical output provided. Copyright 2008 John Wiley & Sons, Ltd.

Estimation of Stature from Footprint Anthropometry Using Regression Analysis: A Study on the Bidayuh Population of East Malaysia

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T. Nataraja Moorthy

2015-05-01

Full Text Available The human foot has been studied for a variety of reasons, i.e., for forensic as well as non-forensic purposes by anatomists, forensic scientists, anthropologists, physicians, podiatrists, and numerous other groups. An aspect of human identification that has received scant attention from forensic anthropologists is the study of human feet and the footprints made by the feet. The present study, conducted during 2013-2014, aimed to derive population specific regression equations to estimate stature from the footprint anthropometry of indigenous adult Bidayuhs in the east of Malaysia. The study sample consisted of 480 bilateral footprints collected using a footprint kit from 240 Bidayuhs (120 males and 120 females, who consented to taking part in the study. Their ages ranged from 18 to 70 years. Stature was measured using a portable body meter device (SECA model 206. The data were analyzed using PASW Statistics version 20. In this investigation, better results were obtained in terms of correlation coefficient (R between stature and various footprint measurements and regression analysis in estimating the stature. The (R values showed a positive and statistically significant (p < 0.001 relationship between the two parameters. The correlation coefficients in the pooled sample (0.861–0.882 were comparatively higher than those of an individual male (0.762-0.795 and female (0.722-0.765. This study provided regression equations to estimate stature from footprints in the Bidayuh population. The result showed that the regression equations without sex indicators performed significantly better than models with gender indications. The regression equations derived for a pooled sample can be used to estimate stature, even when the sex of the footprint is unknown, as in real crime scenes.

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

Directory of Open Access Journals (Sweden)

Morufu Olusola Ibitoye

2016-07-01

Full Text Available The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70% and testing (30% subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2 between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.

Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

Science.gov (United States)

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.

Image Jacobian Matrix Estimation Based on Online Support Vector Regression

Directory of Open Access Journals (Sweden)

Shangqin Mao

2012-10-01

Full Text Available Research into robotics visual servoing is an important area in the field of robotics. It has proven difficult to achieve successful results for machine vision and robotics in unstructured environments without using any a priori camera or kinematic models. In uncalibrated visual servoing, image Jacobian matrix estimation methods can be divided into two groups: the online method and the offline method. The offline method is not appropriate for most natural environments. The online method is robust but rough. Moreover, if the images feature configuration changes, it needs to restart the approximating procedure. A novel approach based on an online support vector regression (OL-SVR algorithm is proposed which overcomes the drawbacks and combines the virtues just mentioned.

Observation of pressure ridges in SAR images of sea ice: Scattering theory and comparison with observations

Science.gov (United States)

Vesecky, J. F.; Daida, J. M.; Shuchman, R. A.; Onstott, R. H.; Camiso, J. C.

1993-01-01

Ridges and keels (hummocks and bummocks) in sea ice flows are important in sea ice research for both scientific and practical reasons. Sea ice movement and deformation is driven by internal and external stresses on the ice. Ridges and keels play important roles in both cases because they determine the external wind and current stresses via drag coefficients. For example, the drag coefficient over sea ice can vary by a factor of several depending on the fluid mechanical roughness length of the surface. This roughness length is thought to be strongly dependent on the ridge structures present. Thus, variations in ridge and keel structure can cause gradients in external stresses which must be balanced by internal stresses and possibly fracture of the ice. Ridging in sea ice is also a sign of fracture. In a practical sense, large ridges form the biggest impediment to surface travel over the ice or penetration through sea ice by ice-strengthened ships. Ridges also play an important role in the damage caused by sea ice to off-shore structures. Hence, observation and measurement of sea ice ridges is an important component of sea ice remote sensing. The research reported here builds on previous work, estimating the characteristics of ridges and leads in sea ice from SAR images. Our objective is to develop methods for quantitative measurement of sea ice ridges from SAR images. To make further progress, in particular, to estimate ridge height, a scattering model for ridges is needed. Our research approach for a ridge scattering model begins with a survey of the geometrical properties of ridges and a comparison with the characteristics of the surrounding ice. For this purpose we have used airborne optical laser (AOL) data collected during the 1987 Greenland Sea Experiment. These data were used to generate a spatial wavenumber spectrum for height variance for a typical ridge - the typical ridge is the average over 10 large ridges. Our first-order model radar scattering includes

Power system state estimation using an iteratively reweighted least squares method for sequential L{sub 1}-regression

Energy Technology Data Exchange (ETDEWEB)

Jabr, R.A. [Electrical, Computer and Communication Engineering Department, Notre Dame University, P.O. Box 72, Zouk Mikhael, Zouk Mosbeh (Lebanon)

2006-02-15

This paper presents an implementation of the least absolute value (LAV) power system state estimator based on obtaining a sequence of solutions to the L{sub 1}-regression problem using an iteratively reweighted least squares (IRLS{sub L1}) method. The proposed implementation avoids reformulating the regression problem into standard linear programming (LP) form and consequently does not require the use of common methods of LP, such as those based on the simplex method or interior-point methods. It is shown that the IRLS{sub L1} method is equivalent to solving a sequence of linear weighted least squares (LS) problems. Thus, its implementation presents little additional effort since the sparse LS solver is common to existing LS state estimators. Studies on the termination criteria of the IRLS{sub L1} method have been carried out to determine a procedure for which the proposed estimator is more computationally efficient than a previously proposed non-linear iteratively reweighted least squares (IRLS) estimator. Indeed, it is revealed that the proposed method is a generalization of the previously reported IRLS estimator, but is based on more rigorous theory. (author)

Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters

Directory of Open Access Journals (Sweden)

V. B. Goryainov

2017-01-01

Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the

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)

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

Science.gov (United States)

Karabatsos, George

2017-02-01

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

Regression and kriging analysis for grid power factor estimation

Directory of Open Access Journals (Sweden)

Rajesh Guntaka

2014-12-01

Full Text Available The measurement of power factor (PF in electrical utility grids is a mainstay of load balancing and is also a critical element of transmission and distribution efficiency. The measurement of PF dates back to the earliest periods of electrical power distribution to public grids. In the wide-area distribution grid, measurement of current waveforms is trivial and may be accomplished at any point in the grid using a current tap transformer. However, voltage measurement requires reference to ground and so is more problematic and measurements are normally constrained to points that have ready and easy access to a ground source. We present two mathematical analysis methods based on kriging and linear least square estimation (LLSE (regression to derive PF at nodes with unknown voltages that are within a perimeter of sample nodes with ground reference across a selected power grid. Our results indicate an error average of 1.884% that is within acceptable tolerances for PF measurements that are used in load balancing tasks.

Unbalanced Regressions and the Predictive Equation

DEFF Research Database (Denmark)

Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...

Bay Ridge Gardens - Mixed-Humid Affordable Multifamily Housing Deep Energy Retrofit

Energy Technology Data Exchange (ETDEWEB)

Lyons, J.; Moore, M.; Thompson, M.

2013-08-01

Under this project, Newport Partners (as part of the BA-PIRC research team) evaluated the installation, measured performance, and cost-effectiveness of efficiency upgrade measures for a tenant-in-place DER at the Bay Ridge multifamily (MF) development in Annapolis, Maryland. The design and construction phase of the Bay Ridge project was completed in August 2012. This report summarizes system commissioning, short-term test results, utility bill data analysis, and analysis of real-time data collected over a one-year period after the retrofit was complete. The Bay Ridge project is comprised of a 'base scope' retrofit which was estimated to achieve a 30%+ savings (relative to pre-retrofit) on 186 apartments, and a 'DER scope' which was estimated to achieve 50% savings (relative to pre-retrofit) on a 12-unit building. The base scope was applied to the entire apartment complex, except for one 12-unit building which underwent the DER scope. A wide range of efficiency measures was applied to pursue this savings target for the DER building, including improvements/replacements of mechanical equipment and distribution systems, appliances, lighting and lighting controls, the building envelope, hot water conservation measures, and resident education. The results of this research build upon the current body of knowledge of multifamily retrofits. Towards this end, the research team has collected and generated data on the selection of measures, their estimated performance, their measured performance, and risk factors and their impact on potential measures.

Large fault fabric of the Ninetyeast Ridge implies near-spreading ridge formation

Digital Repository Service at National Institute of Oceanography (India)

Sager, W.W.; Paul, C.F.; Krishna, K.S.; Pringle, M.S.; Eisin, A.E.; Frey, F.A.; Rao, D.G.; Levchenko, O.V.

of the high ridge. At 26°S, prominent NE-SW 97 oriented lineations extend southwest from the ridge. Some appear to connect with N-S fracture 98 zone troughs east of NER, implying that the NE-SW features are fracture zone scars formed after 99 the change... to the 105 ridge (Fig. 3). This is especially true for NER south of ~4°S. Where KNOX06RR crossed a 106 gravity lineation, negative gradient features correspond to troughs whereas positive gradient 107 features result from igneous basement highs (Fig. 3...

Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

Science.gov (United States)

Wang, Wei; Griswold, Michael E

2016-11-30

The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Magnetic anomalies across the southern Central Indian Ridge: evidence for a new transform fault

Digital Repository Service at National Institute of Oceanography (India)

Chaubey, A.K.; Krishna, K.S.; SubbaRaju, L.V.; Rao, D.G.

, Vol. 37. No. 4. pp. MT-~a56, 1990. 0198-.0149/90 $3.(gl + 0.00 Pnnled in Great Britain. (~ 1990 Pergartma Ptes6 pie Magnetic anomalies across the southern Central Indian Ridge: evidence for a new transform fault A. K. CHAUBEY,* K. S. KRISHNA,* L. V... to the ridge are identified as sea-floor spreading lineations 2.2A,3.3A and 4. A half spreading rate of 2.2 cm y- t is estimated for the last I0 Ma. The ridge jump between the anomalies 2-2A (approx. 2.5 Ma) and a new left lateral transform fault offsetting...

Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: simulation and verification with in vivo data

International Nuclear Information System (INIS)

Fang, Yu-Hua; Kao, Tsair; Liu, Ren-Shyan; Wu, Liang-Chih

2004-01-01

A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13±8.85 and 16.60±9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student's t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification. (orig.)

Project plan for the Background Soil Characterization Project on the Oak Ridge Reservation, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1992-08-01

The Background Soil characterization Project (BSCP) will provide background concentration levels of selected metals, organic compounds, and radionuclides in soils from uncontaminated on-site areas at the Oak Ridge Reservation (ORR), and off-site in the western part of Roane County and the eastern part of Anderson County. The BSCP will establish a database, recommend how to use the data for contaminated site assessment, and provide estimates of the potential human health and environmental risks associated with the background level concentrations of potentially hazardous constituents

Project plan for the Background Soil Characterization Project on the Oak Ridge Reservation, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

1992-08-01

The Background Soil characterization Project (BSCP) will provide background concentration levels of selected metals, organic compounds, and radionuclides in soils from uncontaminated on-site areas at the Oak Ridge Reservation (ORR), and off-site in the western part of Roane County and the eastern part of Anderson County. The BSCP will establish a database, recommend how to use the data for contaminated site assessment, and provide estimates of the potential human health and environmental risks associated with the background level concentrations of potentially hazardous constituents.

Using a Regression Discontinuity Design to Estimate the Impact of Placement Decisions in Developmental Math

Science.gov (United States)

Melguizo, Tatiana; Bos, Johannes M.; Ngo, Federick; Mills, Nicholas; Prather, George

2016-01-01

This study evaluates the effectiveness of math placement policies for entering community college students on these students' academic success in math. We estimate the impact of placement decisions by using a discrete-time survival model within a regression discontinuity framework. The primary conclusion that emerges is that initial placement in a…

Sedimentary characteristics and controlling factors of shelf sand ridges in the Pearl River Mouth Basin, northeast of South China Sea

Directory of Open Access Journals (Sweden)

Xiangtao Zhang

2017-04-01

Full Text Available Shelf sand ridge is a significant type of reservoir in the continental marginal basin, and it has drawn so much attention from sedimentologists and petroleum geologists. We were able to investigate the morphology, distribution, and sedimentary structures of shelf sand ridges systematically in this study based on the integration of high-resolution 3D seismic data, well logging, and cores. These shelf sand ridges are an asymmetrical mound-like structure in profiles, and they developed on an ancient uplift in the forced regression system tract and are onlapped by the overlying strata. In the plane, shelf sand ridges present as linear-shaped, which is different from the classical radial pattern; not to mention, they are separated into two parts by low amplitude tidal muddy channels. Corrugated bedding, tidal bedding, and scouring features are distinguished in cores of shelf sand ridges together with the coarsening up in lithology. All of these sedimentary characteristics indicate that shelf sand ridges deposited in the Pearl River Mouth Basin are reconstructed by the tidal and coastal current.

Soil moisture estimation using multi linear regression with terraSAR-X data

Directory of Open Access Journals (Sweden)

G. García

2016-06-01

Full Text Available The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil’s parameters. Indeed, active microwave sensors (Synthetic Aperture Radar - SAR offer the opportunity to monitor soil moisture (HS at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure, microwave characteristics and land use. Therefore, in this paper we aim at developing a multiple linear regression model to estimate HS on flat agricultural regions using TerraSAR-X satellite data and data from a ground weather station. The results show that the backscatter, the precipitation and the relative humidity are the explanatory variables of HS. The results obtained presented a RMSE of 5.4 and a R2  of about 0.6

A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

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.

Robust Visual Tracking Using the Bidirectional Scale Estimation

Directory of Open Access Journals (Sweden)

An Zhiyong

2017-01-01

Full Text Available Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.

Environmental baseline survey report for West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge and parcel 21D in the vicinity of the East Technology Park, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

King, David A. [Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States). Independent Environmental Assessment and Verification Program

2012-11-29

This environmental baseline survey (EBS) report documents the baseline environmental conditions of five land parcels located near the U.S. Department of Energy?s (DOE?s) East Tennessee Technology Park (ETTP), including West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge, and Parcel 21d. Preparation of this report included the detailed search of federal government records, title documents, aerial photos that may reflect prior uses, and visual inspections of the property and adjacent properties. Interviews with current employees involved in, or familiar with, operations on the real property were also conducted to identify any areas on the property where hazardous substances and petroleum products, or their derivatives, and acutely hazardous wastes may have been released or disposed. In addition, a search was made of reasonably obtainable federal, state, and local government records of each adjacent facility where there has been a release of any hazardous substance or any petroleum product or their derivatives, including aviation fuel and motor oil, and which is likely to cause or contribute to a release of any hazardous substance or any petroleum product or its derivatives, including aviation fuel or motor oil, on the real property. A radiological survey and soil/sediment sampling was conducted to assess baseline conditions of Parcel 21d that were not addressed by the soils-only no-further-investigation (NFI) reports. Groundwater sampling was also conducted to support a Parcel 21d decision. Based on available data West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, and West Pine Ridge are not impacted by site operations and are not subject to actions per the Federal Facility Agreement (FFA). This determination is supported by visual inspections, records searches and interviews, groundwater conceptual modeling, approved NFI reports, analytical data, and risk analysis results. Parcel 21d data, however, demonstrate impacts from site

Environmental baseline survey report for West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge and parcel 21D in the vicinity of the East Technology Park, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

King, David A.

2012-01-01

This environmental baseline survey (EBS) report documents the baseline environmental conditions of five land parcels located near the U.S. Department of Energy's (DOE's) East Tennessee Technology Park (ETTP), including West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge, and Parcel 21d. Preparation of this report included the detailed search of federal government records, title documents, aerial photos that may reflect prior uses, and visual inspections of the property and adjacent properties. Interviews with current employees involved in, or familiar with, operations on the real property were also conducted to identify any areas on the property where hazardous substances and petroleum products, or their derivatives, and acutely hazardous wastes may have been released or disposed. In addition, a search was made of reasonably obtainable federal, state, and local government records of each adjacent facility where there has been a release of any hazardous substance or any petroleum product or their derivatives, including aviation fuel and motor oil, and which is likely to cause or contribute to a release of any hazardous substance or any petroleum product or its derivatives, including aviation fuel or motor oil, on the real property. A radiological survey and soil/sediment sampling was conducted to assess baseline conditions of Parcel 21d that were not addressed by the soils-only no-further-investigation (NFI) reports. Groundwater sampling was also conducted to support a Parcel 21d decision. Based on available data West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, and West Pine Ridge are not impacted by site operations and are not subject to actions per the Federal Facility Agreement (FFA). This determination is supported by visual inspections, records searches and interviews, groundwater conceptual modeling, approved NFI reports, analytical data, and risk analysis results. Parcel 21d data, however, demonstrate impacts from site

Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data

Science.gov (United States)

Sanford, Ward E.; Selnick, David L.

2013-01-01

Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water-balance method was combined with a climate and land-cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971-2000 across the U.S. to obtain long-term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land-cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land-cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land-cover data can also improve those predictions. Using the climate and land-cover data at an 800-m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land-cover data are plentiful.

Stellar atmospheric parameter estimation using Gaussian process regression

Science.gov (United States)

Bu, Yude; Pan, Jingchang

2015-02-01

As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.

The ESASSI-08 cruise in the South Scotia Ridge region: An inverse model property-transport analysis over the Ridge

Science.gov (United States)

Palmer, Margarita; Gomis, Damià; Del Mar Flexas, Maria; Jordà, Gabriel; Naveira-Garabato, Alberto; Jullion, Loic; Tsubouchi, Takamasa

2010-05-01

The ESASSI-08 oceanographic cruise carried out in January 2008 was the most significant milestone of the ESASSI project. ESASSI is the Spanish component of the Synoptic Antarctic Shelf-Slope Interactions (SASSI) study, one of the core projects of the International Polar Year. Hydrographical and biochemical (oxygen, CFCs, nutrients, chlorophyll content, alkalinity, pH, DOC) data were obtained along 11 sections in the South Scotia Ridge (SSR) region, between Elephant and South Orkney Islands. One of the aims of the ESASSI project is to determine the northward outflow of cold and ventilated waters from the Weddell Sea into the Scotia Sea. For that purpose, the accurate estimation of mass, heat, salt, and oxygen transport over the Ridge is requested. An initial analysis of transports across the different sections was first obtained from CTD and ADCP data. The following step has been the application of an inverse method, in order to obtain a better estimation of the net flow for the different water masses present in the region. The set of property-conservation equations considered by the inverse model includes mass, heat and salinity fluxes. The "box" is delimited by the sections along the northern flank of the SSR, between Elephant Island and 50°W, the southern flank of the Ridge, between 51.5°W and 50°W, the 50°W meridian and a diagonal line between Elephant Island and 51.5°W, 61.75°S. Results show that the initial calculations of transports suffered of a significant volume imbalance, due to the inherent errors of ship-ADCP data, the complicated topography and the presence of strong tidal currents in some sections. We present the post-inversion property transports across the rim of the box (and their error bars) for the different water masses.

Methods for estimating disease transmission rates: Evaluating the precision of Poisson regression and two novel methods

DEFF Research Database (Denmark)

Kirkeby, Carsten Thure; Hisham Beshara Halasa, Tariq; Gussmann, Maya Katrin

2017-01-01

the transmission rate. We use data from the two simulation models and vary the sampling intervals and the size of the population sampled. We devise two new methods to determine transmission rate, and compare these to the frequently used Poisson regression method in both epidemic and endemic situations. For most...... tested scenarios these new methods perform similar or better than Poisson regression, especially in the case of long sampling intervals. We conclude that transmission rate estimates are easily biased, which is important to take into account when using these rates in simulation models....

Monopole and dipole estimation for multi-frequency sky maps by linear regression

Science.gov (United States)

Wehus, I. K.; Fuskeland, U.; Eriksen, H. K.; Banday, A. J.; Dickinson, C.; Ghosh, T.; Górski, K. M.; Lawrence, C. R.; Leahy, J. P.; Maino, D.; Reich, P.; Reich, W.

2017-01-01

We describe a simple but efficient method for deriving a consistent set of monopole and dipole corrections for multi-frequency sky map data sets, allowing robust parametric component separation with the same data set. The computational core of this method is linear regression between pairs of frequency maps, often called T-T plots. Individual contributions from monopole and dipole terms are determined by performing the regression locally in patches on the sky, while the degeneracy between different frequencies is lifted whenever the dominant foreground component exhibits a significant spatial spectral index variation. Based on this method, we present two different, but each internally consistent, sets of monopole and dipole coefficients for the nine-year WMAP, Planck 2013, SFD 100 μm, Haslam 408 MHz and Reich & Reich 1420 MHz maps. The two sets have been derived with different analysis assumptions and data selection, and provide an estimate of residual systematic uncertainties. In general, our values are in good agreement with previously published results. Among the most notable results are a relative dipole between the WMAP and Planck experiments of 10-15μK (depending on frequency), an estimate of the 408 MHz map monopole of 8.9 ± 1.3 K, and a non-zero dipole in the 1420 MHz map of 0.15 ± 0.03 K pointing towards Galactic coordinates (l,b) = (308°,-36°) ± 14°. These values represent the sum of any instrumental and data processing offsets, as well as any Galactic or extra-Galactic component that is spectrally uniform over the full sky.

Improved regression models for ventilation estimation based on chest and abdomen movements

International Nuclear Information System (INIS)

Liu, Shaopeng; Gao, Robert; He, Qingbo; Staudenmayer, John; Freedson, Patty

2012-01-01

Non-invasive estimation of minute ventilation is important for quantifying the intensity of physical activity of individuals. In this paper, several improved regression models are presented, based on the measurement of chest and abdomen movements from sensor belts worn by subjects (n = 50) engaged in 14 types of physical activity. Five linear models involving a combination of 11 features were developed, and the effects of different model training approaches and window sizes for computing the features were investigated. The performance of the models was evaluated using experimental data collected during the physical activity protocol. The predicted minute ventilation was compared to the criterion ventilation measured using a bidirectional digital volume transducer housed in a respiratory gas exchange system. The results indicate that the inclusion of breathing frequency and the use of percentile points instead of interdecile ranges over a 60 s window size reduced error by about 43%, when applied to the classical two-degrees-of-freedom model. The mean percentage error of the minute ventilation estimated for all the activities was below 7.5%, verifying reasonably good performance of the models and the applicability of the wearable sensing system for minute ventilation estimation during physical activity. (paper)

Unbalanced Regressions and the Predictive Equation

DEFF Research Database (Denmark)

Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...

Work plan for the High Ranking Facilities Deactivation Project at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1996-03-01

The High Ranking Facilities Deactivation Project (HRFDP), commissioned by the US Department of Energy Nuclear Materials and Facility Stabilization Program, is to place four primary high-risk surplus facilities with 28 associated ancillary facilities at Oak Ridge National Laboratory in a safe, stable, and environmentally sound condition as rapidly and economically as possible. The facilities will be deactivated and left in a condition suitable for an extended period of minimized surveillance and maintenance (S and M) prior to decontaminating and decommissioning (D and D). These four facilities include two reactor facilities containing spent fuel. One of these reactor facilities also contains 55 tons of sodium with approximately 34 tons containing activated sodium-22, 2.5 tons of lithium hydride, approximately 100 tons of potentially contaminated lead, and several other hazardous materials as well as bulk quantities of contaminated scrap metals. The other two facilities to be transferred include a facility with a bank of hot cells containing high levels of transferable contamination and also a facility containing significant quantities of uranyl nitrate and quantities of transferable contamination. This work plan documents the objectives, technical requirements, and detailed work plans--including preliminary schedules, milestones, and conceptual FY 1996 cost estimates--for the Oak Ridge National Laboratory (ORNL). This plan has been developed by the Environmental Restoration (ER) Program of Lockheed Martin Energy Systems (Energy Systems) for the US Department of Energy (DOE) Oak Ridge Operations Office (ORO)

Work plan for the High Ranking Facilities Deactivation Project at Oak Ridge National Laboratory, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-03-01

The High Ranking Facilities Deactivation Project (HRFDP), commissioned by the US Department of Energy Nuclear Materials and Facility Stabilization Program, is to place four primary high-risk surplus facilities with 28 associated ancillary facilities at Oak Ridge National Laboratory in a safe, stable, and environmentally sound condition as rapidly and economically as possible. The facilities will be deactivated and left in a condition suitable for an extended period of minimized surveillance and maintenance (S and M) prior to decontaminating and decommissioning (D and D). These four facilities include two reactor facilities containing spent fuel. One of these reactor facilities also contains 55 tons of sodium with approximately 34 tons containing activated sodium-22, 2.5 tons of lithium hydride, approximately 100 tons of potentially contaminated lead, and several other hazardous materials as well as bulk quantities of contaminated scrap metals. The other two facilities to be transferred include a facility with a bank of hot cells containing high levels of transferable contamination and also a facility containing significant quantities of uranyl nitrate and quantities of transferable contamination. This work plan documents the objectives, technical requirements, and detailed work plans--including preliminary schedules, milestones, and conceptual FY 1996 cost estimates--for the Oak Ridge National Laboratory (ORNL). This plan has been developed by the Environmental Restoration (ER) Program of Lockheed Martin Energy Systems (Energy Systems) for the US Department of Energy (DOE) Oak Ridge Operations Office (ORO).

Engineering estimates versus impact evaluation of energy efficiency projects: Regression discontinuity evidence from a case study

International Nuclear Information System (INIS)

Lang, Corey; Siler, Matthew

2013-01-01

Energy efficiency upgrades have been gaining widespread attention across global channels as a cost-effective approach to addressing energy challenges. The cost-effectiveness of these projects is generally predicted using engineering estimates pre-implementation, often with little ex post analysis of project success. In this paper, for a suite of energy efficiency projects, we directly compare ex ante engineering estimates of energy savings to ex post econometric estimates that use 15-min interval, building-level energy consumption data. In contrast to most prior literature, our econometric results confirm the engineering estimates, even suggesting the engineering estimates were too modest. Further, we find heterogeneous efficiency impacts by time of day, suggesting select efficiency projects can be useful in reducing peak load. - Highlights: • Regression discontinuity used to estimate energy savings from efficiency projects. • Ex post econometric estimates validate ex ante engineering estimates of energy savings. • Select efficiency projects shown to reduce peak load

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

1993-09-01

This Remedial Investigation (RI) Work Plan specifically addresses Chestnut Ridge Operable Unit 1, (OU1) which consists of the Chestnut Ridge Security Pits (CRSP). The CRSP are located {approximately}800 ft southeast of the central portion of the Y-12 Plant atop Chestnut Ridge, which is bounded to the northwest by Bear Creek Valley and to the southeast by Bethel Valley. Operated from 1973 to 1988, the CRSP consisted of a series of trenches used for the disposal of classified hazardous and nonhazardous waste materials. Disposal of hazardous waste materials was discontinued in December 1984, while nonhazardous waste disposal ended on November 8, 1988. An RI is being conducted at this site in response to CERCLA regulations. The overall objectives of the RI are to collect data necessary to evaluate the nature and extent of contaminants of concern (COC), support an ecological risk assessment (ERA) and a human health risk assessment (HHRA), support the evaluation of remedial alternatives, and ultimately develop a Record of Decision for the site. The purpose of this Work Plan is to outline RI activities necessary to define the nature and extent of suspected contaminants at Chestnut Ridge OU1. Potential migration pathways also will be investigated. Data collected during the RI will be used to evaluate the overall risk posed to human health and the environment by OU1.

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1993-09-01

This Remedial Investigation (RI) Work Plan specifically addresses Chestnut Ridge Operable Unit 1, (OU1) which consists of the Chestnut Ridge Security Pits (CRSP). The CRSP are located ∼800 ft southeast of the central portion of the Y-12 Plant atop Chestnut Ridge, which is bounded to the northwest by Bear Creek Valley and to the southeast by Bethel Valley. Operated from 1973 to 1988, the CRSP consisted of a series of trenches used for the disposal of classified hazardous and nonhazardous waste materials. Disposal of hazardous waste materials was discontinued in December 1984, while nonhazardous waste disposal ended on November 8, 1988. An RI is being conducted at this site in response to CERCLA regulations. The overall objectives of the RI are to collect data necessary to evaluate the nature and extent of contaminants of concern (COC), support an ecological risk assessment (ERA) and a human health risk assessment (HHRA), support the evaluation of remedial alternatives, and ultimately develop a Record of Decision for the site. The purpose of this Work Plan is to outline RI activities necessary to define the nature and extent of suspected contaminants at Chestnut Ridge OU1. Potential migration pathways also will be investigated. Data collected during the RI will be used to evaluate the overall risk posed to human health and the environment by OU1

Preliminary results from the first InRidge cruise to the central Indian Ridge

Digital Repository Service at National Institute of Oceanography (India)

Mukhopadhyay, R.; Murthy, K.S.R.; Iyer, S.D.; Rao, M.M.M.; Banerjee, R.; Subrahmanyam, A.S.; Shirodkar, P.V.; Ghose, I.; Ganesan, P.; Rao, A.K.; Suribabu, A.; Ganesh, C.; Naik, G.P.

stream_size 1 stream_content_type text/plain stream_name Inter_Ridge_News_7_40.pdf.txt stream_source_info Inter_Ridge_News_7_40.pdf.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 ...

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

Science.gov (United States)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

Regression with Sparse Approximations of Data

DEFF Research Database (Denmark)

Noorzad, Pardis; Sturm, Bob L.

2012-01-01

We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

Temperature inversions in the vicinity of Oak Ridge, Tennessee, as characterized by tethersonde data

International Nuclear Information System (INIS)

Blasing, T.J.; Wang, J.C.; Lombardi, D.A.

1998-01-01

Accidental releases of hazardous materials to the atmosphere may result from fires that create a buoyant plume which may rise several hundred meters above the ground. For such buoyant release cases, estimates of ground-level concentrations may be as much as a factor of 100 lower than similar, nonbuoyant releases. For the Oak Ridge Reservation, safety analyses often examine buoyant release accident scenarios and resulting downwind, ground-level consequence estimates. For these analyses, careful consideration of buoyant plume rise is important. Plume rise can be limited by a stable vertical atmospheric temperature profile, commonly called an inversion, where the air temperature increases with height. There is a concern that inversions may interact with the complex terrain on the Oak Ridge Reservation, particularly at the Y-12 Plant, which is located in a relatively shallow but narrow valley, to trap the plume and increase ground-level consequences. The purpose of this paper is to review the available meteorological data that provide information on inversions in the Oak Ridge area

Greenland Fracture Zone-East Greenland Ridge(s) revisited: Indications of a C22-change in plate motion?

DEFF Research Database (Denmark)

Døssing, Arne; Funck, T.

2012-01-01

a reinterpretation of the Greenland Fracture Zone -East Greenland Ridge based on new and existing geophysical data. Evidence is shown for two overstepping ridge segments (Segments A and B) of which Segment A corresponds to the already known East Greenland Ridge while Segment B was not detected previously......Changes in the lithospheric stress field, causing axial rift migration and reorientation of the transform, are generally proposed as an explanation for anomalously old crust and/or major aseismic valleys in oceanic ridge-transform-ridge settings. Similarly, transform migration of the Greenland...... Fracture Zone and separation of the 200-km-long, fracture-zone-parallel continental East Greenland Ridge from the Eurasia plate is thought to be related to a major change in relative plate motions between Greenland and Eurasia during the earliest Oligocene (Chron 13 time). This study presents...

Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

International Nuclear Information System (INIS)

Hu, Chao; Jain, Gaurav; Zhang, Puqiang; Schmidt, Craig; Gomadam, Parthasarathy; Gorka, Tom

2014-01-01

Highlights: • We develop a data-driven method for the battery capacity estimation. • Five charge-related features that are indicative of the capacity are defined. • The kNN regression model captures the dependency of the capacity on the features. • Results with 10 years’ continuous cycling data verify the effectiveness of the method. - Abstract: Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the battery health condition by estimating the battery capacity over the life-time. This paper presents a data-driven method for estimating the capacity of Li-ion battery based on the charge voltage and current curves. The contributions of this paper are three-fold: (i) the definition of five characteristic features of the charge curves that are indicative of the capacity, (ii) the development of a non-linear kernel regression model, based on the k-nearest neighbor (kNN) regression, that captures the complex dependency of the capacity on the five features, and (iii) the adaptation of particle swarm optimization (PSO) to finding the optimal combination of feature weights for creating a kNN regression model that minimizes the cross validation (CV) error in the capacity estimation. Verification with 10 years’ continuous cycling data suggests that the proposed method is able to accurately estimate the capacity of Li-ion battery throughout the whole life-time

Estimating the magnitude and frequency of floods for urban and small, rural streams in Georgia, South Carolina, and North Carolina

Science.gov (United States)

Feaster, Toby D.; Gotvald, Anthony J.; Weaver, J. Curtis

2014-01-01

Reliable estimates of the magnitude and frequency of floods are essential for such things as the design of transportation and water-conveyance structures, Flood Insurance Studies, and flood-plain management. The flood-frequency estimates are particularly important in densely populated urban areas. A multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The multistate approach has the advantage over a single state approach of increasing the number of stations available for analysis, expanding the geographical coverage that would allow for application of regional regression equations across state boundaries, and building on a previous flood-frequency investigation of rural streamflow-gaging stations (streamgages) in the Southeastern United States. In addition, streamgages from the inner Coastal Plain of New Jersey were included in the analysis. Generalized least-squares regression techniques were used to generate predictive equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions; the Piedmont-Ridge and Valley, Sand Hills, and Coastal Plain. Incorporation of urban streamgages from New Jersey also allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 2.1 to 53.5 square miles. Explanatory variables in the regression equations included drainage area (DA) and percent of impervious area (IA) for the Piedmont-Ridge and Valley region; DA and percent of developed land for the Sand Hills; and DA, IA, and 24-hour, 50-year maximum precipitation for the Coastal Plain. An application spreadsheet also was developed that can be used to compute the flood-frequency estimates along with the 95-percent prediction

Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

Directory of Open Access Journals (Sweden)

Chuanglin Fang

2015-11-01

Full Text Available Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI values at the city level, and employed Ordinary Least Squares (OLS, Spatial Lag Model (SAR, and Geographically Weighted Regression (GWR to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

Is there a sex difference in palm print ridge density?

Science.gov (United States)

Kanchan, Tanuj; Krishan, Kewal; Aparna, K R; Shyamsundar, S

2013-01-01

Analysis of fingerprints and palm prints at the crime scene is vital to identify the suspect and establish a crime. Dermatoglyphics can even be valuable in identification of a dismembered hand during medicolegal investigations to establish the identity of an individual in cases of mass disasters/mass homicides. The present research studies the variation in ridge density in different areas of the palm prints among men and women. The four prominent areas were analysed on the palm prints that included central prominent part of the thenar eminence (P1), hypothenar region; inner to the proximal axial triradius (P2), medial mount; proximal to the triradius of the second digit (P3) and lateral mount; proximal to the triradius of the fifth digit (P4). The mean palm print ridge density was significantly higher among women than men in all the designated areas in both hands except for the P3 area in the right hand. Statistically significant differences were observed in the palm print ridge density between the different palm areas in men and women in right and left hands. No significant right-left differences were observed in the palm print ridge density in any of the four areas of palm prints among men. In women, right-left differences were observed only in the P3 and P4 areas of palm prints. This preliminary study indicates that though the palm print ridge density is a sexually dimorphic variable, its utility for estimation of sex in forensic identification may be limited owing to significant overlapping of values.

Multiscale analysis of potential fields by a ridge consistency criterion: the reconstruction of the Bishop basement

Science.gov (United States)

Fedi, M.; Florio, G.; Cascone, L.

2012-01-01

We use a multiscale approach as a semi-automated interpreting tool of potential fields. The depth to the source and the structural index are estimated in two steps: first the depth to the source, as the intersection of the field ridges (lines built joining the extrema of the field at various altitudes) and secondly, the structural index by the scale function. We introduce a new criterion, called 'ridge consistency' in this strategy. The criterion is based on the principle that the structural index estimations on all the ridges converging towards the same source should be consistent. If these estimates are significantly different, field differentiation is used to lessen the interference effects from nearby sources or regional fields, to obtain a consistent set of estimates. In our multiscale framework, vertical differentiation is naturally joint to the low-pass filtering properties of the upward continuation, so is a stable process. Before applying our criterion, we studied carefully the errors on upward continuation caused by the finite size of the survey area. To this end, we analysed the complex magnetic synthetic case, known as Bishop model, and evaluated the best extrapolation algorithm and the optimal width of the area extension, needed to obtain accurate upward continuation. Afterwards, we applied the method to the depth estimation of the whole Bishop basement bathymetry. The result is a good reconstruction of the complex basement and of the shape properties of the source at the estimated points.

Vector regression introduced

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.

Quantile Regression Methods

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...

Global survey of lunar wrinkle ridge formation times

Science.gov (United States)

Yue, Z.; Michael, G. G.; Di, K.; Liu, J.

2017-11-01

Wrinkle ridges are a common feature of the lunar maria and record subsequent contraction of mare infill. Constraining the timing of wrinkle ridge formation from crater counts is challenging because they have limited areal extent and it is difficult to determine whether superposed craters post-date ridge formation or have alternatively been uplifted by the deformation. Some wrinkle ridges do allow determination to be made. This is possible where a ridge shows a sufficiently steep boundary or scarp that can be identified as deforming an intersecting crater or the crater obliterates the relief of the ridge. Such boundaries constitute only a small fraction of lunar wrinkle ridge structures yet they are sufficiently numerous to enable us to obtain statistically significant crater counts over systems of structurally related wrinkle ridges. We carried out a global mapping of mare wrinkle ridges, identifying appropriate boundaries for crater identification, and mapping superposed craters. Selected groups of ridges were analyzed using the buffered crater counting method. We found that, except for the ridges in mare Tranquilitatis, the ridge groups formed with average ages between 3.5 and 3.1 Ga ago, or 100-650 Ma after the oldest observable erupted basalts where they are located. We interpret these results to suggest that local stresses from loading by basalt fill are the principal agent responsible for the formation of lunar wrinkle ridges, as others have proposed. We find a markedly longer interval before wrinkle ridge formation in Tranquilitatis which likely indicates a different mechanism of stress accumulation at this site.

Bias and efficiency loss in regression estimates due to duplicated observations: a Monte Carlo simulation

Directory of Open Access Journals (Sweden)

Francesco Sarracino

2017-04-01

Full Text Available Recent studies documented that survey data contain duplicate records. We assess how duplicate records affect regression estimates, and we evaluate the effectiveness of solutions to deal with duplicate records. Results show that the chances of obtaining unbiased estimates when data contain 40 doublets (about 5% of the sample range between 3.5% and 11.5% depending on the distribution of duplicates. If 7 quintuplets are present in the data (2% of the sample, then the probability of obtaining biased estimates ranges between 11% and 20%. Weighting the duplicate records by the inverse of their multiplicity, or dropping superfluous duplicates outperform other solutions in all considered scenarios. Our results illustrate the risk of using data in presence of duplicate records and call for further research on strategies to analyze affected data.

Best management practices plan for the Chestnut Ridge-Filled Coal Ash Pond at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1996-05-01

The Chestnut Ridge Filled Coal Ash Pond (FCAP) Project has been established to satisfy Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) requirements for the Chestnut Ridge Operable Unit 2. FCAP is on Chestnut Ridge, approximately 0.5 miles south of the Y-12 Plant. A 62-foot high earthen dam across Upper McCoy Branch was constructed in 1955 to create a pond to serve as a settling basin for fly and bottom ashes generated by burning coal at the Y-12 Steam Plant. Ash from the steam was mixed with water to form a slurry and then pumped to the crest of Chestnut Ridge and released through a large pipe to flow across the Sluice Channel area and into the pond. The ash slurry eventually overtopped the dam and flowed along Upper McCoy Branch to Rogers Quarry. The purpose of this document is to provide a site-specific Best Management Practices (BMP) Plan for construction associated with environmental restoration activities at the FCAP Site

Ridge interaction features of the Line Islands

Science.gov (United States)

Konter, J. G.; Koppers, A. A. P.; Storm, L. P.

2016-12-01

The sections of Pacific absolute plate motion history that precede the Hawaii-Emperor and Louisville chains are based on three chains: the Line Islands-Mid-Pacific Mountains, the Hess Rise-Shatsky Rise, and the Marshall Islands-Wake Islands (Rurutu hotspot). Although it has been clear that the Line Islands do not define a simple age progression (e.g. Schlanger et al., 1984), the apparent similarity to the Emperor Seamount geographic trend has been used to extend the overall Hawaii-Emperor track further into the past. However, we show here that plate tectonic reconstructions suggest that the Mid-Pacific Mountains (MPMs) and Line Islands (LIs) were erupted near a mid-ocean ridge, and thus these structures do not reflect absolute plate motion. Moverover, the morphology and geochemistry of the volcanoes show similarities with Pukapuka Ridge (e.g. Davis et al., 2002) and the Rano Rahi seamounts, presumed to have a shallow origin. Modern 40Ar/39Ar ages show that the LIs erupted at various times along the entire volcanic chain. The oldest structures formed within 10 Ma of plate formation. Given the short distance to the ridge system, large aseismic volcanic ridges, such as Necker Ridge and Horizon Guyot may simply reflect a connection between MPMs and the ridge, similar to the Pukapuka Ridge. The Line Islands to the south (including Karin Ridge) define short subchains of elongated seamounts that are widespread, resembling the Rano Rahi seamount field. During this time, the plate moved nearly parallel to the ridge system. The change from few large ridges to many subchains may reflect a change in absolute plate motion, similar to the Rano Rahi field. Here, significant MPMs volcanism is no longer connected to the ridge along plate motion. Similar to Pukapuka vs. Rano Rahi, the difference in direction between plate motion and the closest ridge determines whether larger ridges or smaller seamount subchains are formed. The difference between the largest structures (MPMs and LIs

Radiogenic isotopes in enriched mid-ocean ridge basalts from Explorer Ridge, northeast Pacific Ocean

Science.gov (United States)

Cousens, Brian; Weis, Dominique; Constantin, Marc; Scott, Steve

2017-09-01

Extreme gradients in topography related to variations in magma supply are observed on the Southern Explorer Ridge (SER), part of the northern Juan de Fuca ridge system. We report radiogenic isotope (Pb, Sr, Nd, Hf) and geochemical data for twenty-four basalt whole-rock and glass samples collected from the length of the SER and from Explorer Deep, a rift to the north of the SER. Lavas from the SER form a north-south geochemical gradient, dominated by E-MORB at the northern axial high, and range from T-MORB to N-MORB towards the southern deepest part of the ridge. Linear relationships between incompatible element ratios and isotopic ratios in MORB along the ridge are consistent with mixing of magmas beneath the ridge to generate the geographic gradient from E- to N-MORB. The E-MORB have high Sr and Pb, and low Nd and Hf isotopic ratios, typical of enriched mantle that includes a FOZO or HIMU isotopic component. The West Valley and Endeavour segments of the northern Juan de Fuca ridge also include this isotopic component, but the proportion of the FOZO or HIMU component is more extreme in the SER basalts. The FOZO or HIMU component may be garnet-bearing peridotite, or a garnet pyroxenite embedded in peridotite. Recycled garnet pyroxenite better explains the very shallow SER axial high, high Nb/La and La/Sm, and the ;enriched; isotopic compositions.

Beyond the mean estimate: a quantile regression analysis of inequalities in educational outcomes using INVALSI survey data

Directory of Open Access Journals (Sweden)

Antonella Costanzo

2017-09-01

Full Text Available Abstract The number of studies addressing issues of inequality in educational outcomes using cognitive achievement tests and variables from large-scale assessment data has increased. Here the value of using a quantile regression approach is compared with a classical regression analysis approach to study the relationships between educational outcomes and likely predictor variables. Italian primary school data from INVALSI large-scale assessments were analyzed using both quantile and standard regression approaches. Mathematics and reading scores were regressed on students' characteristics and geographical variables selected for their theoretical and policy relevance. The results demonstrated that, in Italy, the role of gender and immigrant status varied across the entire conditional distribution of students’ performance. Analogous results emerged pertaining to the difference in students’ performance across Italian geographic areas. These findings suggest that quantile regression analysis is a useful tool to explore the determinants and mechanisms of inequality in educational outcomes. A proper interpretation of quantile estimates may enable teachers to identify effective learning activities and help policymakers to develop tailored programs that increase equity in education.

Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model

Science.gov (United States)

Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei

2014-10-01

Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

OpenAIRE

Fang, Chuanglin; Liu, Haimeng; Li, Guangdong; Sun, Dongqi; Miao, Zhuang

2015-01-01

Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variati...

Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.

Science.gov (United States)

Jones, Kelly W; Lewis, David J

2015-01-01

Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES)--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that

Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.

Directory of Open Access Journals (Sweden)

Kelly W Jones

Full Text Available Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1 matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2 fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case

Polylinear regression analysis in radiochemistry

International Nuclear Information System (INIS)

Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.

1995-01-01

A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis

Applied Prevalence Ratio estimation with different Regression models: An example from a cross-national study on substance use research.

Science.gov (United States)

Espelt, Albert; Marí-Dell'Olmo, Marc; Penelo, Eva; Bosque-Prous, Marina

2016-06-14

To examine the differences between Prevalence Ratio (PR) and Odds Ratio (OR) in a cross-sectional study and to provide tools to calculate PR using two statistical packages widely used in substance use research (STATA and R). We used cross-sectional data from 41,263 participants of 16 European countries participating in the Survey on Health, Ageing and Retirement in Europe (SHARE). The dependent variable, hazardous drinking, was calculated using the Alcohol Use Disorders Identification Test - Consumption (AUDIT-C). The main independent variable was gender. Other variables used were: age, educational level and country of residence. PR of hazardous drinking in men with relation to women was estimated using Mantel-Haenszel method, log-binomial regression models and poisson regression models with robust variance. These estimations were compared to the OR calculated using logistic regression models. Prevalence of hazardous drinkers varied among countries. Generally, men have higher prevalence of hazardous drinking than women [PR=1.43 (1.38-1.47)]. Estimated PR was identical independently of the method and the statistical package used. However, OR overestimated PR, depending on the prevalence of hazardous drinking in the country. In cross-sectional studies, where comparisons between countries with differences in the prevalence of the disease or condition are made, it is advisable to use PR instead of OR.

Use of instantaneous streamflow measurements to improve regression estimates of index flow for the summer month of lowest streamflow in Michigan

Science.gov (United States)

Holtschlag, David J.

2011-01-01

In Michigan, index flow Q50 is a streamflow characteristic defined as the minimum of median flows for July, August, and September. The state of Michigan uses index flow estimates to help regulate large (greater than 100,000 gallons per day) water withdrawals to prevent adverse effects on characteristic fish populations. At sites where long-term streamgages are located, index flows are computed directly from continuous streamflow records as GageQ50. In an earlier study, a multiple-regression equation was developed to estimate index flows IndxQ50 at ungaged sites. The index equation explains about 94 percent of the variability of index flows at 147 (index) streamgages by use of six explanatory variables describing soil type, aquifer transmissivity, land cover, and precipitation characteristics. This report extends the results of the previous study, by use of Monte Carlo simulations, to evaluate alternative flow estimators, DiscQ50, IntgQ50, SiteQ50, and AugmQ50. The Monte Carlo simulations treated each of the available index streamgages, in turn, as a miscellaneous site where streamflow conditions are described by one or more instantaneous measurements of flow. In the simulations, instantaneous flows were approximated by daily mean flows at the corresponding site. All estimators use information that can be obtained from instantaneous flow measurements and contemporaneous daily mean flow data from nearby long-term streamgages. The efficacy of these estimators was evaluated over a set of measurement intensities in which the number of simulated instantaneous flow measurements ranged from 1 to 100 at a site. The discrete measurement estimator DiscQ50 is based on a simple linear regression developed between information on daily mean flows at five or more streamgages near the miscellaneous site and their corresponding GageQ50 index flows. The regression relation then was used to compute a DiscQ50 estimate at the miscellaneous site by use of the simulated instantaneous flow

Geophysical Investigation of Upper Mantle Anomalies of the Australian-Antarctic Ridge

Science.gov (United States)

Park, S. H.; Choi, H.; Kim, S. S.; Lin, J.

2017-12-01

Australian-Antarctic Ridge (AAR) is situated between the Pacific-Antarctic Ridge (PAR) and Southeast Indian Ridge (SEIR), extending eastward from the Australian-Antarctic Discordance (AAD). Much of the AAR has been remained uncharted until 2011 because of its remoteness and harsh weather conditions. Since 2011, four multidisciplinary expeditions initiated by the Korea Polar Research Institute (KOPRI) have surveyed the little-explored eastern ends of the AAR and investigated the tectonics, geochemistry, and hydrothermal activity of this intermediate spreading system. Recent isotope studies using the new basalt samples from the AAR have led to the new hypothesis of the Southern Ocean mantle domain (SOM), which may have originated from the super-plume activity associated with the Gondwana break-up. In this study, we characterize the geophysics of the Southern Ocean mantle using the newly acquired shipboard bathymetry and available geophysical datasets. First, we computed residual mantle Bouguer gravity anomalies (RMBA), gravity-derived crustal thickness, and residual topography along the AAR in order to obtain a geological proxy for regional variations in magma supply. The results of these analyses revealed that the southern flank of the AAR is associated with shallower seafloor, more negative RMBA, thicker crust, and/or less dense mantle in comparison to the conjugate northern flank. Furthermore, this north-south asymmetry becomes more prominent toward the central ridge segments of the AAR. Interestingly, the along-axis depths of the entire AAR are significantly shallower than the neighboring ridge systems and the global ridges of intermediate spreading rates. Such shallow depths are also correlated with regional negative geoid anomalies. Furthermore, recent mantle tomography models consistently showed that the upper mantle (< 250 km) below the AAR has low S-wave velocities, suggesting that it may be hotter than the nearby ridges. Such regional-scale anomalies of the

Displacement-length ratios and contractional strains of lunar wrinkle ridges in Mare Serenitatis and Mare Tranquillitatis

Science.gov (United States)

Li, Bo; Ling, Zongcheng; Zhang, Jiang; Chen, Jian; Ni, Yuheng; Liu, Chunli

2018-04-01

Wrinkle ridges are complex thrust faults commonly found in lunar mare basalts and caused by compressional stresses from both local basin and global Moon. In this paper, we select 59 single wrinkle ridges in Mare Serenitatis and 39 single wrinkle ridges in Mare Tranquillitatis according to WAC mosaic image. For each wrinkle ridge, several topographic profiles near its midpoint are generated to measure its height and maximum displacement (Dmax) through LOLA DEM data. Then we make 2D plots of displacement-length (L) for ridge population in the two maria. The Dmax-L ratios (γ) are derived by a linear fit method according to the D-L data. The γ value (2.13 × 10-2) of ridges in Mare Tranquillitatis is higher than the γ value (1.73 × 10-2) of ridges in Mare Serenitatis. In the last, the contractional strains (ε) in Mare Serenitatis and Mare Tranquillitatis are estimated to be ∼0.36% and 0.14% (assuming the fault plane dip θ is 25°). The values of the free-air gravity anomalies in Mare Serenitatis range from 78 to 358 mGal higher than those of the gravity anomalies in Mare Tranquillitatis which range from -70 to 120 mGal. The average thickness of basalts in Mare Tranquillitatis is 400 m, while that of basalts in Mare Serenitatis is 798 m. Moreover, the average age for ridge group in Mare Serenitatis is bigger than the wrinkle ridge's age in Mare Tranquillitatis. The formation of ridge group in Mare Serenitatis takes longer time than that in Mare Serenitatis. Therefore, we think the higher value of gravity anomalies, thicker basaltic units and longer formation time for wrinkle ridge in Mare Serenitatis maybe result in the higher value of contractional strain, although the formation of Tranquillitatis basin is earlier than that of Serenitatis basin.

Mathematical models for estimating earthquake casualties and damage cost through regression analysis using matrices

International Nuclear Information System (INIS)

Urrutia, J D; Bautista, L A; Baccay, E B

2014-01-01

The aim of this study was to develop mathematical models for estimating earthquake casualties such as death, number of injured persons, affected families and total cost of damage. To quantify the direct damages from earthquakes to human beings and properties given the magnitude, intensity, depth of focus, location of epicentre and time duration, the regression models were made. The researchers formulated models through regression analysis using matrices and used α = 0.01. The study considered thirty destructive earthquakes that hit the Philippines from the inclusive years 1968 to 2012. Relevant data about these said earthquakes were obtained from Philippine Institute of Volcanology and Seismology. Data on damages and casualties were gathered from the records of National Disaster Risk Reduction and Management Council. This study will be of great value in emergency planning, initiating and updating programs for earthquake hazard reduction in the Philippines, which is an earthquake-prone country.

Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07

Science.gov (United States)

Oden, Timothy D.; Asquith, William H.; Milburn, Matthew S.

2009-01-01

In December 2005, the U.S. Geological Survey in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (total coliform and Escherichia coli), atrazine, and suspended sediment at two U.S. Geological Survey streamflow-gaging stations upstream from Lake Houston near Houston (08068500 Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Texas). The data from the discrete water-quality samples collected during 2005-07, in conjunction with monitored real-time data already being collected - physical properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), streamflow, and rainfall - were used to develop regression models for predicting water-quality constituent concentrations for inflows to Lake Houston. Rainfall data were obtained from a rain gage monitored by Harris County Homeland Security and Emergency Management and colocated with the Spring Creek station. The leaps and bounds algorithm was used to find the best subsets of possible regression models (minimum residual sum of squares for a given number of variables). The potential explanatory or predictive variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, rainfall, and time (to account for seasonal variations inherent in some water-quality data). The response variables at each site were nitrite plus nitrate nitrogen, total phosphorus, organic carbon, Escherichia coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities as a means to estimate concentrations of the various constituents under investigation, with accompanying estimates of measurement uncertainty. Each regression equation can be used to estimate concentrations of a given constituent in real time. In conjunction with estimated concentrations, constituent loads were estimated by multiplying the

Dynamics of the Seychelles-Chagos Thermocline Ridge

Science.gov (United States)

Bulusu, S.

2016-02-01

The southwest tropical Indian Ocean (SWTIO) features a unique, seasonal upwelling of the thermocline also known as the Seychelles-Chagos Thermocline Ridge (SCTR). More recently, this ridge or "dome"-like feature in the thermocline depth at (55°E-65°E, 5°S-12°S) in the SWTIO has been linked to interannual variability in the semi-annual Indian Ocean monsoon seasons as well as the Madden-Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO). The SCTR is a region where the MJO is associated with strong SST variability. Normally more cyclones are found generated in this SCTR region when the thermocline is deeper, which has a positive relation to the arrival of a downwelling Rossby wave from the southeast tropical Indian Ocean. Previous studies have focused their efforts solely on sea surface temperature (SST) because they determined salinity variability to be low, but with the Soil Moisture and Ocean Salinity (SMOS), and Aquarius salinity missions new insight can be shed on the effects that the seasonal upwelling of the thermocline has on Sea Surface Salinity (SSS). Seasonal SSS anomalies these missions will reveal the magnitude of seasonal SSS variability, while Argo depth profiles will show the link between changes in subsurface salinity and temperature structure. A seasonal increase in SST and a decrease in SSS associated with the downwelling of the thermocline have also been shown to occasionally generate MJO events, an extremely important part of climate variability in the Indian ocean. Satellite derives salinity and Argo data can help link changes in surface and subsurface salinity structure to the generation of the important MJO events. This study uses satellite derived salinity from Soil Moisture and Ocean Salinity (SMOS), and Aquarius to see if these satellites can yield new information on seasonal and interannual surface variability. In this study barrier layer thickness (BLT) estimates will be derived from satellite measurements using a

Two-dimensional chronostratigraphic modelling of OSL ages from recent beach-ridge deposits, SE Australia

DEFF Research Database (Denmark)

Tamura, Toru; Cunningham, Alastair C.; Oliver, Thomas S.N.

2018-01-01

Optically-stimulated luminesecne (OSL) dating, in concert with two-dimensional ground-penetrating radar (GPR) profiling, has contributed to significant advances in our understanding of beach-ridge systems and other sedimentary landforms in various settings. For recent beach-ridges, the good OSL...... samples may be larger than the difference in sample ages. Age inversions can be avoided, however, if the stratigraphic constraints are included in the age estimation process. Here, we create a custom Bayesian chronological model for a recent (..., for direct comparison with a GPR profile. The model includes a full ‘burial-dose model’ for each sample and a dose rate term with the modelled ages constrained by the vertical and shore-normal sample order. The modelled ages are visualized by plotting isochrones on the beach-ridge cross section...

Genetic Parameters for Body condition score, Body weigth, Milk yield and Fertility estimated using random regression models

NARCIS (Netherlands)

Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

2003-01-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

The beach ridges of India: A review

Digital Repository Service at National Institute of Oceanography (India)

Kunte, P.D.; Wagle, B.G.

, and is presented in a consolidated form. Beach ridges of the east and west coast of India are grouped in thirteen-beach ridge complexes based on their association. Review indicates that the beach ridges of India are not older than the Holocene age...

Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression

Directory of Open Access Journals (Sweden)

Ana Paula Ferreira de Carvalho

2013-05-01

Full Text Available Radiometric precision is difficult to maintain in orbital images due to several factors (atmospheric conditions, Earth-sun distance, detector calibration, illumination, and viewing angles. These unwanted effects must be removed for radiometric consistency among temporal images, leaving only land-leaving radiances, for optimum change detection. A variety of relative radiometric correction techniques were developed for the correction or rectification of images, of the same area, through use of reference targets whose reflectance do not change significantly with time, i.e., pseudo-invariant features (PIFs. This paper proposes a new technique for radiometric normalization, which uses three sequential methods for an accurate PIFs selection: spectral measures of temporal data (spectral distance and similarity, density scatter plot analysis (ridge method, and robust regression. The spectral measures used are the spectral angle (Spectral Angle Mapper, SAM, spectral correlation (Spectral Correlation Mapper, SCM, and Euclidean distance. The spectral measures between the spectra at times t1 and t2 and are calculated for each pixel. After classification using threshold values, it is possible to define points with the same spectral behavior, including PIFs. The distance and similarity measures are complementary and can be calculated together. The ridge method uses a density plot generated from images acquired on different dates for the selection of PIFs. In a density plot, the invariant pixels, together, form a high-density ridge, while variant pixels (clouds and land cover changes are spread, having low density, facilitating its exclusion. Finally, the selected PIFs are subjected to a robust regression (M-estimate between pairs of temporal bands for the detection and elimination of outliers, and to obtain the optimal linear equation for a given set of target points. The robust regression is insensitive to outliers, i.e., observation that appears to deviate

Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques

Science.gov (United States)

Jones, Kelly W.; Lewis, David J.

2015-01-01

Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented—from protected areas to payments for ecosystem services (PES)—to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing ‘matching’ to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods—an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators—due to the presence of unobservable bias—that lead to differences in conclusions about effectiveness. The Ecuador case

Estimating Penetration Resistance in Agricultural Soils of Ardabil Plain Using Artificial Neural Network and Regression Methods

Directory of Open Access Journals (Sweden)

Gholam Reza Sheykhzadeh

2017-02-01

Full Text Available Introduction: Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance is time consuming and difficult because of high temporal and spatial variability. Therefore, many different regressions and artificial neural network pedotransfer functions have been proposed to estimate penetration resistance from readily available soil variables such as particle size distribution, bulk density (Db and gravimetric water content (θm. The lands of Ardabil Province are one of the main production regions of potato in Iran, thus, obtaining the soil penetration resistance in these regions help with the management of potato production. The objective of this research was to derive pedotransfer functions by using regression and artificial neural network to predict penetration resistance from some soil variations in the agricultural soils of Ardabil plain and to compare the performance of artificial neural network with regression models. Materials and methods: Disturbed and undisturbed soil samples (n= 105 were systematically taken from 0-10 cm soil depth with nearly 3000 m distance in the agricultural lands of the Ardabil plain ((lat 38°15' to 38°40' N, long 48°16' to 48°61' E. The contents of sand, silt and clay (hydrometer method, CaCO3 (titration method, bulk density (cylinder method, particle density (Dp (pychnometer method, organic carbon (wet oxidation method, total porosity(calculating from Db and Dp, saturated (θs and field soil water (θf using the gravimetric method were measured in the laboratory. Mean geometric diameter (dg and standard deviation (σg of soil particles were computed using the percentages of sand, silt and clay. Penetration resistance was measured in situ using cone penetrometer (analog model at 10

Remedial Investigation Report on Chestnut Ridge Operable Unit 2 (Filled Coal Ash Pond/Upper McCoy Branch) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee. Volume 1. Main Text

International Nuclear Information System (INIS)

1994-08-01

This document is a report on the remedial investigation (RI) of Chestnut Ridge Operable Unit (OU) 2 at the Oak Ridge Y-12 Plant. Chestnut Ridge OU 2 consists of Upper McCoy Branch (UMB), the Filled Coal Ash Pond (FCAP), and the area surrounding the Sluice Channel formerly associated with coal ash disposal in the FCAP. Chestnut Ridge OU 2 is located within the U.S. Department of Energy's (DOE's) Oak Ridge Reservation in Anderson County, Tennessee, approximately 24 miles west of Knoxville. The pond is an 8.5-acre area on the southern slope of Chestnut Ridge, 0.5 mile south of the main Y-12 Plant and geographically separated from the Y-12 Plant by Chestnut Ridge. The elevation of the FCAP is ∼ 950 ft above mean sea level (msl), and it is relatively flat and largely vegetated. Two small ponds are usually present at the northeast and northwest comers of the FCAP. The Sluice Channel Area extends ∼1000 ft from the northern margin of the FCAP to the crest of Chestnut Ridge, which has an elevation of ∼1100 ft above msl. The Sluice Channel Area is largely vegetated also. McCoy Branch runs from the top of Chestnut Ridge across the FCAP into Rogers Quarry and out of the quarry where it runs a short distance into Milton Hill Lake at McCoy Embayment, termed UMB. The portion south of Rogers Quarry, within Chestnut Ridge OU 4, is termed Lower McCoy Branch. The DOE Oak Ridge Y-12 Plant disposed of coal ash from its steam plant operations as a slurry that was discharged into an ash retention impoundment; this impoundment is the FCAP. The FCAP was built in 1955 to serve as a settling basin after coal ash slurried over Chestnut Ridge from the Y-12 Plant. The FCAP was constructed by building an earthen dam across the northern tributary of McCoy Branch. The dam was designed to hold 20 years of Y-12 steam plant ash. By July 1967, ash had filled up the impoundment storage behind the dam to within 4 ft of the top

Remedial Investigation Report on Chestnut Ridge Operable Unit 2 (Filled Coal Ash Pond/Upper McCoy Branch) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee. Volume 1. Main Text

Energy Technology Data Exchange (ETDEWEB)

1994-08-01

This document is a report on the remedial investigation (RI) of Chestnut Ridge Operable Unit (OU) 2 at the Oak Ridge Y-12 Plant. Chestnut Ridge OU 2 consists of Upper McCoy Branch (UMB), the Filled Coal Ash Pond (FCAP), and the area surrounding the Sluice Channel formerly associated with coal ash disposal in the FCAP. Chestnut Ridge OU 2 is located within the U.S. Department of Energy`s (DOE`s) Oak Ridge Reservation in Anderson County, Tennessee, approximately 24 miles west of Knoxville. The pond is an 8.5-acre area on the southern slope of Chestnut Ridge, 0.5 mile south of the main Y-12 Plant and geographically separated from the Y-12 Plant by Chestnut Ridge. The elevation of the FCAP is {approximately} 950 ft above mean sea level (msl), and it is relatively flat and largely vegetated. Two small ponds are usually present at the northeast and northwest comers of the FCAP. The Sluice Channel Area extends {approximately}1000 ft from the northern margin of the FCAP to the crest of Chestnut Ridge, which has an elevation of {approximately}1100 ft above msl. The Sluice Channel Area is largely vegetated also. McCoy Branch runs from the top of Chestnut Ridge across the FCAP into Rogers Quarry and out of the quarry where it runs a short distance into Milton Hill Lake at McCoy Embayment, termed UMB. The portion south of Rogers Quarry, within Chestnut Ridge OU 4, is termed Lower McCoy Branch. The DOE Oak Ridge Y-12 Plant disposed of coal ash from its steam plant operations as a slurry that was discharged into an ash retention impoundment; this impoundment is the FCAP. The FCAP was built in 1955 to serve as a settling basin after coal ash slurried over Chestnut Ridge from the Y-12 Plant. The FCAP was constructed by building an earthen dam across the northern tributary of McCoy Branch. The dam was designed to hold 20 years of Y-12 steam plant ash. By July 1967, ash had filled up the impoundment storage behind the dam to within 4 ft of the top.

Preliminary Analysis of the Knipovich Ridge Segmentation - Influence of Focused Magmatism and Ridge Obliquity on an Ultraslow Spreading System

Science.gov (United States)

Okino, K.; Curewitz, D.; Asada, M.; Tamaki, K.

2002-12-01

Bathymetry, gravity and deep-tow sonar image data are used to define the segmentation of a 400 km long portion of the ultraslow-spreading Knipovich Ridge in the Norwegian-Greenland Sea, Northeast Atlantic Ocean. Discrete volcanic centers marked by large volcanic constructions and accompanying short wavelength mantle Bouguer anomaly (MBA) lows generally resemble those of the Gakkel Ridge and the easternmost Southwest Indian Ridge (SWIR). These magmatically robust segment centers are regularly spaced about 85-100 km apart along the ridge, and are characterized by accumulated hummocky terrain, high relief, off-axis seamount chains and significant MBA lows. We suggest that these eruptive centers correspond to areas of enhanced magma flux, and that their spacing reflects the geometry of underlying mantle upwelling cells. The large-scale thermal structure of the mantle primarily controls discrete and focused magmatism, and the relatively wide spacing of these segments may reflect cool mantle beneath the ridge. Segment centers along the southern Knipovich Ridge are characterized by lower relief and smaller MBA anomalies than along the northern section of the ridge. This suggests that ridge obliquity is a secondary control on ridge construction on the Knipovich Ridge, as the obliquity changes from 35° to 49° from north to south, respectively, while spreading rate and axial depth remain approximately constant. The increased obliquity may contribute to decreased effective spreading rates, lower upwelling magma velocity and melt formation, and limited horizontal dike propagation near the surface. We also identify small, magmatically weaker segments with low relief, little or no MBA anomaly, and no off axis expression. We suggest that these segments are either fed by lateral melt migration from adjacent magmatically stronger segments or represent smaller, discrete mantle upwelling centers with short-lived melt supply.

Preliminary analysis of the Knipovich Ridge segmentation: influence of focused magmatism and ridge obliquity on an ultraslow spreading system

Science.gov (United States)

Okino, Kyoko; Curewitz, Daniel; Asada, Miho; Tamaki, Kensaku; Vogt, Peter; Crane, Kathleen

2002-09-01

Bathymetry, gravity and deep-tow sonar image data are used to define the segmentation of a 400 km long portion of the ultraslow-spreading Knipovich Ridge in the Norwegian-Greenland Sea, Northeast Atlantic Ocean. Discrete volcanic centers marked by large volcanic constructions and accompanying short wavelength mantle Bouguer anomaly (MBA) lows generally resemble those of the Gakkel Ridge and the easternmost Southwest Indian Ridge. These magmatically robust segment centers are regularly spaced about 85-100 km apart along the ridge, and are characterized by accumulated hummocky terrain, high relief, off-axis seamount chains and significant MBA lows. We suggest that these eruptive centers correspond to areas of enhanced magma flux, and that their spacing reflects the geometry of underlying mantle upwelling cells. The large-scale thermal structure of the mantle primarily controls discrete and focused magmatism, and the relatively wide spacing of these segments may reflect cool mantle beneath the ridge. Segment centers along the southern Knipovich Ridge are characterized by lower relief and smaller MBA anomalies than along the northern section of the ridge. This suggests that ridge obliquity is a secondary control on ridge construction on the Knipovich Ridge, as the obliquity changes from 35° to 49° from north to south, respectively, while spreading rate and axial depth remain approximately constant. The increased obliquity may contribute to decreased effective spreading rates, lower upwelling magma velocity and melt formation, and limited horizontal dike propagation near the surface. We also identify small, magmatically weaker segments with low relief, little or no MBA anomaly, and no off-axis expression. We suggest that these segments are either fed by lateral melt migration from adjacent magmatically stronger segments or represent smaller, discrete mantle upwelling centers with short-lived melt supply.

Comprehensive integrated planning: A process for the Oak Ridge Reservation, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1998-05-01

The Oak Ridge Comprehensive Integrated Plan is intended to assist the US Department of Energy (DOE) and contractor personnel in implementing a comprehensive integrated planning process consistent with DOE Order 430.1, Life Cycle Asset Management and Oak Ridge Operations Order 430. DOE contractors are charged with developing and producing the Comprehensive Integrated Plan, which serves as a summary document, providing information from other planning efforts regarding vision statements, missions, contextual conditions, resources and facilities, decision processes, and stakeholder involvement. The Comprehensive Integrated Plan is a planning reference that identifies primary issues regarding major changes in land and facility use and serves all programs and functions on-site as well as the Oak Ridge Operations Office and DOE Headquarters. The Oak Ridge Reservation is a valuable national resource and is managed on the basis of the principles of ecosystem management and sustainable development and how mission, economic, ecological, social, and cultural factors are used to guide land- and facility-use decisions. The long-term goals of the comprehensive integrated planning process, in priority order, are to support DOE critical missions and to stimulate the economy while maintaining a quality environment

Comprehensive integrated planning: A process for the Oak Ridge Reservation, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-05-01

The Oak Ridge Comprehensive Integrated Plan is intended to assist the US Department of Energy (DOE) and contractor personnel in implementing a comprehensive integrated planning process consistent with DOE Order 430.1, Life Cycle Asset Management and Oak Ridge Operations Order 430. DOE contractors are charged with developing and producing the Comprehensive Integrated Plan, which serves as a summary document, providing information from other planning efforts regarding vision statements, missions, contextual conditions, resources and facilities, decision processes, and stakeholder involvement. The Comprehensive Integrated Plan is a planning reference that identifies primary issues regarding major changes in land and facility use and serves all programs and functions on-site as well as the Oak Ridge Operations Office and DOE Headquarters. The Oak Ridge Reservation is a valuable national resource and is managed on the basis of the principles of ecosystem management and sustainable development and how mission, economic, ecological, social, and cultural factors are used to guide land- and facility-use decisions. The long-term goals of the comprehensive integrated planning process, in priority order, are to support DOE critical missions and to stimulate the economy while maintaining a quality environment.

Bay Ridge Gardens - Mixed Humid Affordable Multifamily Housing Deep Energy Retrofit

Energy Technology Data Exchange (ETDEWEB)

Lyons, James [Building America Partnership for Improved Residential Construction (BA-PIRC), Cocoa, FL (United States); Moore, Mike [Building America Partnership for Improved Residential Construction (BA-PIRC), Cocoa, FL (United States); Thompson, Margo [Building America Partnership for Improved Residential Construction (BA-PIRC), Cocoa, FL (United States)

2013-08-01

Under this project, Newport Partners (as part of the BA-PIRC research team) evaluated the installation, measured performance, and cost effectiveness of efficiency upgrade measures for a tenant-in-place deep energy retrofit (DER) at the Bay Ridge multifamily development in Annapolis, Maryland. This report summarizes system commissioning, short-term test results, utility bill data analysis, and analysis of real-time data collected over a one-year period after the retrofit was complete. The Bay Ridge project is comprised of a "base scope" retrofit which was estimated to achieve a 30%+ savings (relative to pre-retrofit) on 186 apartments, and a "DER scope" which was estimated to achieve 50% savings (relative to pre-retrofit) on a 12-unit building. A wide range of efficiency measures was applied to pursue this savings target for the DER building, including improvements/replacements of mechanical equipment and distribution systems, appliances, lighting and lighting controls, the building envelope, hot water conservation measures, and resident education. The results of this research build upon the current body of knowledge of multifamily retrofits. Towards this end, the research team has collected and generated data on the selection of measures, their estimated performance, their measured performance, and risk factors and their impact on potential measures.

An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models

International Nuclear Information System (INIS)

Harlim, John; Mahdi, Adam; Majda, Andrew J.

2014-01-01

A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

Science.gov (United States)

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of

Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

Science.gov (United States)

Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

2014-12-01

This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

Science.gov (United States)

Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Leclère, Vincent; Salmon, Joseph

2017-10-01

In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider ℓ 1 penalty to enforce sparsity in such scenarios. Sparsity enforcing methods, the Lasso being a canonical example, are popular candidates to address high dimension. For efficiency, they rely on tuning a parameter trading data fitting versus sparsity. For the Lasso theory to hold this tuning parameter should be proportional to the noise level, yet the latter is often unknown in practice. A possible remedy is to jointly optimize over the regression parameter as well as over the noise level. This has been considered under several names in the literature: Scaled-Lasso, Square-root Lasso, Concomitant Lasso estimation for instance, and could be of interest for uncertainty quantification. In this work, after illustrating numerical difficulties for the Concomitant Lasso formulation, we propose a modification we coined Smoothed Concomitant Lasso, aimed at increasing numerical stability. We propose an efficient and accurate solver leading to a computational cost no more expensive than the one for the Lasso. We leverage on standard ingredients behind the success of fast Lasso solvers: a coordinate descent algorithm, combined with safe screening rules to achieve speed efficiency, by eliminating early irrelevant features.

Nonparametric Mixture of Regression Models.

Science.gov (United States)

Huang, Mian; Li, Runze; Wang, Shaoli

2013-07-01

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

Interpretation of bathymetric and magnetic data from the easternmost segment of Australian-Antarctic Ridge, 156°-161°E

Science.gov (United States)

Choi, H.; Kim, S.; Park, S.

2013-12-01

From 2011 to 2013, Korea Polar Research Institute (KOPRI) conducted a series of geophysical and geochemical expeditions on the longest and easternmost segment of Australian-Antarctic Ridge, located at 61°-63°S, and 156°-161°E. This ridge segment plays an important role in constraining the tectonics of the Antarctic plate. Using IBRV ARAON, the detailed bathymetric data and eleven total magnetic profiles were collected. The studied ridge has spread NNW-SSE direction and tends to be shallower to the west and deeper to the east. The western side of the ridge (156°-157.50°E) shows a broad axial high and a plenty of seamounts as an indicative of massive volcanism. Near the center of the ridge (158°-159°E), a seamount chain is formed stretching toward the south from the ridge. Also, the symmetric seafloor fabric is clearly observed at the eastern portion (158.50°-160°E) of the seamount chain. From the topographic change along the ridge axis, the western part of the ridge appears to have a sufficient magma supply. On the contrary, the eastern side of the ridge (160°-161°E) is characterized by axial valley and relatively deeper depth. Nevertheless, the observed total magnetic field anomalies exhibit symmetric patterns across the ridge axis. Although there have not been enough magnetic survey lines, the spreading rates of the ridge are estimated as the half-spreading rate of 37.7 mm/y and 35.3 mm/y for the western portion of the ridge and 42.3 mm/y for the eastern portion. The studied ridge can be categorized as an intermediate spreading ridge, confirming previous studies based on the spreading rate of global ridge system. Here we will present the preliminary results on bathymetric changes along the ridge axis and its relationship with melt supply distribution, and detailed magnetic properties of the ridge constrained by the observed total field anomalies.

Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation

International Nuclear Information System (INIS)

Jiang, He; Dong, Yao; Wang, Jianzhou; Li, Yuqin

2015-01-01

Highlights: • CS-hard-ridge-RBF and DE-hard-ridge-RBF are proposed to forecast solar radiation. • Pearson and Apriori algorithm are used to analyze correlations between the data. • Hard-ridge penalty is added to reduce the number of nodes in the hidden layer. • CS algorithm and DE algorithm are used to determine the optimal parameters. • Proposed two models have higher forecasting accuracy than RBF and hard-ridge-RBF. - Abstract: Due to the scarcity of equipment and the high costs of maintenance, far fewer observations of solar radiation are made than observations of temperature, precipitation and other weather factors. Therefore, it is increasingly important to study several relevant meteorological factors to accurately forecast solar radiation. For this research, monthly average global solar radiation and 12 meteorological parameters from 1998 to 2010 at four sites in the United States were collected. Pearson correlation coefficients and Apriori association rules were successfully used to analyze correlations between the data, which provided a basis for these relative parameters as input variables. Two effective and innovative methods were developed to forecast monthly average global solar radiation by converting a RBF neural network into a multiple linear regression problem, adding a hard-ridge penalty to reduce the number of nodes in the hidden layer, and applying intelligent optimization algorithms, such as the cuckoo search algorithm (CS) and differential evolution (DE), to determine the optimal center and scale parameters. The experimental results show that the proposed models produce much more accurate forecasts than other models

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

Oak Ridge Leadership Computing Facility (OLCF)

Data.gov (United States)

Federal Laboratory Consortium — The Oak Ridge Leadership Computing Facility (OLCF) was established at Oak Ridge National Laboratory in 2004 with the mission of standing up a supercomputer 100 times...

Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.

Science.gov (United States)

Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L

2012-03-06

Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

US Department of Energy Oak Ridge Operations Environmental Management Public Involvement Plan for the Oak Ridge Reservation

International Nuclear Information System (INIS)

1996-03-01

This document was prepared in accordance with CERCLA requirements for writing community relations plans. It includes information on how the DOE Oak Ridge Operations Office prepares and executes Environmental Management Community relations activities. It is divided into three sections: the public involvement plan, public involvement in Oak Ridge, and public involvement in 1995. Four appendices are also included: environmental management in Oak Ridge; community and regional overview; key laws, agreements, and policy; and principal contacts

Regression analysis and transfer function in estimating the parameters of central pulse waves from brachial pulse wave.

Science.gov (United States)

Chai Rui; Li Si-Man; Xu Li-Sheng; Yao Yang; Hao Li-Ling

2017-07-01

This study mainly analyzed the parameters such as ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO) and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. These parameters extracted from the central pulse wave invasively measured were compared with the parameters measured from the brachial pulse waves by a regression model and a transfer function model. The accuracy of the parameters which were estimated by the regression model and the transfer function model was compared too. Our findings showed that in addition to the k value, the above parameters of the central pulse wave and the brachial pulse wave invasively measured had positive correlation. Both the regression model parameters including A_slope, DBP, SEVR and the transfer function model parameters had good consistency with the parameters invasively measured, and they had the same effect of consistency. The regression equations of the three parameters were expressed by Y'=a+bx. The SBP, PP, SV, CO of central pulse wave could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

InRidge program: Preliminary results from the first cruise

Digital Repository Service at National Institute of Oceanography (India)

Mukhopadhyay, R.; Murthy, K.S.R.; Iyer, S.D.; Rao, M.M.M.; Banerjee, R.; Subrahmanyam, A.S.; Shirodkar, P.V.; Ghose, I.

The first cruise under India's own Ridge research initiative, InRidge collected new data on bathymetry, free-air gravity and magnetic anomalies across the ridge axis between the Vema and Zhivago transform faults in the Central Indian Ridge...

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl

2013-01-01

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng

2013-11-05

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Forecasting exchange rates: a robust regression approach

OpenAIRE

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...

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Does presence of a mid-ocean ridge enhance biomass and biodiversity?

Directory of Open Access Journals (Sweden)

Imants G Priede

Full Text Available In contrast to generally sparse biological communities in open-ocean settings, seamounts and ridges are perceived as areas of elevated productivity and biodiversity capable of supporting commercial fisheries. We investigated the origin of this apparent biological enhancement over a segment of the North Mid-Atlantic Ridge (MAR using sonar, corers, trawls, traps, and a remotely operated vehicle to survey habitat, biomass, and biodiversity. Satellite remote sensing provided information on flow patterns, thermal fronts, and primary production, while sediment traps measured export flux during 2007-2010. The MAR, 3,704,404 km(2 in area, accounts for 44.7% lower bathyal habitat (800-3500 m depth in the North Atlantic and is dominated by fine soft sediment substrate (95% of area on a series of flat terraces with intervening slopes either side of the ridge axis contributing to habitat heterogeneity. The MAR fauna comprises mainly species known from continental margins with no evidence of greater biodiversity. Primary production and export flux over the MAR were not enhanced compared with a nearby reference station over the Porcupine Abyssal Plain. Biomasses of benthic macrofauna and megafauna were similar to global averages at the same depths totalling an estimated 258.9 kt C over the entire lower bathyal north MAR. A hypothetical flat plain at 3500 m depth in place of the MAR would contain 85.6 kt C, implying an increase of 173.3 kt C attributable to the presence of the Ridge. This is approximately equal to 167 kt C of estimated pelagic biomass displaced by the volume of the MAR. There is no enhancement of biological productivity over the MAR; oceanic bathypelagic species are replaced by benthic fauna otherwise unable to survive in the mid ocean. We propose that globally sea floor elevation has no effect on deep sea biomass; pelagic plus benthic biomass is constant within a given surface productivity regime.

Regression calibration with more surrogates than mismeasured variables

KAUST Repository

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

KAUST Repository

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.

Site characterization report for Building 3515 at Oak Ridge National Laboratory, Oak Ridge, Tennessee. Environmental Restoration Program

International Nuclear Information System (INIS)

1994-08-01

Building 3515 at Oak Ridge National Laboratory (ORNL), also known as the Fission Product Pilot Plant, is a surplus facility in the main plant area to the east of the South Tank Farm slated for decontamination and decommissioning (D ampersand D). The building consists of two concrete cells (north and south) on a concrete pad and was used to extract radioisotopes of ruthenium, strontium, cesium, cerium, rhenium and other elements from aqueous fission product waste. Site characterization activities of the building were initiated. The objective of the site characterization was to provide information necessary for engineering evaluation and planning of D ampersand D approaches, planning for personal protection of D ampersand D workers, and estimating waste volumes from D ampersand D activities. This site characterization report documents the investigation with a site description, a summary of characterization methods, chemical and radiological sample analysis results, field measurement results, and waste volume estimates

Quality assurance project plan for the Chestnut Ridge Fly Ash Pond Stabilization Project at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1996-07-01

The Chestnut Ridge Fly Ash Pond Stabilization (CRFAPS) Project will stabilize a 19-m-high (62-ft-high) earthen embankment across Upper McCoy Branch situated along the southern slope of Chestnut Ridge. This task will be accomplished by raising the crest of the embankment, reinforcing the face of the embankment, removing trees from the face and top of the embankment, and repairing the emergency spillway. The primary responsibilities of the team members are: Lockheed Martin Energy Systems, Inc., (Energy Systems) will be responsible for project integration, technical support, Title 3 field support, environmental oversight, and quality assurance (QA) oversight of the project; Foster Wheeler Environmental Corporation (FWENC) will be responsible for design and home office Title 3 support; MK-Ferguson of Oak Ridge Company (MK-F) will be responsible for health and safety, construction, and procurement of construction materials. Each of the team members has a QA program approved by the US Department of Energy (DOE) Oak Ridge Operations. This project-specific QA project plan (QAPP), which is applicable to all project activities, identifies and integrates the specific QA requirements from the participant's QA programs that are necessary for this project

Quantile Regression With Measurement Error

KAUST Repository

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.

Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression.

Science.gov (United States)

van de Kassteele, Jan; Zwakhals, Laurens; Breugelmans, Oscar; Ameling, Caroline; van den Brink, Carolien

2017-07-01

Local policy makers increasingly need information on health-related indicators at smaller geographic levels like districts or neighbourhoods. Although more large data sources have become available, direct estimates of the prevalence of a health-related indicator cannot be produced for neighbourhoods for which only small samples or no samples are available. Small area estimation provides a solution, but unit-level models for binary-valued outcomes that can handle both non-linear effects of the predictors and spatially correlated random effects in a unified framework are rarely encountered. We used data on 26 binary-valued health-related indicators collected on 387,195 persons in the Netherlands. We associated the health-related indicators at the individual level with a set of 12 predictors obtained from national registry data. We formulated a structured additive regression model for small area estimation. The model captured potential non-linear relations between the predictors and the outcome through additive terms in a functional form using penalized splines and included a term that accounted for spatially correlated heterogeneity between neighbourhoods. The registry data were used to predict individual outcomes which in turn are aggregated into higher geographical levels, i.e. neighbourhoods. We validated our method by comparing the estimated prevalences with observed prevalences at the individual level and by comparing the estimated prevalences with direct estimates obtained by weighting methods at municipality level. We estimated the prevalence of the 26 health-related indicators for 415 municipalities, 2599 districts and 11,432 neighbourhoods in the Netherlands. We illustrate our method on overweight data and show that there are distinct geographic patterns in the overweight prevalence. Calibration plots show that the estimated prevalences agree very well with observed prevalences at the individual level. The estimated prevalences agree reasonably well with the

The number of subjects per variable required in linear regression analyses.

Science.gov (United States)

Austin, Peter C; Steyerberg, Ewout W

2015-06-01

To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

Science.gov (United States)

Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D

2015-05-01

Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical

Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals

Science.gov (United States)

Almirall, Daniel; Griffin, Beth Ann; McCaffrey, Daniel F.; Ramchand, Rajeev; Yuen, Robert A.; Murphy, Susan A.

2014-01-01

This article considers the problem of examining time-varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the time-varying moderators are also the sole time-varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time-varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. We illustrate the methodology in a case study to assess if time-varying substance use moderates treatment effects on future substance use. PMID:23873437

An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran

Energy Technology Data Exchange (ETDEWEB)

Azadeh, A; Seraj, O [Department of Industrial Engineering and Research Institute of Energy Management and Planning, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, P.O. Box 11365-4563 (Iran); Saberi, M [Department of Industrial Engineering, University of Tafresh (Iran); Institute for Digital Ecosystems and Business Intelligence, Curtin University of Technology, Perth (Australia)

2010-06-15

This study presents an integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression (FR) or time series and the integrated algorithm could be an ideal substitute for such cases. At First, preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred Auto Regression Moving Average (ARMA) model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, the preferred model from fuzzy regression and time series model is selected by the Granger-Newbold. Also, the impact of data preprocessing on the fuzzy regression performance is considered. Monthly electricity consumption of Iran from March 1994 to January 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with other intelligent tools such as Genetic Algorithm (GA) and Artificial Neural Network (ANN). (author)

ELESTRES: performance of nuclear fuel, circumferential ridging, and multiaxial elastic-plastic stresses in sheaths

International Nuclear Information System (INIS)

Tayal, M.

1986-10-01

The finite element code ELESTRES models the two-dimensional axisymmetric behaviour of a CANDU fuel element during normal operation. The main focus of the code is to estimate temperatures, fission gas release, and axial variations of deformation/stresses in the pellet and in the sheath. Thus the code is able to predict details like stresses/strains at circumferential ridges. This paper describes the current version of ELESTRES. The emphasis is on a recent addition: multiaxial stresses in the sheath near circumferential ridges. For accuracy in the critical region, a fine mesh is used near the ridge. To keep computing costs low, a coarse mesh is used near the midplane of the pellet. Predictions of ELESTRES show good agreement with abouth 80 measurements of fission-gas-release. In this paper, we also present ELESTRES predictions of hoop strains in sheaths, for two irradiations: element ABS and bundle GB. For both irradiations, predictions, compare favourably with measurements. An illustrative example shows that near circumferential ridges, bending contributes to multiaxial stresses in the sheath. This can have a significant effect on sheath integrity, such as during stress-corrosion-cracking due to power-increases, or during corrosion-assisted-fatigue due to power cycling

Petrological systematics of mid-ocean ridge basalts: Constraints on melt generation beneath ocean ridges

Science.gov (United States)

Langmuir, Charles H.; Klein, Emily M.; Plank, Terry

Mid-ocean ridge basalts (MORB) are a consequence of pressure-release melting beneath ocean ridges, and contain much information concerning melt formation, melt migration and heterogeneity within the upper mantle. MORB major element chemical systematics can be divided into global and local aspects, once they have been corrected for low pressure fractionation and interlaboratory biases. Regional average compositions for ridges unaffected by hot spots ("normal" ridges) can be used to define the global correlations among normalized Na2O, FeO, TiO2 and SiO2 contents, CaO/Al2O3 ratios, axial depth and crustal thickness. Back-arc basins show similar correlations, but are offset to lower FeO and TiO2 contents. Some hot spots, such as the Azores and Galapagos, disrupt the systematics of nearby ridges and have the opposite relationships between FeO, Na2O and depth over distances of 1000 km. Local variations in basalt chemistry from slow- and fast-spreading ridges are distinct from one another. On slow-spreading ridges, correlations among the elements cross the global vector of variability at a high angle. On the fast-spreading East Pacific Rise (EPR), correlations among the elements are distinct from both global and slow-spreading compositional vectors, and involve two components of variation. Spreading rate does not control the global correlations, but influences the standard deviations of axial depth, crustal thickness, and MgO contents of basalts. Global correlations are not found in very incompatible trace elements, even for samples far from hot spots. Moderately compatible trace elements for normal ridges, however, correlate with the major elements. Trace element systematics are significantly different for the EPR and the mid-Atlantic Ridge (MAR). Normal portions of the MAR are very depleted in REE, with little variability; hot spots cause large long wavelength variations in REE abundances. Normal EPR basalts are significantly more enriched than MAR basalts from normal

An aerial radiological survey of the Oak Ridge Reservation and surrounding area, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

Maurer, R.J.

1989-09-01

An aerial radiological survey of the Oak Ridge Reservation (ORR) and surrounding area in Oak Ridge, Tennessee, was conducted from September 12--29, 1989. The purpose of the survey was to measure and document the site's terrestrial radiological environment for use in effective environmental management and emergency response planning. The aerial survey was flown at an altitude of 91 meters (300 feet) along a series of parallel lines 152 meters (500 feet) apart. The survey encompassed an area of 440 square kilometers (170 square miles) as defined by the Tennessee Valley Authority Map S-16A of the entire Oak Ridge Reservation and adjacent area. The results of the aerial survey are reported as inferred exposure rates at 1 meter above ground level (AGL) in the form of a radiation contour map. Typical background exposure rates were found to vary from 5 to 14 microroentgens per hour (μR/h). The man-made radionuclides, cobalt-60, cesium-137, and protactinium-234m (a radioisotope indicative of depleted uranium), were detected at several facilities on the site. In support of the aerial survey, ground-based exposure rate and soil sample measurements were obtained at several locations within the survey boundary. In addition to the large scale aerial survey, two special flyovers were requested by the Department of Energy. The first request was to conduct a survey of a 1-mile x 2-mile area in south Knoxville, Tennessee. The area had been used previously to store contaminated scrap metals from operations at the Oak Ridge site. The second request was to fly several passes over a 5-mile length of railroad tracks leading from the Oak Ridge Y-12 Plant, north through the city of Oak Ridge. The railroad tracks had been previously used in the transport of cesium-137

Impact of regression methods on improved effects of soil structure on soil water retention estimates

Science.gov (United States)

Nguyen, Phuong Minh; De Pue, Jan; Le, Khoa Van; Cornelis, Wim

2015-06-01

Increasing the accuracy of pedotransfer functions (PTFs), an indirect method for predicting non-readily available soil features such as soil water retention characteristics (SWRC), is of crucial importance for large scale agro-hydrological modeling. Adding significant predictors (i.e., soil structure), and implementing more flexible regression algorithms are among the main strategies of PTFs improvement. The aim of this study was to investigate whether the improved effect of categorical soil structure information on estimating soil-water content at various matric potentials, which has been reported in literature, could be enduringly captured by regression techniques other than the usually applied linear regression. Two data mining techniques, i.e., Support Vector Machines (SVM), and k-Nearest Neighbors (kNN), which have been recently introduced as promising tools for PTF development, were utilized to test if the incorporation of soil structure will improve PTF's accuracy under a context of rather limited training data. The results show that incorporating descriptive soil structure information, i.e., massive, structured and structureless, as grouping criterion can improve the accuracy of PTFs derived by SVM approach in the range of matric potential of -6 to -33 kPa (average RMSE decreased up to 0.005 m3 m-3 after grouping, depending on matric potentials). The improvement was primarily attributed to the outperformance of SVM-PTFs calibrated on structureless soils. No improvement was obtained with kNN technique, at least not in our study in which the data set became limited in size after grouping. Since there is an impact of regression techniques on the improved effect of incorporating qualitative soil structure information, selecting a proper technique will help to maximize the combined influence of flexible regression algorithms and soil structure information on PTF accuracy.

Carbon isotopes and concentrations in mid-oceanic ridge basalts

International Nuclear Information System (INIS)

Pineau, F.; Javoy, M.

1983-01-01

In order to estimate carbon fluxes at mid-ocean ridges and carbon isotopic compositions in the convective mantle, we have studied carbon concentrations and isotopic compositions in tholeiitic glasses from the FAMOUS zone (Mid-Atlantic Ridge at 36 0 N) and East Pacific Rise from 21 0 N (RITA zone) to 20 0 S. These samples correspond essentially to the whole spectrum of spreading rates (2-16 cm/yr). The contain: -CO 2 vesicles in various quantities (3-220 ppm C) with delta 13 C between -4 and -9per mille relative to PDB, in the range of carbonatites and diamonds. - Carbonate carbon (3-100 ppm C) with delta 13 C between -2.6 and -20.0per mille relative to PDB. - Dissolved carbon at a concentration of 170+-10 ppm under 250 bar pressure with delta 13 C from -9 to -21per mille relative to PDB. This dissolved carbon, not contained in large CO 2 vesicles, corresponds to a variety of chemical forms among which part of the above carbonates, microscopic CO 2 bubbles and graphite. The lightest portions of this dissolved carbon are extracted at low temperatures (400-600 0 C) whereas the CO 2 from the vesicles is extracted near fusion temperature. These features can be explained by outgassing processes in two steps from the source region of the magma: (1) equilibrium outgassing before the second percolation threshold, where micron size bubbles are continuously reequilibrated with the magma; (2) distillation after the second percolation threshold when larger bubbles travel faster than magma concentrations to the surface. The second step may begin at different depths apparently related to the spreading rate, shallower for fast-spreading ridges than for slow-spreading ridges. (orig./WL)

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

Science.gov (United States)

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

Science.gov (United States)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of

Contextualization of Holocene beach ridge systems for relative sea-level reconstruction using the SRTM elevation model

DEFF Research Database (Denmark)

Sander, Lasse; Raniolo, Luís Ariél; Alberdi, Ernesto

2014-01-01

for the WGS84 ellipsoid. On a beach ridge plain at Caleta de los Loros, Río Negro, Argentina, we observed a good correlation of GPS-RTK (GPS-Real Time Kinematic) measurements (estimated vertical accuracy:

Robust mislabel logistic regression without modeling mislabel probabilities.

Science.gov (United States)

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Dynamical instability produces transform faults at mid-ocean ridges.

Science.gov (United States)

Gerya, Taras

2010-08-27

Transform faults at mid-ocean ridges--one of the most striking, yet enigmatic features of terrestrial plate tectonics--are considered to be the inherited product of preexisting fault structures. Ridge offsets along these faults therefore should remain constant with time. Here, numerical models suggest that transform faults are actively developing and result from dynamical instability of constructive plate boundaries, irrespective of previous structure. Boundary instability from asymmetric plate growth can spontaneously start in alternate directions along successive ridge sections; the resultant curved ridges become transform faults within a few million years. Fracture-related rheological weakening stabilizes ridge-parallel detachment faults. Offsets along the transform faults change continuously with time by asymmetric plate growth and discontinuously by ridge jumps.

Quantile regression theory and applications

CERN Document Server

Davino, Cristina; Vistocco, Domenico

2013-01-01

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and

Directly measured currents and estimated transport pathways of Atlantic Water between 59.5°N and the Iceland–Faroes–Scotland Ridge

Directory of Open Access Journals (Sweden)

Katelin H. Childers

2015-11-01

Full Text Available Using vessel-mounted acoustic Doppler current profiler data from four different routes between Scotland, Iceland and Greenland, we map out the mean flow of water in the top 400 m of the northeastern North Atlantic. The poleward transport east of the Reykjanes Ridge (RR decreases from ~8.5 to 10 Sv (1 Sverdrup=106 m3 s−1 at 59.5°N to 61°N to 6 Sv crossing the Iceland–Faroes–Scotland Ridge. The two longest ~1200 km transport integrals have 1.4–0.94 Sv uncertainty, respectively. The overall decrease in transport can in large measure be accounted for by a ~1.5 Sv flow across the RR into the Irminger Sea north of 59.5°N and by a ~0.5 Sv overflow of dense water along the Iceland–Faroes Ridge. A remaining 0.5 Sv flux divergence is at the edge of detectability, but if real could be accounted for through wintertime convection to >400 m and densification of upper ocean water. The topography of the Iceland Basin and the banks west of Scotland play a fundamental role in controlling flow pathways towards and past Iceland, the Faroes and Scotland. Most water flows north unimpeded through the Iceland Basin, some in the centre of the basin along the Maury Channel, and some along Hatton Bank, turning east along the northern slopes of George Bligh Bank, Lousy Bank and Bill Bailey's Bank, whereupon the flow splits with ~3 Sv turning northwest towards the Iceland–Faroes Ridge and the remainder continuing east towards and north of the Wyville-Thomson Ridge (WTR to the Scotland slope thereby increasing the Slope Current transport from ~1.5 Sv south of the WTR to 3.5 Sv in the Faroes–Shetland Channel.

Estimation of a Reactor Core Power Peaking Factor Using Support Vector Regression and Uncertainty Analysis

International Nuclear Information System (INIS)

Bae, In Ho; Naa, Man Gyun; Lee, Yoon Joon; Park, Goon Cherl

2009-01-01

The monitoring of detailed 3-dimensional (3D) reactor core power distribution is a prerequisite in the operation of nuclear power reactors to ensure that various safety limits imposed on the LPD and DNBR, are not violated during nuclear power reactor operation. The LPD and DNBR should be calculated in order to perform the two major functions of the core protection calculator system (CPCS) and the core operation limit supervisory system (COLSS). The LPD at the hottest part of a hot fuel rod, which is related to the power peaking factor (PPF, F q ), is more important than the LPD at any other position in a reactor core. The LPD needs to be estimated accurately to prevent nuclear fuel rods from melting. In this study, support vector regression (SVR) and uncertainty analysis have been applied to estimation of reactor core power peaking factor

Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

Science.gov (United States)

2017-03-23

Logistic Regression to Estimate the Median Will-Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle, B.S...not the other. We are able to give logistic regression models to program managers that identify several program characteristics for either...considered acceptable. We recommend the use of our logistic models as a tool to manage a portfolio of programs in order to gain potential elusive

On the use of a regression model for trend estimates from ground-based atmospheric observations in the Southern hemisphere

CSIR Research Space (South Africa)

Bencherif, H

2010-09-01

Full Text Available The present reports on the use of a multi-regression model adapted at Reunion University for temperature and ozone trend estimates. Depending on the location of the observing site, the studied geophysical signal is broken down in form of a sum...

Wrinkle Ridges and Young Fresh Crater

Science.gov (United States)

2002-01-01

(Released 10 May 2002) The Science Wrinkle ridges are a very common landform on Mars, Mercury, Venus, and the Moon. These ridges are linear to arcuate asymmetric topographic highs commonly found on smooth plains. The origin of wrinkle ridges is not certain and two leading hypotheses have been put forth by scientists over the past 40 years. The volcanic model calls for the extrusion of high viscosity lavas along linear conduits. This thick lava accumulated over these conduits and formed the ridges. The other model is tectonic and advocates that the ridges are formed by compressional faulting and folding. Today's THEMIS image is of the ridged plains of Lunae Planum located between Kasei Valles and Valles Marineris in the northern hemisphere of the planet. Wrinkle ridges are found mostly along the eastern side of the image. The broadest wrinkle ridges in this image are up to 2 km wide. A 3 km diameter young fresh crater is located near the bottom of the image. The crater's ejecta blanket is also clearly seen surrounding the sharp well-defined crater rim. These features are indicative of a very young crater that has not been subjected to erosional processes. The Story The great thing about the solar system is that planets are both alike and different. They're all foreign enough to be mysterious and intriguing, and yet familiar enough to be seen as planetary 'cousins.' By comparing them, we can learn a lot about how planets form and then evolve geologically over time. Crinkled over smooth plains, the long, wavy raised landforms seen here are called 'wrinkle ridges,' and they've been found on Mars, Mercury, Venus, and the Moon - that is, on rocky bodies that are a part of our inner solar system. We know from this observation that planets (and large-enough moons) follow similar processes. What we don't know for sure is HOW these processes work. Scientists have been trying to understand how wrinkle ridges form for 40 years, and they still haven't reached a conclusion. That

Comparison of Regression Analysis and Transfer Function in Estimating the Parameters of Central Pulse Waves from Brachial Pulse Wave.

Science.gov (United States)

Chai, Rui; Xu, Li-Sheng; Yao, Yang; Hao, Li-Ling; Qi, Lin

2017-01-01

This study analyzed ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO), and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. Invasively measured parameters were compared with parameters measured from brachial pulse waves by regression model and transfer function model. Accuracy of parameters estimated by regression and transfer function model, was compared too. Findings showed that k value, central pulse wave and brachial pulse wave parameters invasively measured, correlated positively. Regression model parameters including A_slope, DBP, SEVR, and transfer function model parameters had good consistency with parameters invasively measured. They had same effect of consistency. SBP, PP, SV, and CO could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

Exploration of the Pine Ridge Uranium Deposits, Powder River Basin, Wyoming

International Nuclear Information System (INIS)

Doelger, Mark J.; Sundell, Kent A.

2014-01-01

Summary of Exploration in Pine Ridge District: • Use of outcrop mapping integrated with oil and gas subsurface data and available well logs resulted in a geologic model for this previously unexplored area. • Proprietary drilling by Stakeholder over the past two years has confirmed the geologic model of large mineralized alteration cells in staked fluvial sandstone sequences. • The target-rich area of potential extends over nine contiguous townships where Stakeholder has leased over 70,000 acres. • Adjacent mature in-situ projects provide strong analogs and demonstrate amenability for the ore bodies at shallow, intermediate, and deep depths. • These project attributes, with discoveries by Stakeholder are expected to result in future yellow cake production with partner or successor to Stakeholder, and warrants naming this the Pine Ridge District. • Potential resource is an estimated 66 to 72 million pounds

Oak Ridge Reservation environmental report for 1989

International Nuclear Information System (INIS)

Jacobs, V.A.; Wilson, A.R.

1990-10-01

This two-volume report, the Oak Ridge Reservation Environmental Report for 1989, is the nineteenth in an annual series that began in 1971. It reports the results of a comprehensive, year-round program to monitor the impact of operations at the three major US Department of Energy (DOE) production and research installations in Oak Ridge on the immediate areas' and surrounding region's groundwater and surface waters, soil, air quality, vegetation and wildlife, and through these multiple and varied pathways, the resident human population. Information is presented for the environmental monitoring Quality Assurance (QA) Program, audits and reviews, waste management activities, land special environmental studies. Data are included for the Oak Ridge Y-12 Plant, Oak Ridge National Laboratory (ORNL), and Oak Ridge Gaseous Diffusion Plant (ORGDP). Volume 1 presents narratives, summaries, and conclusions based on environmental monitoring at the three DOE installations and in the surrounding environs during calendar year (CY) 1989. Volume 1 is intended to be a ''stand-alone'' report about the Oak Ridge Reservation (ORR) for the reader who does not want an in-depth review of 1989 data. Volume 2 presents the detailed data from which these conclusions have been drawn and should be used in conjunction with Volume 1

Oak Ridge Reservation environmental report for 1989

Energy Technology Data Exchange (ETDEWEB)

Jacobs, V.A.; Wilson, A.R. (eds.)

1990-10-01

This two-volume report, the Oak Ridge Reservation Environmental Report for 1989, is the nineteenth in an annual series that began in 1971. It reports the results of a comprehensive, year-round program to monitor the impact of operations at the three major US Department of Energy (DOE) production and research installations in Oak Ridge on the immediate areas' and surrounding region's groundwater and surface waters, soil, air quality, vegetation and wildlife, and through these multiple and varied pathways, the resident human population. Information is presented for the environmental monitoring Quality Assurance (QA) Program, audits and reviews, waste management activities, land special environmental studies. Data are included for the Oak Ridge Y-12 Plant, Oak Ridge National Laboratory (ORNL), and Oak Ridge Gaseous Diffusion Plant (ORGDP). Volume 1 presents narratives, summaries, and conclusions based on environmental monitoring at the three DOE installations and in the surrounding environs during calendar year (CY) 1989. Volume 1 is intended to be a stand-alone'' report about the Oak Ridge Reservation (ORR) for the reader who does not want an in-depth review of 1989 data. Volume 2 presents the detailed data from which these conclusions have been drawn and should be used in conjunction with Volume 1.

Hierarchical Matching and Regression with Application to Photometric Redshift Estimation

Science.gov (United States)

Murtagh, Fionn

2017-06-01

This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.

Influence diagnostics in meta-regression model.

Science.gov (United States)

Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

2017-09-01

This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

Science.gov (United States)

Shahid, Muhammad; Pandremmenou, Katerina; Kondi, Lisimachos P.; Rossholm, Andreas; Lövström, Benny

2016-09-01

Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.

Alveolar ridge rehabilitation to increase full denture retention and stability

Directory of Open Access Journals (Sweden)

Mefina Kuntjoro

2010-12-01

Full Text Available Background: Atrophic mandibular alveolar ridge generally complicates prostetic restoration expecially full denture. Low residual alveolar ridge and basal seat can cause unstable denture, permanent ulcer, pain, neuralgia, and mastication difficulty. Pre-proshetic surgery is needed to improve denture retention and stability. Augmentation is a major surgery to increase vertical height of the atrophic mandible while vestibuloplasty is aimed to increase the denture bearing area. Purpose: The augmentation and vestibuloplasty was aimed to provide stability and retentive denture atrophic mandibular alveolar ridge. Case: A 65 years old woman patient complained about uncomfortable denture. Clinical evaluate showed flat ridge in the anterior mandible, flabby tissue and candidiasis, while residual ridge height was classified into class IV. Case management: Augmentation using autograph was conducted as the mandible vertical height is less than 15 mm. Autograph was used to achieve better bone quantity and quality. Separated alveolar ridge was conducted from left to right canine region and was elevated 0.5 mm from the previous position to get new ridge in the anterior region. The separated alveolar ridge was fixated by using T-plate and ligature wire. Three months after augmentation fixation appliances was removed vestibuloplasty was performed to increase denture bearing area that can make a stable and retentive denture. Conclusion: Augmentation and vestibuloplasty can improve flat ridge to become prominent.Latar belakang: Ridge mandibula yang atrofi pada umumnya mempersulit pembuatan restorasi prostetik terutama gigi tiruan lengkap (GTL. Residual alveolar ridge dan basal seat yang rendah menyebabkan gigi tiruan menjadi tidak stabil, menimbulkan ulser permanen, nyeri, neuralgia, dan kesulitan mengunyah. Tujuan: Augmentasi dan vestibuloplasti pada ridge mandibula yang atrofi dilakukan untuk menciptakan gigi tiruan yang stabil dan retentive. Kasus: Pasien wanita

riskRegression

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....

Ridge and Furrow Fields

DEFF Research Database (Denmark)

Møller, Per Grau

2016-01-01

Ridge and furrow is a specific way of ploughing which makes fields of systematic ridges and furrows like a rubbing washboard. They are part of an overall openfield system, but the focus in this paper is on the functionality of the fields. There are many indications that agro-technological reasons...... systems and the establishment of basic structures like villages (with churches) and townships and states (in northern Europe). The fields can be considered as a resilient structure lasting for 800 years, along with the same basic physical structures in society....

Final report on the Background Soil Characterization Project at the Oak Ridge Reservation, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1993-10-01

The Background Soil Characterization Project (BSCP) will provide background concentration levels of selected metals organic compounds, and radionuclides in soils from uncontaminated on-site areas at the Oak Ridge Reservation (ORR), and off-site in the western part of Roane County and the eastern part of Anderson County. The BSCP will establish a database, recommend how to use the data for contaminated site assessment, and provide estimates of the potential human health and environmental risks associated with the background level concentrations of potentially hazardous constituents. This volume contains the data from the Background Soil Characterization Project. When available, the following validation qualifiers are used in the appendixes. When validation qualifiers are not available, the corresponding contract laboratory data qualifiers appearing on the next page are used

Background stratified Poisson regression analysis of cohort data.

Science.gov (United States)

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Study Heterogeneity and Estimation of Prevalence of Primary Aldosteronism: A Systematic Review and Meta-Regression Analysis.

Science.gov (United States)

Käyser, Sabine C; Dekkers, Tanja; Groenewoud, Hans J; van der Wilt, Gert Jan; Carel Bakx, J; van der Wel, Mark C; Hermus, Ad R; Lenders, Jacques W; Deinum, Jaap

2016-07-01

For health care planning and allocation of resources, realistic estimation of the prevalence of primary aldosteronism is necessary. Reported prevalences of primary aldosteronism are highly variable, possibly due to study heterogeneity. Our objective was to identify and explain heterogeneity in studies that aimed to establish the prevalence of primary aldosteronism in hypertensive patients. PubMed, EMBASE, Web of Science, Cochrane Library, and reference lists from January 1, 1990, to January 31, 2015, were used as data sources. Description of an adult hypertensive patient population with confirmed diagnosis of primary aldosteronism was included in this study. Dual extraction and quality assessment were the forms of data extraction. Thirty-nine studies provided data on 42 510 patients (nine studies, 5896 patients from primary care). Prevalence estimates varied from 3.2% to 12.7% in primary care and from 1% to 29.8% in referral centers. Heterogeneity was too high to establish point estimates (I(2) = 57.6% in primary care; 97.1% in referral centers). Meta-regression analysis showed higher prevalences in studies 1) published after 2000, 2) from Australia, 3) aimed at assessing prevalence of secondary hypertension, 4) that were retrospective, 5) that selected consecutive patients, and 6) not using a screening test. All studies had minor or major flaws. This study demonstrates that it is pointless to claim low or high prevalence of primary aldosteronism based on published reports. Because of the significant impact of a diagnosis of primary aldosteronism on health care resources and the necessary facilities, our findings urge for a prevalence study whose design takes into account the factors identified in the meta-regression analysis.

Geology along the Blue Ridge Parkway in Virginia

Science.gov (United States)

Carter, Mark W.; Southworth, C. Scott; Tollo, Richard P.; Merschat, Arthur J.; Wagner, Sara; Lazor, Ava; Aleinikoff, John N.

2017-01-01

Detailed geologic mapping and new SHRIMP (sensitive high-resolution ion microprobe) U-Pb zircon, Ar/Ar, Lu-Hf, 14C, luminescence (optically stimulated), thermochronology (fission-track), and palynology reveal the complex Mesoproterozoic to Quaternary geology along the ~350 km length of the Blue Ridge Parkway in Virginia. Traversing the boundary of the central and southern Appalachians, rocks along the parkway showcase the transition from the para-autochthonous Blue Ridge anticlinorium of northern and central Virginia to the allochthonous eastern Blue Ridge in southern Virginia. From mile post (MP) 0 near Waynesboro, Virginia, to ~MP 124 at Roanoke, the parkway crosses the unconformable to faulted boundary between Mesoproterozoic basement in the core of the Blue Ridge anticlinorium and Neoproterozoic to Cambrian metasedimentary and metavolcanic cover rocks on the western limb of the structure. Mesoproterozoic basement rocks comprise two groups based on SHRIMP U-Pb zircon geochronology: Group I rocks (1.2-1.14 Ga) are strongly foliated orthogneisses, and Group II rocks (1.08-1.00 Ga) are granitoids that mostly lack obvious Mesoproterozoic deformational features.Neoproterozoic to Cambrian cover rocks on the west limb of the anticlinorium include the Swift Run and Catoctin Formations, and constituent formations of the Chilhowee Group. These rocks unconformably overlie basement, or abut basement along steep reverse faults. Rocks of the Chilhowee Group are juxtaposed against Cambrian rocks of the Valley and Ridge province along southeast- and northwest-dipping, high-angle reverse faults. South of the James River (MP 64), Chilhowee Group and basement rocks occupy the hanging wall of the nearly flat-lying Blue Ridge thrust fault and associated splays.South of the Red Valley high-strain zone (MP 144.5), the parkway crosses into the wholly allochthonous eastern Blue Ridge, comprising metasedimentary and meta-igneous rocks assigned to the Wills Ridge, Ashe, and Alligator

Posterior Mandibular Ridge Resorption Associated with Different Retentive Systems for Overdentures: A 7-Year Retrospective Preliminary Study.

Science.gov (United States)

Elsyad, Moustafa Abdou; Mohamed, Shahinaz Sayed; Shawky, Ahmad Fathalla

This retrospective study compared posterior mandibular residual ridge resorption with two different retentive mechanisms for overdentures after 7 years. A convenience sample of 18 edentulous men was assigned to one of two equal groups. Two implants were placed in the mandibular canine areas for each patient using the conventional two-stage surgical protocol, and the implants were splinted with a round bar 3 months later. New mandibular overdentures were then connected to the bars with clips (clip-retained overdentures, CR group) or resilient liners (resilient liner-retained overdentures, RR group). Posterior mandibular ridge resorption (PMRR) was recorded using proportional measurements and posterior area index (PAI) on panoramic radiographs taken immediately after overdenture insertion (T 0 ) and 7 years later (T 7 ). A linear regression model was used to verify the relationship between PAI and the following considerations: attachment type, age, initial mandibular ridge height, period of mandibular edentulism, number of previously worn dentures, and relining events. After 7 years, the RR group demonstrated a significantly (P = .014) higher change in PAI (0.11 ± 0.02) than the CR group (0.06 ± 0.04). The average PMRR for each mm of posterior mandibular ridge was 0.79 mm (0.11 mm/year) in the CR group and 1.4 mm (0.2 mm/year) in the RR group. Attachment type, initial mandibular ridge height, and relining times were significantly correlated with change in the PAI (P = .004, P = .035, and P = .045, respectively). Within the limitations of this preliminary study's design, it was observed that following a 7-year period of use, resilient liner attachments for bar/implant-retained overdentures appear to be associated with greater posterior mandibular ridge resorption when compared to clip attachments.

Logistic regression for dichotomized counts.

Science.gov (United States)

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Bird mortality associated with wind turbines at the Buffalo Ridge wind resource area, Minnesota

Science.gov (United States)

Osborn, R.G.; Higgins, K.F.; Usgaard, R.E.; Dieter, C.D.; Neiger, R.D.

2000-01-01

Recent technological advances have made wind power a viable source of alternative energy production and the number of windplant facilities has increased in the United States. Construction was completed on a 73 turbine, 25 megawatt windplant on Buffalo Ridge near Lake Benton, Minnesota in Spring 1994. The number of birds killed at existing windplants in California caused concern about the potential impacts of the Buffalo Ridge facility on the avian community. From April 1994 through Dec. 1995 we searched the Buffalo Ridge windplant site for dead birds. Additionally, we evaluated search efficiency, predator scavenging rates and rate of carcass decomposition. During 20 mo of monitoring we found 12 dead birds. Collisions with wind turbines were suspected for 8 of the 12 birds. During observer efficiency trials searchers found 78.8% of carcasses. Scavengers removed 39.5% of carcasses during scavenging trials. All carcasses remained recognizable during 7 d decomposition trials. After correction for biases we estimated that approximately 36 ?? 12 birds (bird per turbine) were killed at the Buffalo Ridge windplant in 1 y. Although windplants do not appear to be more detrimental to birds than other man-made structures, proper facility sitting is an important first consideration in order to avoid unnecessary fatalities.

Estimation of the laser cutting operating cost by support vector regression methodology

Science.gov (United States)

Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

2016-09-01

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

AMPLITUDES OF DISJUNCTIVE DISLOCATIONS IN THE KNIPOVICH RIDGE FLANKS (NORTHERN ATLANTIC AS AN INDICATOR OF MODERN REGIONAL GEODYNAMICS

Directory of Open Access Journals (Sweden)

S. Yu. Sokolov

2017-01-01

Full Text Available This article presents the first map showing the vertical amplitudes of modern disjunctive dislocations inNorthern Atlantic, based on the estimated phase shifts of reflected waves recorded by high-frequency seismic acoustic surveys. The amplitude distribution pattern is mosaic with alternating areas of compression and extension in the flanks of the Knipovich rift system. The modern structure of the Knipovich Ridge, including two strike-slip faults, represents a local rift in the pull-apart setting. The asymmetry of stresses and the presence of compression in the ridge flanks is evidenced by the distribution of the focal mechanisms of strong earthquakes related to reverse faults. In the southeastern Knipovich Ridge, tectonic activity is marked by the asymmetric pattern of the epicenters of small earthquakes.

Glacial modulation of mid-ocean ridge magmatism and anomalous Pacific Antarctic Ridge volcanism during Termination II

Science.gov (United States)

Asimow, P. D.; Lewis, M.; Lund, D. C.; Seeley, E.; McCart, S.; Mudahy, A.

2017-12-01

Glacially-driven sea level rise and fall may modulate submarine volcanism by superposing pressure changes on the tectonic decompression that causes melt production in the mantle below mid-ocean ridges. A number of recent studies have considered whether this effect is recorded in the periodicity of ridge flank bathymetry (Tolstoy, 2015; Crowley et al., 2015) but interpretation of the bathymetric data remains controversial (Goff, 2016; Olive et al., 2016). We have pursued an independent approach using hydrothermal metals in well-dated near-ridge sediment cores. Along the full length of the East Pacific Rise, in areas of the ocean with widely variable biologic productivity, there are large and consistent rises in Fe, Mn, and As concentrations during the last two glacial terminations. We interpret these cores as records of excess hydrothermal flux due to delayed delivery to the axis of excess melt generated by the preceding falls in sea level. Here we discuss the potentially related discovery, in a core near the Pacific Antarctic Ridge (PAR), of a 10 cm thick layer of basaltic ash shards up to 250 mm in size, coincident with the penultimate deglaciation (Termination II). Although the site was 8 km off-axis at the time, the glasses have major element, volatile, and trace element composition consistent with more evolved members of the axial MORB suite from the nearby ridge axis. Their morphologies are typical of pyroclastic deposits created by explosive submarine volcanism (Clague et al., 2009). We propose that a period of low magmatic flux following a sea-level rise caused cooling of crustal magmatic systems, more advanced fractionation in the axial magma chamber, and increases in viscosity and volatile concentration. We hypothesize subsequent arrival of high magmatic flux during Termination II then reactivated the system and triggered an unusually vigorous series of explosive eruptions along this segment of the PAR. Ash layers recording large eruptions such as this one

Ridge Orientations of the Ridge-Forming Unit, Sinus Meridiani, Mars-A Fluvial Explanation

Science.gov (United States)

Wilkinson, M. Justin; Herridge, A.

2013-01-01

Imagery and MOLA data were used in an analysis of the ridge-forming rock unit (RFU) exposed in Sinus Meridiani (SM). This unit shows parallels at different scales with fluvial sedimentary bodies. We propose the terrestrial megafan as the prime analog for the RFU, and likely for other members of the layered units. Megafans are partial cones of fluvial sediment, with radii up to hundreds of km. Although recent reviews of hypotheses for the RFU units exclude fluvial hypotheses [1], inverted ridges in the deserts of Oman have been suggested as putative analogs for some ridges [2], apparently without appreciating The wider context in which these ridges have formed is a series of megafans [3], a relatively unappreciated geomorphic feature. It has been argued that these units conform to the megafan model at the regional, subregional and local scales [4]. At the regional scale suites of terrestrial megafans are known to cover large areas at the foot of uplands on all continents - a close parallel with the setting of the Meridiani sediments at the foot of the southern uplands of Mars, with its incised fluvial systems leading down the regional NW slope [2, 3] towards the sedimentary units. At the subregional scale the layering and internal discontinuities of the Meridiani rocks are consistent, inter alia, with stacked fluvial units [4]. Although poorly recognized as such, the prime geomorphic environment in which stream channel networks cover large areas, without intervening hillslopes, is the megafan [see e.g. 4]. Single megafans can reach 200,000 km2 [5]. Megafans thus supply an analog for areas where channel-like ridges (as a palimpsest of a prior landscape) cover the intercrater plains of Meridiani [6]. At the local, or river-reach scale, the numerous sinuous features of the RFU are suggestive of fluvial channels. Cross-cutting relationships, a common feature of channels on terrestrial megafans, are ubiquitous. Desert megafans show cemented paleo-channels as inverted

Regional regression equations for the estimation of selected monthly low-flow duration and frequency statistics at ungaged sites on streams in New Jersey

Science.gov (United States)

Watson, Kara M.; McHugh, Amy R.

2014-01-01

Regional regression equations were developed for estimating monthly flow-duration and monthly low-flow frequency statistics for ungaged streams in Coastal Plain and non-coastal regions of New Jersey for baseline and current land- and water-use conditions. The equations were developed to estimate 87 different streamflow statistics, which include the monthly 99-, 90-, 85-, 75-, 50-, and 25-percentile flow-durations of the minimum 1-day daily flow; the August–September 99-, 90-, and 75-percentile minimum 1-day daily flow; and the monthly 7-day, 10-year (M7D10Y) low-flow frequency. These 87 streamflow statistics were computed for 41 continuous-record streamflow-gaging stations (streamgages) with 20 or more years of record and 167 low-flow partial-record stations in New Jersey with 10 or more streamflow measurements. The regression analyses used to develop equations to estimate selected streamflow statistics were performed by testing the relation between flow-duration statistics and low-flow frequency statistics for 32 basin characteristics (physical characteristics, land use, surficial geology, and climate) at the 41 streamgages and 167 low-flow partial-record stations. The regression analyses determined drainage area, soil permeability, average April precipitation, average June precipitation, and percent storage (water bodies and wetlands) were the significant explanatory variables for estimating the selected flow-duration and low-flow frequency statistics. Streamflow estimates were computed for two land- and water-use conditions in New Jersey—land- and water-use during the baseline period of record (defined as the years a streamgage had little to no change in development and water use) and current land- and water-use conditions (1989–2008)—for each selected station using data collected through water year 2008. The baseline period of record is representative of a period when the basin was unaffected by change in development. The current period is

Hydrothermal plume anomalies over the southwest Indian ridge: magmatic control

Science.gov (United States)

Yue, X.; Li, H.; Tao, C.; Ren, J.; Zhou, J.; Chen, J.; Chen, S.; Wang, Y.

2017-12-01

Here we firstly reported the extensive survey results of the hydrothermal activity along the ultra-slow spreading southwest Indian ridge (SWIR). The study area is located at segment 27, between the Indomed and Gallieni transform faults, SWIR. The seismic crustal thickness reaches 9.5km in this segment (Li et al., 2015), which is much thicker than normal crustal. The anomaly thickened crust could be affected by the Crozet hotspot or highly focused melt delivery from the mantle. The Duanqiao hydrothermal field was reported at the ridge valley of the segment by Tao et al (2009). The Deep-towed Hydrothermal Detection System (DHDS) was used to collect information related with hydrothermal activity, like temperature, turbidity, oxidation-reduction potential (ORP) and seabed types. There are 15 survey lines at the interval of 2 to 3 km which are occupied about 1300 km2 in segment 27. After processing the raw data, including wiping out random noise points, 5-points moving average processing and subtracting the ambient, we got anomalous Nephelometric Turbidity Units values (ΔNTU). And dE/dt was used to identify the ORP anomalous as the raw data is easily influenced by electrode potentials drifting (Baker et al., 2016). According to the results of water column turbidity and ORP distributions, we confirmed three hydrothermal anomaly fields named A1, A2 and A3. The three fields are all located in the western part of the segment. The A1 field lies on the ridge valley, west side of Duanqiao field. The A2 and A3 field lie on the northern and southern of the ridge valley, respectively. We propose that recent magmatic activity probably focus on the western part of segment 27.And the extensive distribution of hydrothermal plume in the segment is the result of the discrete magma intrusion. References Baker E T, et al. How many vent fields? New estimates of vent field populations on ocean ridges from precise mapping of hydrothermal discharge locations. EPSL, 2016, 449:186-196. Li J

Computed statistics at streamgages, and methods for estimating low-flow frequency statistics and development of regional regression equations for estimating low-flow frequency statistics at ungaged locations in Missouri

Science.gov (United States)

Southard, Rodney E.

2013-01-01

The weather and precipitation patterns in Missouri vary considerably from year to year. In 2008, the statewide average rainfall was 57.34 inches and in 2012, the statewide average rainfall was 30.64 inches. This variability in precipitation and resulting streamflow in Missouri underlies the necessity for water managers and users to have reliable streamflow statistics and a means to compute select statistics at ungaged locations for a better understanding of water availability. Knowledge of surface-water availability is dependent on the streamflow data that have been collected and analyzed by the U.S. Geological Survey for more than 100 years at approximately 350 streamgages throughout Missouri. The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, computed streamflow statistics at streamgages through the 2010 water year, defined periods of drought and defined methods to estimate streamflow statistics at ungaged locations, and developed regional regression equations to compute selected streamflow statistics at ungaged locations. Streamflow statistics and flow durations were computed for 532 streamgages in Missouri and in neighboring States of Missouri. For streamgages with more than 10 years of record, Kendall’s tau was computed to evaluate for trends in streamflow data. If trends were detected, the variable length method was used to define the period of no trend. Water years were removed from the dataset from the beginning of the record for a streamgage until no trend was detected. Low-flow frequency statistics were then computed for the entire period of record and for the period of no trend if 10 or more years of record were available for each analysis. Three methods are presented for computing selected streamflow statistics at ungaged locations. The first method uses power curve equations developed for 28 selected streams in Missouri and neighboring States that have multiple streamgages on the same streams. Statistical

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

Science.gov (United States)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

Penalized estimation for competing risks regression with applications to high-dimensional covariates

DEFF Research Database (Denmark)

Ambrogi, Federico; Scheike, Thomas H.

2016-01-01

of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95: (1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily...... Research 19: (1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25: (7), 890-896). The aim of this work is to consider how to do penalized regression in the presence...... implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according...

Aerodynamic roughness length related to non-aggregated tillage ridges

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M. Kardous

2005-11-01

Full Text Available Wind erosion in agricultural soils is dependent, in part, on the aerodynamic roughness length (z₀ produced by tillage ridges. Although previous studies have related z₀ to ridge characteristics (ridge height (RH and spacing (RS, these relationships have not been tested for tillage ridges observed in the North African agricultural fields. In these regions, due to climate and soil conditions, small plowing tools are largely used. Most of these tools produce non-aggregated and closely-spaced small ridges. Thus, experiments were conducted in a 7-m long wind tunnel to measure z₀ for 11 ridge types covering the range of geometric characteristics frequently observed in south Tunisia. Experimental results suggest that RH²/RS is the first order parameter controlling z₀. A strong relationship between z₀ and RH²/RS is proposed for a wide range of ridge characteristics.

Estimating exposure of terrestrial wildlife to contaminants

Energy Technology Data Exchange (ETDEWEB)

Sample, B.E.; Suter, G.W. II

1994-09-01

This report describes generalized models for the estimation of contaminant exposure experienced by wildlife on the Oak Ridge Reservation. The primary exposure pathway considered is oral ingestion, e.g. the consumption of contaminated food, water, or soil. Exposure through dermal absorption and inhalation are special cases and are not considered hereIN. Because wildlife mobile and generally consume diverse diets and because environmental contamination is not spatial homogeneous, factors to account for variation in diet, movement, and contaminant distribution have been incorporated into the models. To facilitate the use and application of the models, life history parameters necessary to estimate exposure are summarized for 15 common wildlife species. Finally, to display the application of the models, exposure estimates were calculated for four species using data from a source operable unit on the Oak Ridge Reservation.

Estimating exposure of terrestrial wildlife to contaminants

International Nuclear Information System (INIS)

Sample, B.E.; Suter, G.W. II.

1994-09-01

This report describes generalized models for the estimation of contaminant exposure experienced by wildlife on the Oak Ridge Reservation. The primary exposure pathway considered is oral ingestion, e.g. the consumption of contaminated food, water, or soil. Exposure through dermal absorption and inhalation are special cases and are not considered hereIN. Because wildlife mobile and generally consume diverse diets and because environmental contamination is not spatial homogeneous, factors to account for variation in diet, movement, and contaminant distribution have been incorporated into the models. To facilitate the use and application of the models, life history parameters necessary to estimate exposure are summarized for 15 common wildlife species. Finally, to display the application of the models, exposure estimates were calculated for four species using data from a source operable unit on the Oak Ridge Reservation

Background stratified Poisson regression analysis of cohort data

International Nuclear Information System (INIS)

Richardson, David B.; Langholz, Bryan

2012-01-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

Directory of Open Access Journals (Sweden)

Maria Gabriela Campolina Diniz Peixoto

2014-05-01

Full Text Available The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524 of test-day milk yield (TDMY from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects, whereas the contemporary group, calving age (linear and quadratic effects and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Calendar year 1995 groundwater quality report for the Chestnut Ridge Hydrogeological Regime, Oak Ridge Y-12 Plant, Oak Ridge, Tennessee. 1995 Groundwater quality data and calculated rate of contaminant migration

International Nuclear Information System (INIS)

1996-02-01

This annual groundwater quality report (GWQR) contains groundwater quality data obtained during the 1995 calendar year (CY) at several hazardous and nonhazardous waste management facilities associated with the U.S. Department of Energy (DOE) Y-12 Plant located on the DOE Oak Ridge Reservation (ORR) southeast of Oak Ridge, Tennessee. These sites are located south of the Y-12 Plant in the Chestnut Ridge Hydrogeologic Regime (Chestnut Ridge Regime), which is one of three regimes defined for the purposes of groundwater quality monitoring at the Y-12 Plant. The Environmental Management Department of the Y-12 Plant Health, Safety, Environment, and Accountability (HSEA) Organization manages the groundwater monitoring activities in each regime as part of the Y-12 Plant Groundwater Protection Program (GWPP). The U.S. Environmental Protection Agency (EPA) identification number for the Y-12 Plant is TN

A Case Report of Ridge Augmentation using Onlay Interpositional Graft: An Approach to Improve Prosthetic Prognosis of a Deficit Ridge

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Devanand Shetty

2014-01-01

Full Text Available Background: Periodontal therapy has developed beyond the scope of the treatment of periodontal pathoses. Periodontal plastic surgery consists of the reconstructive procedures designed to enhance the both function and esthetics. Deficient ridges pose a severe problem to the restorative dentist in restoring the natural form, function and esthetics of the prosthesis replacing the natural dentition. Depending upon the severity, location of these defects and the prosthetic option chosen, hard and soft tissue ridge augmentation or non-surgical approach or a combination may help to address them. The present clinical report describes a soft tissue ridge augmentation of a localized ridge defect in maxillary aesthetic region using onlay interpositional graft followed by fixed partial denture.

Evaluation of Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) for Water Quality Monitoring: A Case Study for the Estimation of Salinity

Science.gov (United States)

Nazeer, Majid; Bilal, Muhammad

2018-04-01

Landsat-5 Thematic Mapper (TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square (OLS) regression and Geographically Weighted Regression (GWR) based on in situ data of October 2009. Results show that the coefficient of determination ( R 2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher ( R 2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay (north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant (32 practical salinity units) towards the open sea.

ENVIRONMENTAL BASELINE SURVEY REPORT FOR WEST BLACK OAK RIDGE, EAST BLACK OAK RIDGE, MCKINNEY RIDGE, WEST PINE RIDGE, AND PARCEL 21D IN THE VICINITY OF THE EAST TENNESSEE TECHNOLOGY PARK, OAK RIDGE, TENNESSEE

Energy Technology Data Exchange (ETDEWEB)

David A. King

2012-11-29

This environmental baseline survey (EBS) report documents the baseline environmental conditions of five land parcels located near the U.S. Department of Energy’s (DOE’s) East Tennessee Technology Park (ETTP), including West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge, and Parcel 21d. The goal is to obtain all media no-further-investigation (NFI) determinations for the subject parcels considering existing soils. To augment the existing soils-only NFI determinations, samples of groundwater, surface water, soil, and sediment were collected to support all media NFI decisions. The only updates presented here are those that were made after the original issuance of the NFI documents. In the subject parcel where the soils NFI determination was not completed for approval (Parcel 21d), the full process has been performed to address the soils as well. Preparation of this report included the detailed search of federal government records, title documents, aerial photos that may reflect prior uses, and visual inspections of the property and adjacent properties. Interviews with current employees involved in, or familiar with, operations on the real property were also conducted to identify any areas on the property where hazardous substances and petroleum products, or their derivatives, and acutely hazardous wastes may have been released or disposed. In addition, a search was made of reasonably obtainable federal, state, and local government records of each adjacent facility where there has been a release of any hazardous substance or any petroleum product or their derivatives, including aviation fuel and motor oil, and which is likely to cause or contribute to a release of any hazardous substance or any petroleum product or its derivatives, including aviation fuel or motor oil, on the real property. A radiological survey and soil/sediment sampling was conducted to assess baseline conditions of Parcel 21d that were not addressed by the soils-only NFI

Effects of Cocos Ridge Collision on the Western Caribbean: Is there a Panama Block?

Science.gov (United States)

Kobayashi, D.; La Femina, P. C.; Geirsson, H.; Chichaco, E.; Abrego M, A. A.; Fisher, D. M.; Camacho, E. I.

2011-12-01

earthquake relocation and geologic data are used to define block boundaries and fault geometries. We invert the three-dimensional GPS velocity vectors and earthquake slip vectors to estimate the magnitude and spatial distribution of interplate mechanical coupling on active plate and block boundaries around the Panama block; the Middle America Trench - South Panama Deformed Belt, the Central Costa Rican Deformed Belt, and the North Panama Deformed Belt in particular, and the rates of relative plate motion between the Panama block and the adjacent Cocos, Nazca, and Caribbean plates. This study tests whether the Panama block responds to the ridge collision as a rigid tectonic block or as a deforming zone consisting of multiple blocks.

Fragility estimation for seismically isolated nuclear structures by high confidence low probability of failure values and bi-linear regression

International Nuclear Information System (INIS)

Carausu, A.

1996-01-01

A method for the fragility estimation of seismically isolated nuclear power plant structure is proposed. The relationship between the ground motion intensity parameter (e.g. peak ground velocity or peak ground acceleration) and the response of isolated structures is expressed in terms of a bi-linear regression line, whose coefficients are estimated by the least-square method in terms of available data on seismic input and structural response. The notion of high confidence low probability of failure (HCLPF) value is also used for deriving compound fragility curves for coupled subsystems. (orig.)

Sosyal Bilimlerde Yanlı Regresyon Tahmin Edicilerinin Kullanılması

Directory of Open Access Journals (Sweden)

Orkun ÇOŞKUNTUNCEL

2010-12-01

Full Text Available Regression analysis is a statistical technique for investigating and modeling the relationship between variables and this technique occur in almost every field. Least squares estimation which known classical method is most useable regression coefficients estimation in regression analysis. However this method is very sensitive to outliers and multicollinearity in the data. The aim of this study is to propose biased regression methods (Ridge regression and Liu estimator for the model parameter of a regression models that can combat with the multicollinearity in social science

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

Directory of Open Access Journals (Sweden)

Gascón Adrià

2017-10-01

Full Text Available We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013, and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

A deep structural ridge beneath central India

Science.gov (United States)

Agrawal, P. K.; Thakur, N. K.; Negi, J. G.

A joint-inversion of magnetic satellite (MAGSAT) and free air gravity data has been conducted to quantitatively investigate the cause for Bouguer gravity anomaly over Central Indian plateaus and possible fold consequences beside Himalayan zone in the Indian sub-continent due to collision between Indian and Eurasian plates. The appropriate inversion with 40 km crustal depth model has delineated after discriminating high density and magnetisation models, for the first time, about 1500 km long hidden ridge structure trending NW-SE. The structure is parallel to Himalayan fold axis and the Indian Ocean ridge in the Arabian Sea. A quantitative relief model across a representative anomaly profile confirms the ridge structure with its highest point nearly 6 km higher than the surrounding crustal level in peninsular India. The ridge structure finds visible support from the astro-geoidal contours.

A gentle introduction to quantile regression for ecologists

Science.gov (United States)

Cade, B.S.; Noon, B.R.

2003-01-01

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

SEPARATION PHENOMENA LOGISTIC REGRESSION

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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.

Metallogenesis along the Indian Ocean Ridge System

Digital Repository Service at National Institute of Oceanography (India)

Banerjee, R.; Ray, Dwijesh

including India. Among these studies majority were concentrated around the Central Indian Ridge and the Southwest Indian Ridge areas, while a few observations were made around the rest of the areas in the IORS. The findings of these studies are discussed...

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

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

Ridge Waveguide Structures in Magnesium-Doped Lithium Niobate

Science.gov (United States)

Himmer, Phillip; Battle, Philip; Suckow, William; Switzer, Greg

2011-01-01

This work proposes to establish the feasibility of fabricating isolated ridge waveguides in 5% MgO:LN. Ridge waveguides in MgO:LN will significantly improve power handling and conversion efficiency, increase photonic component integration, and be well suited to spacebased applications. The key innovation in this effort is to combine recently available large, high-photorefractive-damage-threshold, z-cut 5% MgO:LN with novel ridge fabrication techniques to achieve high-optical power, low-cost, high-volume manufacturing of frequency conversion structures. The proposed ridge waveguide structure should maintain the characteristics of the periodically poled bulk substrate, allowing for the efficient frequency conversion typical of waveguides and the high optical damage threshold and long lifetimes typical of the 5% doped bulk substrate. The low cost and large area of 5% MgO:LN wafers, and the improved performance of the proposed ridge waveguide structure, will enhance existing measurement capabilities as well as reduce the resources required to achieve high-performance specifications. The purpose of the ridge waveguides in MgO:LN is to provide platform technology that will improve optical power handling and conversion efficiency compared to existing waveguide technology. The proposed ridge waveguide is produced using standard microfabrication techniques. The approach is enabled by recent advances in inductively coupled plasma etchers and chemical mechanical planarization techniques. In conjunction with wafer bonding, this fabrication methodology can be used to create arbitrarily shaped waveguides allowing complex optical circuits to be engineered in nonlinear optical materials such as magnesium doped lithium niobate. Researchers here have identified NLO (nonlinear optical) ridge waveguide structures as having suitable value to be the leading frequency conversion structures. Its value is based on having the low-cost fabrication necessary to satisfy the challenging pricing

Estimation of residual stress in welding of dissimilar metals at nuclear power plants using cascaded support vetor regression

Energy Technology Data Exchange (ETDEWEB)

Koo, Young Do; Yoo, Kwae Hwan; Na, Man Gyun [Dept. of Nuclear Engineering, Chosun University, Gwangju (Korea, Republic of)

2017-06-15

Residual stress is a critical element in determining the integrity of parts and the lifetime of welded structures. It is necessary to estimate the residual stress of a welding zone because residual stress is a major reason for the generation of primary water stress corrosion cracking in nuclear power plants. That is, it is necessary to estimate the distribution of the residual stress in welding of dissimilar metals under manifold welding conditions. In this study, a cascaded support vector regression (CSVR) model was presented to estimate the residual stress of a welding zone. The CSVR model was serially and consecutively structured in terms of SVR modules. Using numerical data obtained from finite element analysis by a subtractive clustering method, learning data that explained the characteristic behavior of the residual stress of a welding zone were selected to optimize the proposed model. The results suggest that the CSVR model yielded a better estimation performance when compared with a classic SVR model.

Anatomy of a shoreface sand ridge revisted using foraminifera: False Cape Shoals, Virginia/North Carolina inner shelf

Science.gov (United States)

Robinson, Marci M.; McBride, Randolph A.

2008-01-01

Certain details regarding the origin and evolution of shelf sand ridges remain elusive. Knowledge of their internal stratigraphy and microfossil distribution is necessary to define the origin and to determine the processes that modify sand ridges. Fourteen vibracores from False Cape Shoal A, a well-developed shoreface-attached sand ridge on the Virginia/North Carolina inner continental shelf, were examined to document the internal stratigraphy and benthic foraminiferal assemblages, as well as to reconstruct the depositional environments recorded in down-core sediments. Seven sedimentary and foraminiferal facies correspond to the following stratigraphic units: fossiliferous silt, barren sand, clay to sandy clay, laminated and bioturbated sand, poorly sorted massive sand, fine clean sand, and poorly sorted clay to gravel. The units represent a Pleistocene estuary and shoreface, a Holocene estuary, ebb tidal delta, modern shelf, modern shoreface, and swale fill, respectively. The succession of depositional environments reflects a Pleistocene sea-level highstand and subsequent regression followed by the Holocene transgression in which barrier island/spit systems formed along the Virginia/North Carolina inner shelf not, vert, ~5.2 ka and migrated landward and an ebb tidal delta that was deposited, reworked, and covered by shelf sand.

Estimated prevalence of halitosis: a systematic review and meta-regression analysis.

Science.gov (United States)

Silva, Manuela F; Leite, Fábio R M; Ferreira, Larissa B; Pola, Natália M; Scannapieco, Frank A; Demarco, Flávio F; Nascimento, Gustavo G

2018-01-01

This study aims to conduct a systematic review to determine the prevalence of halitosis in adolescents and adults. Electronic searches were performed using four different databases without restrictions: PubMed, Scopus, Web of Science, and SciELO. Population-based observational studies that provided data about the prevalence of halitosis in adolescents and adults were included. Additionally, meta-analyses, meta-regression, and sensitivity analyses were conducted to synthesize the evidence. A total of 584 articles were initially found and considered for title and abstract evaluation. Thirteen articles met inclusion criteria. The combined prevalence of halitosis was found to be 31.8% (95% CI 24.6-39.0%). Methodological aspects such as the year of publication and the socioeconomic status of the country where the study was conducted seemed to influence the prevalence of halitosis. Our results demonstrated that the estimated prevalence of halitosis was 31.8%, with high heterogeneity between studies. The results suggest a worldwide trend towards a rise in halitosis prevalence. Given the high prevalence of halitosis and its complex etiology, dental professionals should be aware of their roles in halitosis prevention and treatment.

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

Science.gov (United States)

Yang, Duo; Zhang, Xu; Pan, Rui; Wang, Yujie; Chen, Zonghai

2018-04-01

The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In order to provide an accurate and reliable SOH estimation, a novel Gaussian process regression (GPR) model based on charging curve is proposed in this paper. Different from other researches where SOH is commonly estimated by cycle life, in this work four specific parameters extracted from charging curves are used as inputs of the GPR model instead of cycle numbers. These parameters can reflect the battery aging phenomenon from different angles. The grey relational analysis method is applied to analyze the relational grade between selected features and SOH. On the other hand, some adjustments are made in the proposed GPR model. Covariance function design and the similarity measurement of input variables are modified so as to improve the SOH estimate accuracy and adapt to the case of multidimensional input. Several aging data from NASA data repository are used for demonstrating the estimation effect by the proposed method. Results show that the proposed method has high SOH estimation accuracy. Besides, a battery with dynamic discharging profile is used to verify the robustness and reliability of this method.

Design assessment for the Bethel Valley FFA Upgrades at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1995-09-01

This report describes the proposed upgrades to Building 3025 and the Evaporator Area at Oak Ridge National Laboratory. Design assessments, specifications and drawings are provided. Building 3025 is a general purpose research facility utilized by the Materials and Ceramics Division to conduct research on irradiated materials. The Evaporator Area, building 2531, serves as the collection point for all low-level liquid wastes generated at the Oak Ridge National Laboratory

Estimation of snowpack matching ground-truth data and MODIS satellite-based observations by using regression kriging

Science.gov (United States)

Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Pulido-Velazquez, David

2016-04-01

The estimation of Snow Water Equivalent (SWE) is essential for an appropriate assessment of the available water resources in Alpine catchment. The hydrologic regime in these areas is dominated by the storage of water in the snowpack, which is discharged to rivers throughout the melt season. An accurate estimation of the resources will be necessary for an appropriate analysis of the system operation alternatives using basin scale management models. In order to obtain an appropriate estimation of the SWE we need to know the spatial distribution snowpack and snow density within the Snow Cover Area (SCA). Data for these snow variables can be extracted from in-situ point measurements and air-borne/space-borne remote sensing observations. Different interpolation and simulation techniques have been employed for the estimation of the cited variables. In this paper we propose to estimate snowpack from a reduced number of ground-truth data (1 or 2 campaigns per year with 23 observation point from 2000-2014) and MODIS satellite-based observations in the Sierra Nevada Mountain (Southern Spain). Regression based methodologies has been used to study snowpack distribution using different kind of explicative variables: geographic, topographic, climatic. 40 explicative variables were considered: the longitude, latitude, altitude, slope, eastness, northness, radiation, maximum upwind slope and some mathematical transformation of each of them [Ln(v), (v)^-1; (v)^2; (v)^0.5). Eight different structure of regression models have been tested (combining 1, 2, 3 or 4 explicative variables). Y=B0+B1Xi (1); Y=B0+B1XiXj (2); Y=B0+B1Xi+B2Xj (3); Y=B0+B1Xi+B2XjXl (4); Y=B0+B1XiXk+B2XjXl (5); Y=B0+B1Xi+B2Xj+B3Xl (6); Y=B0+B1Xi+B2Xj+B3XlXk (7); Y=B0+B1Xi+B2Xj+B3Xl+B4Xk (8). Where: Y is the snow depth; (Xi, Xj, Xl, Xk) are the prediction variables (any of the 40 variables); (B0, B1, B2, B3) are the coefficients to be estimated. The ground data are employed to calibrate the multiple regressions. In

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

Science.gov (United States)

van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

2017-03-21

Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Geomorphological investigation of multiphase glacitectonic composite ridge systems in Svalbard

Science.gov (United States)

Lovell, Harold; Benn, Douglas I.; Lukas, Sven; Spagnolo, Matteo; Cook, Simon J.; Swift, Darrel A.; Clark, Chris D.; Yde, Jacob C.; Watts, Tom

2018-01-01

Some surge-type glaciers on the High-Arctic archipelago of Svalbard have large glacitectonic composite ridge systems at their terrestrial margins. These have formed by rapid glacier advance into proglacial sediments during the active surge phase, creating multicrested moraine complexes. Such complexes can be formed during single surge advances or multiple surges to successively less-extensive positions. The few existing studies of composite ridge systems have largely relied on detailed information on internal structure and sedimentology to reconstruct their formation and links to surge processes. However, natural exposures of internal structure are commonly unavailable, and the creation of artificial exposures is often problematic in fragile Arctic environments. To compensate for these issues, we investigate the potential for reconstructing composite ridge system formation based on geomorphological evidence alone, focusing on clear morphostratigraphic relationships between ridges within the moraine complex and relict meltwater channels/outwash fans. Based on mapping at the margins of Finsterwalderbreen (in Van Keulenfjorden) and Grønfjordbreen (in Grønfjorden), we show that relict meltwater channels that breach outer parts of the composite ridge systems are in most cases truncated upstream within the ridge complex by an inner pushed ridge or ridges at their ice-proximal extents. Our interpretation of this relationship is that the entire composite ridge system is unlikely to have formed during the same glacier advance but is instead the product of multiple advances to successively less-extensive positions, whereby younger ridges are emplaced on the ice-proximal side of older ridges. This indicates that the Finsterwalderbreen composite ridge system has been formed by multiple separate advances, consistent with the cyclicity of surges. Being able to identify the frequency and magnitude of former surges is important as it provides insight into the past behaviour of

Oak Ridge reservation land-use plan

Energy Technology Data Exchange (ETDEWEB)

Bibb, W. R.; Hardin, T. H.; Hawkins, C. C.; Johnson, W. A.; Peitzsch, F. C.; Scott, T. H.; Theisen, M. R.; Tuck, S. C.

1980-03-01

This study establishes a basis for long-range land-use planning to accommodate both present and projected DOE program requirements in Oak Ridge. In addition to technological requirements, this land-use plan incorporates in-depth ecological concepts that recognize multiple uses of land as a viable option. Neither environmental research nor technological operations need to be mutually exclusive in all instances. Unique biological areas, as well as rare and endangered species, need to be protected, and human and environmental health and safety must be maintained. The plan is based on the concept that the primary use of DOE land resources must be to implement the overall DOE mission in Oak Ridge. This document, along with the base map and overlay maps, provides a reasonably detailed description of the DOE Oak Ridge land resources and of the current and potential uses of the land. A description of the land characteristics, including geomorphology, agricultural productivity and soils, water courses, vegetation, and terrestrial and aquatic animal habitats, is presented to serve as a resource document. Essentially all DOE land in the Oak Ridge area is being fully used for ongoing DOE programs or has been set aside as protected areas.

Calendar year 1993 groundwater quality report for the Chestnut Ridge Hydrogeologic Regime, Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1994-02-01

This annual groundwater report contains groundwater quality data obtained during the 1993 calendar year (CY) at several hazardous and non-hazardous waste-management facilities associated with the US Department of Energy (DOE) Y-12 Plant located on the DOE Oak Ridge Reservation (ORR) southeast of Oak Ridge, Tennessee. These sites are located south of the Y-12 Plant in the Chestnut Ridge Hydrogeologic Regime (Chestnut Ridge Regime), which is one of three regimes defined for the purposes of groundwater quality monitoring at the Y-12 Plant. The Environmental Management Department of the Y-12 Plant Health, Safety, Environment, and Accountability Organization manages the groundwater monitoring activities in each regime as part of the Y-12 Plant Groundwater Protection Program (GWPP). The annual groundwater report for the Chestnut Ridge Regime is completed in two-parts; Part 1 (this report) containing the groundwater quality data and Part 2 containing a detailed evaluation of the data. The primary purpose of this report is to serve as a reference for the groundwater quality data obtained each year under the lead of the Y-12 Plant GWPP. However, because it contains information needed to comply with Resource Conservation and Recovery Act (RCRA) interim status assessment monitoring and reporting requirements, this report is submitted to the Tennessee Department of Health and Environment (TDEC) by the RCRA reporting deadline

Regression Discontinuity and Randomized Controlled Trial Estimates: An Application to The Mycotic Ulcer Treatment Trials.

Science.gov (United States)

Oldenburg, Catherine E; Venkatesh Prajna, N; Krishnan, Tiruvengada; Rajaraman, Revathi; Srinivasan, Muthiah; Ray, Kathryn J; O'Brien, Kieran S; Glymour, M Maria; Porco, Travis C; Acharya, Nisha R; Rose-Nussbaumer, Jennifer; Lietman, Thomas M

2018-08-01

We compare results from regression discontinuity (RD) analysis to primary results of a randomized controlled trial (RCT) utilizing data from two contemporaneous RCTs for treatment of fungal corneal ulcers. Patients were enrolled in the Mycotic Ulcer Treatment Trials I and II (MUTT I & MUTT II) based on baseline visual acuity: patients with acuity ≤ 20/400 (logMAR 1.3) enrolled in MUTT I, and >20/400 in MUTT II. MUTT I investigated the effect of topical natamycin versus voriconazole on best spectacle-corrected visual acuity. MUTT II investigated the effect of topical voriconazole plus placebo versus topical voriconazole plus oral voriconazole. We compared the RD estimate (natamycin arm of MUTT I [N = 162] versus placebo arm of MUTT II [N = 54]) to the RCT estimate from MUTT I (topical natamycin [N = 162] versus topical voriconazole [N = 161]). In the RD, patients receiving natamycin had mean improvement of 4-lines of visual acuity at 3 months (logMAR -0.39, 95% CI: -0.61, -0.17) compared to topical voriconazole plus placebo, and 2-lines in the RCT (logMAR -0.18, 95% CI: -0.30, -0.05) compared to topical voriconazole. The RD and RCT estimates were similar, although the RD design overestimated effects compared to the RCT.

Linear regression in astronomy. I

Science.gov (United States)

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.

Mixed Frequency Data Sampling Regression Models: The R Package midasr

Directory of Open Access Journals (Sweden)

Eric Ghysels

2016-08-01

Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

Directory of Open Access Journals (Sweden)

Guoqi Qian

2016-01-01

Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.

Oak Ridge low-level waste disposal facility designs

International Nuclear Information System (INIS)

Van Hoesen, S.D.; Jones, L.S.

1991-01-01

The strategic planning process that culuminates in the identification, selection, construction, and ultimate operation of treatment, storage, and disposal facilities for all types of low-level waste (LLW) generated on the Oak Ridge Reservation (ORR) was conducted under the Low-Level Waste Disposal Development and Demonstration (LLWDDD) Program. This program considered management of various concentrations of short half-life radionuclides generated principally at Oak Ridge National Laboratory (ORNL) and long half-life radionuclides (principally uranium) generated at the Oak Ridge Y-12 Plant and the Oak Ridge K-25 Plant. The LLWDDD Program is still ongoing and involves four phases: (1) alternative identification and evaluation, (2) technology demonstration, (3) limited operational implementation, and (4) full operational implementation. This document provides a discussion of these phases

Lateral ridge split and immediate implant placement in moderately resorbed alveolar ridges: How much is the added width?

Directory of Open Access Journals (Sweden)

Amin Rahpeyma

2013-01-01

Full Text Available Background: Lateral ridge split technique is a way to solve the problem of the width in narrow ridges with adequate height. Simultaneous insertion of dental implants will considerably reduce the edentulism time. Materials and Methods: Twenty-five patients who were managed with ridge splitting technique were enrolled. Thirty-eight locations in both jaws with near equal distribution in quadrants received 82 dental fixtures. Beta Tricalcium phosphate (Cerasorb® was used as biomaterial to fill the intercortical space. Submerged implants were used and 3 months later healing caps were placed. Direct bone measurements before and after split were done with a Collis. Patients were clinically re-evaluated at least 6 months after implant loading. All the data were analyzed by Statistical Package for Social Sciences (SPSS software version 11.5 (SPSS Inc, Chicago Illinois, USA. Frequency of edentulous spaces and pre/post operative bone width was analyzed. Paired t-test was used for statistical analysis. Difference was considered significant if P value was less than 0.05. Results: Mean value for presplit width was 3.2 ± 0.34 mm while post-split mean width was 5.57 ± 0.49 mm. Mean gain in crest ridge after ridge splitting was 2 ± 0.3 mm. Statistical analysis showed significant differences in width before and after operation ((P < 0.05. All implants (n = 82 survived and were in full function at follow up (at least 6 months after implant loading. Conclusion: Ridge splitting technique in both jaws showed the predictable outcomes, if appropriate cases selected and special attention paid to details; then the waiting time between surgery and beginning of prosthodontic treatment can be reduced to 3 month.

Remedial investigation work plan for Chestnut Ridge Operable Unit 4 (Rogers Quarry/Lower McCoy Branch) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

1993-09-01

The Oak Ridge Y-12 Plant includes - 800 acres near the northeast comer of the reservation and adjacent to the city of Oak Ridge (Fig. 1-1). The plant is a manufacturing and developmental engineering facility that produced components for various nuclear weapons systems and provides engineering support to other Energy Systems facilities. More than 200 contaminated sites have been identified at the Y-12 Plant that resulted from past waste management practices. Many of the sites have operable units (OUs) based on priority and on investigative and remediation requirements. This Remedial Investigation RI work plan specifically addresses Chestnut Ridge OU 4. Chestnut Ridge OU 4 consists of Rogers Quarry and Lower McCoy Branch (MCB). Rogers Quarry, which is also known as Old Rogers Quarry or Bethel Valley Quarry was used for quarrying from the late 1940s or early 1950s until about 1960. Since that time, the quarry has been used for disposal of coal ash and materials from Y-12 production operations, including classified materials. Disposal of coal ash ended in July 1993. An RI is being conducted at this site in response to CERCLA regulations. The overall objectives of the RI are to collect data necessary to evaluate the nature and extent of contaminants of concern, support an Ecological Risk Assessment and a Human Health Risk Assessment, support the evaluation of remedial alternatives, and ultimately develop a Record of Decision for the site. The purpose of this work plan is to outline RI activities necessary to define the nature and extent of suspected contaminants at Chestnut Ridge OU 4. Potential migration pathways also will be investigated. Data collected during the RI will be used to evaluate the risk posed to human health and the environment by OU 4.

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit; Genton, Marc G.

2017-01-01

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit

2017-04-05

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Linear regression and the normality assumption.

Science.gov (United States)

Schmidt, Amand F; Finan, Chris

2017-12-16

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

Orientationally ordered ridge structures of aluminum films on hydrogen terminated silicon

DEFF Research Database (Denmark)

Quaade, Ulrich; Pantleon, Karen

2006-01-01

Films of aluminum deposited onto Si(100) substrates show a surface structure of parallel ridges. On films deposited on oxidized silicon substrates the direction of the ridges is arbitrary, but on films deposited on hydrogen-terminated Si(100) the ridges are oriented parallel to the < 110 > direct......Films of aluminum deposited onto Si(100) substrates show a surface structure of parallel ridges. On films deposited on oxidized silicon substrates the direction of the ridges is arbitrary, but on films deposited on hydrogen-terminated Si(100) the ridges are oriented parallel to the ... > directions on the silicon substrate. The ridge structure appears when the film thickness is above 500 nm, and increasing the film thickness makes the structure more distinct. Anodic oxidation enhances the structure even further. X-ray diffraction indicates that grains in the film have mostly (110) facets...

Development and Application of Watershed Regressions for Pesticides (WARP) for Estimating Atrazine Concentration Distributions in Streams

Science.gov (United States)

Larson, Steven J.; Crawford, Charles G.; Gilliom, Robert J.

2004-01-01

Regression models were developed for predicting atrazine concentration distributions in rivers and streams, using the Watershed Regressions for Pesticides (WARP) methodology. Separate regression equations were derived for each of nine percentiles of the annual distribution of atrazine concentrations and for the annual time-weighted mean atrazine concentration. In addition, seasonal models were developed for two specific periods of the year--the high season, when the highest atrazine concentrations are expected in streams, and the low season, when concentrations are expected to be low or undetectable. Various nationally available watershed parameters were used as explanatory variables, including atrazine use intensity, soil characteristics, hydrologic parameters, climate and weather variables, land use, and agricultural management practices. Concentration data from 112 river and stream stations sampled as part of the U.S. Geological Survey's National Water-Quality Assessment and National Stream Quality Accounting Network Programs were used for computing the concentration percentiles and mean concentrations used as the response variables in regression models. Tobit regression methods, using maximum likelihood estimation, were used for developing the models because some of the concentration values used for the response variables were censored (reported as less than a detection threshold). Data from 26 stations not used for model development were used for model validation. The annual models accounted for 62 to 77 percent of the variability in concentrations among the 112 model development stations. Atrazine use intensity (the amount of atrazine used in the watershed divided by watershed area) was the most important explanatory variable in all models, but additional watershed parameters significantly increased the amount of variability explained by the models. Predicted concentrations from all 10 models were within a factor of 10 of the observed concentrations at most

Estimating Engineering and Manufacturing Development Cost Risk Using Logistic and Multiple Regression

National Research Council Canada - National Science Library

Bielecki, John

2003-01-01

.... Previous research has demonstrated the use of a two-step logistic and multiple regression methodology to predicting cost growth produces desirable results versus traditional single-step regression...

Bias in regression coefficient estimates upon different treatments of ...

African Journals Online (AJOL)

MS and PW consistently overestimated the population parameter. EM and RI, on the other hand, tended to consistently underestimate the population parameter under non-monotonic pattern. Keywords: Missing data, bias, regression, percent missing, non-normality, missing pattern > East African Journal of Statistics Vol.

A wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry

Science.gov (United States)

Wang, Jianhua; Yang, Yanxi

2018-05-01

We present a new wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry (2-D WTP). First of all, the maximum value point is extracted from two-dimensional wavelet transform coefficient modulus, and the local extreme value points over 90% of maximum value are also obtained, they both constitute wavelet ridge candidates. Then, the gradient of rotate factor is introduced into the Abid's cost function, and the logarithmic Logistic model is used to adjust and improve the cost function weights so as to obtain more reasonable value estimation. At last, the dynamic programming method is used to accurately find the optimal wavelet ridge, and the wrapped phase can be obtained by extracting the phase at the ridge. Its advantage is that, the fringe pattern with low signal-to-noise ratio can be demodulated accurately, and its noise immunity will be better. Meanwhile, only one fringe pattern is needed to projected to measured object, so dynamic three-dimensional (3-D) measurement in harsh environment can be realized. Computer simulation and experimental results show that, for the fringe pattern with noise pollution, the 3-D surface recovery accuracy by the proposed algorithm is increased. In addition, the demodulation phase accuracy of Morlet, Fan and Cauchy mother wavelets are compared.

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

Science.gov (United States)

Granato, Gregory E.

2006-01-01

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

Tectonics of ridge-transform intersections at the Kane fracture zone

Science.gov (United States)

Karson, J. A.; Dick, H. J. B.

1983-03-01

The Kane Transform offsets spreading-center segments of the Mid-Atlantic Ridge by about 150 km at 24° N latitude. In terms of its first-order morphological, geological, and geophysical characteristics it appears to be typical of long-offset (>100 km), slow-slipping (2 cm yr-1) ridge-ridge transform faults. High-resolution geological observations were made from deep-towed ANGUS photographs and the manned submersible ALVIN at the ridge-transform intersections and indicate similar relationships in these two regions. These data indicate that over a distance of about 20 km as the spreading axes approach the fracture zone, the two flanks of each ridge axis behave in very different ways. Along the flanks that intersect the active transform zone the rift valley floor deepens and the surface expression of volcanism becomes increasingly narrow and eventually absent at the intersection where only a sediment-covered ‘nodal basin’ exists. The adjacent median valley walls have structural trends that are oblique to both the ridge and the transform and have as much as 4 km of relief. These are tectonically active regions that have only a thin (young volcanics passes laterally into median valley walls with a simple block-faulted character where only volcanic rocks have been found. Along strike toward the fracture zone, the youngest volcanics form linear constructional volcanic ridges that transect the entire width of the fracture zone valley. These volcanics are continuous with the older-looking, slightly faulted volcanic terrain that floors the non-transform fracture zone valleys. These observations document the asymmetric nature of seafloor spreading near ridge-transform intersections. An important implication is that the crust and lithosphere across different portions of the fracture zone will have different geological characteristics. Across the active transform zone two lithosphere plate edges formed at ridge-transform corners are faulted against one another. In the non

Alternatives evaluation for the decontamination and decommissioning of buildings 3506 and 3515 at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1994-01-01

This is an alternative evaluation document that records the evaluation process and justification for choosing the alternative recommended for the decontamination and decommissioning (D ampersand D) of the 3506 and 3515 buildings at the Oak Ridge National Laboratory (ORNL). The alternatives for the D ampersand D of the two buildings were: (1) no action (continued surveillance and maintenance), (2) decontamination for free release, (3) entombment in place, (4) partial dismantlement, and (5) complete dismantlement. Soil remediation is not included in any of the alternatives. The recommended alternative for the D ampersand D of Building 3506 is partial dismantlement at an estimated cost of $936, 000 in escalated dollars. The cost estimate for complete dismantlement is $1,384,000. The recommended alternative for the D ampersand D of Building 3515 is complete dismantlement at an estimated cost of $3,733,000 in escalated dollars. This alternative is recommended, because the soils below the foundation of the 3515 building are highly contaminated, and removing the foundation in the D ampersand D project results in lower overall worker risk, costs, and improved post-D ampersand D site conditions. A further recommendation is to revise these cost estimates after the conclusion of the ongoing characterization study. The results of the characterization of the two buildings is expected to change some of the assumptions and resolve some of the uncertainties in the development of these estimates

Internal doses in Oak Ridge. The Internet beams

International Nuclear Information System (INIS)

Passchier, W.F.

1997-01-01

A brief overview is given of the information, presented by the Radiation Internal Dose Information Center (RIDIC) of the Oak Ridge Associated Universities in Oak Ridge, TN, USA, via Internet (www.orau.gov/ehsd/ridic.htm)

Preliminary shielding estimates for the proposed Oak Ridge National Laboratory (ORNL) Radioactive Ion Beam Facility (RIBF)

International Nuclear Information System (INIS)

Johnson, J.O.; Gabriel, T.A.; Lillie, R.A.

1996-01-01

The Oak Ridge National Laboratory (ORNL) has proposed designing and implementing a new target-ion source for production and injection of negative radioactive ion beams into the Hollifield tandem accelerator. This new facility, referred to as the Radioactive Ion Beam Facility (RIBF), will primarily be used to advance the scientific communities' capabilities for performing state-of-the-art cross-section measurements. Beams of protons or other light, stable ions from the Oak Ridge Isochronous Cyclotron (ORIC) will be stopped in the RIBF target ion source and the resulting radioactive atoms will be ionized, charge exchanged, accelerated, and injected into the tandem accelerator. The ORIC currently operates with proton energies up to 60 MeV and beam currents up to 100 microamps with a maximum beam power less than 2.0 kW. The proposed RIBF will require upgrading the ORIC to generate proton energies up to 200 MeV and beam currents up to 200 microamps for optimum performance. This report summarizes the results of a preliminary one-dimensional shielding analysis of the proposed upgrade to the ORIC and design of the RIBF. The principal objective of the shielding analysis was to determine the feasibility of such an upgrade with respect to existing shielding from the facility structure, and additional shielding requirements for the 200 MeV ORIC machine and RIBF target room

Top Incomes, Heavy Tails, and Rank-Size Regressions

Directory of Open Access Journals (Sweden)

Christian Schluter

2018-03-01

Full Text Available In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading parametric income models (so the upper income tail is less heavy than estimated, which leads to test size distortions and undermines inference. For practical work, we propose a sensitivity analysis based on regression diagnostics in order to assess the likely impact of the distortion. The methods are illustrated using data on top incomes in the UK.

A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data

KAUST Repository

Gazioglu, Suzan

2013-05-25

Primary analysis of case-control studies focuses on the relationship between disease (D) and a set of covariates of interest (Y, X). A secondary application of the case-control study, often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated due to the case-control sampling, and to avoid the biased sampling that arises from the design, it is typical to use the control data only. In this paper, we develop penalized regression spline methodology that uses all the data, and improves precision of estimation compared to using only the controls. A simulation study and an empirical example are used to illustrate the methodology.

A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data

KAUST Repository

Gazioglu, Suzan; Wei, Jiawei; Jennings, Elizabeth M.; Carroll, Raymond J.

2013-01-01

Primary analysis of case-control studies focuses on the relationship between disease (D) and a set of covariates of interest (Y, X). A secondary application of the case-control study, often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated due to the case-control sampling, and to avoid the biased sampling that arises from the design, it is typical to use the control data only. In this paper, we develop penalized regression spline methodology that uses all the data, and improves precision of estimation compared to using only the controls. A simulation study and an empirical example are used to illustrate the methodology.