Characteristics and Properties of a Simple Linear Regression Model

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Kowal Robert

2016-12-01

Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Despite the passage of time, it continues to raise interest both from the theoretical side as well as from the application side. One of the many fundamental questions in the model concerns determining derivative characteristics and studying the properties existing in their scope, referring to the first of these aspects. The literature of the subject provides several classic solutions in that regard. In the paper, a completely new design is proposed, based on the direct application of variance and its properties, resulting from the non-correlation of certain estimators with the mean, within the scope of which some fundamental dependencies of the model characteristics are obtained in a much more compact manner. The apparatus allows for a simple and uniform demonstration of multiple dependencies and fundamental properties in the model, and it does it in an intuitive manner. The results were obtained in a classic, traditional area, where everything, as it might seem, has already been thoroughly studied and discovered.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

Science.gov (United States)

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection

OpenAIRE

Kwan, Johnny S. H.; Kung, Annie W. C.; Sham, Pak C.

2011-01-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias. © The Author(s) 2011.

A modified approach to estimating sample size for simple logistic regression with one continuous covariate.

Science.gov (United States)

Novikov, I; Fund, N; Freedman, L S

2010-01-15

Different methods for the calculation of sample size for simple logistic regression (LR) with one normally distributed continuous covariate give different results. Sometimes the difference can be large. Furthermore, some methods require the user to specify the prevalence of cases when the covariate equals its population mean, rather than the more natural population prevalence. We focus on two commonly used methods and show through simulations that the power for a given sample size may differ substantially from the nominal value for one method, especially when the covariate effect is large, while the other method performs poorly if the user provides the population prevalence instead of the required parameter. We propose a modification of the method of Hsieh et al. that requires specification of the population prevalence and that employs Schouten's sample size formula for a t-test with unequal variances and group sizes. This approach appears to increase the accuracy of the sample size estimates for LR with one continuous covariate.

Statistical approach for selection of regression model during validation of bioanalytical method

Directory of Open Access Journals (Sweden)

Natalija Nakov

2014-06-01

Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.

Regression methods for medical research

CERN Document Server

Tai, Bee Choo

2013-01-01

Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the

Regression analysis by example

CERN Document Server

Chatterjee, Samprit

2012-01-01

Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

Regression Analysis by Example. 5th Edition

Science.gov (United States)

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

A prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2, based on simple clinical parameters.

Science.gov (United States)

Koeneman, Margot M; van Lint, Freyja H M; van Kuijk, Sander M J; Smits, Luc J M; Kooreman, Loes F S; Kruitwagen, Roy F P M; Kruse, Arnold J

2017-01-01

This study aims to develop a prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2 (CIN 2) lesions based on simple clinicopathological parameters. The study was conducted at Maastricht University Medical Center, the Netherlands. The prediction model was developed in a retrospective cohort of 129 women with a histologic diagnosis of CIN 2 who were managed by watchful waiting for 6 to 24months. Five potential predictors for spontaneous regression were selected based on the literature and expert opinion and were analyzed in a multivariable logistic regression model, followed by backward stepwise deletion based on the Wald test. The prediction model was internally validated by the bootstrapping method. Discriminative capacity and accuracy were tested by assessing the area under the receiver operating characteristic curve (AUC) and a calibration plot. Disease regression within 24months was seen in 91 (71%) of 129 patients. A prediction model was developed including the following variables: smoking, Papanicolaou test outcome before the CIN 2 diagnosis, concomitant CIN 1 diagnosis in the same biopsy, and more than 1 biopsy containing CIN 2. Not smoking, Papanicolaou class predictive of disease regression. The AUC was 69.2% (95% confidence interval, 58.5%-79.9%), indicating a moderate discriminative ability of the model. The calibration plot indicated good calibration of the predicted probabilities. This prediction model for spontaneous regression of CIN 2 may aid physicians in the personalized management of these lesions. Copyright © 2016 Elsevier Inc. All rights reserved.

BOX-COX REGRESSION METHOD IN TIME SCALING

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ATİLLA GÖKTAŞ

2013-06-01

Full Text Available Box-Cox regression method with λj, for j = 1, 2, ..., k, power transformation can be used when dependent variable and error term of the linear regression model do not satisfy the continuity and normality assumptions. The situation obtaining the smallest mean square error  when optimum power λj, transformation for j = 1, 2, ..., k, of Y has been discussed. Box-Cox regression method is especially appropriate to adjust existence skewness or heteroscedasticity of error terms for a nonlinear functional relationship between dependent and explanatory variables. In this study, the advantage and disadvantage use of Box-Cox regression method have been discussed in differentiation and differantial analysis of time scale concept.

Effective Surfactants Blend Concentration Determination for O/W Emulsion Stabilization by Two Nonionic Surfactants by Simple Linear Regression.

Science.gov (United States)

Hassan, A K

2015-01-01

In this work, O/W emulsion sets were prepared by using different concentrations of two nonionic surfactants. The two surfactants, tween 80(HLB=15.0) and span 80(HLB=4.3) were used in a fixed proportions equal to 0.55:0.45 respectively. HLB value of the surfactants blends were fixed at 10.185. The surfactants blend concentration is starting from 3% up to 19%. For each O/W emulsion set the conductivity was measured at room temperature (25±2°), 40, 50, 60, 70 and 80°. Applying the simple linear regression least squares method statistical analysis to the temperature-conductivity obtained data determines the effective surfactants blend concentration required for preparing the most stable O/W emulsion. These results were confirmed by applying the physical stability centrifugation testing and the phase inversion temperature range measurements. The results indicated that, the relation which represents the most stable O/W emulsion has the strongest direct linear relationship between temperature and conductivity. This relationship is linear up to 80°. This work proves that, the most stable O/W emulsion is determined via the determination of the maximum R² value by applying of the simple linear regression least squares method to the temperature-conductivity obtained data up to 80°, in addition to, the true maximum slope is represented by the equation which has the maximum R² value. Because the conditions would be changed in a more complex formulation, the method of the determination of the effective surfactants blend concentration was verified by applying it for more complex formulations of 2% O/W miconazole nitrate cream and the results indicate its reproducibility.

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.

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...

Multi-step polynomial regression method to model and forecast malaria incidence.

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Chandrajit Chatterjee

Full Text Available Malaria is one of the most severe problems faced by the world even today. Understanding the causative factors such as age, sex, social factors, environmental variability etc. as well as underlying transmission dynamics of the disease is important for epidemiological research on malaria and its eradication. Thus, development of suitable modeling approach and methodology, based on the available data on the incidence of the disease and other related factors is of utmost importance. In this study, we developed a simple non-linear regression methodology in modeling and forecasting malaria incidence in Chennai city, India, and predicted future disease incidence with high confidence level. We considered three types of data to develop the regression methodology: a longer time series data of Slide Positivity Rates (SPR of malaria; a smaller time series data (deaths due to Plasmodium vivax of one year; and spatial data (zonal distribution of P. vivax deaths for the city along with the climatic factors, population and previous incidence of the disease. We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step methods for induction of variables in the non-linear regression analysis along with applied Gauss-Markov models, and ANOVA for testing the prediction, validity and constructing the confidence intervals. The results execute the applicability of our method for different types of data, the autoregressive nature of forecasting, and show high prediction power for both SPR and P. vivax deaths, where the one-lag SPR values plays an influential role and proves useful for better prediction. Different climatic factors are identified as playing crucial role on shaping the disease curve. Further, disease incidence at zonal level and the effect of causative factors on different zonal clusters indicate the pattern of malaria prevalence in the city

Sample size determination for logistic regression on a logit-normal distribution.

Science.gov (United States)

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

Simple method for the estimation of glomerular filtration rate

Energy Technology Data Exchange (ETDEWEB)

Groth, T [Group for Biomedical Informatics, Uppsala Univ. Data Center, Uppsala (Sweden); Tengstroem, B [District General Hospital, Skoevde (Sweden)

1977-02-01

A simple method is presented for indirect estimation of the glomerular filtration rate from two venous blood samples, drawn after a single injection of a small dose of (/sup 125/I)sodium iothalamate (10 ..mu..Ci). The method does not require exact dosage, as the first sample, taken after a few minutes (t=5 min) after injection, is used to normilize the value of the second sample, which should be taken in between 2 to 4 h after injection. The glomerular filtration rate, as measured by standard insulin clearance, may then be predicted from the logarithm of the normalized value and linear regression formulas with a standard error of estimate of the order of 1 to 2 ml/min/1.73 m/sup 2/. The slope-intercept method for direct estimation of glomerular filtration rate is also evaluated and found to significantly underestimate standard insulin clearance. The normalized 'single-point' method is concluded to be superior to the slope-intercept method and more sophisticated methods using curve fitting technique, with regard to predictive force and clinical applicability.

Substoichiometric method in the simple radiometric analysis

International Nuclear Information System (INIS)

Ikeda, N.; Noguchi, K.

1979-01-01

The substoichiometric method is applied to simple radiometric analysis. Two methods - the standard reagent method and the standard sample method - are proposed. The validity of the principle of the methods is verified experimentally in the determination of silver by the precipitation method, or of zinc by the ion-exchange or solvent-extraction method. The proposed methods are simple and rapid compared with the conventional superstoichiometric method. (author)

Thermal Efficiency Degradation Diagnosis Method Using Regression Model

International Nuclear Information System (INIS)

Jee, Chang Hyun; Heo, Gyun Young; Jang, Seok Won; Lee, In Cheol

2011-01-01

This paper proposes an idea for thermal efficiency degradation diagnosis in turbine cycles, which is based on turbine cycle simulation under abnormal conditions and a linear regression model. The correlation between the inputs for representing degradation conditions (normally unmeasured but intrinsic states) and the simulation outputs (normally measured but superficial states) was analyzed with the linear regression model. The regression models can inversely response an associated intrinsic state for a superficial state observed from a power plant. The diagnosis method proposed herein is classified into three processes, 1) simulations for degradation conditions to get measured states (referred as what-if method), 2) development of the linear model correlating intrinsic and superficial states, and 3) determination of an intrinsic state using the superficial states of current plant and the linear regression model (referred as inverse what-if method). The what-if method is to generate the outputs for the inputs including various root causes and/or boundary conditions whereas the inverse what-if method is the process of calculating the inverse matrix with the given superficial states, that is, component degradation modes. The method suggested in this paper was validated using the turbine cycle model for an operating power plant

A Simple Method for Determination of Critical Swimming Velocity in Swimming Flume

OpenAIRE

高橋, 繁浩; 若吉, 浩二; Shigehiro, TAKAHASHI; Kohji, WAKAYOSHI; 中京大学; 奈良教育大学教育学部

2001-01-01

The purpose of this study was to investigate a simple method for determination of critical swimming velocity (Vcri). Vcri is defined by Wakayoshi et al. (1992) as the swimming speed which could theoretically be maintained forever without exhaustion, and is expressed as the slope of a regression line between swimming distance (D) and swimming time (T) obtained at various swimming speeds. To determine Vcri, 20 well-trained swimmers were measured at several swimming speeds ranging from 1.25 m/se...

A method for fitting regression splines with varying polynomial order in the linear mixed model.

Science.gov (United States)

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Linear Regression Analysis

CERN Document Server

Seber, George A F

2012-01-01

Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

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...

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

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

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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.

Testing hypotheses for differences between linear regression lines

Science.gov (United States)

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

TEMPERATURE PREDICTION IN 3013 CONTAINERS IN K AREA MATERIAL STORAGE (KAMS) FACILITY USING REGRESSION METHODS

International Nuclear Information System (INIS)

Gupta, N

2008-01-01

3013 containers are designed in accordance with the DOE-STD-3013-2004. These containers are qualified to store plutonium (Pu) bearing materials such as PuO2 for 50 years. DOT shipping packages such as the 9975 are used to store the 3013 containers in the K-Area Material Storage (KAMS) facility at Savannah River Site (SRS). DOE-STD-3013-2004 requires that a comprehensive surveillance program be set up to ensure that the 3013 container design parameters are not violated during the long term storage. To ensure structural integrity of the 3013 containers, thermal analyses using finite element models were performed to predict the contents and component temperatures for different but well defined parameters such as storage ambient temperature, PuO 2 density, fill heights, weights, and thermal loading. Interpolation is normally used to calculate temperatures if the actual parameter values are different from the analyzed values. A statistical analysis technique using regression methods is proposed to develop simple polynomial relations to predict temperatures for the actual parameter values found in the containers. The analysis shows that regression analysis is a powerful tool to develop simple relations to assess component temperatures

Finding-equal regression method and its application in predication of U resources

International Nuclear Information System (INIS)

Cao Huimo

1995-03-01

The commonly adopted deposit model method in mineral resources predication has two main part: one is model data that show up geological mineralization law for deposit, the other is statistics predication method that accords with characters of the data namely pretty regression method. This kind of regression method may be called finding-equal regression, which is made of the linear regression and distribution finding-equal method. Because distribution finding-equal method is a data pretreatment which accords with advanced mathematical precondition for the linear regression namely equal distribution theory, and this kind of data pretreatment is possible of realization. Therefore finding-equal regression not only can overcome nonlinear limitations, that are commonly occurred in traditional linear regression or other regression and always have no solution, but also can distinguish outliers and eliminate its weak influence, which would usually appeared when Robust regression possesses outlier in independent variables. Thus this newly finding-equal regression stands the best status in all kind of regression methods. Finally, two good examples of U resource quantitative predication are provided

Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation

Directory of Open Access Journals (Sweden)

Sharad Damodar Gore

2009-10-01

Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.

Simple-MSSM: a simple and efficient method for simultaneous multi-site saturation mutagenesis.

Science.gov (United States)

Cheng, Feng; Xu, Jian-Miao; Xiang, Chao; Liu, Zhi-Qiang; Zhao, Li-Qing; Zheng, Yu-Guo

2017-04-01

To develop a practically simple and robust multi-site saturation mutagenesis (MSSM) method that enables simultaneously recombination of amino acid positions for focused mutant library generation. A general restriction enzyme-free and ligase-free MSSM method (Simple-MSSM) based on prolonged overlap extension PCR (POE-PCR) and Simple Cloning techniques. As a proof of principle of Simple-MSSM, the gene of eGFP (enhanced green fluorescent protein) was used as a template gene for simultaneous mutagenesis of five codons. Forty-eight randomly selected clones were sequenced. Sequencing revealed that all the 48 clones showed at least one mutant codon (mutation efficiency = 100%), and 46 out of the 48 clones had mutations at all the five codons. The obtained diversities at these five codons are 27, 24, 26, 26 and 22, respectively, which correspond to 84, 75, 81, 81, 69% of the theoretical diversity offered by NNK-degeneration (32 codons; NNK, K = T or G). The enzyme-free Simple-MSSM method can simultaneously and efficiently saturate five codons within one day, and therefore avoid missing interactions between residues in interacting amino acid networks.

A simple method for HPLC retention time prediction: linear calibration using two reference substances.

Science.gov (United States)

Sun, Lei; Jin, Hong-Yu; Tian, Run-Tao; Wang, Ming-Juan; Liu, Li-Na; Ye, Liu-Ping; Zuo, Tian-Tian; Ma, Shuang-Cheng

2017-01-01

Analysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (t R ) and predicted t R in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of t R accurately. In the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The t R of compounds on a HPLC column can be calculated by standard retention time and linear relationship. The method was validated in two medicines on 30 columns. It was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Science.gov (United States)

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Alternative regression models to assess increase in childhood BMI

OpenAIRE

Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

2008-01-01

Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...

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.

A simple and accurate onset detection method for a measured bell-shaped speed profile

Directory of Open Access Journals (Sweden)

Lior Botzer

2009-06-01

Full Text Available Motor control neuroscientists measure limb trajectories and extract the onset of the movement for a variety of purposes. Such trajectories are often aligned relative to the onset of individual movement before the features of that movement are extracted and their properties are inspected. Onset detection is performed either manually or automatically, typically by selecting a velocity threshold. Here, we present a simple onset detection algorithm that is more accurate than the conventional velocity threshold technique. The proposed method is based on a simple regression and follows the minimum acceleration with constraints model, in which the initial phase of the bell-shaped movement is modeled by a cubic power of the time. We demonstrate the performance of the suggested method and compare it to the velocity threshold technique and to manual onset detection by a group of motor control experts. The database for this comparison consists of simulated minimum jerk trajectories and recorded reaching movements.

Linear regression

CERN Document Server

Olive, David J

2017-01-01

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

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.

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Correlation of concentration of modified cassava flour for banana fritter flour using simple linear regression

Science.gov (United States)

Herminiati, A.; Rahman, T.; Turmala, E.; Fitriany, C. G.

2017-12-01

The purpose of this study was to determine the correlation of different concentrations of modified cassava flour that was processed for banana fritter flour. The research method consists of two stages: (1) to determine the different types of flour: cassava flour, modified cassava flour-A (using the method of the lactid acid bacteria), and modified cassava flour-B (using the method of the autoclaving cooling cycle), then conducted on organoleptic test and physicochemical analysis; (2) to determine the correlation of concentration of modified cassava flour for banana fritter flour, by design was used simple linear regression. The factors were used different concentrations of modified cassava flour-B (y1) 40%, (y2) 50%, and (y3) 60%. The response in the study includes physical analysis (whiteness of flour, water holding capacity-WHC, oil holding capacity-OHC), chemical analysis (moisture content, ash content, crude fiber content, starch content), and organoleptic (color, aroma, taste, texture). The results showed that the type of flour selected from the organoleptic test was modified cassava flour-B. Analysis results of modified cassava flour-B component containing whiteness of flour 60.42%; WHC 41.17%; OHC 21.15%; moisture content 4.4%; ash content 1.75%; crude fiber content 1.86%; starch content 67.31%. The different concentrations of modified cassava flour-B with the results of the analysis provides correlation to the whiteness of flour, WHC, OHC, moisture content, ash content, crude fiber content, and starch content. The different concentrations of modified cassava flour-B does not affect the color, aroma, taste, and texture.

Simple gas chromatographic method for furfural analysis.

Science.gov (United States)

Gaspar, Elvira M S M; Lopes, João F

2009-04-03

A new, simple, gas chromatographic method was developed for the direct analysis of 5-hydroxymethylfurfural (5-HMF), 2-furfural (2-F) and 5-methylfurfural (5-MF) in liquid and water soluble foods, using direct immersion SPME coupled to GC-FID and/or GC-TOF-MS. The fiber (DVB/CAR/PDMS) conditions were optimized: pH effect, temperature, adsorption and desorption times. The method is simple and accurate (RSDfurfurals will contribute to characterise and quantify their presence in the human diet.

Identification of Influential Points in a Linear Regression Model

Directory of Open Access Journals (Sweden)

Jan Grosz

2011-03-01

Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

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 and Path Analysis Method to Analyze Factors influencing Students’ Achievement

Science.gov (United States)

Noeryanti, N.; Suryowati, K.; Setyawan, Y.; Aulia, R. R.

2018-04-01

Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student (internal factors) consist of intelligence (X1), health (X2), interest (X3), and motivation of students (X4). The external factors consist of family environment (X5), school environment (X6), and society environment (X7). The objects of this research are eighth grade students of the school year 2016/2017 at SMPN 1 Jiwan Madiun sampled by using simple random sampling. Primary data are obtained by distributing questionnaires. The method used in this study is binary logistic regression analysis that aims to identify internal and external factors that affect student’s achievement and how the trends of them. Path Analysis was used to determine the factors that influence directly, indirectly or totally on student’s achievement. Based on the results of binary logistic regression, variables that affect student’s achievement are interest and motivation. And based on the results obtained by path analysis, factors that have a direct impact on student’s achievement are students’ interest (59%) and students’ motivation (27%). While the factors that have indirect influences on students’ achievement, are family environment (97%) and school environment (37).

Improving ASTER GDEM Accuracy Using Land Use-Based Linear Regression Methods: A Case Study of Lianyungang, East China

Directory of Open Access Journals (Sweden)

Xiaoyan Yang

2018-04-01

Full Text Available The Advanced Spaceborne Thermal-Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM is important to a wide range of geographical and environmental studies. Its accuracy, to some extent associated with land-use types reflecting topography, vegetation coverage, and human activities, impacts the results and conclusions of these studies. In order to improve the accuracy of ASTER GDEM prior to its application, we investigated ASTER GDEM errors based on individual land-use types and proposed two linear regression calibration methods, one considering only land use-specific errors and the other considering the impact of both land-use and topography. Our calibration methods were tested on the coastal prefectural city of Lianyungang in eastern China. Results indicate that (1 ASTER GDEM is highly accurate for rice, wheat, grass and mining lands but less accurate for scenic, garden, wood and bare lands; (2 despite improvements in ASTER GDEM2 accuracy, multiple linear regression calibration requires more data (topography and a relatively complex calibration process; (3 simple linear regression calibration proves a practicable and simplified means to systematically investigate and improve the impact of land-use on ASTER GDEM accuracy. Our method is applicable to areas with detailed land-use data based on highly accurate field-based point-elevation measurements.

Simple method for calculating island widths

International Nuclear Information System (INIS)

Cary, J.R.; Hanson, J.D.; Carreras, B.A.; Lynch, V.E.

1989-01-01

A simple method for calculating magnetic island widths has been developed. This method uses only information obtained from integrating along the closed field line at the island center. Thus, this method is computationally less intensive than the usual method of producing surfaces of section of sufficient detail to locate and resolve the island separatrix. This method has been implemented numerically and used to analyze the buss work islands of ATF. In this case the method proves to be accurate to at least within 30%. 7 refs

Simple Calculation Programs for Biology Immunological Methods

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. Simple Calculation Programs for Biology Immunological Methods. Computation of Ab/Ag Concentration from EISA data. Graphical Method; Raghava et al., 1992, J. Immuno. Methods 153: 263. Determination of affinity of Monoclonal Antibody. Using non-competitive ...

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.

Stochastic development regression using method of moments

DEFF Research Database (Denmark)

Kühnel, Line; Sommer, Stefan Horst

2017-01-01

This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....

On two flexible methods of 2-dimensional regression analysis

Czech Academy of Sciences Publication Activity Database

Volf, Petr

2012-01-01

Roč. 18, č. 4 (2012), s. 154-164 ISSN 1803-9782 Grant - others:GA ČR(CZ) GAP209/10/2045 Institutional support: RVO:67985556 Keywords : regression analysis * Gordon surface * prediction error * projection pursuit Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/SI/volf-on two flexible methods of 2-dimensional regression analysis.pdf

A Simple Preparation Method for Diphosphoimidazole

DEFF Research Database (Denmark)

Rosenberg, T.

1964-01-01

A simple method for the preparation of diphosphoimidazole is presented that involves direct phosphorylation of imidazole by phosphorus oxychloride in alkaline aqueous solution. Details are given on the use of diphosphoimidazole in preparing sodium phosphoramidate and certain phosphorylated amino...

A multiple regression method for genomewide association studies ...

Indian Academy of Sciences (India)

Bujun Mei

2018-06-07

Jun 7, 2018 ... Similar to the typical genomewide association tests using LD ... new approach performed validly when the multiple regression based on linkage method was employed. .... the model, two groups of scenarios were simulated.

Steganalysis using logistic regression

Science.gov (United States)

Lubenko, Ivans; Ker, Andrew D.

2011-02-01

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

A simple method for human peripheral blood monocyte Isolation

Directory of Open Access Journals (Sweden)

Marcos C de Almeida

2000-04-01

Full Text Available We describe a simple method using percoll gradient for isolation of highly enriched human monocytes. High numbers of fully functional cells are obtained from whole blood or buffy coat cells. The use of simple laboratory equipment and a relatively cheap reagent makes the described method a convenient approach to obtaining human monocytes.

Tracking time-varying parameters with local regression

DEFF Research Database (Denmark)

Joensen, Alfred Karsten; Nielsen, Henrik Aalborg; Nielsen, Torben Skov

2000-01-01

This paper shows that the recursive least-squares (RLS) algorithm with forgetting factor is a special case of a varying-coe\\$cient model, and a model which can easily be estimated via simple local regression. This observation allows us to formulate a new method which retains the RLS algorithm, bu......, but extends the algorithm by including polynomial approximations. Simulation results are provided, which indicates that this new method is superior to the classical RLS method, if the parameter variations are smooth....

Multitask Quantile Regression under the Transnormal Model.

Science.gov (United States)

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2016-01-01

We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.

Determination of gaussian peaks in gamma spectra by iterative regression

International Nuclear Information System (INIS)

Nordemann, D.J.R.

1987-05-01

The parameters of the peaks in gamma-ray spectra are determined by a simple iterative regression method. For each peak, the parameters are associated with a gaussian curve (3 parameters) located above a linear continuum (2 parameters). This method may produces the complete result of the calculation of statistical uncertainties and an accuracy higher than others methods. (author) [pt

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.

Simple Calculation Programs for Biology Other Methods

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. Simple Calculation Programs for Biology Other Methods. Hemolytic potency of drugs. Raghava et al., (1994) Biotechniques 17: 1148. FPMAP: methods for classification and identification of microorganisms 16SrRNA. graphical display of restriction and fragment map of ...

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...

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...

"Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results"

Directory of Open Access Journals (Sweden)

Jason W. Osborne

2012-06-01

Full Text Available Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These outcomes represent important social science lines of research: retention in, or dropout from school, using illicit drugs, underage alcohol consumption, antisocial behavior, purchasing decisions, voting patterns, risky behavior, and so on. The goal of this paper is to briefly lead the reader through the surprisingly simple mathematics that underpins logistic regression: probabilities, odds, odds ratios, and logits. Anyone with spreadsheet software or a scientific calculator can follow along, and in turn, this knowledge can be used to make much more interesting, clear, and accurate presentations of results (especially to non-technical audiences. In particular, I will share an example of an interaction in logistic regression, how it was originally graphed, and how the graph was made substantially more user-friendly by converting the original metric (logits to a more readily interpretable metric (probability through three simple steps.

Detection of epistatic effects with logic regression and a classical linear regression model.

Science.gov (United States)

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Statistical methods in regression and calibration analysis of chromosome aberration data

International Nuclear Information System (INIS)

Merkle, W.

1983-01-01

The method of iteratively reweighted least squares for the regression analysis of Poisson distributed chromosome aberration data is reviewed in the context of other fit procedures used in the cytogenetic literature. As an application of the resulting regression curves methods for calculating confidence intervals on dose from aberration yield are described and compared, and, for the linear quadratic model a confidence interval is given. Emphasis is placed on the rational interpretation and the limitations of various methods from a statistical point of view. (orig./MG)

An improved partial least-squares regression method for Raman spectroscopy

Science.gov (United States)

Momenpour Tehran Monfared, Ali; Anis, Hanan

2017-10-01

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

Simple and inexpensive method for CT-guided stereotaxy

Energy Technology Data Exchange (ETDEWEB)

Wester, K; Sortland, O; Hauglie-Hanssen, E

1981-01-01

A simple and inexpensive method for CT-guided stereotaxy is described. The method requires neither sophisticated computer programs nor additional stereotactic equipment, such as special head holders for the CT, and can be easily obtained without technical assistance. The method is designed to yield the vertical coordinates.

FATAL, General Experiment Fitting Program by Nonlinear Regression Method

International Nuclear Information System (INIS)

Salmon, L.; Budd, T.; Marshall, M.

1982-01-01

1 - Description of problem or function: A generalized fitting program with a free-format keyword interface to the user. It permits experimental data to be fitted by non-linear regression methods to any function describable by the user. The user requires the minimum of computer experience but needs to provide a subroutine to define his function. Some statistical output is included as well as 'best' estimates of the function's parameters. 2 - Method of solution: The regression method used is based on a minimization technique devised by Powell (Harwell Subroutine Library VA05A, 1972) which does not require the use of analytical derivatives. The method employs a quasi-Newton procedure balanced with a steepest descent correction. Experience shows this to be efficient for a very wide range of application. 3 - Restrictions on the complexity of the problem: The current version of the program permits functions to be defined with up to 20 parameters. The function may be fitted to a maximum of 400 points, preferably with estimated values of weight given

A computer tool for a minimax criterion in binary response and heteroscedastic simple linear regression models.

Science.gov (United States)

Casero-Alonso, V; López-Fidalgo, J; Torsney, B

2017-01-01

Binary response models are used in many real applications. For these models the Fisher information matrix (FIM) is proportional to the FIM of a weighted simple linear regression model. The same is also true when the weight function has a finite integral. Thus, optimal designs for one binary model are also optimal for the corresponding weighted linear regression model. The main objective of this paper is to provide a tool for the construction of MV-optimal designs, minimizing the maximum of the variances of the estimates, for a general design space. MV-optimality is a potentially difficult criterion because of its nondifferentiability at equal variance designs. A methodology for obtaining MV-optimal designs where the design space is a compact interval [a, b] will be given for several standard weight functions. The methodology will allow us to build a user-friendly computer tool based on Mathematica to compute MV-optimal designs. Some illustrative examples will show a representation of MV-optimal designs in the Euclidean plane, taking a and b as the axes. The applet will be explained using two relevant models. In the first one the case of a weighted linear regression model is considered, where the weight function is directly chosen from a typical family. In the second example a binary response model is assumed, where the probability of the outcome is given by a typical probability distribution. Practitioners can use the provided applet to identify the solution and to know the exact support points and design weights. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Regression Methods for Virtual Metrology of Layer Thickness in Chemical Vapor Deposition

DEFF Research Database (Denmark)

Purwins, Hendrik; Barak, Bernd; Nagi, Ahmed

2014-01-01

The quality of wafer production in semiconductor manufacturing cannot always be monitored by a costly physical measurement. Instead of measuring a quantity directly, it can be predicted by a regression method (Virtual Metrology). In this paper, a survey on regression methods is given to predict...... average Silicon Nitride cap layer thickness for the Plasma Enhanced Chemical Vapor Deposition (PECVD) dual-layer metal passivation stack process. Process and production equipment Fault Detection and Classification (FDC) data are used as predictor variables. Various variable sets are compared: one most...... algorithm, and Support Vector Regression (SVR). On a test set, SVR outperforms the other methods by a large margin, being more robust towards changes in the production conditions. The method performs better on high-dimensional multivariate input data than on the most predictive variables alone. Process...

Comparing parametric and nonparametric regression methods for panel data

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...

A simple and rapid method to estimate radiocesium in man

International Nuclear Information System (INIS)

Kindl, P.; Steger, F.

1990-09-01

A simple and rapid method for monitoring internal contamination of radiocesium in man was developed. This method is based on measurements of the γ-rays emitted from the muscular parts between the thights by a simple NaJ(Tl)-system. The experimental procedure, the calibration, the estimation of the body activity and results are explained and discussed. (Authors)

Fitting program for linear regressions according to Mahon (1996)

Energy Technology Data Exchange (ETDEWEB)

2018-01-09

This program takes the users' Input data and fits a linear regression to it using the prescription presented by Mahon (1996). Compared to the commonly used York fit, this method has the correct prescription for measurement error propagation. This software should facilitate the proper fitting of measurements with a simple Interface.

The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics

Directory of Open Access Journals (Sweden)

Ronald de Vlaming

2015-01-01

Full Text Available In 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 commonly used methods in animal breeding, (iii the computational feasibility, and (iv the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis. Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N<10,000 the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP. However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.

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....

On the calibration process of film dosimetry: OLS inverse regression versus WLS inverse prediction

International Nuclear Information System (INIS)

Crop, F; Thierens, H; Rompaye, B Van; Paelinck, L; Vakaet, L; Wagter, C De

2008-01-01

The purpose of this study was both putting forward a statistically correct model for film calibration and the optimization of this process. A reliable calibration is needed in order to perform accurate reference dosimetry with radiographic (Gafchromic) film. Sometimes, an ordinary least squares simple linear (in the parameters) regression is applied to the dose-optical-density (OD) curve with the dose as a function of OD (inverse regression) or sometimes OD as a function of dose (inverse prediction). The application of a simple linear regression fit is an invalid method because heteroscedasticity of the data is not taken into account. This could lead to erroneous results originating from the calibration process itself and thus to a lower accuracy. In this work, we compare the ordinary least squares (OLS) inverse regression method with the correct weighted least squares (WLS) inverse prediction method to create calibration curves. We found that the OLS inverse regression method could lead to a prediction bias of up to 7.3 cGy at 300 cGy and total prediction errors of 3% or more for Gafchromic EBT film. Application of the WLS inverse prediction method resulted in a maximum prediction bias of 1.4 cGy and total prediction errors below 2% in a 0-400 cGy range. We developed a Monte-Carlo-based process to optimize calibrations, depending on the needs of the experiment. This type of thorough analysis can lead to a higher accuracy for film dosimetry

Comparison of ν-support vector regression and logistic equation for ...

African Journals Online (AJOL)

Due to the complexity and high non-linearity of bioprocess, most simple mathematical models fail to describe the exact behavior of biochemistry systems. As a novel type of learning method, support vector regression (SVR) owns the powerful capability to characterize problems via small sample, nonlinearity, high dimension ...

The analysis of nonstationary time series using regression, correlation and cointegration

DEFF Research Database (Denmark)

Johansen, Søren

2012-01-01

There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference using the cointegrated vector autoregressive model. Finally we...... analyse some monthly data from US on interest rates as an illustration of the methods...

A Simple UV Spectrophotometric Method for the Determination of ...

African Journals Online (AJOL)

The method was also used in the determination of the content of levofloxacin in two commercial brands of levofloxacin in the Nigerian market. Results: The regression data for the calibration plots exhibited good linear relationship (r = 0.999) over a concentration range of 0.25 – 12.0 ìg/ml and the linear regression equation ...

A simple approximation method for dilute Ising systems

International Nuclear Information System (INIS)

Saber, M.

1996-10-01

We describe a simple approximate method to analyze dilute Ising systems. The method takes into consideration the fluctuations of the effective field, and is based on a probability distribution of random variables which correctly accounts for all the single site kinematic relations. It is shown that the simplest approximation gives satisfactory results when compared with other methods. (author). 12 refs, 2 tabs

The Efficiency of OLS Estimators of Structural Parameters in a Simple Linear Regression Model in the Calibration of the Averages Scheme

Directory of Open Access Journals (Sweden)

Kowal Robert

2016-12-01

Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Multiple areas of research function within its scope. One of the many fundamental questions in the model concerns proving the efficiency of the most commonly used OLS estimators and examining their properties. In the literature of the subject one can find taking back to this scope and certain solutions in that regard. Methodically, they are borrowed from the multiple regression model or also from a boundary partial model. Not everything, however, is here complete and consistent. In the paper a completely new scheme is proposed, based on the implementation of the Cauchy-Schwarz inequality in the arrangement of the constraint aggregated from calibrated appropriately secondary constraints of unbiasedness which in a result of choice the appropriate calibrator for each variable directly leads to showing this property. A separate range-is a matter of choice of such a calibrator. These deliberations, on account of the volume and kinds of the calibration, were divided into a few parts. In the one the efficiency of OLS estimators is proven in a mixed scheme of the calibration by averages, that is preliminary, and in the most basic frames of the proposed methodology. In these frames the future outlines and general premises constituting the base of more distant generalizations are being created.

Mapping urban environmental noise: a land use regression method.

Science.gov (United States)

Xie, Dan; Liu, Yi; Chen, Jining

2011-09-01

Forecasting and preventing urban noise pollution are major challenges in urban environmental management. Most existing efforts, including experiment-based models, statistical models, and noise mapping, however, have limited capacity to explain the association between urban growth and corresponding noise change. Therefore, these conventional methods can hardly forecast urban noise at a given outlook of development layout. This paper, for the first time, introduces a land use regression method, which has been applied for simulating urban air quality for a decade, to construct an urban noise model (LUNOS) in Dalian Municipality, Northwest China. The LUNOS model describes noise as a dependent variable of surrounding various land areas via a regressive function. The results suggest that a linear model performs better in fitting monitoring data, and there is no significant difference of the LUNOS's outputs when applied to different spatial scales. As the LUNOS facilitates a better understanding of the association between land use and urban environmental noise in comparison to conventional methods, it can be regarded as a promising tool for noise prediction for planning purposes and aid smart decision-making.

Treating experimental data of inverse kinetic method by unitary linear regression analysis

International Nuclear Information System (INIS)

Zhao Yusen; Chen Xiaoliang

2009-01-01

The theory of treating experimental data of inverse kinetic method by unitary linear regression analysis was described. Not only the reactivity, but also the effective neutron source intensity could be calculated by this method. Computer code was compiled base on the inverse kinetic method and unitary linear regression analysis. The data of zero power facility BFS-1 in Russia were processed and the results were compared. The results show that the reactivity and the effective neutron source intensity can be obtained correctly by treating experimental data of inverse kinetic method using unitary linear regression analysis and the precision of reactivity measurement is improved. The central element efficiency can be calculated by using the reactivity. The result also shows that the effect to reactivity measurement caused by external neutron source should be considered when the reactor power is low and the intensity of external neutron source is strong. (authors)

Interpretation of commonly used statistical regression models.

Science.gov (United States)

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Science.gov (United States)

de los Campos, Gustavo; Hickey, John M.; Pong-Wong, Ricardo; Daetwyler, Hans D.; Calus, Mario P. L.

2013-01-01

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade. PMID:22745228

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

Science.gov (United States)

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

Model-based bootstrapping when correcting for measurement error with application to logistic regression.

Science.gov (United States)

Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne

2018-03-01

When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.

[Analysis on the accuracy of simple selection method of Fengshi (GB 31)].

Science.gov (United States)

Li, Zhixing; Zhang, Haihua; Li, Suhe

2015-12-01

To explore the accuracy of simple selection method of Fengshi (GB 31). Through the study of the ancient and modern data,the analysis and integration of the acupuncture books,the comparison of the locations of Fengshi (GB 31) by doctors from all dynasties and the integration of modern anatomia, the modern simple selection method of Fengshi (GB 31) is definite, which is the same as the traditional way. It is believed that the simple selec tion method is in accord with the human-oriented thought of TCM. Treatment by acupoints should be based on the emerging nature and the individual difference of patients. Also, it is proposed that Fengshi (GB 31) should be located through the integration between the simple method and body surface anatomical mark.

Analysis of interactive fixed effects dynamic linear panel regression with measurement error

OpenAIRE

Nayoung Lee; Hyungsik Roger Moon; Martin Weidner

2011-01-01

This paper studies a simple dynamic panel linear regression model with interactive fixed effects in which the variable of interest is measured with error. To estimate the dynamic coefficient, we consider the least-squares minimum distance (LS-MD) estimation method.

The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration

Directory of Open Access Journals (Sweden)

Søren Johansen

2012-06-01

Full Text Available There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference using the cointegrated vector autoregressive model. Finally we analyse some monthly data from US on interest rates as an illustration of the methods.

A Simple and Specific Stability- Indicating RP-HPLC Method for Routine Assay of Adefovir Dipivoxil in Bulk and Tablet Dosage Form.

Science.gov (United States)

Darsazan, Bahar; Shafaati, Alireza; Mortazavi, Seyed Alireza; Zarghi, Afshin

2017-01-01

A simple and reliable stability-indicating RP-HPLC method was developed and validated for analysis of adefovir dipivoxil (ADV).The chromatographic separation was performed on a C 18 column using a mixture of acetonitrile-citrate buffer (10 mM at pH 5.2) 36:64 (%v/v) as mobile phase, at a flow rate of 1.5 mL/min. Detection was carried out at 260 nm and a sharp peak was obtained for ADV at a retention time of 5.8 ± 0.01 min. No interferences were observed from its stress degradation products. The method was validated according to the international guidelines. Linear regression analysis of data for the calibration plot showed a linear relationship between peak area and concentration over the range of 0.5-16 μg/mL; the regression coefficient was 0.9999and the linear regression equation was y = 24844x-2941.3. The detection (LOD) and quantification (LOQ) limits were 0.12 and 0.35 μg/mL, respectively. The results proved the method was fast (analysis time less than 7 min), precise, reproducible, and accurate for analysis of ADV over a wide range of concentration. The proposed specific method was used for routine quantification of ADV in pharmaceutical bulk and a tablet dosage form.

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....

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].

Gallium determination with Rodamina B: a simple method

International Nuclear Information System (INIS)

Queiroz, R.R.U. de.

1981-01-01

A simple method for determining gallium with Rhodamine B, by the modification of the method proposed by Onishi and Sandell. The complex (RH) GaCl 4 is extracted with a mixture benzene-ethylacetate (3:1 V/V), from an aqueous medium 6 M in hydrochloric acid. The interference of foreign ions is studied. (C.G.C.) [pt

Credit Scoring Problem Based on Regression Analysis

OpenAIRE

Khassawneh, Bashar Suhil Jad Allah

2014-01-01

ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

A simple and efficient electrochemical reductive method for ...

Indian Academy of Sciences (India)

Administrator

This approach opens up a new, practical and green reducing method to prepare large- scale graphene. ... has the following significant advantages: (1) It is simple to operate. .... The authors thank the National High Technology Research.

Solution of the schrodinger equation in one dimension by simple method for a simple step potential

International Nuclear Information System (INIS)

Ertik, H.

2005-01-01

The coefficients of the transmission and reflection for the simple-step barrier potential were calculated by a simple method. Their values were entirely different from those often encountered in the literature. Especially in the case that the total energy is equal to the barrier potential, the value of 0,20 for the reflection coefficient was obtained whereas this is zero in the literature. This may be considered as an interesting point

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

Science.gov (United States)

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

A Simple HPLC Bioanalytical Method for the Determination of ...

African Journals Online (AJOL)

Purpose: To develop a simple, accurate, and precise high performance chromatography (HPLC) method with spectrophotometric detection for the determination of doxorubicin hydrochloride in rat plasma. Methods: Doxorubicin hydrochloride and daunorubicin hydrochloride (internal standard, IS) were separated on a C18 ...

Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods

Science.gov (United States)

Helmreich, James E.; Krog, K. Peter

2018-01-01

We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…

Suppression Situations in Multiple Linear Regression

Science.gov (United States)

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

Science.gov (United States)

Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

A simple method for multiday imaging of slice cultures.

Science.gov (United States)

Seidl, Armin H; Rubel, Edwin W

2010-01-01

The organotypic slice culture (Stoppini et al. A simple method for organotypic cultures of nervous tissue. 1991;37:173-182) has become the method of choice to answer a variety of questions in neuroscience. For many experiments, however, it would be beneficial to image or manipulate a slice culture repeatedly, for example, over the course of many days. We prepared organotypic slice cultures of the auditory brainstem of P3 and P4 mice and kept them in vitro for up to 4 weeks. Single cells in the auditory brainstem were transfected with plasmids expressing fluorescent proteins by way of electroporation (Haas et al. Single-cell electroporation for gene transfer in vivo. 2001;29:583-591). The culture was then placed in a chamber perfused with oxygenated ACSF and the labeled cell imaged with an inverted wide-field microscope repeatedly for multiple days, recording several time-points per day, before returning the slice to the incubator. We describe a simple method to image a slice culture preparation during the course of multiple days and over many continuous hours, without noticeable damage to the tissue or photobleaching. Our method uses a simple, inexpensive custom-built insulator constructed around the microscope to maintain controlled temperature and uses a perfusion chamber as used for in vitro slice recordings. (c) 2009 Wiley-Liss, Inc.

Understanding logistic regression analysis.

Science.gov (United States)

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Simple Synthesis Method for Alumina Nanoparticle

Directory of Open Access Journals (Sweden)

Daniel Damian

2017-11-01

Full Text Available Globally, the human population steady increase, expansion of urban areas, excessive industrialization including in agriculture, caused not only decrease to depletion of non-renewable resources, a rapid deterioration of the environment with negative impact on water quality, soil productivity and of course quality of life in general. This paper aims to prepare size controlled nanoparticles of aluminum oxide using a simple synthesis method. The morphology and dimensions of nanomaterial was investigated using modern analytical techniques: SEM/EDAX and XRD spectroscopy.

Percutaneous Method of Management of Simple Bone Cyst

Directory of Open Access Journals (Sweden)

O. P. Lakhwani

2013-01-01

Full Text Available Introduction. Simple bone cyst or unicameral bone cysts are benign osteolytic lesions seen in metadiaphysis of long bones in growing children. Various treatment modalities with variable outcomes have been described in the literature. The case report illustrates the surgical technique of minimally invasive method of treatment. Case Study. A 14-year-old boy was diagnosed as active simple bone cyst proximal humerus with pathological fracture. The patient was treated by minimally invasive percutaneous curettage with titanium elastic nail (TENS and allogenic bone grafting mixed with bone marrow under image intensifier guidance. Results. Pathological fracture was healed and allograft filled in the cavity was well taken up. The patient achieved full range of motion with successful outcome. Conclusion. Minimally invasive percutaneous method using elastic intramedullary nail gives benefit of curettage cyst decompression and stabilization of fracture. Allogenic bone graft fills the cavity and healing of lesion by osteointegration. This method may be considered with advantage of minimally invasive technique in treatment of benign cystic lesions of bone, and the level of evidence was therapeutic level V.

Percutaneous Method of Management of Simple Bone Cyst

Science.gov (United States)

Lakhwani, O. P.

2013-01-01

Introduction. Simple bone cyst or unicameral bone cysts are benign osteolytic lesions seen in metadiaphysis of long bones in growing children. Various treatment modalities with variable outcomes have been described in the literature. The case report illustrates the surgical technique of minimally invasive method of treatment. Case Study. A 14-year-old boy was diagnosed as active simple bone cyst proximal humerus with pathological fracture. The patient was treated by minimally invasive percutaneous curettage with titanium elastic nail (TENS) and allogenic bone grafting mixed with bone marrow under image intensifier guidance. Results. Pathological fracture was healed and allograft filled in the cavity was well taken up. The patient achieved full range of motion with successful outcome. Conclusion. Minimally invasive percutaneous method using elastic intramedullary nail gives benefit of curettage cyst decompression and stabilization of fracture. Allogenic bone graft fills the cavity and healing of lesion by osteointegration. This method may be considered with advantage of minimally invasive technique in treatment of benign cystic lesions of bone, and the level of evidence was therapeutic level V. PMID:23819089

Comparison of methods for the analysis of relatively simple mediation models.

Science.gov (United States)

Rijnhart, Judith J M; Twisk, Jos W R; Chinapaw, Mai J M; de Boer, Michiel R; Heymans, Martijn W

2017-09-01

Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Science.gov (United States)

Chen, X.; Vierling, Lee; Deering, D.

2005-01-01

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a

A simple Ultraviolet spectrophotometric method for the determination of etoricoxib in dosage formulations

Directory of Open Access Journals (Sweden)

Shipra Singh

2012-01-01

Full Text Available The present study was undertaken to develop a validated, rapid, simple, and low-cost ultraviolet (UV spectrophotometric method for estimating Etoricoxib (ETX in pharmaceutical formulations. The analysis was performed on λ max 233 nm using 0.1 M HCl as blank/diluent. The proposed method was validated on International Conference on Harmonization (ICH guidelines including parameters as linearity, accuracy, precision, reproducibility, and specificity. The proposed method was also used to access the content of the ETX in two commercial brands of Indian market. Beer′s law was obeyed in concentration range of 0.1-0.5 μg/ml, and the regression equation was Y = 0.418x + 0.018. The mean accuracy values for 0.1 μg/ml and 0.2 μg/ml concentration of ETX were found to be 99.76 ± 0.52% and 99.12 ± 0.84, respectively, and relative standard deviation (RSD of interday and intraday was less than 2%. The developed method was suitable and specific to the analysis of ETX even in the presence of common excipients. The method was applied on two different marketed brands and ETX contents were 98.5 ± 0.56 and 99.33 ± 0.44, respectively, of labeled claim. The proposed method was validated as per ICH guidelines and statistically good results were obtained. This method can be employed for routine analysis of ETX in bulk and commercial formulations.

A simple Ultraviolet spectrophotometric method for the determination of etoricoxib in dosage formulations.

Directory of Open Access Journals (Sweden)

S Singh

2012-01-01

Full Text Available The present study was undertaken to develop a validated, rapid, simple, and low-cost ultraviolet (UV spectrophotometric method for estimating Etoricoxib (ETX in pharmaceutical formulations. The analysis was performed on Î max 233 nm using 0.1 M HCl as blank/diluent. The proposed method was validated on International Conference on Harmonization (ICH guidelines including parameters as linearity, accuracy, precision, reproducibility, and specificity. The proposed method was also used to access the content of the ETX in two commercial brands of Indian market. Beer′s law was obeyed in concentration range of 0.1-0.5 μg/ml, and the regression equation was Y = 0.418x + 0.018. The mean accuracy values for 0.1 μg/ml and 0.2 μg/ml concentration of ETX were found to be 99.76 ± 0.52% and 99.12 ± 0.84, respectively, and relative standard deviation (RSD of interday and intraday was less than 2%. The developed method was suitable and specific to the analysis of ETX even in the presence of common excipients. The method was applied on two different marketed brands and ETX contents were 98.5 ± 0.56 and 99.33 ± 0.44, respectively, of labeled claim. The proposed method was validated as per ICH guidelines and statistically good results were obtained. This method can be employed for routine analysis of ETX in bulk and commercial formulations.

A nonparametric approach to calculate critical micelle concentrations: the local polynomial regression method

Energy Technology Data Exchange (ETDEWEB)

Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)

2004-02-01

The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)

Simple equation method for nonlinear partial differential equations and its applications

Directory of Open Access Journals (Sweden)

Taher A. Nofal

2016-04-01

Full Text Available In this article, we focus on the exact solution of the some nonlinear partial differential equations (NLPDEs such as, Kodomtsev–Petviashvili (KP equation, the (2 + 1-dimensional breaking soliton equation and the modified generalized Vakhnenko equation by using the simple equation method. In the simple equation method the trial condition is the Bernoulli equation or the Riccati equation. It has been shown that the method provides a powerful mathematical tool for solving nonlinear wave equations in mathematical physics and engineering problems.

A simple finite element method for linear hyperbolic problems

International Nuclear Information System (INIS)

Mu, Lin; Ye, Xiu

2017-01-01

Here, we introduce a simple finite element method for solving first order hyperbolic equations with easy implementation and analysis. Our new method, with a symmetric, positive definite system, is designed to use discontinuous approximations on finite element partitions consisting of arbitrary shape of polygons/polyhedra. Error estimate is established. Extensive numerical examples are tested that demonstrate the robustness and flexibility of the method.

Penalized regression procedures for variable selection in the potential outcomes framework.

Science.gov (United States)

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L

2015-05-10

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple 'impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data, and imputation are drawn. A difference least absolute shrinkage and selection operator algorithm is defined, along with its multiple imputation analogs. The procedures are illustrated using a well-known right-heart catheterization dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Easy methods for extracting individual regression slopes: Comparing SPSS, R, and Excel

Directory of Open Access Journals (Sweden)

Roland Pfister

2013-10-01

Full Text Available Three different methods for extracting coefficientsof linear regression analyses are presented. The focus is on automatic and easy-to-use approaches for common statistical packages: SPSS, R, and MS Excel / LibreOffice Calc. Hands-on examples are included for each analysis, followed by a brief description of how a subsequent regression coefficient analysis is performed.

Simple Calculation Programs for Biology Methods in Molecular ...

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. Simple Calculation Programs for Biology Methods in Molecular Biology. GMAP: A program for mapping potential restriction sites. RE sites in ambiguous and non-ambiguous DNA sequence; Minimum number of silent mutations required for introducing a RE sites; Set ...

A Simple and Effective Image Normalization Method to Monitor Boreal Forest Change in a Siberian Burn Chronosequence across Sensors and across Time

Science.gov (United States)

Chen, X.; Vierling, L. A.; Deering, D. W.

2004-12-01

Satellite data offer unique perspectives for monitoring and quantifying land cover change, however, the radiometric consistency among co-located multi-temporal images is difficult to maintain due to variations in sensors and atmosphere. To detect accurate landscape change using multi-temporal images, we developed a new relative radiometric normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on 9 June 1990 (Landsat 4), 20 June 2000, and 26 August 2001 (Landsat 7) for analyses over boreal forests near the Siberian city of Krasnoyarsk. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Reduced Simple Ratio (RSR) were investigated in the normalization study. The temporally invariant cluster (TIC) centers were identified through a point density map of the base image and the target image and a normalization regression line was created through all TIC centers. The target image digital data were then converted using the regression function so that the two images could be compared using the resulting common radiometric scale. We found that EVI was very sensitive to vegetation structure and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. NDVI was a very effective vegetation index to reduce the influence of shadow, while EVI was very sensitive to shadowing. After normalization, correlations of NDVI and EVI with field collected total Leaf Area Index (LAI) data in 2000 and 2001 were significantly improved; the r-square values in these regressions increased from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI ¡°cancellation effect¡± where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of

Linear Regression with a Randomly Censored Covariate: Application to an Alzheimer's Study.

Science.gov (United States)

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2017-01-01

The association between maternal age of onset of dementia and amyloid deposition (measured by in vivo positron emission tomography (PET) imaging) in cognitively normal older offspring is of interest. In a regression model for amyloid, special methods are required due to the random right censoring of the covariate of maternal age of onset of dementia. Prior literature has proposed methods to address the problem of censoring due to assay limit of detection, but not random censoring. We propose imputation methods and a survival regression method that do not require parametric assumptions about the distribution of the censored covariate. Existing imputation methods address missing covariates, but not right censored covariates. In simulation studies, we compare these methods to the simple, but inefficient complete case analysis, and to thresholding approaches. We apply the methods to the Alzheimer's study.

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)

Non-invasive diagnostic methods for atherosclerosis and use in assessing progression and regression in hypercholesterolemia

International Nuclear Information System (INIS)

Tsushima, Motoo; Fujii, Shigeki; Yutani, Chikao; Yamamoto, Akira; Naitoh, Hiroaki.

1990-01-01

We evaluated the wall thickening and stenosis rate (ASI), the calcification rate (ACI), and the wall thickening and calcification stenosis rate (SCI) of the lower abdominal aorta calculated by the 12 sector method from simple or enhanced computed tomography. The intra-observer variation of the calculation of ASI was 5.7% and that of ACI was 2.4%. In 9 patients who underwent an autopsy examination, ACI was significantly correlated with the rate of the calcification dimension to the whole objective area of the abdominal aorta (r=0.856, p<0.01). However, there were no correlations between ASI and the surface involvement or the atherosclerotic index obtained by the point-counting method of the autopsy materials. In the analysis of 40 patients with atherosclerotic vascular diseases, ASI and ACI were also highly correlated with the percentage volume of the arterial wall in relation to the whole volume of the observed artery (r=0.852, p<0.0001) and also the percentage calcification volume (r=0.913, p<0.0001) calculated by the computed method, respectively. The percentage of atherosclerotic vascular diseases increased in the group of both high ASI (over 10%) and high ACI (over 20%). We used SCI as a reliable index when the progression and regression of atherosclerosis was considered. Among patients of hypercholesterolemia consisting of 15 with familial hypercholesterolemia (FH) and 6 non-FH patients, the change of SCI (d-SCI) was significantly correlated with the change of total cholesterol concentration (d-TC) after the treatment (r=0.466, p<0.05) and the change of the right Achilles' tendon thickening (d-ATT) was also correlated with d-TC (r=0.634, p<0.005). However, no correlation between d-SCI and d-ATT was observed. In conclusion, CT indices of atherosclerosis were useful as a noninvasive quantitative diagnostic method and we were able to use them to assess the progression and regression of atherosclerosis. (author)

A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary

Science.gov (United States)

Gillis, Nicolas; Luce, Robert

2018-01-01

A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary. As opposed to previously proposed models, this model yields a smooth optimization problem where the sparsity is enforced through linear constraints. We show that the Euclidean projection on the polyhedron defined by these constraints can be computed efficiently, and propose a fast gradient method to solve our model. We compare our algorithm with several state-of-the-art methods on synthetic data sets and real-world hyperspectral images.

A SIMPLE METHOD FOR THE EXTRACTION AND QUANTIFICATION OF PHOTOPIGMENTS FROM SYMBIODINIUM SPP.

Science.gov (United States)

John E. Rogers and Dragoslav Marcovich. Submitted. Simple Method for the Extraction and Quantification of Photopigments from Symbiodinium spp.. Limnol. Oceanogr. Methods. 19 p. (ERL,GB 1192). We have developed a simple, mild extraction procedure using methanol which, when...

Convert a low-cost sensor to a colorimeter using an improved regression method

Science.gov (United States)

Wu, Yifeng

2008-01-01

Closed loop color calibration is a process to maintain consistent color reproduction for color printers. To perform closed loop color calibration, a pre-designed color target should be printed, and automatically measured by a color measuring instrument. A low cost sensor has been embedded to the printer to perform the color measurement. A series of sensor calibration and color conversion methods have been developed. The purpose is to get accurate colorimetric measurement from the data measured by the low cost sensor. In order to get high accuracy colorimetric measurement, we need carefully calibrate the sensor, and minimize all possible errors during the color conversion. After comparing several classical color conversion methods, a regression based color conversion method has been selected. The regression is a powerful method to estimate the color conversion functions. But the main difficulty to use this method is to find an appropriate function to describe the relationship between the input and the output data. In this paper, we propose to use 1D pre-linearization tables to improve the linearity between the input sensor measuring data and the output colorimetric data. Using this method, we can increase the accuracy of the regression method, so as to improve the accuracy of the color conversion.

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

Detection of Outliers in Regression Model for Medical Data

Directory of Open Access Journals (Sweden)

Stephen Raj S

2017-07-01

Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.

Application of NIRS coupled with PLS regression as a rapid, non-destructive alternative method for quantification of KBA in Boswellia sacra

Science.gov (United States)

Al-Harrasi, Ahmed; Rehman, Najeeb Ur; Mabood, Fazal; Albroumi, Muhammaed; Ali, Liaqat; Hussain, Javid; Hussain, Hidayat; Csuk, René; Khan, Abdul Latif; Alam, Tanveer; Alameri, Saif

2017-09-01

In the present study, for the first time, NIR spectroscopy coupled with PLS regression as a rapid and alternative method was developed to quantify the amount of Keto-β-Boswellic Acid (KBA) in different plant parts of Boswellia sacra and the resin exudates of the trunk. NIR spectroscopy was used for the measurement of KBA standards and B. sacra samples in absorption mode in the wavelength range from 700-2500 nm. PLS regression model was built from the obtained spectral data using 70% of KBA standards (training set) in the range from 0.1 ppm to 100 ppm. The PLS regression model obtained was having R-square value of 98% with 0.99 corelationship value and having good prediction with RMSEP value 3.2 and correlation of 0.99. It was then used to quantify the amount of KBA in the samples of B. sacra. The results indicated that the MeOH extract of resin has the highest concentration of KBA (0.6%) followed by essential oil (0.1%). However, no KBA was found in the aqueous extract. The MeOH extract of the resin was subjected to column chromatography to get various sub-fractions at different polarity of organic solvents. The sub-fraction at 4% MeOH/CHCl3 (4.1% of KBA) was found to contain the highest percentage of KBA followed by another sub-fraction at 2% MeOH/CHCl3 (2.2% of KBA). The present results also indicated that KBA is only present in the gum-resin of the trunk and not in all parts of the plant. These results were further confirmed through HPLC analysis and therefore it is concluded that NIRS coupled with PLS regression is a rapid and alternate method for quantification of KBA in Boswellia sacra. It is non-destructive, rapid, sensitive and uses simple methods of sample preparation.

A simple flow-concentration modelling method for integrating water ...

African Journals Online (AJOL)

A simple flow-concentration modelling method for integrating water quality and ... flow requirements are assessed for maintenance low flow, drought low flow ... the instream concentrations of chemical constituents that will arise from different ...

An NCME Instructional Module on Data Mining Methods for Classification and Regression

Science.gov (United States)

Sinharay, Sandip

2016-01-01

Data mining methods for classification and regression are becoming increasingly popular in various scientific fields. However, these methods have not been explored much in educational measurement. This module first provides a review, which should be accessible to a wide audience in education measurement, of some of these methods. The module then…

A simple and secure method to fix laparoscopic trocars in children.

Science.gov (United States)

Yip, K F; Tam, P K H; Li, M K W

2006-04-01

We introduce a simple method of fixing trocars to the abdominal wall in children. Before anchoring the trocar, a piece of Tegaderm polyurethrane adhesive (3M Healthcare, St. Paul, Minnesota) is attached to the trocar. A silk stitch is anchored to neighboring skin, and then transfixed over the shaft of the trocar through the adhesive. Both inward and outward movement of the trocar can be restrained. This method is simple, fast, secure, and can be applied to trocars of any size.

The efficiency of the centroid method compared to a simple average

DEFF Research Database (Denmark)

Eskildsen, Jacob Kjær; Kristensen, Kai; Nielsen, Rikke

Based on empirical data as well as a simulation study this paper gives recommendations with respect to situations wheere a simple avarage of the manifest indicators can be used as a close proxy for the centroid method and when it cannot.......Based on empirical data as well as a simulation study this paper gives recommendations with respect to situations wheere a simple avarage of the manifest indicators can be used as a close proxy for the centroid method and when it cannot....

A simple statistical method for catch comparison studies

DEFF Research Database (Denmark)

Holst, René; Revill, Andrew

2009-01-01

For analysing catch comparison data, we propose a simple method based on Generalised Linear Mixed Models (GLMM) and use polynomial approximations to fit the proportions caught in the test codend. The method provides comparisons of fish catch at length by the two gears through a continuous curve...... with a realistic confidence band. We demonstrate the versatility of this method, on field data obtained from the first known testing in European waters of the Rhode Island (USA) 'Eliminator' trawl. These data are interesting as they include a range of species with different selective patterns. Crown Copyright (C...

Bayesian median regression for temporal gene expression data

Science.gov (United States)

Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.

2007-09-01

Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.

Correcting for cryptic relatedness by a regression-based genomic control method

Directory of Open Access Journals (Sweden)

Yang Yaning

2009-12-01

Full Text Available Abstract Background Genomic control (GC method is a useful tool to correct for the cryptic relatedness in population-based association studies. It was originally proposed for correcting for the variance inflation of Cochran-Armitage's additive trend test by using information from unlinked null markers, and was later generalized to be applicable to other tests with the additional requirement that the null markers are matched with the candidate marker in allele frequencies. However, matching allele frequencies limits the number of available null markers and thus limits the applicability of the GC method. On the other hand, errors in genotype/allele frequencies may cause further bias and variance inflation and thereby aggravate the effect of GC correction. Results In this paper, we propose a regression-based GC method using null markers that are not necessarily matched in allele frequencies with the candidate marker. Variation of allele frequencies of the null markers is adjusted by a regression method. Conclusion The proposed method can be readily applied to the Cochran-Armitage's trend tests other than the additive trend test, the Pearson's chi-square test and other robust efficiency tests. Simulation results show that the proposed method is effective in controlling type I error in the presence of population substructure.

The Prediction Properties of Inverse and Reverse Regression for the Simple Linear Calibration Problem

Science.gov (United States)

Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.

2010-01-01

The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.

A simple three dimensional wide-angle beam propagation method

Science.gov (United States)

Ma, Changbao; van Keuren, Edward

2006-05-01

The development of three dimensional (3-D) waveguide structures for chip scale planar lightwave circuits (PLCs) is hampered by the lack of effective 3-D wide-angle (WA) beam propagation methods (BPMs). We present a simple 3-D wide-angle beam propagation method (WA-BPM) using Hoekstra’s scheme along with a new 3-D wave equation splitting method. The applicability, accuracy and effectiveness of our method are demonstrated by applying it to simulations of wide-angle beam propagation and comparing them with analytical solutions.

Simple methods of aligning four-circle diffractometers with crystal reflections

Energy Technology Data Exchange (ETDEWEB)

Mitsui, Y [Tokyo Univ. (Japan). Faculty of Pharmaceutical Sciences

1979-08-01

Simple methods of aligning four-circle diffractometers with crystal reflections are devised. They provide the methods to check (1) perpendicularity of chi plane to the incident beam, (2) zero point of 2theta and linearity of focus-chi center-receiving aperture and (3) zero point of chi.

Simple PVT quantitative method of Kr under high pure N2 condition

International Nuclear Information System (INIS)

Li Xuesong; Zhang Zibin; Wei Guanyi; Chen Liyun; Zhai Lihua

2005-01-01

A simple PVT quantitative method of Kr in the high pure N 2 was studied. Pressure, volume and temperature of the sample gas were measured by three individual methods to obtain the sum sample with food uncertainty. The ratio of Kr/N 2 could measured by GAM 400 quadrupole mass spectrometer. So the quantity of Kr could be calculated with the two measured data above. This method can be suited for quantitative analysis of other simple composed noble gas sample with high pure carrying gas. (authors)

Nonparametric regression using the concept of minimum energy

International Nuclear Information System (INIS)

Williams, Mike

2011-01-01

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

Type I error rates of rare single nucleotide variants are inflated in tests of association with non-normally distributed traits using simple linear regression methods.

Science.gov (United States)

Schwantes-An, Tae-Hwi; Sung, Heejong; Sabourin, Jeremy A; Justice, Cristina M; Sorant, Alexa J M; Wilson, Alexander F

2016-01-01

In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log 10 and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs. Tests of association were performed with simple linear regression and average type I error rates were determined for minor allele frequency classes. Rare SNVs (minor allele frequency < 0.05) showed inflated type I error rates for non-normally distributed traits that increased as the minor allele frequency decreased. The inflation of average type I error rates increased as the significance threshold decreased. Normally distributed traits did not show inflated type I error rates with respect to the minor allele frequency for rare SNVs. There was no consistent effect of transformation on the uniformity of the distribution of the location of SNVs with a type I error.

A simple method for determining split renal function from dynamic {sup 99m}Tc-MAG3 scintigraphic data

Energy Technology Data Exchange (ETDEWEB)

Wesolowski, Michal J.; Watson, Gage; Wanasundara, Surajith N.; Babyn, Paul [University of Saskatchewan, Department of Medical Imaging, Saskatoon, SK (Canada); Conrad, Gary R. [University of Kentucky College of Medicine, Department of Radiology, Lexington, KY (United States); Samal, Martin [Charles University Prague and the General University Hospital in Prague, Department of Nuclear Medicine, First Faculty of Medicine, Praha 2 (Czech Republic); Wesolowski, Carl A. [University of Saskatchewan, Department of Medical Imaging, Saskatoon, SK (Canada); Memorial University of Newfoundland, Department of Radiology, St. John' s, NL (Canada)

2016-03-15

Commonly used methods for determining split renal function (SRF) from dynamic scintigraphic data require extrarenal background subtraction and additional correction for intrarenal vascular activity. The use of these additional regions of interest (ROIs) can produce inaccurate results and be challenging, e.g. if the heart is out of the camera field of view. The purpose of this study was to evaluate a new method for determining SRF called the blood pool compensation (BPC) technique, which is simple to implement, does not require extrarenal background correction and intrinsically corrects for intrarenal vascular activity. In the BPC method SRF is derived from a parametric plot of the curves generated by one blood-pool and two renal ROIs. Data from 107 patients who underwent {sup 99m}Tc-MAG3 scintigraphy were used to determine SRF values. Values calculated using the BPC method were compared to those obtained with the integral (IN) and Patlak-Rutland (PR) techniques using Bland-Altman plotting and Passing-Bablok regression. The interobserver variability of the BPC technique was also assessed for two observers. The SRF values obtained with the BPC method did not differ significantly from those obtained with the PR method and showed no consistent bias, while SRF values obtained with the IN method showed significant differences with some bias in comparison to those obtained with either the PR or BPC method. No significant interobserver variability was found between two observers calculating SRF using the BPC method. The BPC method requires only three ROIs to produce reliable estimates of SRF, was simple to implement, and in this study yielded statistically equivalent results to the PR method with appreciable interobserver agreement. As such, it adds a new reliable method for quality control of monitoring relative kidney function. (orig.)

Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination.

Science.gov (United States)

Chaurasia, Ashok; Harel, Ofer

2015-02-10

Tests for regression coefficients such as global, local, and partial F-tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F-tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data. Copyright © 2014 John Wiley & Sons, Ltd.

A simple method for generating exactly solvable quantum mechanical potentials

CERN Document Server

Williams, B W

1993-01-01

A simple transformation method permitting the generation of exactly solvable quantum mechanical potentials from special functions solving second-order differential equations is reviewed. This method is applied to Gegenbauer polynomials to generate an attractive radial potential. The relationship of this method to the determination of supersymmetric quantum mechanical superpotentials is discussed, and the superpotential for the radial potential is also derived. (author)

Simple prediction method of lumbar lordosis for planning of lumbar corrective surgery: radiological analysis in a Korean population.

Science.gov (United States)

Lee, Chong Suh; Chung, Sung Soo; Park, Se Jun; Kim, Dong Min; Shin, Seong Kee

2014-01-01

This study aimed at deriving a lordosis predictive equation using the pelvic incidence and to establish a simple prediction method of lumbar lordosis for planning lumbar corrective surgery in Asians. Eighty-six asymptomatic volunteers were enrolled in the study. The maximal lumbar lordosis (MLL), lower lumbar lordosis (LLL), pelvic incidence (PI), and sacral slope (SS) were measured. The correlations between the parameters were analyzed using Pearson correlation analysis. Predictive equations of lumbar lordosis through simple regression analysis of the parameters and simple predictive values of lumbar lordosis using PI were derived. The PI strongly correlated with the SS (r = 0.78), and a strong correlation was found between the SS and LLL (r = 0.89), and between the SS and MLL (r = 0.83). Based on these correlations, the predictive equations of lumbar lordosis were found (SS = 0.80 + 0.74 PI (r = 0.78, R (2) = 0.61), LLL = 5.20 + 0.87 SS (r = 0.89, R (2) = 0.80), MLL = 17.41 + 0.96 SS (r = 0.83, R (2) = 0.68). When PI was between 30° to 35°, 40° to 50° and 55° to 60°, the equations predicted that MLL would be PI + 10°, PI + 5° and PI, and LLL would be PI - 5°, PI - 10° and PI - 15°, respectively. This simple calculation method can provide a more appropriate and simpler prediction of lumbar lordosis for Asian populations. The prediction of lumbar lordosis should be used as a reference for surgeons planning to restore the lumbar lordosis in lumbar corrective surgery.

Comparing the index-flood and multiple-regression methods using L-moments

Science.gov (United States)

Malekinezhad, H.; Nachtnebel, H. P.; Klik, A.

In arid and semi-arid regions, the length of records is usually too short to ensure reliable quantile estimates. Comparing index-flood and multiple-regression analyses based on L-moments was the main objective of this study. Factor analysis was applied to determine main influencing variables on flood magnitude. Ward’s cluster and L-moments approaches were applied to several sites in the Namak-Lake basin in central Iran to delineate homogeneous regions based on site characteristics. Homogeneity test was done using L-moments-based measures. Several distributions were fitted to the regional flood data and index-flood and multiple-regression methods as two regional flood frequency methods were compared. The results of factor analysis showed that length of main waterway, compactness coefficient, mean annual precipitation, and mean annual temperature were the main variables affecting flood magnitude. The study area was divided into three regions based on the Ward’s method of clustering approach. The homogeneity test based on L-moments showed that all three regions were acceptably homogeneous. Five distributions were fitted to the annual peak flood data of three homogeneous regions. Using the L-moment ratios and the Z-statistic criteria, GEV distribution was identified as the most robust distribution among five candidate distributions for all the proposed sub-regions of the study area, and in general, it was concluded that the generalised extreme value distribution was the best-fit distribution for every three regions. The relative root mean square error (RRMSE) measure was applied for evaluating the performance of the index-flood and multiple-regression methods in comparison with the curve fitting (plotting position) method. In general, index-flood method gives more reliable estimations for various flood magnitudes of different recurrence intervals. Therefore, this method should be adopted as regional flood frequency method for the study area and the Namak-Lake basin

The Bland-Altman Method Should Not Be Used in Regression Cross-Validation Studies

Science.gov (United States)

O'Connor, Daniel P.; Mahar, Matthew T.; Laughlin, Mitzi S.; Jackson, Andrew S.

2011-01-01

The purpose of this study was to demonstrate the bias in the Bland-Altman (BA) limits of agreement method when it is used to validate regression models. Data from 1,158 men were used to develop three regression equations to estimate maximum oxygen uptake (R[superscript 2] = 0.40, 0.61, and 0.82, respectively). The equations were evaluated in a…

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

Directory of Open Access Journals (Sweden)

Hailun Wang

2017-01-01

Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

Logistic regression for risk factor modelling in stuttering research.

Science.gov (United States)

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Simple statistical methods for software engineering data and patterns

CERN Document Server

Pandian, C Ravindranath

2015-01-01

Although there are countless books on statistics, few are dedicated to the application of statistical methods to software engineering. Simple Statistical Methods for Software Engineering: Data and Patterns fills that void. Instead of delving into overly complex statistics, the book details simpler solutions that are just as effective and connect with the intuition of problem solvers.Sharing valuable insights into software engineering problems and solutions, the book not only explains the required statistical methods, but also provides many examples, review questions, and case studies that prov

The modified simple equation method for solving some fractional ...

Indian Academy of Sciences (India)

... and processes in various areas of natural science. Thus, many effective and powerful methods have been established and improved. In this study, we establish exact solutions of the time fractional biological population model equation and nonlinearfractional Klein–Gordon equation by using the modified simple equation ...

A simple method of dosimetry for E-beam radiation

International Nuclear Information System (INIS)

Spencer, D.S.; Thalacker, V.P.; Chasman, J.N.; Siegel, S.

1985-01-01

A simple method utilizing a photochromic 'intensity label' for monitoring electron-beam sources was evaluated. The labels exhibit a color change upon exposure to UV or e-beam radiation. A correlation was found between absorbed energy and Gardner Color Index at low electron-beam doses. (author)

Simple and convenient method for culturing anaerobic bacteria.

OpenAIRE

Behbehani, M J; Jordan, H V; Santoro, D L

1982-01-01

A simple and convenient method for culturing anaerobic bacteria is described. Cultures can be grown in commercially available flasks normally used for preparation of sterile external solutions. A special disposable rubber flask closure maintains anaerobic conditions in the flask after autoclaving. Growth of a variety of anaerobic oral bacteria was comparable to that obtained after anaerobic incubation of broth cultures in Brewer Anaerobic Jars.

A simple method for one-loop renormalization in curved space-time

Energy Technology Data Exchange (ETDEWEB)

Markkanen, Tommi [Helsinki Institute of Physics and Department of Physics, P.O. Box 64, FI-00014, University of Helsinki (Finland); Tranberg, Anders, E-mail: tommi.markkanen@helsinki.fi, E-mail: anders.tranberg@uis.no [Niels Bohr International Academy and Discovery Center, Niels Bohr Institute, Blegdamsvej 17, 2100 Copenhagen (Denmark)

2013-08-01

We present a simple method for deriving the renormalization counterterms from the components of the energy-momentum tensor in curved space-time. This method allows control over the finite parts of the counterterms and provides explicit expressions for each term separately. As an example, the method is used for the self-interacting scalar field in a Friedmann-Robertson-Walker metric in the adiabatic approximation, where we calculate the renormalized equation of motion for the field and the renormalized components of the energy-momentum tensor to fourth adiabatic order while including interactions to one-loop order. Within this formalism the trace anomaly, including contributions from interactions, is shown to have a simple derivation. We compare our results to those obtained by two standard methods, finding agreement with the Schwinger-DeWitt expansion but disagreement with adiabatic subtractions for interacting theories.

A simple method for DNA isolation from Xanthomonas spp.

Directory of Open Access Journals (Sweden)

Gomes Luiz Humberto

2000-01-01

Full Text Available A simple DNA isolation method was developed with routine chemicals that yields high quality and integrity preparations when compared to some of the most well known protocols. The method described does not require the use of lysing enzymes, water bath and the DNA was obtained within 40 minutes The amount of nucleic acid extracted (measured in terms of absorbancy at 260 nm from strains of Xanthomonas spp., Pseudomonas spp. and Erwinia spp. was two to five times higher than that of the most commonly used method.

Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level

Science.gov (United States)

Doran, Kara S.; Howd, Peter A.; Sallenger,, Asbury H.

2016-01-04

This report documents the development of statistical tools used to quantify the hazard presented by the response of sea-level elevation to natural or anthropogenic changes in climate and ocean circulation. A hazard is a physical process (or processes) that, when combined with vulnerability (or susceptibility to the hazard), results in risk. This study presents the development and comparison of new and existing sea-level analysis methods, exploration of the strengths and weaknesses of the methods using synthetic time series, and when appropriate, synthesis of the application of the method to observed sea-level time series. These reports are intended to enhance material presented in peer-reviewed journal articles where it is not always possible to provide the level of detail that might be necessary to fully support or recreate published results.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

Science.gov (United States)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Comparison of methods for the analysis of relatively simple mediation models

NARCIS (Netherlands)

Rijnhart, Judith J.M.; Twisk, Jos W.R.; Chinapaw, Mai J.M.; de Boer, Michiel R.; Heymans, Martijn W.

2017-01-01

Background/aims Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural

Lowest-order constrained variational method for simple many-fermion systems

International Nuclear Information System (INIS)

Alexandrov, I.; Moszkowski, S.A.; Wong, C.W.

1975-01-01

The authors study the potential energy of many-fermion systems calculated by the lowest-order constrained variational (LOCV) method of Pandharipande. Two simple two-body interactions are used. For a simple hard-core potential in a dilute Fermi gas, they find that the Huang-Yang exclusion correction can be used to determine a healing distance. The result is close to the older Pandharipande prescription for the healing distance. For a hard core plus attractive exponential potential, the LOCV result agrees closely with the lowest-order separation method of Moszkowski and Scott. They find that the LOCV result has a shallow minimum as a function of the healing distance at the Moszkowski-Scott separation distance. The significance of the absence of a Brueckner dispersion correction in the LOCV result is discussed. (Auth.)

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

Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting

Science.gov (United States)

Sutawinaya, IP; Astawa, INGA; Hariyanti, NKD

2018-01-01

Heavy rainfall can cause disaster, therefore need a forecast to predict rainfall intensity. Main factor that cause flooding is there is a high rainfall intensity and it makes the river become overcapacity. This will cause flooding around the area. Rainfall factor is a dynamic factor, so rainfall is very interesting to be studied. In order to support the rainfall forecasting, there are methods that can be used from Artificial Intelligence (AI) to statistic. In this research, we used Adaline for AI method and Regression for statistic method. The more accurate forecast result shows the method that used is good for forecasting the rainfall. Through those methods, we expected which is the best method for rainfall forecasting here.

Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

Energy Technology Data Exchange (ETDEWEB)

Lee, Myoung Keon [Agency for Defense Development, Daejeon (Korea, Republic of); Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon [Chungnam Nat’l Univ., Daejeon (Korea, Republic of)

2016-10-15

This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

International Nuclear Information System (INIS)

Lee, Myoung Keon; Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon

2016-01-01

This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

ON THE EFFECTS OF THE PRESENCE AND METHODS OF THE ELIMINATION HETEROSCEDASTICITY AND AUTOCORRELATION IN THE REGRESSION MODEL

Directory of Open Access Journals (Sweden)

Nina L. Timofeeva

2014-01-01

Full Text Available The article presents the methodological and technical bases for the creation of regression models that adequately reflect reality. The focus is on methods of removing residual autocorrelation in models. Algorithms eliminating heteroscedasticity and autocorrelation of the regression model residuals: reweighted least squares method, the method of Cochran-Orkutta are given. A model of "pure" regression is build, as well as to compare the effect on the dependent variable of the different explanatory variables when the latter are expressed in different units, a standardized form of the regression equation. The scheme of abatement techniques of heteroskedasticity and autocorrelation for the creation of regression models specific to the social and cultural sphere is developed.

Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

Science.gov (United States)

Gusriani, N.; Firdaniza

2018-03-01

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

Science.gov (United States)

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2010-01-01

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

Science.gov (United States)

Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

2012-01-01

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

A simple method for validation and verification of pipettes mounted on automated liquid handlers

DEFF Research Database (Denmark)

Stangegaard, Michael; Hansen, Anders Johannes; Frøslev, Tobias Guldberg

 We have implemented a simple method for validation and verification of the performance of pipettes mounted on automated liquid handlers as necessary for laboratories accredited under ISO 17025. An 8-step serial dilution of Orange G was prepared in quadruplicates in a flat bottom 96-well microtit...... available. In conclusion, we have set up a simple solution for the continuous validation of automated liquid handlers used for accredited work. The method is cheap, simple and easy to use for aqueous solutions but requires a spectrophotometer that can read microtiter plates....... We have implemented a simple method for validation and verification of the performance of pipettes mounted on automated liquid handlers as necessary for laboratories accredited under ISO 17025. An 8-step serial dilution of Orange G was prepared in quadruplicates in a flat bottom 96-well microtiter...

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 comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

Science.gov (United States)

Ozdemir, Adnan; Altural, Tolga

2013-03-01

This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets. The field surveys performed as part of this investigation mapped the locations of 90 landslides that had been identified in the Sultan Mountains of south-western Turkey. The landslide influence parameters used for this study are geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transportation capacity index, distance to drainage, distance to fault, drainage density, fault density, and spring density maps. The relationships between landslide distributions and these parameters were analysed using the three methods, and the results of these methods were then used to calculate the landslide susceptibility of the entire study area. The accuracy of the final landslide susceptibility maps was evaluated based on the landslides observed during the fieldwork, and the accuracy of the models was evaluated by calculating each model's relative operating characteristic curve. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the frequency ratio, logistic regression, and weights of evidence methods are 0.976, 0.952, and 0.937, respectively. These results indicate that the frequency ratio and weights of evidence models are relatively good estimators of landslide susceptibility in the study area. Specifically, the results of the correlation analysis show a high correlation between the frequency ratio and weights of evidence results, and the frequency ratio and logistic regression methods exhibit correlation coefficients of 0.771 and 0.727, respectively. The frequency ratio model is simple, and its input, calculation and output processes are

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

A simple immunoblotting method after separation of proteins in agarose gel

DEFF Research Database (Denmark)

Koch, C; Skjødt, K; Laursen, I

1985-01-01

A simple and sensitive method for immunoblotting of proteins after separation in agarose gels is described. It involves transfer of proteins onto nitrocellulose paper simply by diffusion through pressure, a transfer which only takes about 10 min. By this method we have demonstrated the existence ...

A simple method of fitting ill-conditioned polynomials to data

International Nuclear Information System (INIS)

Buckler, A.N.; Lawrence, J.

1979-04-01

A very simple transformation of the independent variable x is shown to cure the ill-conditioning when some polynomial series are fitted to given Y values. Numerical examples are given to illustrate the power of the method. (author)

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.

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

Science.gov (United States)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

An Introduction to Graphical and Mathematical Methods for Detecting Heteroscedasticity in Linear Regression.

Science.gov (United States)

Thompson, Russel L.

Homoscedasticity is an important assumption of linear regression. This paper explains what it is and why it is important to the researcher. Graphical and mathematical methods for testing the homoscedasticity assumption are demonstrated. Sources of homoscedasticity and types of homoscedasticity are discussed, and methods for correction are…

Regression: The Apple Does Not Fall Far From the Tree.

Science.gov (United States)

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Identification of Super Phenix steam generator by a simple polynomial model

International Nuclear Information System (INIS)

Rousseau, I.

1981-01-01

This note suggests a method of identification for the steam generator of the Super-Phenix fast neutron power plant for simple polynomial models. This approach is justified in the selection of the adaptive control. The identification algorithms presented will be applied to multivariable input-output behaviours. The results obtained with the representation in self-regressive form and by simple polynomial models will be compared and the effect of perturbations on the output signal will be tested, in order to select a good identification algorithm for multivariable adaptive regulation [fr

The simple modelling method for storm- and grey-water quality ...

African Journals Online (AJOL)

The simple modelling method for storm- and grey-water quality management applied to Alexandra settlement. ... objectives optimally consist of educational programmes, erosion and sediment control, street sweeping, removal of sanitation system overflows, impervious cover reduction, downspout disconnections, removal of ...

A Simple Method for Identifying the Acromioclavicular Joint During Arthroscopic Procedures

OpenAIRE

Javed, Saqib; Heasley, Richard; Ravenscroft, Matt

2013-01-01

Arthroscopic acromioclavicular joint excision is performed via an anterior portal and is technically demanding. We present a simple method for identifying the acromioclavicular joint during arthroscopic procedures.

A simple method for estimating thermal response of building ...

African Journals Online (AJOL)

This paper develops a simple method for estimating the thermal response of building materials in the tropical climatic zone using the basic heat equation. The efficacy of the developed model has been tested with data from three West African cities, namely Kano (lat. 12.1 ºN) Nigeria, Ibadan (lat. 7.4 ºN) Nigeria and Cotonou ...

Analysing inequalities in Germany a structured additive distributional regression approach

CERN Document Server

Silbersdorff, Alexander

2017-01-01

This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators. This new approach allows the observed discrepancy between the individuals’ realities and the abstract representation of those realities to be explicitly taken into consideration using the arithmetic mean alone. In turn, the method is applied to the question of economic inequality in Germany.

Real-time prediction of respiratory motion based on local regression methods

International Nuclear Information System (INIS)

Ruan, D; Fessler, J A; Balter, J M

2007-01-01

Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths

Fill rate estimation in periodic review policies with lost sales using simple methods

Energy Technology Data Exchange (ETDEWEB)

Cardós, M.; Guijarro Tarradellas, E.; Babiloni Griñón, E.

2016-07-01

Purpose: The exact estimation of the fill rate in the lost sales case is complex and time consuming. However, simple and suitable methods are needed for its estimation so that inventory managers could use them. Design/methodology/approach: Instead of trying to compute the fill rate in one step, this paper focuses first on estimating the probabilities of different on-hand stock levels so that the fill rate is computed later. Findings: As a result, the performance of a novel proposed method overcomes the other methods and is relatively simple to compute. Originality/value: Existing methods for estimating stock levels are examined, new procedures are proposed and their performance is assessed.

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

A SOCIOLOGICAL ANALYSIS OF THE CHILDBEARING COEFFICIENT IN THE ALTAI REGION BASED ON METHOD OF FUZZY LINEAR REGRESSION

Directory of Open Access Journals (Sweden)

Sergei Vladimirovich Varaksin

2017-06-01

Full Text Available Purpose. Construction of a mathematical model of the dynamics of childbearing change in the Altai region in 2000–2016, analysis of the dynamics of changes in birth rates for multiple age categories of women of childbearing age. Methodology. A auxiliary analysis element is the construction of linear mathematical models of the dynamics of childbearing by using fuzzy linear regression method based on fuzzy numbers. Fuzzy linear regression is considered as an alternative to standard statistical linear regression for short time series and unknown distribution law. The parameters of fuzzy linear and standard statistical regressions for childbearing time series were defined with using the built in language MatLab algorithm. Method of fuzzy linear regression is not used in sociological researches yet. Results. There are made the conclusions about the socio-demographic changes in society, the high efficiency of the demographic policy of the leadership of the region and the country, and the applicability of the method of fuzzy linear regression for sociological analysis.

Understanding poisson regression.

Science.gov (United States)

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

Marginal longitudinal semiparametric regression via penalized splines

KAUST Repository

Al Kadiri, M.

2010-08-01

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.

Marginal longitudinal semiparametric regression via penalized splines

KAUST Repository

Al Kadiri, M.; Carroll, R.J.; Wand, M.P.

2010-01-01

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.

A Simple DTC-SVM method for Matrix Converter Drives Using a Deadbeat Scheme

DEFF Research Database (Denmark)

Lee, Kyo-Beum; Blaabjerg, Frede; Lee, Kwang-Won

2005-01-01

In this paper, a simple direct torque control (DTC) method for sensorless matrix converter drives is proposed, which is characterized by a simple structure, minimal torque ripple and unity input power factor. Also a good sensorless speed-control performance in the low speed operation is obtained,...

12 CFR 334.25 - Reasonable and simple methods of opting out.

Science.gov (United States)

2010-01-01

... STATEMENTS OF GENERAL POLICY FAIR CREDIT REPORTING Affiliate Marketing § 334.25 Reasonable and simple methods... or processed at an Internet Web site, if the consumer agrees to the electronic delivery of... opt-out under the Act, and the affiliate marketing opt-out under the Act, by a single method, such as...

A New Quantile Regression Model to forecast one-day-ahead Value-at-Risk

OpenAIRE

Steine, Sturla Aavik; Eliassen, Markus Thorsø

2014-01-01

This master thesis focuses on the problem of forecasting volatility and Value-at-Risk (VaR) in the nancial markets. There are numerous methods for calculating VaR. However, research in this area has not currently reached one universally accepted method that can produce good VaR estimates across dierent data series, and VaR prediction and quality testing is still a very challenging statistical problem. The thesis has two main purposes, the rst is to propose a simple quantile regression mod...

The Box-and-Dot Method: A Simple Strategy for Counting Significant Figures

Science.gov (United States)

Stephenson, W. Kirk

2009-01-01

A visual method for counting significant digits is presented. This easy-to-learn (and easy-to-teach) method, designated the box-and-dot method, uses the device of "boxing" significant figures based on two simple rules, then counting the number of digits in the boxes. (Contains 4 notes.)

A Simple and Automatic Method for Locating Surgical Guide Hole

Science.gov (United States)

Li, Xun; Chen, Ming; Tang, Kai

2017-12-01

Restoration-driven surgical guides are widely used in implant surgery. This study aims to provide a simple and valid method of automatically locating surgical guide hole, which can reduce operator's experiences and improve the design efficiency and quality of surgical guide. Few literatures can be found on this topic and the paper proposed a novel and simple method to solve this problem. In this paper, a local coordinate system for each objective tooth is geometrically constructed in CAD system. This coordinate system well represents dental anatomical features and the center axis of the objective tooth (coincide with the corresponding guide hole axis) can be quickly evaluated in this coordinate system, finishing the location of the guide hole. The proposed method has been verified by comparing two types of benchmarks: manual operation by one skilled doctor with over 15-year experiences (used in most hospitals) and automatic way using one popular commercial package Simplant (used in few hospitals).Both the benchmarks and the proposed method are analyzed in their stress distribution when chewing and biting. The stress distribution is visually shown and plotted as a graph. The results show that the proposed method has much better stress distribution than the manual operation and slightly better than Simplant, which will significantly reduce the risk of cervical margin collapse and extend the wear life of the restoration.

A simple reversed phase high-performance liquid chromatography (RP-HPLC method for determination of curcumin in aqueous humor of rabbit

Directory of Open Access Journals (Sweden)

Akhilesh Mishra

2014-01-01

Full Text Available This article describes a simple and rapid method for determination of curcumin (diferuloylmethane in aqueous humor of rabbit using high-performance liquid chromatography (HPLC. Analysis was performed using a C-18 column (250 × 4.6 mm, 5 μ luna by isocratic elution with a mobile phase containing 25 mM potassium dihydrogen orthophosphate (pH 3.5: Acetonitrile (40:60 and detection at 424 nm using a photodiode array (PDA detector for curcumin. The regression data for curcumin showed a good linear relationship with r 2 > 0.998 over the concentration range of 0.1-10 μg ml−1 . Relative standard deviations (RSD for the intraday and interday coefficient of variations for the assay were less than 5.0 and 8.5, respectively. The recovery of the method was between 79.8-83.6%. The quantification limit of the method for curcumin was 0.01 μg ml−1 . This method has good accuracy, precision, and quantitation limit. It is also concluded that the method is useful for measuring very low curcumin concentrations in aqueous humor.

Method of Factor Extraction and Simple Structure of Data from Diverse Scientific Areas.

Science.gov (United States)

Thorndike, Robert M.

To study the applicability of simple structure logic for factorial data from scientific disciplines outside psychology, four correlation matrices from each of six scientific areas were factor analyzed by five factoring methods. Resulting factor matrices were compared on two objective criteria of simple structure before and after rotation.…

Aid and growth regressions

DEFF Research Database (Denmark)

Hansen, Henrik; Tarp, Finn

2001-01-01

This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...

Assessing the performance of variational methods for mixed logistic regression models

Czech Academy of Sciences Publication Activity Database

Rijmen, F.; Vomlel, Jiří

2008-01-01

Roč. 78, č. 8 (2008), s. 765-779 ISSN 0094-9655 R&D Projects: GA MŠk 1M0572 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Mixed models * Logistic regression * Variational methods * Lower bound approximation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.353, year: 2008

Quantification of endocrine disruptors and pesticides in water by gas chromatography-tandem mass spectrometry. Method validation using weighted linear regression schemes.

Science.gov (United States)

Mansilha, C; Melo, A; Rebelo, H; Ferreira, I M P L V O; Pinho, O; Domingues, V; Pinho, C; Gameiro, P

2010-10-22

A multi-residue methodology based on a solid phase extraction followed by gas chromatography-tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC-MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness. Copyright © 2010 Elsevier B.V. All rights reserved.

A Simple Method for Dynamic Scheduling in a Heterogeneous Computing System

OpenAIRE

Žumer, Viljem; Brest, Janez

2002-01-01

A simple method for the dynamic scheduling on a heterogeneous computing system is proposed in this paper. It was implemented to minimize the parallel program execution time. The proposed method decomposes the program workload into computationally homogeneous subtasks, which may be of the different size, depending on the current load of each machine in a heterogeneous computing system.

Method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1972-01-01

Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.

Double-lock technique: a simple method to secure abdominal wall closure

International Nuclear Information System (INIS)

Jategaonkar, P.A.; Yadav, S.P.

2013-01-01

Secure closure of a laparotomy incision remains an important aspect of any abdominal operation with the aim to avoid the postoperative morbidity and hasten the patient's recovery. Depending on the operator's preference and experience, it may be done by the continuous or the interrupted methods either using a non-absorbable or delayed-absorbable suture. We describe a simple, secure and quick technique of abdominal wall closure which involves continuous suture inter-locked doubly after every third bite. This simple and easy to use mass closure technique can be easily mastered by any member of the surgical team and does not need any assistant. It amalgamates the advantages of both, the continuous and the interrupted methods of closures. To our knowledge, such a technique has not been reported in the literature. (author)

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.

USE OF THE SIMPLE LINEAR REGRESSION MODEL IN MACRO-ECONOMICAL ANALYSES

Directory of Open Access Journals (Sweden)

Constantin ANGHELACHE

2011-10-01

Full Text Available The article presents the fundamental aspects of the linear regression, as a toolbox which can be used in macroeconomic analyses. The article describes the estimation of the parameters, the statistical tests used, the homoscesasticity and heteroskedasticity. The use of econometrics instrument in macroeconomics is an important factor that guarantees the quality of the models, analyses, results and possible interpretation that can be drawn at this level.

Some new, simple and efficient stereological methods and their use in pathological research and diagnosis

DEFF Research Database (Denmark)

Gundersen, H J; Bendtsen, T F; Korbo, L

1988-01-01

Stereology is a set of simple and efficient methods for quantitation of three-dimensional microscopic structures which is specifically tuned to provide reliable data from sections. Within the last few years, a number of new methods has been developed which are of special interest to pathologists...... are invariably simple and easy....

Process control and optimization with simple interval calculation method

DEFF Research Database (Denmark)

Pomerantsev, A.; Rodionova, O.; Høskuldsson, Agnar

2006-01-01

for the quality improvement in the course of production. The latter is an active quality optimization, which takes into account the actual history of the process. The advocate approach is allied to the conventional method of multivariate statistical process control (MSPC) as it also employs the historical process......Methods of process control and optimization are presented and illustrated with a real world example. The optimization methods are based on the PLS block modeling as well as on the simple interval calculation methods of interval prediction and object status classification. It is proposed to employ...... the series of expanding PLS/SIC models in order to support the on-line process improvements. This method helps to predict the effect of planned actions on the product quality and thus enables passive quality control. We have also considered an optimization approach that proposes the correcting actions...

Regression analysis of sparse asynchronous longitudinal data.

Science.gov (United States)

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

2015-09-01

We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

A simple and fast method for extraction and quantification of cryptophyte phycoerythrin

DEFF Research Database (Denmark)

Thoisen, Christina Vinum; Hansen, Benni Winding; Nielsen, Søren Laurentius

2017-01-01

The microalgal pigment phycoerythrin (PE) is of commercial interest as natural colorant in food and cosmetics, as well as fluoroprobes for laboratory analysis. Several methods for extraction and quantification of PE are available but they comprise typically various extraction buffers, repetitive...... freeze-thaw cycles and liquid nitrogen, making extraction procedures more complicated. A simple method for extraction of PE from cryptophytes is described using standard laboratory materials and equipment. Filters with the cryptophyte were frozen (−80 °C) and added phosphate buffer for extraction at 4 °C...... followed by absorbance measurement. The cryptophyte Rhodomonas salina was used as a model organism. •Simple method for extraction and quantification of phycoerythrin from cryptophytes. •Minimal usage of equipment and chemicals, and low labor costs. •Applicable for industrial and biological purposes....

Alpins and thibos vectorial astigmatism analyses: proposal of a linear regression model between methods

Directory of Open Access Journals (Sweden)

Giuliano de Oliveira Freitas

2013-10-01

Full Text Available PURPOSE: To determine linear regression models between Alpins descriptive indices and Thibos astigmatic power vectors (APV, assessing the validity and strength of such correlations. METHODS: This case series prospectively assessed 62 eyes of 31 consecutive cataract patients with preoperative corneal astigmatism between 0.75 and 2.50 diopters in both eyes. Patients were randomly assorted among two phacoemulsification groups: one assigned to receive AcrySof®Toric intraocular lens (IOL in both eyes and another assigned to have AcrySof Natural IOL associated with limbal relaxing incisions, also in both eyes. All patients were reevaluated postoperatively at 6 months, when refractive astigmatism analysis was performed using both Alpins and Thibos methods. The ratio between Thibos postoperative APV and preoperative APV (APVratio and its linear regression to Alpins percentage of success of astigmatic surgery, percentage of astigmatism corrected and percentage of astigmatism reduction at the intended axis were assessed. RESULTS: Significant negative correlation between the ratio of post- and preoperative Thibos APVratio and Alpins percentage of success (%Success was found (Spearman's ρ=-0.93; linear regression is given by the following equation: %Success = (-APVratio + 1.00x100. CONCLUSION: The linear regression we found between APVratio and %Success permits a validated mathematical inference concerning the overall success of astigmatic surgery.

Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings

International Nuclear Information System (INIS)

Chung, William

2012-01-01

Highlights: ► Fuzzy linear regression method is used for developing benchmarking systems. ► The systems can be used to benchmark energy efficiency of commercial buildings. ► The resulting benchmarking model can be used by public users. ► The resulting benchmarking model can capture the fuzzy nature of input–output data. -- Abstract: Benchmarking systems from a sample of reference buildings need to be developed to conduct benchmarking processes for the energy efficiency of commercial buildings. However, not all benchmarking systems can be adopted by public users (i.e., other non-reference building owners) because of the different methods in developing such systems. An approach for benchmarking the energy efficiency of commercial buildings using statistical regression analysis to normalize other factors, such as management performance, was developed in a previous work. However, the field data given by experts can be regarded as a distribution of possibility. Thus, the previous work may not be adequate to handle such fuzzy input–output data. Consequently, a number of fuzzy structures cannot be fully captured by statistical regression analysis. This present paper proposes the use of fuzzy linear regression analysis to develop a benchmarking process, the resulting model of which can be used by public users. An illustrative example is given as well.

A new fuzzy regression model based on interval-valued fuzzy neural network and its applications to management

Directory of Open Access Journals (Sweden)

Somaye Yeylaghi

2017-06-01

Full Text Available In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods.

A New and Simple Method for Crosstalk Estimation in Homogeneous Trench-Assisted Multi-Core Fibers

DEFF Research Database (Denmark)

Ye, Feihong; Tu, Jiajing; Saitoh, Kunimasa

2014-01-01

A new and simple method for inter-core crosstalk estimation in homogeneous trench-assisted multi-core fibers is presented. The crosstalk calculated by this method agrees well with experimental measurement data for two kinds of fabricated 12-core fibers.......A new and simple method for inter-core crosstalk estimation in homogeneous trench-assisted multi-core fibers is presented. The crosstalk calculated by this method agrees well with experimental measurement data for two kinds of fabricated 12-core fibers....

Simple method to generate and fabricate stochastic porous scaffolds

Energy Technology Data Exchange (ETDEWEB)

Yang, Nan, E-mail: y79nzw@163.com; Gao, Lilan; Zhou, Kuntao

2015-11-01

Considerable effort has been made to generate regular porous structures (RPSs) using function-based methods, although little effort has been made for constructing stochastic porous structures (SPSs) using the same methods. In this short communication, we propose a straightforward method for SPS construction that is simple in terms of methodology and the operations used. Using our method, we can obtain a SPS with functionally graded, heterogeneous and interconnected pores, target pore size and porosity distributions, which are useful for applications in tissue engineering. The resulting SPS models can be directly fabricated using additive manufacturing (AM) techniques. - Highlights: • Random porous structures are constructed based on their regular counterparts. • Functionally graded random pores can be constructed easily. • The scaffolds can be directly fabricated using additive manufacturing techniques.

Development of nondestructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression

Energy Technology Data Exchange (ETDEWEB)

Lee, Sang Dae; Lohumi, Santosh; Cho, Byoung Kwan [Dept. of Biosystems Machinery Engineering, Chungnam National University, Daejeon (Korea, Republic of); Kim, Moon Sung [United States Department of Agriculture Agricultural Research Service, Washington (United States); Lee, Soo Hee [Life and Technology Co.,Ltd., Hwasung (Korea, Republic of)

2014-08-15

This study was conducted to develop a non-destructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression(PLSR). Garlic and ginger powder, which are used as natural seasoning and in health supplement foods, were selected for this experiment. Samples were adulterated with corn starch in concentrations of 5-35%. PLSR models for adulterated garlic and ginger powders were developed and their performances evaluated using cross validation. The R{sup 2}{sub c} and SEC of an optimal PLSR model were 0.99 and 2.16 for the garlic powder samples, and 0.99 and 0.84 for the ginger samples, respectively. The variable importance in projection (VIP) score is a useful and simple tool for the evaluation of the importance of each variable in a PLSR model. After the VIP scores were taken pre-selection, the Raman spectrum data was reduced by one third. New PLSR models, based on a reduced number of wavelengths selected by the VIP scores technique, gave good predictions for the adulterated garlic and ginger powder samples.

Development of nondestructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression

International Nuclear Information System (INIS)

Lee, Sang Dae; Lohumi, Santosh; Cho, Byoung Kwan; Kim, Moon Sung; Lee, Soo Hee

2014-01-01

This study was conducted to develop a non-destructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression(PLSR). Garlic and ginger powder, which are used as natural seasoning and in health supplement foods, were selected for this experiment. Samples were adulterated with corn starch in concentrations of 5-35%. PLSR models for adulterated garlic and ginger powders were developed and their performances evaluated using cross validation. The R 2 c and SEC of an optimal PLSR model were 0.99 and 2.16 for the garlic powder samples, and 0.99 and 0.84 for the ginger samples, respectively. The variable importance in projection (VIP) score is a useful and simple tool for the evaluation of the importance of each variable in a PLSR model. After the VIP scores were taken pre-selection, the Raman spectrum data was reduced by one third. New PLSR models, based on a reduced number of wavelengths selected by the VIP scores technique, gave good predictions for the adulterated garlic and ginger powder samples.

Post-processing through linear regression

Science.gov (United States)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

A simple method to adapt time sampling of the analog signal

International Nuclear Information System (INIS)

Kalinin, Yu.G.; Martyanov, I.S.; Sadykov, Kh.; Zastrozhnova, N.N.

2004-01-01

In this paper we briefly describe the time sampling method, which is adapted to the speed of the signal change. Principally, this method is based on a simple idea--the combination of discrete integration with differentiation of the analog signal. This method can be used in nuclear electronics research into the characteristics of detectors and the shape of the pulse signal, pulse and transitive characteristics of inertial systems of processing of signals, etc

Cox regression with missing covariate data using a modified partial likelihood method

DEFF Research Database (Denmark)

Martinussen, Torben; Holst, Klaus K.; Scheike, Thomas H.

2016-01-01

Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard...

Study of a simple method and software for quantitative measurement of rCBF with 99Tcm-ECD SPECT brain imaging

International Nuclear Information System (INIS)

Shu Boxue; Lai Huaan; Li Zhigang; Shi An

2000-01-01

Objective: To create a simple, practical, stable and easy to popularize rCBF quantitative measurement method. Methods: 1) Creating attenuation correction factor (δ) of brain; 2) Proving a factor (ρ) between planar image and tomographic image; 3) Creating SPECT system to determine the dead time and to correct linear regression equation; 4) Measuring lung retardation rate (R 1 ); 5) Improving Nickel model and editing the software; 6) Clinical application; The modified method was performed in 24 subjects, including 15 healthy controls, 8 patients with epilepsy in intermission and 1 patient with brain infarction. Results: δ = 1.7, ρ = 2.23, R 1 -1 ·100 g -1 . The rCBFs of foci in 8 cases of epilepsy were obviously decreased, (22.5∼34.2) mL·min -1 ·100 g -1 , and in the case of brain infarction was only 7.2 mL·min -1 ·100 g -1 . Conclusions: The method is reliable, practical and easy to perform with good quality control. Overall, it is of high clinical value

Simple method for correct enumeration of Staphylococcus aureus

DEFF Research Database (Denmark)

Haaber, J.; Cohn, M. T.; Petersen, A.

2016-01-01

culture. When grown in such liquid cultures, the human pathogen Staphylococcus aureus is characterized by its aggregation of single cells into clusters of variable size. Here, we show that aggregation during growth in the laboratory standard medium tryptic soy broth (TSB) is common among clinical...... and laboratory S. aureus isolates and that aggregation may introduce significant bias when applying standard enumeration methods on S. aureus growing in laboratory batch cultures. We provide a simple and efficient sonication procedure, which can be applied prior to optical density measurements to give...

Simple Screening Methods for Drought and Heat Tolerance in Cowpea

International Nuclear Information System (INIS)

Singh, B. B.

2000-10-01

Success in breeding for drought tolerance has not been as pronounced as for other traits. This is partly due to lack of simple, cheap and reliable screening methods to select drought tolerant plants/progenies from the segregating populations and partly due to complexity of factors involved in drought tolerance. Measuring drought tolerance through physiological parameters is expensive, time consuming and difficult to use for screening large numbers of lines and segregating populations. Since several factors/mechanisms (in shoot and root) operate independently and/or jointly to enable plants to cope with drought stress, drought tolerance appears as a complex trait. However, if these factors/mechanisms can be separated and studied individually, the components leading to drought tolerance will appear less complex and may be easy to manipulate by breeders. We have developed a simple box screening method for shoot drought tolerance in cowpea, which eliminates the effects of roots and permits non-destructive visual identification of shoot dehydration tolerance. We have also developed a 'root-box pin-board' method to study two dimensional root architecture of individual plants. Using these methods, we have identified two mechanisms of shoot drought tolerance in cowpea which are controlled by single dominant genes and major difference for root architecture among cowpea varieties. Combining deep and dense root system with shoot dehydration tolerance results into highly drought tolerant plants

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

Science.gov (United States)

Steinhardt, Charles L.; Jermyn, Adam S.

2018-02-01

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

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.

A simple and efficient method for isolating small RNAs from different plant species

Directory of Open Access Journals (Sweden)

de Folter Stefan

2011-02-01

Full Text Available Abstract Background Small RNAs emerged over the last decade as key regulators in diverse biological processes in eukaryotic organisms. To identify and study small RNAs, good and efficient protocols are necessary to isolate them, which sometimes may be challenging due to the composition of specific tissues of certain plant species. Here we describe a simple and efficient method to isolate small RNAs from different plant species. Results We developed a simple and efficient method to isolate small RNAs from different plant species by first comparing different total RNA extraction protocols, followed by streamlining the best one, finally resulting in a small RNA extraction method that has no need of first total RNA extraction and is not based on the commercially available TRIzol® Reagent or columns. This small RNA extraction method not only works well for plant tissues with high polysaccharide content, like cactus, agave, banana, and tomato, but also for plant species like Arabidopsis or tobacco. Furthermore, the obtained small RNA samples were successfully used in northern blot assays. Conclusion Here we provide a simple and efficient method to isolate small RNAs from different plant species, such as cactus, agave, banana, tomato, Arabidopsis, and tobacco, and the small RNAs from this simplified and low cost method is suitable for downstream handling like northern blot assays.

An introduction to g methods.

Science.gov (United States)

Naimi, Ashley I; Cole, Stephen R; Kennedy, Edward H

2017-04-01

Robins' generalized methods (g methods) provide consistent estimates of contrasts (e.g. differences, ratios) of potential outcomes under a less restrictive set of identification conditions than do standard regression methods (e.g. linear, logistic, Cox regression). Uptake of g methods by epidemiologists has been hampered by limitations in understanding both conceptual and technical details. We present a simple worked example that illustrates basic concepts, while minimizing technical complications. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Temporal trends in sperm count: a systematic review and meta-regression analysis.

Science.gov (United States)

Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson; Mendiola, Jaime; Weksler-Derri, Dan; Mindlis, Irina; Pinotti, Rachel; Swan, Shanna H

2017-11-01

Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group. PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models. SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because

A simple and fast method for extraction and quantification of cryptophyte phycoerythrin.

Science.gov (United States)

Thoisen, Christina; Hansen, Benni Winding; Nielsen, Søren Laurentius

2017-01-01

The microalgal pigment phycoerythrin (PE) is of commercial interest as natural colorant in food and cosmetics, as well as fluoroprobes for laboratory analysis. Several methods for extraction and quantification of PE are available but they comprise typically various extraction buffers, repetitive freeze-thaw cycles and liquid nitrogen, making extraction procedures more complicated. A simple method for extraction of PE from cryptophytes is described using standard laboratory materials and equipment. The cryptophyte cells on the filters were disrupted at -80 °C and added phosphate buffer for extraction at 4 °C followed by absorbance measurement. The cryptophyte Rhodomonas salina was used as a model organism. •Simple method for extraction and quantification of phycoerythrin from cryptophytes.•Minimal usage of equipment and chemicals, and low labor costs.•Applicable for industrial and biological purposes.

A simple encapsulation method for organic optoelectronic devices

International Nuclear Information System (INIS)

Sun Qian-Qian; An Qiao-Shi; Zhang Fu-Jun

2014-01-01

The performances of organic optoelectronic devices, such as organic light emitting diodes and polymer solar cells, have rapidly improved in the past decade. The stability of an organic optoelectronic device has become a key problem for further development. In this paper, we report one simple encapsulation method for organic optoelectronic devices with a parafilm, based on ternary polymer solar cells (PSCs). The power conversion efficiencies (PCE) of PSCs with and without encapsulation decrease from 2.93% to 2.17% and from 2.87% to 1.16% after 168-hours of degradation under an ambient environment, respectively. The stability of PSCs could be enhanced by encapsulation with a parafilm. The encapsulation method is a competitive choice for organic optoelectronic devices, owing to its low cost and compatibility with flexible devices. (atomic and molecular physics)

Fungible weights in logistic regression.

Science.gov (United States)

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Geographically weighted regression based methods for merging satellite and gauge precipitation

Science.gov (United States)

Chao, Lijun; Zhang, Ke; Li, Zhijia; Zhu, Yuelong; Wang, Jingfeng; Yu, Zhongbo

2018-03-01

Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate hydrological forecasting. To improve the spatial resolution and quality of satellite precipitation, a three-step satellite and gauge precipitation merging method was formulated in this study: (1) bilinear interpolation is first applied to downscale coarser satellite precipitation to a finer resolution (PS); (2) the (mixed) geographically weighted regression methods coupled with a weighting function are then used to estimate biases of PS as functions of gauge observations (PO) and PS; and (3) biases of PS are finally corrected to produce a merged precipitation product. Based on the above framework, eight algorithms, a combination of two geographically weighted regression methods and four weighting functions, are developed to merge CMORPH (CPC MORPHing technique) precipitation with station observations on a daily scale in the Ziwuhe Basin of China. The geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were used for merging precipitation to avoid the artificial spatial autocorrelation resulting from traditional interpolation methods. The results show that the combination of the MGWR and BI-square function (MGWR-BI) has the best performance (R = 0.863 and RMSE = 7.273 mm/day) among the eight algorithms. The MGWR-BI algorithm was then applied to produce hourly merged precipitation product. Compared to the original CMORPH product (R = 0.208 and RMSE = 1.208 mm/hr), the quality of the merged data is significantly higher (R = 0.724 and RMSE = 0.706 mm/hr). The developed merging method not only improves the spatial resolution and quality of the satellite product but also is easy to implement, which is valuable for hydrological modeling and other applications.

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.

Determination of Urine Albumin by New Simple High-Performance Liquid Chromatography Method.

Science.gov (United States)

Klapkova, Eva; Fortova, Magdalena; Prusa, Richard; Moravcova, Libuse; Kotaska, Karel

2016-11-01

A simple high-performance liquid chromatography (HPLC) method was developed for the determination of albumin in patients' urine samples without coeluting proteins and was compared with the immunoturbidimetric determination of albumin. Urine albumin is important biomarker in diabetic patients, but part of it is immuno-nonreactive. Albumin was determined by high-performance liquid chromatography (HPLC), UV detection at 280 nm, Zorbax 300SB-C3 column. Immunoturbidimetric analysis was performed using commercial kit on automatic biochemistry analyzer COBAS INTEGRA ® 400, Roche Diagnostics GmbH, Manheim, Germany. The HLPC method was fully validated. No significant interference with other proteins (transferrin, α-1-acid glycoprotein, α-1-antichymotrypsin, antitrypsin, hemopexin) was found. The results from 301 urine samples were compared with immunochemical determination. We found a statistically significant difference between these methods (P = 0.0001, Mann-Whitney test). New simple HPLC method was developed for the determination of urine albumin without coeluting proteins. Our data indicate that the HPLC method is highly specific and more sensitive than immunoturbidimetry. © 2016 Wiley Periodicals, Inc.

A simple method suitable to study de novo root organogenesis

Directory of Open Access Journals (Sweden)

Xiaodong eChen

2014-05-01

Full Text Available De novo root organogenesis is the process in which adventitious roots regenerate from detached or wounded plant tissues or organs. In tissue culture, appropriate types and concentrations of plant hormones in the medium are critical for inducing adventitious roots. However, in natural conditions, regeneration from detached organs is likely to rely on endogenous hormones. To investigate the actions of endogenous hormones and the molecular mechanisms guiding de novo root organogenesis, we developed a simple method to imitate natural conditions for adventitious root formation by culturing Arabidopsis thaliana leaf explants on B5 medium without additive hormones. Here we show that the ability of the leaf explants to regenerate roots depends on the age of the leaf and on certain nutrients in the medium. Based on these observations, we provide examples of how this method can be used in different situations, and how it can be optimized. This simple method could be used to investigate the effects of various physiological and molecular changes on the regeneration of adventitious roots. It is also useful for tracing cell lineage during the regeneration process by differential interference contrast observation of -glucuronidase staining, and by live imaging of proteins labeled with fluorescent tags.

Post-processing through linear regression

Directory of Open Access Journals (Sweden)

B. Van Schaeybroeck

2011-03-01

Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.

These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

COMPARISON OF PARTIAL LEAST SQUARES REGRESSION METHOD ALGORITHMS: NIPALS AND PLS-KERNEL AND AN APPLICATION

Directory of Open Access Journals (Sweden)

ELİF BULUT

2013-06-01

Full Text Available Partial Least Squares Regression (PLSR is a multivariate statistical method that consists of partial least squares and multiple linear regression analysis. Explanatory variables, X, having multicollinearity are reduced to components which explain the great amount of covariance between explanatory and response variable. These components are few in number and they don’t have multicollinearity problem. Then multiple linear regression analysis is applied to those components to model the response variable Y. There are various PLSR algorithms. In this study NIPALS and PLS-Kernel algorithms will be studied and illustrated on a real data set.

A simple method for determination of carmine in food samples based on cloud point extraction and spectrophotometric detection.

Science.gov (United States)

Heydari, Rouhollah; Hosseini, Mohammad; Zarabi, Sanaz

2015-01-01

In this paper, a simple and cost effective method was developed for extraction and pre-concentration of carmine in food samples by using cloud point extraction (CPE) prior to its spectrophotometric determination. Carmine was extracted from aqueous solution using Triton X-100 as extracting solvent. The effects of main parameters such as solution pH, surfactant and salt concentrations, incubation time and temperature were investigated and optimized. Calibration graph was linear in the range of 0.04-5.0 μg mL(-1) of carmine in the initial solution with regression coefficient of 0.9995. The limit of detection (LOD) and limit of quantification were 0.012 and 0.04 μg mL(-1), respectively. Relative standard deviation (RSD) at low concentration level (0.05 μg mL(-1)) of carmine was 4.8% (n=7). Recovery values in different concentration levels were in the range of 93.7-105.8%. The obtained results demonstrate the proposed method can be applied satisfactory to determine the carmine in food samples. Copyright © 2015 Elsevier B.V. All rights reserved.

A simple transformation independent method for outlier definition.

Science.gov (United States)

Johansen, Martin Berg; Christensen, Peter Astrup

2018-04-10

Definition and elimination of outliers is a key element for medical laboratories establishing or verifying reference intervals (RIs). Especially as inclusion of just a few outlying observations may seriously affect the determination of the reference limits. Many methods have been developed for definition of outliers. Several of these methods are developed for the normal distribution and often data require transformation before outlier elimination. We have developed a non-parametric transformation independent outlier definition. The new method relies on drawing reproducible histograms. This is done by using defined bin sizes above and below the median. The method is compared to the method recommended by CLSI/IFCC, which uses Box-Cox transformation (BCT) and Tukey's fences for outlier definition. The comparison is done on eight simulated distributions and an indirect clinical datasets. The comparison on simulated distributions shows that without outliers added the recommended method in general defines fewer outliers. However, when outliers are added on one side the proposed method often produces better results. With outliers on both sides the methods are equally good. Furthermore, it is found that the presence of outliers affects the BCT, and subsequently affects the determined limits of current recommended methods. This is especially seen in skewed distributions. The proposed outlier definition reproduced current RI limits on clinical data containing outliers. We find our simple transformation independent outlier detection method as good as or better than the currently recommended methods.

Using container weights to determine irrigation needs: A simple method

Science.gov (United States)

R. Kasten Dumroese; Mark E. Montville; Jeremiah R. Pinto

2015-01-01

Proper irrigation can reduce water use, water waste, and incidence of disease. Knowing when to irrigate plants in container nurseries can be determined by weighing containers. This simple method is quantifiable, which is a benefit when more than one worker is responsible for irrigation. Irrigation is necessary when the container weighs some target as a proportion of...

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

Science.gov (United States)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

SPLINE LINEAR REGRESSION USED FOR EVALUATING FINANCIAL ASSETS 1

Directory of Open Access Journals (Sweden)

Liviu GEAMBAŞU

2010-12-01

Full Text Available One of the most important preoccupations of financial markets participants was and still is the problem of determining more precise the trend of financial assets prices. For solving this problem there were written many scientific papers and were developed many mathematical and statistical models in order to better determine the financial assets price trend. If until recently the simple linear models were largely used due to their facile utilization, the financial crises that affected the world economy starting with 2008 highlight the necessity of adapting the mathematical models to variation of economy. A simple to use model but adapted to economic life realities is the spline linear regression. This type of regression keeps the continuity of regression function, but split the studied data in intervals with homogenous characteristics. The characteristics of each interval are highlighted and also the evolution of market over all the intervals, resulting reduced standard errors. The first objective of the article is the theoretical presentation of the spline linear regression, also referring to scientific national and international papers related to this subject. The second objective is applying the theoretical model to data from the Bucharest Stock Exchange

Radiation transport benchmarks for simple geometries with void regions using the spherical harmonics method

International Nuclear Information System (INIS)

Kobayashi, K.

2009-01-01

In 2001, an international cooperation on the 3D radiation transport benchmarks for simple geometries with void region was performed under the leadership of E. Sartori of OECD/NEA. There were contributions from eight institutions, where 6 contributions were by the discrete ordinate method and only two were by the spherical harmonics method. The 3D spherical harmonics program FFT3 by the finite Fourier transformation method has been improved for this presentation, and benchmark solutions for the 2D and 3D simple geometries with void region by the FFT2 and FFT3 are given showing fairly good accuracy. (authors)

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

Science.gov (United States)

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

A Simple and Reliable Method of Design for Standalone Photovoltaic Systems

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Srinivasarao, Mantri; Sudha, K. Rama; Bhanu, C. V. K.

2017-06-01

Standalone photovoltaic (SAPV) systems are seen as a promoting method of electrifying areas of developing world that lack power grid infrastructure. Proliferations of these systems require a design procedure that is simple, reliable and exhibit good performance over its life time. The proposed methodology uses simple empirical formulae and easily available parameters to design SAPV systems, that is, array size with energy storage. After arriving at the different array size (area), performance curves are obtained for optimal design of SAPV system with high amount of reliability in terms of autonomy at a specified value of loss of load probability (LOLP). Based on the array to load ratio (ALR) and levelized energy cost (LEC) through life cycle cost (LCC) analysis, it is shown that the proposed methodology gives better performance, requires simple data and is more reliable when compared with conventional design using monthly average daily load and insolation.

Determination of carbohydrates present in Saccharomyces cerevisiae using mid-infrared spectroscopy and partial least squares regression

OpenAIRE

Plata, Maria R.; Koch, Cosima; Wechselberger, Patrick; Herwig, Christoph; Lendl, Bernhard

2013-01-01

A fast and simple method to control variations in carbohydrate composition of Saccharomyces cerevisiae, baker's yeast, during fermentation was developed using mid-infrared (mid-IR) spectroscopy. The method allows for precise and accurate determinations with minimal or no sample preparation and reagent consumption based on mid-IR spectra and partial least squares (PLS) regression. The PLS models were developed employing the results from reference analysis of the yeast cells. The reference anal...

Simple measurement of 14C in the environment using gel suspension method

International Nuclear Information System (INIS)

Wakabayashi, Genichiro; Oura, Hirotaka; Nagao, Kenjiro; Okai, Tomio; Matoba, Masaru; Kakiuchi, Hideki; Momoshima, Noriyuki; Kawamura, Hidehisa

1999-01-01

A gel suspension method using N-lauroyl-L-glutamic-α, γ-dibutylamide as gelling agent and calcium carbonate as sample was studied and it was proved a more simple measurement method of 14 C in environment than the ordinary method. 100, 20 and 7 ml of sample could introduce 3.6, 0.72 and 0.252 g of carbon, respectively. When 100 ml and 20 ml of vial introduced the maximum carbon, the lower limit of detection was about 0.3 dpm/g-C and 0.5 dpm/g-C, respectively. These values showed that this method was able to determine 14 C in the environment. The value of sample has been constant for two years or more. This fact indicated the sample prepared by this method was good for repeat measurement and long-term storage. Many samples prepared by the same calcium carbonate showed almost same values. The concentrations of 14 C in the growth rings of a tree and in rice in the environment were determined and the results agreed with the values in the references. From these above results, this method is more simple measurement method of 14 C in the environment than the ordinary method and can apply to determine 14 C in and around the nuclear installation. (S.Y.)

The Research of Regression Method for Forecasting Monthly Electricity Sales Considering Coupled Multi-factor

Science.gov (United States)

Wang, Jiangbo; Liu, Junhui; Li, Tiantian; Yin, Shuo; He, Xinhui

2018-01-01

The monthly electricity sales forecasting is a basic work to ensure the safety of the power system. This paper presented a monthly electricity sales forecasting method which comprehensively considers the coupled multi-factors of temperature, economic growth, electric power replacement and business expansion. The mathematical model is constructed by using regression method. The simulation results show that the proposed method is accurate and effective.

Alternative regression models to assess increase in childhood BMI

Directory of Open Access Journals (Sweden)

Mansmann Ulrich

2008-09-01

Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

A simple enzymic method for the synthesis of [32P]phosphoenolpyruvate

International Nuclear Information System (INIS)

Parra, F.

1982-01-01

A rapid and simple enzymic method is described for the synthesis of [ 32 P]phosphoenolpyruvate from [ 32 P]Psub(i), with a reproducible yield of 74%. The final product was shown to be a good substrate for pyruvate kinase (EC 2.7.1.40). (author)

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.

Applications of Monte Carlo method to nonlinear regression of rheological data

Science.gov (United States)

Kim, Sangmo; Lee, Junghaeng; Kim, Sihyun; Cho, Kwang Soo

2018-02-01

In rheological study, it is often to determine the parameters of rheological models from experimental data. Since both rheological data and values of the parameters vary in logarithmic scale and the number of the parameters is quite large, conventional method of nonlinear regression such as Levenberg-Marquardt (LM) method is usually ineffective. The gradient-based method such as LM is apt to be caught in local minima which give unphysical values of the parameters whenever the initial guess of the parameters is far from the global optimum. Although this problem could be solved by simulated annealing (SA), the Monte Carlo (MC) method needs adjustable parameter which could be determined in ad hoc manner. We suggest a simplified version of SA, a kind of MC methods which results in effective values of the parameters of most complicated rheological models such as the Carreau-Yasuda model of steady shear viscosity, discrete relaxation spectrum and zero-shear viscosity as a function of concentration and molecular weight.

Further Insight and Additional Inference Methods for Polynomial Regression Applied to the Analysis of Congruence

Science.gov (United States)

Cohen, Ayala; Nahum-Shani, Inbal; Doveh, Etti

2010-01-01

In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. Although this method is increasingly applied in congruence research, its complexity relative to other methods for assessing congruence (e.g., difference score methods) was one of the…

Comparing treatment effects after adjustment with multivariable Cox proportional hazards regression and propensity score methods

NARCIS (Netherlands)

Martens, Edwin P; de Boer, Anthonius; Pestman, Wiebe R; Belitser, Svetlana V; Stricker, Bruno H Ch; Klungel, Olaf H

PURPOSE: To compare adjusted effects of drug treatment for hypertension on the risk of stroke from propensity score (PS) methods with a multivariable Cox proportional hazards (Cox PH) regression in an observational study with censored data. METHODS: From two prospective population-based cohort

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

Science.gov (United States)

Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

Energy Technology Data Exchange (ETDEWEB)

Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be [Vrije Universiteit Brussel (VUB), Department of Mechanical Engineering, Research Group Fluid Mechanics and Thermodynamics, Pleinlaan 2, 1050 Brussels (Belgium); Raisee, Mehrdad [School of Mechanical Engineering, College of Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran (Iran, Islamic Republic of); Ghorbaniasl, Ghader; Contino, Francesco; Lacor, Chris [Vrije Universiteit Brussel (VUB), Department of Mechanical Engineering, Research Group Fluid Mechanics and Thermodynamics, Pleinlaan 2, 1050 Brussels (Belgium)

2017-03-01

Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selection criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels.

Science.gov (United States)

Kaneko, Hiromasa

2018-02-26

To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of an objective variable using subregression models, only the submodels with ADs that cover a query sample, i.e., the sample is inside the model's AD, are used. By constructing submodels and changing a list of selected explanatory variables, the union of the submodels' ADs, which defines the overall AD, becomes large, and the prediction performance is enhanced for diverse compounds. By analyzing a quantitative structure-activity relationship data set and a quantitative structure-property relationship data set, it is confirmed that the ADs can be enlarged and the estimation performance of regression models is improved compared with traditional methods.

A simple method for generation of back-ground-free gamma-ray spectra

International Nuclear Information System (INIS)

Kawarasaki, Y.

1976-01-01

A simple and versatile method of generating background-free γ-ray spectra is presented. This method is equivalent to the generation of a continuous background baseline over the entire energy range of spectra corresponding to the original ones obtained with a Ge(Li) detector. These background curves can not be generally expressed in a single and simple analytic form nor in the form of a power series. These background-free spectra thus obtained make it feasible to assign many tiny peaks at the stage of visual inspection of the spectra, which is difficult to do with the original ones. The automatic peak-finding and peak area calculation procedures are both applicable to these background-free spectra. Examples of the application are illustrated. The effect of the peak-shape distortion is also discussed. (Auth.)

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.

Regression modeling of ground-water flow

Science.gov (United States)

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

Science.gov (United States)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

Simple Stacking Methods for Silicon Micro Fuel Cells

Directory of Open Access Journals (Sweden)

Gianmario Scotti

2014-08-01

Full Text Available We present two simple methods, with parallel and serial gas flows, for the stacking of microfabricated silicon fuel cells with integrated current collectors, flow fields and gas diffusion layers. The gas diffusion layer is implemented using black silicon. In the two stacking methods proposed in this work, the fluidic apertures and gas flow topology are rotationally symmetric and enable us to stack fuel cells without an increase in the number of electrical or fluidic ports or interconnects. Thanks to this simplicity and the structural compactness of each cell, the obtained stacks are very thin (~1.6 mm for a two-cell stack. We have fabricated two-cell stacks with two different gas flow topologies and obtained an open-circuit voltage (OCV of 1.6 V and a power density of 63 mW·cm−2, proving the viability of the design.

Discriminative Elastic-Net Regularized Linear Regression.

Science.gov (United States)

Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

2017-03-01

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards: Smoke Detection Using an Autologistic Regression Classifier.

Science.gov (United States)

Wolters, Mark A; Dean, C B

2017-01-01

Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring and cataloguing atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and nonhazard regions using the autologistic regression model, which may be viewed as a spatial extension of logistic regression. The method includes a novel and simple approach to parameter estimation that makes it well suited to handling the large and high-dimensional datasets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to the identification of forest fire smoke.

Simple method of obtaining the band strengths in the electronic spectra of diatomic molecules

International Nuclear Information System (INIS)

Gowda, L.S.; Balaji, V.N.

1977-01-01

It is shown that relative band strengths of diatomic molecules for which the product of Franck-Condon factor and r-centroid is approximately equal to 1 for (0,0) band can be determined by a simple method which is in good agreement with the smoothed array of experimental values. Such values for the Swan bands of the C 2 molecule are compared with the band strengths of the simple method. It is noted that the Swan bands are one of the outstanding features of R- and N-type stars and of the heads of comets

A Simple Combinatorial Codon Mutagenesis Method for Targeted Protein Engineering.

Science.gov (United States)

Belsare, Ketaki D; Andorfer, Mary C; Cardenas, Frida S; Chael, Julia R; Park, Hyun June; Lewis, Jared C

2017-03-17

Directed evolution is a powerful tool for optimizing enzymes, and mutagenesis methods that improve enzyme library quality can significantly expedite the evolution process. Here, we report a simple method for targeted combinatorial codon mutagenesis (CCM). To demonstrate the utility of this method for protein engineering, CCM libraries were constructed for cytochrome P450 BM3 , pfu prolyl oligopeptidase, and the flavin-dependent halogenase RebH; 10-26 sites were targeted for codon mutagenesis in each of these enzymes, and libraries with a tunable average of 1-7 codon mutations per gene were generated. Each of these libraries provided improved enzymes for their respective transformations, which highlights the generality, simplicity, and tunability of CCM for targeted protein engineering.

The APT model as reduced-rank regression

NARCIS (Netherlands)

Bekker, P.A.; Dobbelstein, P.; Wansbeek, T.J.

Integrating the two steps of an arbitrage pricing theory (APT) model leads to a reduced-rank regression (RRR) model. So the results on RRR can be used to estimate APT models, making estimation very simple. We give a succinct derivation of estimation of RRR, derive the asymptotic variance of RRR

transformation of independent variables in polynomial regression ...

African Journals Online (AJOL)

Ada

preferable when possible to work with a simple functional form in transformed variables rather than with a more complicated form in the original variables. In this paper, it is shown that linear transformations applied to independent variables in polynomial regression models affect the t ratio and hence the statistical ...

The simple method to co-register planar image with photograph

International Nuclear Information System (INIS)

Jang, Sung June; Kim, Seok Ki; Kang, Keon Wook

2005-01-01

Generally scintigraphic image presents the highly specific functional information. Sometimes, there can be limited information of patients anatomical landmark required to identify the lesion in planar nuclear medicine image. In this study, we applied the simple fusion method of planar scintigraphy and plain photography and validated the techniques with our own software. We used three fiducial marks which were comprised with Tc-99m. We obtained planar image with single head gamma camera (ARGUS ADAC laboratory, USA) and photograph using a general digital camera (CANON JAPAN). The coordinates of three marks were obtained in photograph and planar scintigraphy image. Based on these points, we took affine transformation and then fused these two images. To evaluate the precision, we compared with different depth. To find out the depth of lesion, the images were acquired in different angles and we compared the real depth and the geometrically calculated depth. At the same depth with mark, the each discordance was less than 1 mm. When the photograph were taken at the distance with 1 m and 2 m, the point 30 cm off the center were discordant in 5 mm and 2 mm each. We used this method in the localization of the remnant thyroid tissue on I-131 whole body scan with photo image. The simple method to co-register planar image with photography was reliable and easy to use. By this method, we could localize the lesion on the planar scintigraphy more accurately with other planar images (i.e. photograph) and predict the depth of the lesion without tomographic image

A method to determine the necessity for global signal regression in resting-state fMRI studies.

Science.gov (United States)

Chen, Gang; Chen, Guangyu; Xie, Chunming; Ward, B Douglas; Li, Wenjun; Antuono, Piero; Li, Shi-Jiang

2012-12-01

In resting-state functional MRI studies, the global signal (operationally defined as the global average of resting-state functional MRI time courses) is often considered a nuisance effect and commonly removed in preprocessing. This global signal regression method can introduce artifacts, such as false anticorrelated resting-state networks in functional connectivity analyses. Therefore, the efficacy of this technique as a correction tool remains questionable. In this article, we establish that the accuracy of the estimated global signal is determined by the level of global noise (i.e., non-neural noise that has a global effect on the resting-state functional MRI signal). When the global noise level is low, the global signal resembles the resting-state functional MRI time courses of the largest cluster, but not those of the global noise. Using real data, we demonstrate that the global signal is strongly correlated with the default mode network components and has biological significance. These results call into question whether or not global signal regression should be applied. We introduce a method to quantify global noise levels. We show that a criteria for global signal regression can be found based on the method. By using the criteria, one can determine whether to include or exclude the global signal regression in minimizing errors in functional connectivity measures. Copyright © 2012 Wiley Periodicals, Inc.

Multiscale methods coupling atomistic and continuum mechanics: analysis of a simple case

OpenAIRE

Blanc , Xavier; Le Bris , Claude; Legoll , Frédéric

2007-01-01

International audience; The description and computation of fine scale localized phenomena arising in a material (during nanoindentation, for instance) is a challenging problem that has given birth to many multiscale methods. In this work, we propose an analysis of a simple one-dimensional method that couples two scales, the atomistic one and the continuum mechanics one. The method includes an adaptive criterion in order to split the computational domain into two subdomains, that are described...

A simple method for affinity purification of radiolabeled monoclonal antibodies

Energy Technology Data Exchange (ETDEWEB)

Juweid, M; Sato, J; Paik, C; Onay-Basaran, S; Weinstein, J N; Neumann, R D [National Cancer Inst., Bethesda, MD (United States)

1993-04-01

A simple method is described for affinity purification of radiolabeled antibodies using glutaraldehyde-fixed tumor target cells. The cell-bound antibody fraction is removed from the cells by an acid wash and then immediately subjected to buffer-exchange chromatography. The method was applied to the D3 murine monoclonal antibody which binds to a 290 kDa antigen on the surface of Line 10 guinea pig carcinoma cells. No alteration in the molecular size profile was detected after acid washing. Purification resulted in a significant increase in immunoreactivity by an average of 14 [+-] 47% (SD; range 4-30%). (author).

Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

Directory of Open Access Journals (Sweden)

Yunfeng Wu

2014-01-01

Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

A simple method of screening for metabolic bone disease

International Nuclear Information System (INIS)

Broughton, R.B.K.; Evans, W.D.

1982-01-01

The purpose of this investigation was to find a simple method -to be used as an adjunct to the conventional bone scintigram- that could differentiate between decreased bone metabolism or mass, i.e., osteoporosis -normal bone- and the group of conditions of increased bone metabolism or mass namely, osteomalacia, renal osteodystrophy, hyperparathyroidism and Paget's disease. The Fogelman's method using the bone to soft tissue ratios from region of interest analysis at 4 hours post injection, was adopted. An initial experience in measuring a value for the count rate density in lumbar vertebrae at 1 hr post injection during conventional bone scintigraphy appears to give a clear indication of the overall rate of bone metabolism. The advantage over whole body retention methods is that the scan performed at the end of the metabolic study will reveal localized bone disease that may otherwise not be anticipated

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 ...

Exploring simple assessment methods for lighting quality with architecture and design students

DEFF Research Database (Denmark)

Madsen, Merete

2006-01-01

that cannot be assessed by simple equations or rules-of-thumb. Balancing the many an often contradictory aspects of energy efficiency and high quality lighting design is a complex undertaking not just for students. The work described in this paper is one result of an academic staff exchange between...... the Schools of Architecture in Copenhagen and Victoria University of Wellington (New Zealand). The authors explore two approaches to teaching students simple assessment methods that can contribute to making more informed decisions about the luminous environment and its quality. One approach deals...... with the assessment of luminance ratios in relation to computer work and presents in that context some results from an experiment undertaken to introduce the concept of luminance ratios and preferred luminance ranges to architeture students. In the other approach a Danish method for assissing the luminance...

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

Science.gov (United States)

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

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.

12 CFR 717.25 - Reasonable and simple methods of opting out.

Science.gov (United States)

2010-01-01

... simple methods for exercising an opt-out right do not include— (i) Requiring the consumer to write his or... out. (a) In general. You must not use eligibility information about a consumer that you receive from an affiliate to make a solicitation to the consumer about your products or services, unless the...

A simple red-ox titrimetric method for the evaluation of photo ...

Indian Academy of Sciences (India)

Unknown

tal conditions in a relatively short duration in R&D labora- tories having basic analytical facilities. The method suggested here could also be adopted to study the photo- catalytic activity of other transition metal oxide based catalysts. For establishing this technique, we have moni- tored a simple one-electron transfer red-ox ...

A simple method for the prevention of endometrial autolysis in hysterectomy specimens

OpenAIRE

Houghton, J P; Roddy, S; Carroll, S; McCluggage, W G

2004-01-01

Aims: Uteri are among the most common surgical pathology specimens. Assessment of the endometrium is often difficult because of pronounced tissue autolysis. This study describes a simple method to prevent endometrial autolysis and aid in interpretation of the endometrium.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

Science.gov (United States)

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various

A simple method for estimating the entropy of neural activity

International Nuclear Information System (INIS)

Berry II, Michael J; Tkačik, Gašper; Dubuis, Julien; Marre, Olivier; Da Silveira, Rava Azeredo

2013-01-01

The number of possible activity patterns in a population of neurons grows exponentially with the size of the population. Typical experiments explore only a tiny fraction of the large space of possible activity patterns in the case of populations with more than 10 or 20 neurons. It is thus impossible, in this undersampled regime, to estimate the probabilities with which most of the activity patterns occur. As a result, the corresponding entropy—which is a measure of the computational power of the neural population—cannot be estimated directly. We propose a simple scheme for estimating the entropy in the undersampled regime, which bounds its value from both below and above. The lower bound is the usual ‘naive’ entropy of the experimental frequencies. The upper bound results from a hybrid approximation of the entropy which makes use of the naive estimate, a maximum entropy fit, and a coverage adjustment. We apply our simple scheme to artificial data, in order to check their accuracy; we also compare its performance to those of several previously defined entropy estimators. We then apply it to actual measurements of neural activity in populations with up to 100 cells. Finally, we discuss the similarities and differences between the proposed simple estimation scheme and various earlier methods. (paper)

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.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

Science.gov (United States)

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Single-electron multiplication statistics as a combination of Poissonian pulse height distributions using constraint regression methods

International Nuclear Information System (INIS)

Ballini, J.-P.; Cazes, P.; Turpin, P.-Y.

1976-01-01

Analysing the histogram of anode pulse amplitudes allows a discussion of the hypothesis that has been proposed to account for the statistical processes of secondary multiplication in a photomultiplier. In an earlier work, good agreement was obtained between experimental and reconstructed spectra, assuming a first dynode distribution including two Poisson distributions of distinct mean values. This first approximation led to a search for a method which could give the weights of several Poisson distributions of distinct mean values. Three methods have been briefly exposed: classical linear regression, constraint regression (d'Esopo's method), and regression on variables subject to error. The use of these methods gives an approach of the frequency function which represents the dispersion of the punctual mean gain around the whole first dynode mean gain value. Comparison between this function and the one employed in Polya distribution allows the statement that the latter is inadequate to describe the statistical process of secondary multiplication. Numerous spectra obtained with two kinds of photomultiplier working under different physical conditions have been analysed. Then two points are discussed: - Does the frequency function represent the dynode structure and the interdynode collection process. - Is the model (the multiplication process of all dynodes but the first one, is Poissonian) valid whatever the photomultiplier and the utilization conditions. (Auth.)

Study (Prediction of Main Pipes Break Rates in Water Distribution Systems Using Intelligent and Regression Methods

Directory of Open Access Journals (Sweden)

Massoud Tabesh

2011-07-01

Full Text Available Optimum operation of water distribution networks is one of the priorities of sustainable development of water resources, considering the issues of increasing efficiency and decreasing the water losses. One of the key subjects in optimum operational management of water distribution systems is preparing rehabilitation and replacement schemes, prediction of pipes break rate and evaluation of their reliability. Several approaches have been presented in recent years regarding prediction of pipe failure rates which each one requires especial data sets. Deterministic models based on age and deterministic multi variables and stochastic group modeling are examples of the solutions which relate pipe break rates to parameters like age, material and diameters. In this paper besides the mentioned parameters, more factors such as pipe depth and hydraulic pressures are considered as well. Then using multi variable regression method, intelligent approaches (Artificial neural network and neuro fuzzy models and Evolutionary polynomial Regression method (EPR pipe burst rate are predicted. To evaluate the results of different approaches, a case study is carried out in a part ofMashhadwater distribution network. The results show the capability and advantages of ANN and EPR methods to predict pipe break rates, in comparison with neuro fuzzy and multi-variable regression methods.

A simple method of fabricating mask-free microfluidic devices for biological analysis.

KAUST Repository

Yi, Xin; Kodzius, Rimantas; Gong, Xiuqing; Xiao, Kang; Wen, Weijia

2010-01-01

We report a simple, low-cost, rapid, and mask-free method to fabricate two-dimensional (2D) and three-dimensional (3D) microfluidic chip for biological analysis researches. In this fabrication process, a laser system is used to cut through paper

A simple method to evaluate the composition of tissue-equivalent phantom materials

International Nuclear Information System (INIS)

Geske, G.

1977-01-01

A description is given of a method to calculate the composition of phantom materials with given density and radiation-physical parameters mixed of components, of which are known their chemical composition and their effective specific volumes. By an example of a simple composition with three components the method is illustrated. The results of this example and some experimental details that must be considered are discussed. (orig.) [de

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

Directory of Open Access Journals (Sweden)

Xiangbing Zhou

2018-04-01

Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

Use of eddy-covariance methods to "calibrate" simple estimators of evapotranspiration

Science.gov (United States)

Sumner, David M.; Geurink, Jeffrey S.; Swancar, Amy

2017-01-01

Direct measurement of actual evapotranspiration (ET) provides quantification of this large component of the hydrologic budget, but typically requires long periods of record and large instrumentation and labor costs. Simple surrogate methods of estimating ET, if “calibrated” to direct measurements of ET, provide a reliable means to quantify ET. Eddy-covariance measurements of ET were made for 12 years (2004-2015) at an unimproved bahiagrass (Paspalum notatum) pasture in Florida. These measurements were compared to annual rainfall derived from rain gage data and monthly potential ET (PET) obtained from a long-term (since 1995) U.S. Geological Survey (USGS) statewide, 2-kilometer, daily PET product. The annual proportion of ET to rainfall indicates a strong correlation (r2=0.86) to annual rainfall; the ratio increases linearly with decreasing rainfall. Monthly ET rates correlated closely (r2=0.84) to the USGS PET product. The results indicate that simple surrogate methods of estimating actual ET show positive potential in the humid Florida climate given the ready availability of historical rainfall and PET.

Validity of a Simple Method for Measuring Force-Velocity-Power Profile in Countermovement Jump.

Science.gov (United States)

Jiménez-Reyes, Pedro; Samozino, Pierre; Pareja-Blanco, Fernando; Conceição, Filipe; Cuadrado-Peñafiel, Víctor; González-Badillo, Juan José; Morin, Jean-Benoît

2017-01-01

To analyze the reliability and validity of a simple computation method to evaluate force (F), velocity (v), and power (P) output during a countermovement jump (CMJ) suitable for use in field conditions and to verify the validity of this computation method to compute the CMJ force-velocity (F-v) profile (including unloaded and loaded jumps) in trained athletes. Sixteen high-level male sprinters and jumpers performed maximal CMJs under 6 different load conditions (0-87 kg). A force plate sampling at 1000 Hz was used to record vertical ground-reaction force and derive vertical-displacement data during CMJ trials. For each condition, mean F, v, and P of the push-off phase were determined from both force-plate data (reference method) and simple computation measures based on body mass, jump height (from flight time), and push-off distance and used to establish the linear F-v relationship for each individual. Mean absolute bias values were 0.9% (± 1.6%), 4.7% (± 6.2%), 3.7% (± 4.8%), and 5% (± 6.8%) for F, v, P, and slope of the F-v relationship (S Fv ), respectively. Both methods showed high correlations for F-v-profile-related variables (r = .985-.991). Finally, all variables computed from the simple method showed high reliability, with ICC >.980 and CV push-off distance, and jump height are known.

Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow

Directory of Open Access Journals (Sweden)

Mohammad Azmi

2012-07-01

Full Text Available Different statistical, non-statistical and black-box methods have been used in forecasting processes. Among statistical methods, K-nearest neighbour non-parametric regression method (K-NN due to its natural simplicity and mathematical base is one of the recommended methods for forecasting processes. In this study, K-NN method is explained completely. Besides, development and improvement approaches such as best neighbour estimation, data transformation functions, distance functions and proposed extrapolation method are described. K-NN method in company with its development approaches is used in streamflow forecasting of Zayandeh-Rud Dam upper basin. Comparing between final results of classic K-NN method and modified K-NN (number of neighbour 5, transformation function of Range Scaling, distance function of Mahanalobis and proposed extrapolation method shows that modified K-NN in criteria of goodness of fit, root mean square error, percentage of volume of error and correlation has had performance improvement 45% , 59% and 17% respectively. These results approve necessity of applying mentioned approaches to derive more accurate forecasts.

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

Science.gov (United States)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

Science.gov (United States)

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p logistic regression model for the classification of risk groups for PTB.

Development and Validation of a Simple High Performance Liquid Chromatography/UV Method for Simultaneous Determination of Urinary Uric Acid, Hypoxanthine, and Creatinine in Human Urine

Directory of Open Access Journals (Sweden)

Nimanthi Wijemanne

2018-01-01

Full Text Available Uric acid and hypoxanthine are produced in the catabolism of purine. Abnormal urinary levels of these products are associated with many diseases and therefore it is necessary to have a simple and rapid method to detect them. Hence, we report a simple reverse phase high performance liquid chromatography (HPLC/UV technique, developed and validated for simultaneous analysis of uric acid, hypoxanthine, and creatinine in human urine. Urine was diluted appropriately and eluted with C-18 column 100 mm × 4.6 mm with a C-18 precolumn 25 mm × 4.6 mm in series. Potassium phosphate buffer (20 mM, pH 7.25 at a flow rate of 0.40 mL/min was employed as the solvent and peaks were detected at 235 nm. Tyrosine was used as the internal standard. The experimental conditions offered a good separation of analytes without interference of endogenous substances. The calibration curves were linear for all test compounds with a regression coefficient, r2>0.99. Uric acid, creatinine, tyrosine, and hypoxanthine were eluted at 5.2, 6.1, 7.2, and 8.3 min, respectively. Intraday and interday variability were less than 4.6% for all the analytes investigated and the recovery ranged from 98 to 102%. The proposed HPLC procedure is a simple, rapid, and low cost method with high accuracy with minimum use of organic solvents. This method was successfully applied for the determination of creatinine, hypoxanthine, and uric acid in human urine.

A simple method of chaos control for a class of chaotic discrete-time systems

International Nuclear Information System (INIS)

Jiang Guoping; Zheng Weixing

2005-01-01

In this paper, a simple method is proposed for chaos control for a class of discrete-time chaotic systems. The proposed method is built upon the state feedback control and the characteristic of ergodicity of chaos. The feedback gain matrix of the controller is designed using a simple criterion, so that control parameters can be selected via the pole placement technique of linear control theory. The new controller has a feature that it only uses the state variable for control and does not require the target equilibrium point in the feedback path. Moreover, the proposed control method cannot only overcome the so-called 'odd eigenvalues number limitation' of delayed feedback control, but also control the chaotic systems to the specified equilibrium points. The effectiveness of the proposed method is demonstrated by a two-dimensional discrete-time chaotic system

Time-adaptive quantile regression

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

2008-01-01

and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

12 CFR 571.25 - Reasonable and simple methods of opting out.

Science.gov (United States)

2010-01-01

... CREDIT REPORTING Affiliate Marketing § 571.25 Reasonable and simple methods of opting out. (a) In general... out, such as a form that can be electronically mailed or processed at an Internet Web site, if the... (15 U.S.C. 6801 et seq.), the affiliate sharing opt-out under the Act, and the affiliate marketing opt...

16 CFR 680.25 - Reasonable and simple methods of opting out.

Science.gov (United States)

2010-01-01

... AFFILIATE MARKETING § 680.25 Reasonable and simple methods of opting out. (a) In general. You must not use... a form that can be electronically mailed or processed at an Internet Web site, if the consumer..., 15 U.S.C. 6801 et seq., the affiliate sharing opt-out under the Act, and the affiliate marketing opt...

Multiple predictor smoothing methods for sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Helton, Jon Craig; Storlie, Curtis B.

2006-08-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.

Multiple predictor smoothing methods for sensitivity analysis

International Nuclear Information System (INIS)

Helton, Jon Craig; Storlie, Curtis B.

2006-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

A Simple Method to Measure Nematodes' Propulsive Thrust and the Nematode Ratchet.

Science.gov (United States)

Bau, Haim; Yuan, Jinzhou; Raizen, David

2015-11-01

Since the propulsive thrust of micro organisms provides a more sensitive indicator of the animal's health and response to drugs than motility, a simple, high throughput, direct measurement of the thrust is desired. Taking advantage of the nematode C. elegans being heavier than water, we devised a simple method to determine the propulsive thrust of the animals by monitoring their velocity when swimming along an inclined plane. We find that the swimming velocity is a linear function of the sin of the inclination angle. This method allows us to determine, among other things, the animas' propulsive thrust as a function of genotype, drugs, and age. Furthermore, taking advantage of the animals' inability to swim over a stiff incline, we constructed a sawteeth ratchet-like track that restricts the animals to swim in a predetermined direction. This research was supported, in part, by NIH NIA Grant 5R03AG042690-02.

Method validation using weighted linear regression models for quantification of UV filters in water samples.

Science.gov (United States)

da Silva, Claudia Pereira; Emídio, Elissandro Soares; de Marchi, Mary Rosa Rodrigues

2015-01-01

This paper describes the validation of a method consisting of solid-phase extraction followed by gas chromatography-tandem mass spectrometry for the analysis of the ultraviolet (UV) filters benzophenone-3, ethylhexyl salicylate, ethylhexyl methoxycinnamate and octocrylene. The method validation criteria included evaluation of selectivity, analytical curve, trueness, precision, limits of detection and limits of quantification. The non-weighted linear regression model has traditionally been used for calibration, but it is not necessarily the optimal model in all cases. Because the assumption of homoscedasticity was not met for the analytical data in this work, a weighted least squares linear regression was used for the calibration method. The evaluated analytical parameters were satisfactory for the analytes and showed recoveries at four fortification levels between 62% and 107%, with relative standard deviations less than 14%. The detection limits ranged from 7.6 to 24.1 ng L(-1). The proposed method was used to determine the amount of UV filters in water samples from water treatment plants in Araraquara and Jau in São Paulo, Brazil. Copyright © 2014 Elsevier B.V. All rights reserved.

Analyzing Big Data with the Hybrid Interval Regression Methods

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Chia-Hui Huang

2014-01-01

Full Text Available Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM to analyze big data. Recently, the smooth support vector machine (SSVM was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.

A simple and fast method for extraction and quantification of cryptophyte phycoerythrin

OpenAIRE

Thoisen, Christina; Hansen, Benni Winding; Nielsen, S?ren Laurentius

2017-01-01

The microalgal pigment phycoerythrin (PE) is of commercial interest as natural colorant in food and cosmetics, as well as fluoroprobes for laboratory analysis. Several methods for extraction and quantification of PE are available but they comprise typically various extraction buffers, repetitive freeze-thaw cycles and liquid nitrogen, making extraction procedures more complicated. A simple method for extraction of PE from cryptophytes is described using standard laboratory materials and equip...

Dimension Reduction and Discretization in Stochastic Problems by Regression Method

DEFF Research Database (Denmark)

Ditlevsen, Ove Dalager

1996-01-01

The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation, ...

Present status and future of simple measuring methods; Suishitsu kan`i sokutei gijutsu no genjo to hatten no hoko

Energy Technology Data Exchange (ETDEWEB)

Urano, K.; Ishii, S. [Yokohama National University, Yokohama (Japan)

1998-05-10

This paper discusses simple measuring methods for water quality. There are various purposes for measuring water quality. It is not necessary to measure the water quality in an official way by taking a lot of labor and cost. Simple measuring methods are often adopted. By applying simple methods, measuring points and frequency of main discharge processes can be increased, resulting in the detailed evaluation and monitoring. Easy safety check for earth filling waste and soil pollutants is realized. Daily inspection and atmospheric measurements at accidents and disasters are easily promoted. The simple methods require easy and rapid operation, sensibility, accuracy and reproducibility suitable for the purpose, small size apparatus, low cost for measurements, and safety against used harmful matters. Coloring reaction is utilized for most of the measuring principles of simple methods, which include tests using test papers, pack tests, colorimetric tests, and tests using photoelectric colorimeter. The bacteria detection includes titration methods using dropping bottles, tablets and syringes, and tests using test papers. A measuring kit for the enzyme immunity method is commercialized. Cooperation of experts, measuring operators at the site and citizens is also indispensable. 7 refs., 3 figs., 2 tabs.

Simple method for absolute calibration of geophones, seismometers, and other inertial vibration sensors

International Nuclear Information System (INIS)

Kann, Frank van; Winterflood, John

2005-01-01

A simple but powerful method is presented for calibrating geophones, seismometers, and other inertial vibration sensors, including passive accelerometers. The method requires no cumbersome or expensive fixtures such as shaker platforms and can be performed using a standard instrument commonly available in the field. An absolute calibration is obtained using the reciprocity property of the device, based on the standard mathematical model for such inertial sensors. It requires only simple electrical measurement of the impedance of the sensor as a function of frequency to determine the parameters of the model and hence the sensitivity function. The method is particularly convenient if one of these parameters, namely the suspended mass is known. In this case, no additional mechanical apparatus is required and only a single set of impedance measurements yields the desired calibration function. Moreover, this measurement can be made with the device in situ. However, the novel and most powerful aspect of the method is its ability to accurately determine the effective suspended mass. For this, the impedance measurement is made with the device hanging from a simple spring or flexible cord (depending on the orientation of its sensitive axis). To complete the calibration, the device is weighed to determine its total mass. All the required calibration parameters, including the suspended mass, are then determined from a least-squares fit to the impedance as a function of frequency. A demonstration using both a 4.5 Hz geophone and a 1 Hz seismometer shows that the method can yield accurate absolute calibrations with an error of 0.1% or better, assuming no a priori knowledge of any parameters

A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

Science.gov (United States)

Boucher, Thomas F.; Ozanne, Marie V.; Carmosino, Marco L.; Dyar, M. Darby; Mahadevan, Sridhar; Breves, Elly A.; Lepore, Kate H.; Clegg, Samuel M.

2015-05-01

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k-nearest neighbor (kNN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO2, Fe2O3, CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na2O, K2O, TiO2, and P2O5, the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high dimensionality of the data (6144 channels

Direct and regression methods do not give different estimates of digestible and metabolizable energy of wheat for pigs.

Science.gov (United States)

Bolarinwa, O A; Adeola, O

2012-12-01

Digestible and metabolizable energy contents of feed ingredients for pigs can be determined by direct or indirect methods. There are situations when only the indirect approach is suitable and the regression method is a robust indirect approach. This study was conducted to compare the direct and regression methods for determining the energy value of wheat for pigs. Twenty-four barrows with an average initial BW of 31 kg were assigned to 4 diets in a randomized complete block design. The 4 diets consisted of 969 g wheat/kg plus minerals and vitamins (sole wheat) for the direct method, corn (Zea mays)-soybean (Glycine max) meal reference diet (RD), RD + 300 g wheat/kg, and RD + 600 g wheat/kg. The 3 corn-soybean meal diets were used for the regression method and wheat replaced the energy-yielding ingredients, corn and soybean meal, so that the same ratio of corn and soybean meal across the experimental diets was maintained. The wheat used was analyzed to contain 883 g DM, 15.2 g N, and 3.94 Mcal GE/kg. Each diet was fed to 6 barrows in individual metabolism crates for a 5-d acclimation followed by a 5-d total but separate collection of feces and urine. The DE and ME for the sole wheat diet were 3.83 and 3.77 Mcal/kg DM, respectively. Because the sole wheat diet contained 969 g wheat/kg, these translate to 3.95 Mcal DE/kg DM and 3.89 Mcal ME/kg DM. The RD used for the regression approach yielded 4.00 Mcal DE and 3.91 Mcal ME/kg DM diet. Increasing levels of wheat in the RD linearly reduced (P direct method (3.95 and 3.89 Mcal/kg DM) did not differ (0.78 < P < 0.89) from those obtained using the regression method (3.96 and 3.88 Mcal/kg DM).

A Comparison of Multidimensional Item Selection Methods in Simple and Complex Test Designs

Directory of Open Access Journals (Sweden)

Eren Halil ÖZBERK

2017-03-01

Full Text Available In contrast with the previous studies, this study employed various test designs (simple and complex which allow the evaluation of the overall ability score estimations across multiple real test conditions. In this study, four factors were manipulated, namely the test design, number of items per dimension, correlation between dimensions and item selection methods. Using the generated item and ability parameters, dichotomous item responses were generated in by using M3PL compensatory multidimensional IRT model with specified correlations. MCAT composite ability score accuracy was evaluated using absolute bias (ABSBIAS, correlation and the root mean square error (RMSE between true and estimated ability scores. The results suggest that the multidimensional test structure, number of item per dimension and correlation between dimensions had significant effect on item selection methods for the overall score estimations. For simple structure test design it was found that V1 item selection has the lowest absolute bias estimations for both long and short tests while estimating overall scores. As the model gets complex KL item selection method performed better than other two item selection method.

Simple, reliable, and nondestructive method for the measurement of vacuum pressure without specialized equipment.

Science.gov (United States)

Yuan, Jin-Peng; Ji, Zhong-Hua; Zhao, Yan-Ting; Chang, Xue-Fang; Xiao, Lian-Tuan; Jia, Suo-Tang

2013-09-01

We present a simple, reliable, and nondestructive method for the measurement of vacuum pressure in a magneto-optical trap. The vacuum pressure is verified to be proportional to the collision rate constant between cold atoms and the background gas with a coefficient k, which can be calculated by means of the simple ideal gas law. The rate constant for loss due to collisions with all background gases can be derived from the total collision loss rate by a series of loading curves of cold atoms under different trapping laser intensities. The presented method is also applicable for other cold atomic systems and meets the miniaturization requirement of commercial applications.

True phosphorus digestibility and the endogenous phosphorus outputs associated with brown rice for weanling pigs measured by the simple linear regression analysis technique.

Science.gov (United States)

Yang, H; Li, A K; Yin, Y L; Li, T J; Wang, Z R; Wu, G; Huang, R L; Kong, X F; Yang, C B; Kang, P; Deng, J; Wang, S X; Tan, B E; Hu, Q; Xing, F F; Wu, X; He, Q H; Yao, K; Liu, Z J; Tang, Z R; Yin, F G; Deng, Z Y; Xie, M Y; Fan, M Z

2007-03-01

The objectives of this study were to determine true phosphorus (P) digestibility, degradability of phytate-P complex and the endogenous P outputs associated with brown rice feeding in weanling pigs by using the simple linear regression analysis technique. Six barrows with an average initial body weight of 12.5 kg were fitted with a T-cannula and fed six diets according to a 6 × 6 Latin-square design. Six maize starch-based diets, containing six levels of P at 0.80, 1.36, 1.93, 2.49, 3.04, and 3.61 g/kg per kg dry-matter (DM) intake (DMI), were formulated with brown rice. Each experimental period lasted 10 days. After a 7-day adaptation, all faecal samples were collected on days 8 and 9. Ileal digesta samples were collected for a total of 24 h on day 10. The apparent ileal and faecal P digestibility values of brown rice were affected ( P Linear relationships ( P simple regression analysis technique. There were no differences ( P>0.05) in true P digestibility values (57.7 ± 5.4 v. 58.2 ± 5.9%), phytate P degradability (76.4 ± 6.7 v. 79.0 ± 4.4%) and the endogenous P outputs (0.812 ± 0..096 v. 0.725 ± 0.083 g/kg DMI) between the ileal and the faecal levels. The endogenous faecal P output represented 14 and 25% of the National Research Council (1998) recommended daily total and available P requirements in the weanling pig, respectively. About 58% of the total P in brown rice could be digested and absorbed by the weanling pig. Our results suggest that the large intestine of the weanling pigs does not play a significant role in the digestion of P in brown rice. Diet formulation on the basis of total or apparent P digestibility with brown rice may lead to P overfeeding and excessive P excretion in pigs.

A simple bacterial turbidimetric method for detection of some radurized foods

International Nuclear Information System (INIS)

Gautam, S.; Sharma, Arun; Thomas, Paul

1998-01-01

A simple and quick method for detection of irradiated food is proposed. The method is based on the principle of microbial contribution to the development of turbidity in a clear medium. It employs measurement of absorbance at 600 nm of the medium after the test commodity has been suspended and shaken in it for a fixed interval. The differences in the bacterial turbidity from irradiated and nonirradiated samples are quite marked so as to allow identification of the irradiated foods like fish, lamb meat, chicken and mushroom. (author)

Accurate and simple measurement method of complex decay schemes radionuclide activity

International Nuclear Information System (INIS)

Legrand, J.; Clement, C.; Bac, C.

1975-01-01

A simple method for the measurement of the activity is described. It consists of using a well-type sodium iodide crystal whose efficiency mith monoenergetic photon rays has been computed or measured. For each radionuclide with a complex decay scheme a total efficiency is computed; it is shown that the efficiency is very high, near 100%. The associated incertainty is low, in spite of the important uncertainties on the different parameters used in the computation. The method has been applied to the measurement of the 152 Eu primary reference [fr

A SIMPLE AND EFFECTIVE CURSIVE WORD SEGMENTATION METHOD

NARCIS (Netherlands)

nicchiotti, G.; Rimassa, S.; Scagliola, C.

2004-01-01

A simple procedure for cursive word oversegmentation is presented, which is based on the analysis of the handwritten profiles and on the extraction of ``white holes\\'\\'. It follows the policy of using simple rules on complex data and sophisticated rules on simpler data. Experimental results show

Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

International Nuclear Information System (INIS)

Sun Zhong-Hua; Jiang Fan

2010-01-01

In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. (rapid communication)

A simple multistage closed-(box+reservoir model of chemical evolution

Directory of Open Access Journals (Sweden)

Caimmi R.

2011-01-01

Full Text Available Simple closed-box (CB models of chemical evolution are extended on two respects, namely (i simple closed-(box+reservoir (CBR models allowing gas outflow from the box into the reservoir (Hartwick 1976 or gas inflow into the box from the reservoir (Caimmi 2007 with rate proportional to the star formation rate, and (ii simple multistage closed-(box+reservoir (MCBR models allowing different stages of evolution characterized by different inflow or outflow rates. The theoretical differential oxygen abundance distribution (TDOD predicted by the model maintains close to a continuous broken straight line. An application is made where a fictitious sample is built up from two distinct samples of halo stars and taken as representative of the inner Galactic halo. The related empirical differential oxygen abundance distribution (EDOD is represented, to an acceptable extent, as a continuous broken line for two viable [O/H]-[Fe/H] empirical relations. The slopes and the intercepts of the regression lines are determined, and then used as input parameters to MCBR models. Within the errors (-+σ, regression line slopes correspond to a large inflow during the earlier stage of evolution and to low or moderate outflow during the subsequent stages. A possible inner halo - outer (metal-poor bulge connection is also briefly discussed. Quantitative results cannot be considered for applications to the inner Galactic halo, unless selection effects and disk contamination are removed from halo samples, and discrepancies between different oxygen abundance determination methods are explained.

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

Landslide susceptibility mapping on a global scale using the method of logistic regression

Directory of Open Access Journals (Sweden)

L. Lin

2017-08-01

Full Text Available This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

Enforcing Co-expression Within a Brain-Imaging Genomics Regression Framework.

Science.gov (United States)

Zille, Pascal; Calhoun, Vince D; Wang, Yu-Ping

2017-06-28

Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this work, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities. In the following, we propose to rely on a 'CCA-type' formulation in order to regularize the classical multimodal sparse regression problem (essentially incorporating both CCA and regression models within a unified formulation). The underlying motivation is to extract discriminative variables that are also co-expressed across modalities. We first show that the simplest formulation of such model can be expressed as a special case of collaborative learning methods. After discussing its limitation, we propose an extended, more flexible formulation, and introduce a simple and efficient alternating minimization algorithm to solve the associated optimization problem.We explore the parameter space and provide some guidelines regarding parameter selection. Both the original and extended versions are then compared on a simple toy dataset and a more advanced simulated imaging genomics dataset in order to illustrate the benefits of the latter. Finally, we validate the proposed formulation using single nucleotide polymorphisms (SNP) data and functional magnetic resonance imaging (fMRI) data from a population of adolescents (n = 362 subjects, age 16.9 ± 1.9 years from the Philadelphia Neurodevelopmental Cohort) for the study of learning ability. Furthermore, we carry out a significance

A Study of Simple α Source Preparation Using a Micro-coprecipitation Method

International Nuclear Information System (INIS)

Lee, Myung Ho; Park, Taehong; Song, Byung Chul; Park, Jong Ho; Song, Kyuseok

2012-01-01

This study presents a rapid and simple α source preparation method for a radioactive waste sample. The recovery of 239 Pu, 232 U and 243 Am using a micro-coprecipitation method was over 95%. The α-peak resolution of Pu and Am isotopes through the micro-coprecipitation method is enough to discriminate the Pu and Am isotopes from other Pu and Am isotopes. The determination of the Pu and Am isotopes using the micro-coprecipitation method was applied to the radioactive waste sample, so that the activity concentrations of the Pu and Am isotopes using the micro-coprecipitation method in the radioactive waste sample were similar to those using the electrodeposition method

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.

A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

International Nuclear Information System (INIS)

Boucher, Thomas F.; Ozanne, Marie V.; Carmosino, Marco L.; Dyar, M. Darby; Mahadevan, Sridhar; Breves, Elly A.; Lepore, Kate H.; Clegg, Samuel M.

2015-01-01

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k-nearest neighbor (kNN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO 2 , Fe 2 O 3 , CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na 2 O, K 2 O, TiO 2 , and P 2 O 5 , the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high dimensionality of the data (6144

Confidence intervals for distinguishing ordinal and disordinal interactions in multiple regression.

Science.gov (United States)

Lee, Sunbok; Lei, Man-Kit; Brody, Gene H

2015-06-01

Distinguishing between ordinal and disordinal interaction in multiple regression is useful in testing many interesting theoretical hypotheses. Because the distinction is made based on the location of a crossover point of 2 simple regression lines, confidence intervals of the crossover point can be used to distinguish ordinal and disordinal interactions. This study examined 2 factors that need to be considered in constructing confidence intervals of the crossover point: (a) the assumption about the sampling distribution of the crossover point, and (b) the possibility of abnormally wide confidence intervals for the crossover point. A Monte Carlo simulation study was conducted to compare 6 different methods for constructing confidence intervals of the crossover point in terms of the coverage rate, the proportion of true values that fall to the left or right of the confidence intervals, and the average width of the confidence intervals. The methods include the reparameterization, delta, Fieller, basic bootstrap, percentile bootstrap, and bias-corrected accelerated bootstrap methods. The results of our Monte Carlo simulation study suggest that statistical inference using confidence intervals to distinguish ordinal and disordinal interaction requires sample sizes more than 500 to be able to provide sufficiently narrow confidence intervals to identify the location of the crossover point. (c) 2015 APA, all rights reserved).

Nonlinear regression analysis for evaluating tracer binding parameters using the programmable K1003 desk computer

International Nuclear Information System (INIS)

Sarrach, D.; Strohner, P.

1986-01-01

The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated

A computational approach to compare regression modelling strategies in prediction research.

Science.gov (United States)

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

New simple method for fast and accurate measurement of volumes

International Nuclear Information System (INIS)

Frattolillo, Antonio

2006-01-01

A new simple method is presented, which allows us to measure in just a few minutes but with reasonable accuracy (less than 1%) the volume confined inside a generic enclosure, regardless of the complexity of its shape. The technique proposed also allows us to measure the volume of any portion of a complex manifold, including, for instance, pipes and pipe fittings, valves, gauge heads, and so on, without disassembling the manifold at all. To this purpose an airtight variable volume is used, whose volume adjustment can be precisely measured; it has an overall capacity larger than that of the unknown volume. Such a variable volume is initially filled with a suitable test gas (for instance, air) at a known pressure, as carefully measured by means of a high precision capacitive gauge. By opening a valve, the test gas is allowed to expand into the previously evacuated unknown volume. A feedback control loop reacts to the resulting finite pressure drop, thus contracting the variable volume until the pressure exactly retrieves its initial value. The overall reduction of the variable volume achieved at the end of this process gives a direct measurement of the unknown volume, and definitively gets rid of the problem of dead spaces. The method proposed actually does not require the test gas to be rigorously held at a constant temperature, thus resulting in a huge simplification as compared to complex arrangements commonly used in metrology (gas expansion method), which can grant extremely accurate measurement but requires rather expensive equipments and results in time consuming methods, being therefore impractical in most applications. A simple theoretical analysis of the thermodynamic cycle and the results of experimental tests are described, which demonstrate that, in spite of its simplicity, the method provides a measurement accuracy within 0.5%. The system requires just a few minutes to complete a single measurement, and is ready immediately at the end of the process. The

A simple micro-photometric method for urinary iodine determination.

Science.gov (United States)

Grimm, Gabriele; Lindorfer, Heidelinde; Kieweg, Heidi; Marculescu, Rodrig; Hoffmann, Martha; Gessl, Alois; Sager, Manfred; Bieglmayer, Christian

2011-10-01

Urinary iodide concentration (UIC) is useful to evaluate nutritional iodine status. In clinical settings UIC helps to exclude blocking of the thyroid gland by excessive endogenous iodine, if diagnostic or therapeutic administration of radio-iodine is indicated. Therefore, this study established a simple test for the measurement of UIC. UIC was analyzed in urine samples of 200 patients. Samples were pre-treated at 95°C for 45 min with ammonium persulfate in a thermal cycler, followed by a photometric Sandell-Kolthoff reaction (SK) carried out in microtiter plates. For method comparison, UIC was analyzed in 30 samples by inductivity coupled plasma mass spectro-metry (ICP-MS) as a reference method. Incubation conditions were optimized concerning recovery. The photometric test correlated well to the reference method (SK=0.91*ICP-MS+1, r=0.962) and presented with a functional sensitivity of 20 μg/L. UIC of patient samples ranged from photometric test provides satisfactory results and can be performed with the basic equipment of a clinical laboratory.

Recursive least squares method of regression coefficients estimation as a special case of Kalman filter

Science.gov (United States)

Borodachev, S. M.

2016-06-01

The simple derivation of recursive least squares (RLS) method equations is given as special case of Kalman filter estimation of a constant system state under changing observation conditions. A numerical example illustrates application of RLS to multicollinearity problem.

A simple two-step method to fabricate highly transparent ITO/polymer nanocomposite films

International Nuclear Information System (INIS)

Liu, Haitao; Zeng, Xiaofei; Kong, Xiangrong; Bian, Shuguang; Chen, Jianfeng

2012-01-01

Highlights: ► A simple two-step method without further surface modification step was employed. ► ITO nanoparticles were easily to be uniformly dispersed in polymer matrix. ► ITO/polymer nanocomposite film had high transparency and UV/IR blocking properties. - Abstract: Transparent functional indium tin oxide (ITO)/polymer nanocomposite films were fabricated via a simple approach with two steps. Firstly, the functional monodisperse ITO nanoparticles were synthesized via a facile nonaqueous solvothermal method using bifunctional chemical agent (N-methyl-pyrrolidone, NMP) as the reaction solvent and surface modifier. Secondly, the ITO/acrylics polyurethane (PUA) nanocomposite films were fabricated by a simple sol-solution mixing method without any further surface modification step as often employed traditionally. Flower-like ITO nanoclusters with about 45 nm in diameter were mono-dispersed in ethyl acetate and each nanocluster was assembled by nearly spherical nanoparticles with primary size of 7–9 nm in diameter. The ITO nanoclusters exhibited an excellent dispersibility in polymer matrix of PUA, remaining their original size without any further agglomeration. When the loading content of ITO nanoclusters reached to 5 wt%, the transparent functional nanocomposite film featured a high transparency more than 85% in the visible light region (at 550 nm), meanwhile cutting off near-infrared radiation about 50% at 1500 nm and blocking UV ray about 45% at 350 nm. It could be potential for transparent functional coating materials applications.

A simple and convenient method for the simultaneous in vitro study of metformin and glimepiride tablets.

Science.gov (United States)

Ahmed, Rehan

2014-11-01

A simple and convenient method was developed for the simultaneous determination of metformin HCl and glimepiride in tablet dosage form of different pharmaceuticals companies. This method was validated and proved to be applicable for assay determination in intermediate and finished staged. More over a single medium dissolution of metformin HCl and glimepiride was established and the media was evaluated for comparative studies for different formulations. Reverse phase HPLC equipped with UV detector was used for the determination of metformin HCl and glimepiride. A mixture of acetonitrile and ammonium acetate buffer 0.05M pH 3.0 was used as mobile phase at flow rate of 1.0ml/min. Promocil C18 5µ 100Aº 4.6 x 100mm C18 silica column was used and detection was carried out at 270nm. Method was found to be linear over the range of 4ppm to 16ppm for glimepiride and 170ppm to 680ppm for metformin HCl. Regression co-efficient were found to be 0.9949 and 0.9864 for glimepiride and metformin HCl respectively. Dissolution was performed in 500ml 0.2% sodium lauryl sulfate at 37°C for 45min using paddle apparatus. Dissolution of glimepiride was found to be 98.60% and 101.08% in Orinase Met1 tablet and Amaryl M tablet respectively whereas metformin was found 99.41% and 98.59% in Orinase Met 1 tablet and Amaryl M tablet. RSD for all the dissolutions was less than 2.0% after completion.

Prenatal diagnosis of Caudal Regression Syndrome : a case report

Directory of Open Access Journals (Sweden)

Celikaslan Nurgul

2001-12-01

Full Text Available Abstract Background Caudal regression is a rare syndrome which has a spectrum of congenital malformations ranging from simple anal atresia to absence of sacral, lumbar and possibly lower thoracic vertebrae, to the most severe form which is known as sirenomelia. Maternal diabetes, genetic predisposition and vascular hypoperfusion have been suggested as possible causative factors. Case presentation We report a case of caudal regression syndrome diagnosed in utero at 22 weeks' of gestation. Prenatal ultrasound examination revealed a sudden interruption of the spine and "frog-like" position of lower limbs. Termination of pregnancy and autopsy findings confirmed the diagnosis. Conclusion Prenatal ultrasonographic diagnosis of caudal regression syndrome is possible at 22 weeks' of gestation by ultrasound examination.

Thermodynamic Analysis of Simple Gas Turbine Cycle with Multiple Regression Modelling and Optimization

Directory of Open Access Journals (Sweden)

Abdul Ghafoor Memon

2014-03-01

Full Text Available In this study, thermodynamic and statistical analyses were performed on a gas turbine system, to assess the impact of some important operating parameters like CIT (Compressor Inlet Temperature, PR (Pressure Ratio and TIT (Turbine Inlet Temperature on its performance characteristics such as net power output, energy efficiency, exergy efficiency and fuel consumption. Each performance characteristic was enunciated as a function of operating parameters, followed by a parametric study and optimization. The results showed that the performance characteristics increase with an increase in the TIT and a decrease in the CIT, except fuel consumption which behaves oppositely. The net power output and efficiencies increase with the PR up to certain initial values and then start to decrease, whereas the fuel consumption always decreases with an increase in the PR. The results of exergy analysis showed the combustion chamber as a major contributor to the exergy destruction, followed by stack gas. Subsequently, multiple regression models were developed to correlate each of the response variables (performance characteristic with the predictor variables (operating parameters. The regression model equations showed a significant statistical relationship between the predictor and response variables.

Predicting Taxi-Out Time at Congested Airports with Optimization-Based Support Vector Regression Methods

Directory of Open Access Journals (Sweden)

Guan Lian

2018-01-01

Full Text Available Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.

An Introduction to the Hybrid Approach of Neural Networks and the Linear Regression Model : An Illustration in the Hedonic Pricing Model of Building Costs

OpenAIRE

浅野, 美代子; マーコ, ユー K.W.

2007-01-01

This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...

A simple and rapid method of purification of impure plutonium oxide

International Nuclear Information System (INIS)

Michael, K.M.; Rakshe, P.R.; Dharmpurikar, G.R.; Thite, B.S.; Lokhande, Manisha; Sinalkar, Nitin; Dakshinamoorthy, A.; Munshi, S.K.; Dey, P.K.

2007-01-01

Impure plutonium oxides are conventionally purified by dissolution in HNO 3 in presence of HF followed by ion exchange separation and oxalate precipitation. The method is tedious and use of HF enhances corrosion of the plant equipment's. A simple and rapid method has been developed for the purification of the oxide by leaching with various reagents like DM water, NaOH and oxalic acid. A combination of DM water followed by hot leaching with 0.4 M oxalic acid could bring down the impurity levels in the oxide to the desired level required for fuel fabrication. (author)

A simple method for percutaneous resection of osteoid osteoma

International Nuclear Information System (INIS)

Kamrani, Reza S.; Kiani, K.; Mazlouman, Shahriar J.

2007-01-01

To introduce a method that can be performed with minimal equipments available to most orthopedic surgeons and precludes the extensive anesthetic and ablative requirements. A percutaneous lead tunnel was first established in the cortex next to the nidus under computerized tomography guidance with local anesthesia; then the nidus was curetted in the operating room through the lead tunnel. The study was performed in Shariati Hospital in Tehran, Iran, from September 2002 to December 2005. Nineteen patients were treated with this method with 94.7% cure rate. The diagnosis was histologically confirmed in 16 cases (84.2%). Failure occurred in one patient. The patients had a mean follow-up of 13.5 months with no recurrence of symptoms with mean hospitalization time of 1.6 days. This technique is simple, minimally invasive and effective. It needs no especial equipments and provides the material for tissue diagnosis. (author)

Hybrid ARIMAX quantile regression method for forecasting short term electricity consumption in east java

Science.gov (United States)

Prastuti, M.; Suhartono; Salehah, NA

2018-04-01

The need for energy supply, especially for electricity in Indonesia has been increasing in the last past years. Furthermore, the high electricity usage by people at different times leads to the occurrence of heteroscedasticity issue. Estimate the electricity supply that could fulfilled the community’s need is very important, but the heteroscedasticity issue often made electricity forecasting hard to be done. An accurate forecast of electricity consumptions is one of the key challenges for energy provider to make better resources and service planning and also take control actions in order to balance the electricity supply and demand for community. In this paper, hybrid ARIMAX Quantile Regression (ARIMAX-QR) approach was proposed to predict the short-term electricity consumption in East Java. This method will also be compared to time series regression using RMSE, MAPE, and MdAPE criteria. The data used in this research was the electricity consumption per half-an-hour data during the period of September 2015 to April 2016. The results show that the proposed approach can be a competitive alternative to forecast short-term electricity in East Java. ARIMAX-QR using lag values and dummy variables as predictors yield more accurate prediction in both in-sample and out-sample data. Moreover, both time series regression and ARIMAX-QR methods with addition of lag values as predictor could capture accurately the patterns in the data. Hence, it produces better predictions compared to the models that not use additional lag variables.

A Powerful Test for Comparing Multiple Regression Functions.

Science.gov (United States)

Maity, Arnab

2012-09-01

In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

Calculation of U, Ra, Th and K contents in uranium ore by multiple linear regression method

International Nuclear Information System (INIS)

Lin Chao; Chen Yingqiang; Zhang Qingwen; Tan Fuwen; Peng Guanghui

1991-01-01

A multiple linear regression method was used to compute γ spectra of uranium ore samples and to calculate contents of U, Ra, Th, and K. In comparison with the inverse matrix method, its advantage is that no standard samples of pure U, Ra, Th and K are needed for obtaining response coefficients

A simple scintigraphic method for continuous monitoring of gastric emptying

Energy Technology Data Exchange (ETDEWEB)

Lipp, R.W.; Hammer, H.F.; Schnedl, W.; Dobnig, H.; Passath, A.; Leb, G.; Krejs, G.J. (Graz Univ. (Austria). Div. of Nuclear Medicine and Endocrinology)

1993-03-01

A new and simple scintigraphic method for the measurement of gastric emptying was developed and validated. The test meal consists of 200 g potato mash mixed with 0.5 g Dowex 2X8 particles (mesh 20-50) labelled with 37 MBq (1 mCi) technetium-99m. After ingestion of the meal, sequential dynamic 15-s anteroposterior exposures in the supine position are obtained for 90 min. A second recording sequence of 20 min is added after a 30-min interval. The results can be displayed as immediate cine-replay, as time-activity diagrams and/or as acitivty retention values. Complicated mathematical fittings are not necessary. The method lends itself equally to the testing of in- and outpatients. (orig.).

A simple and reliable method reducing sulfate to sulfide for multiple sulfur isotope analysis.

Science.gov (United States)

Geng, Lei; Savarino, Joel; Savarino, Clara A; Caillon, Nicolas; Cartigny, Pierre; Hattori, Shohei; Ishino, Sakiko; Yoshida, Naohiro

2018-02-28

Precise analysis of four sulfur isotopes of sulfate in geological and environmental samples provides the means to extract unique information in wide geological contexts. Reduction of sulfate to sulfide is the first step to access such information. The conventional reduction method suffers from a cumbersome distillation system, long reaction time and large volume of the reducing solution. We present a new and simple method enabling the process of multiple samples at one time with a much reduced volume of reducing solution. One mL of reducing solution made of HI and NaH 2 PO 2 was added to a septum glass tube with dry sulfate. The tube was heated at 124°C and the produced H 2 S was purged with inert gas (He or N 2 ) through gas-washing tubes and then collected by NaOH solution. The collected H 2 S was converted into Ag 2 S by adding AgNO 3 solution and the co-precipitated Ag 2 O was removed by adding a few drops of concentrated HNO 3 . Within 2-3 h, a 100% yield was observed for samples with 0.2-2.5 μmol Na 2 SO 4 . The reduction rate was much slower for BaSO 4 and a complete reduction was not observed. International sulfur reference materials, NBS-127, SO-5 and SO-6, were processed with this method, and the measured against accepted δ 34 S values yielded a linear regression line which had a slope of 0.99 ± 0.01 and a R 2 value of 0.998. The new methodology is easy to handle and allows us to process multiple samples at a time. It has also demonstrated good reproducibility in terms of H 2 S yield and for further isotope analysis. It is thus a good alternative to the conventional manual method, especially when processing samples with limited amount of sulfate available. © 2017 The Authors. Rapid Communications in Mass Spectrometry Pubished by John Wiley & Sons Ltd.

A simple optical method for measuring the vibration amplitude of a speaker

OpenAIRE

UEDA, Masahiro; YAMAGUCHI, Toshihiko; KAKIUCHI, Hiroki; SUGA, Hiroshi

1999-01-01

A simple optical method has been proposed for measuring the vibration amplitude of a speaker vibrating with a frequency of approximately 10 kHz. The method is based on a multiple reflection between a vibrating speaker plane and a mirror parallel to that speaker plane. The multiple reflection can magnify a dispersion of the laser beam caused by the vibration, and easily make a measurement of the amplitude. The measuring sensitivity ranges between sub-microns and 1 mm. A preliminary experim...

A simple method to downscale daily wind statistics to hourly wind data

OpenAIRE

Guo, Zhongling

2013-01-01

Wind is the principal driver in the wind erosion models. The hourly wind speed data were generally required for precisely wind erosion modeling. In this study, a simple method to generate hourly wind speed data from daily wind statistics (daily average and maximum wind speeds together or daily average wind speed only) was established. A typical windy location with 3285 days (9 years) measured hourly wind speed data were used to validate the downscaling method. The results showed that the over...

Simulation Opportunity Index, A Simple and Effective Method to Boost the Hydrocarbon Recovery

KAUST Repository

Saputra, Wardana

2016-01-01

This paper describes how the SOI software helps as a simple, fast, and accurate way to obtain the higher hydrocarbon production than that of trial-error method and previous studies in two different fields located in offshore Indonesia. On one hand, the proposed method could save money by minimizing the required number of wells. On the other hand, it could maximize profit by maximizing recovery.

The regression-calibration method for fitting generalized linear models with additive measurement error

OpenAIRE

James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll

2003-01-01

This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...

Linear regression metamodeling as a tool to summarize and present simulation model results.

Science.gov (United States)

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Boucher, Thomas F., E-mail: boucher@cs.umass.edu [School of Computer Science, University of Massachusetts Amherst, 140 Governor' s Drive, Amherst, MA 01003, United States. (United States); Ozanne, Marie V. [Department of Astronomy, Mount Holyoke College, South Hadley, MA 01075 (United States); Carmosino, Marco L. [School of Computer Science, University of Massachusetts Amherst, 140 Governor' s Drive, Amherst, MA 01003, United States. (United States); Dyar, M. Darby [Department of Astronomy, Mount Holyoke College, South Hadley, MA 01075 (United States); Mahadevan, Sridhar [School of Computer Science, University of Massachusetts Amherst, 140 Governor' s Drive, Amherst, MA 01003, United States. (United States); Breves, Elly A.; Lepore, Kate H. [Department of Astronomy, Mount Holyoke College, South Hadley, MA 01075 (United States); Clegg, Samuel M. [Los Alamos National Laboratory, P.O. Box 1663, MS J565, Los Alamos, NM 87545 (United States)

2015-05-01

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k-nearest neighbor (kNN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO{sub 2}, Fe{sub 2}O{sub 3}, CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na{sub 2}O, K{sub 2}O, TiO{sub 2}, and P{sub 2}O{sub 5}, the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high

Wrist arthrography: a simple method

Energy Technology Data Exchange (ETDEWEB)

Berna-Serna, Juan D.; Reus, Manuel; Alonso, Jose [Virgen de la Arrixaca University Hospital, Department of Radiology, El Palmar (Murcia) (Spain); Martinez, Francisco; Domenech-Ratto, Gines [University of Murcia, Department of Human Anatomy, Faculty of Medicine, Murcia (Spain)

2006-02-01

A technique of wrist arthrography is presented using an adhesive marker-plate with radiopaque coordinates to identify precisely sites for puncture arthrography of the wrist and to obviate the need for fluoroscopic guidance. Radiocarpal joint arthrography was performed successfully in all 24 cases, 14 in the cadaveric wrists and 10 in the live patients. The arthrographic procedure described in this study is simple, safe, and rapid, and has the advantage of precise localisation of the site for puncture without need for fluoroscopic guidance. (orig.)

Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

Lesion size affects diagnostic performance of IOTA logistic regression models, IOTA simple rules and risk of malignancy index in discriminating between benign and malignant adnexal masses.

Science.gov (United States)

Di Legge, A; Testa, A C; Ameye, L; Van Calster, B; Lissoni, A A; Leone, F P G; Savelli, L; Franchi, D; Czekierdowski, A; Trio, D; Van Holsbeke, C; Ferrazzi, E; Scambia, G; Timmerman, D; Valentin, L

2012-09-01

To estimate the ability to discriminate between benign and malignant adnexal masses of different size using: subjective assessment, two International Ovarian Tumor Analysis (IOTA) logistic regression models (LR1 and LR2), the IOTA simple rules and the risk of malignancy index (RMI). We used a multicenter IOTA database of 2445 patients with at least one adnexal mass, i.e. the database previously used to prospectively validate the diagnostic performance of LR1 and LR2. The masses were categorized into three subgroups according to their largest diameter: small tumors (diameter IOTA simple rules and the RMI were applied to each of the three groups. Sensitivity, specificity, positive and negative likelihood ratio (LR+, LR-), diagnostic odds ratio (DOR) and area under the receiver-operating characteristics curve (AUC) were used to describe diagnostic performance. A moving window technique was applied to estimate the effect of tumor size as a continuous variable on the AUC. The reference standard was the histological diagnosis of the surgically removed adnexal mass. The frequency of invasive malignancy was 10% in small tumors, 19% in medium-sized tumors and 40% in large tumors; 11% of the large tumors were borderline tumors vs 3% and 4%, respectively, of the small and medium-sized tumors. The type of benign histology also differed among the three subgroups. For all methods, sensitivity with regard to malignancy was lowest in small tumors (56-84% vs 67-93% in medium-sized tumors and 74-95% in large tumors) while specificity was lowest in large tumors (60-87%vs 83-95% in medium-sized tumors and 83-96% in small tumors ). The DOR and the AUC value were highest in medium-sized tumors and the AUC was largest in tumors with a largest diameter of 7-11 cm. Tumor size affects the performance of subjective assessment, LR1 and LR2, the IOTA simple rules and the RMI in discriminating correctly between benign and malignant adnexal masses. The likely explanation, at least in part, is

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

Science.gov (United States)

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

A methodology for the design of experiments in computational intelligence with multiple regression models.

Science.gov (United States)

Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A methodology for the design of experiments in computational intelligence with multiple regression models

Directory of Open Access Journals (Sweden)

Carlos Fernandez-Lozano

2016-12-01

Full Text Available The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

Filtration Isolation of Nucleic Acids: A Simple and Rapid DNA Extraction Method.

Science.gov (United States)

McFall, Sally M; Neto, Mário F; Reed, Jennifer L; Wagner, Robin L

2016-08-06

FINA, filtration isolation of nucleic acids, is a novel extraction method which utilizes vertical filtration via a separation membrane and absorbent pad to extract cellular DNA from whole blood in less than 2 min. The blood specimen is treated with detergent, mixed briefly and applied by pipet to the separation membrane. The lysate wicks into the blotting pad due to capillary action, capturing the genomic DNA on the surface of the separation membrane. The extracted DNA is retained on the membrane during a simple wash step wherein PCR inhibitors are wicked into the absorbent blotting pad. The membrane containing the entrapped DNA is then added to the PCR reaction without further purification. This simple method does not require laboratory equipment and can be easily implemented with inexpensive laboratory supplies. Here we describe a protocol for highly sensitive detection and quantitation of HIV-1 proviral DNA from 100 µl whole blood as a model for early infant diagnosis of HIV that could readily be adapted to other genetic targets.

Consistency analysis of subspace identification methods based on a linear regression approach

DEFF Research Database (Denmark)

Knudsen, Torben

2001-01-01

In the literature results can be found which claim consistency for the subspace method under certain quite weak assumptions. Unfortunately, a new result gives a counter example showing inconsistency under these assumptions and then gives new more strict sufficient assumptions which however does n...... not include important model structures as e.g. Box-Jenkins. Based on a simple least squares approach this paper shows the possible inconsistency under the weak assumptions and develops only slightly stricter assumptions sufficient for consistency and which includes any model structure...

Sensitive and simple method for measuring wire tensions

International Nuclear Information System (INIS)

Atac, M.; Mishina, M.

1982-08-01

Measuring tension of wires in drift chambers and multiwire proportional chambers after construction is an important process because sometimes wires get loose after soldering, crimping or glueing. One needs to sort out wires which have tensions below a required minimum value to prevent electrostatic instabilities. There have been several methods reported on this subject in which the wires were excited either with sinusoidal current under magnetic field or with sinusoidal voltage electrostatically coupled to the wire, searching for a resonating frequency with which the wires vibrate mechanically. Then the vibration is detected either visually, optically or with magnetic pick-up directly touching the wires. Any of these is only applicable to the usual multiwire chamber which has open access to the wire plane. They also need fairly large excitation currents to induce a detectable vibration to the wires. Here we report a very simple method that can be used for any type of wire chamber or proportional tube system for measuring wire tension. Only a very small current is required for the wire excitation to obtain a large enough signal because it detects the induced emf voltage across a wire. A sine-wave oscillator and a digital voltmeter are sufficient devices aside from a permanent magnet to provide the magnetic field around the wire. A useful application of this method to a large system is suggested

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

Regression of environmental noise in LIGO data

International Nuclear Information System (INIS)

Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G

2015-01-01

We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)

A simple method to approximate liver size on cross-sectional images using living liver models

International Nuclear Information System (INIS)

Muggli, D.; Mueller, M.A.; Karlo, C.; Fornaro, J.; Marincek, B.; Frauenfelder, T.

2009-01-01

Aim: To assess whether a simple. diameter-based formula applicable to cross-sectional images can be used to calculate the total liver volume. Materials and methods: On 119 cross-sectional examinations (62 computed tomography and 57 magnetic resonance imaging) a simple, formula-based method to approximate the liver volume was evaluated. The total liver volume was approximated measuring the largest craniocaudal (cc), ventrodorsal (vd), and coronal (cor) diameters by two readers and implementing the equation: Vol estimated =ccxvdxcorx0.31. Inter-rater reliability, agreement, and correlation between liver volume calculation and virtual liver volumetry were analysed. Results: No significant disagreement between the two readers was found. The formula correlated significantly with the volumetric data (r > 0.85, p < 0.0001). In 81% of cases the error of the approximated volume was <10% and in 92% of cases <15% compared to the volumetric data. Conclusion: Total liver volume can be accurately estimated on cross-sectional images using a simple, diameter-based equation.

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.

Thermoluminescence dating of chinese porcelain using a regression method of saturating exponential in pre-dose technique

International Nuclear Information System (INIS)

Wang Weida; Xia Junding; Zhou Zhixin; Leung, P.L.

2001-01-01

Thermoluminescence (TL) dating using a regression method of saturating exponential in pre-dose technique was described. 23 porcelain samples from past dynasties of China were dated by this method. The results show that the TL ages are in reasonable agreement with archaeological dates within a standard deviation of 27%. Such error can be accepted in porcelain dating

Simple and efficient methods for the accurate evaluation of patterning effects in ultrafast photonic switches

DEFF Research Database (Denmark)

Xu, Jing; Ding, Yunhong; Peucheret, Christophe

2011-01-01

Although patterning effects (PEs) are known to be a limiting factor of ultrafast photonic switches based on semiconductor optical amplifiers (SOAs), a simple approach for their evaluation in numerical simulations and experiments is missing. In this work, we experimentally investigate and verify...... as well as the operation bit rate. Furthermore, a simple and effective method for probing the maximum PEs is demonstrated, which may relieve the computational effort or the experimental difficulties associated with the use of long PRBSs for the simulation or characterization of SOA-based switches. Good...... agrement with conventional PRBS characterization is obtained. The method is suitable for quick and systematic estimation and optimization of the switching performance....

Note on a simple test method for estimaing J/sub Ic/

International Nuclear Information System (INIS)

Whipple, T.A.; McHenry, H.I.

1980-01-01

Fracture toughness testing is generally a time-consuming and expensive procedure; therefore, there has been a significant amount of effort directed toward developing an inexpensive and rapid method of estimating the fracture toughness of materials. In this paper, a simple method for estimating J/sub Ic/ through the use of small, notched, bend bars is evaluated. The test only involves the measurement of the energy necessary to fracture the sample. Initial tests on Fe-18Cr-3Ni-13Mn and 304L stainless steel at 76 and 4 0 K have yielded results consistent with other fracture toughness tests, for materials in the low- to medium-toughness range

A simple headspace equilibration method for measuring dissolved methane

Science.gov (United States)

Magen, C; Lapham, L.L.; Pohlman, John W.; Marshall, Kristin N.; Bosman, S.; Casso, Michael; Chanton, J.P.

2014-01-01

Dissolved methane concentrations in the ocean are close to equilibrium with the atmosphere. Because methane is only sparingly soluble in seawater, measuring it without contamination is challenging for samples collected and processed in the presence of air. Several methods for analyzing dissolved methane are described in the literature, yet none has conducted a thorough assessment of the method yield, contamination issues during collection, transport and storage, and the effect of temperature changes and preservative. Previous extraction methods transfer methane from water to gas by either a "sparge and trap" or a "headspace equilibration" technique. The gas is then analyzed for methane by gas chromatography. Here, we revisit the headspace equilibration technique and describe a simple, inexpensive, and reliable method to measure methane in fresh and seawater, regardless of concentration. Within the range of concentrations typically found in surface seawaters (2-1000 nmol L-1), the yield of the method nears 100% of what is expected from solubility calculation following the addition of known amount of methane. In addition to being sensitive (detection limit of 0.1 ppmv, or 0.74 nmol L-1), this method requires less than 10 min per sample, and does not use highly toxic chemicals. It can be conducted with minimum materials and does not require the use of a gas chromatograph at the collection site. It can therefore be used in various remote working environments and conditions.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

Science.gov (United States)

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

Science.gov (United States)

Zhu, Xiaofeng; Suk, Heung-Il; Wang, Li; Lee, Seong-Whan; Shen, Dinggang

2017-05-01

In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ 2,1 -norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs

Science.gov (United States)

Lee, Eugene K.; Kurokawa, Yosuke K.; Tu, Robin; George, Steven C.; Khine, Michelle

2015-07-01

Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.

Development of the simple evaluation method of the soil biomass by the ATP measurement

Czech Academy of Sciences Publication Activity Database

Urashima, Y.; Nakajima, M.; Kaneda, Satoshi; Murakami, T.

2007-01-01

Roč. 78, č. 2 (2007), s. 187-190 ISSN 0029-0610 Institutional research plan: CEZ:AV0Z60660521 Keywords : simple evaluation method * soil biomass * ATP measurement Subject RIV: EH - Ecology, Behaviour

Applied regression analysis a research tool

CERN Document Server

Pantula, Sastry; Dickey, David

1998-01-01

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

A simple and inexpensive method for genomic restriction mapping analysis

International Nuclear Information System (INIS)

Huang, C.H.; Lam, V.M.S.; Tam, J.W.O.

1988-01-01

The Southern blotting procedure for the transfer of DNA fragments from agarose gels to nitrocellulose membranes has revolutionized nucleic acid detection methods, and it forms the cornerstone of research in molecular biology. Basically, the method involves the denaturation of DNA fragments that have been separated on an agarose gel, the immobilization of the fragments by transfer to a nitrocellulose membrane, and the identification of the fragments of interest through hybridization to /sup 32/P-labeled probes and autoradiography. While the method is sensitive and applicable to both genomic and cloned DNA, it suffers from the disadvantages of being time consuming and expensive, and fragments of greater than 15 kb are difficult to transfer. Moreover, although theoretically the nitrocellulose membrane can be washed and hybridized repeatedly using different probes, in practice, the membrane becomes brittle and difficult to handle after a few cycles. A direct hybridization method for pure DNA clones was developed in 1975 but has not been widely exploited. The authors report here a modification of their procedure as applied to genomic DNA. The method is simple, rapid, and inexpensive, and it does not involve transfer to nitrocellulose membranes

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)

A Simple Method for Measuring the Verticality of Small-Diameter Driven Wells

DEFF Research Database (Denmark)

Kjeldsen, Peter; Skov, Bent

1994-01-01

The presence of stones, solid waste, and other obstructions can deflect small-diameter driven wells during installation, leading to deviations of the well from its intended position. This could lead to erroneous results, especially for measurements of ground water levels by water level meters....... A simple method was developed to measure deviations from the intended positions of well screens and determine correction factors required for proper measurement of ground water levels in nonvertical wells. The method is based upon measurement of the hydrostatic pressure in the bottom of a water column...... ground water flow directions....

A Nonmonotone Trust Region Method for Nonlinear Programming with Simple Bound Constraints

International Nuclear Information System (INIS)

Chen, Z.-W.; Han, J.-Y.; Xu, D.-C.

2001-01-01

In this paper we propose a nonmonotone trust region algorithm for optimization with simple bound constraints. Under mild conditions, we prove the global convergence of the algorithm. For the monotone case it is also proved that the correct active set can be identified in a finite number of iterations if the strict complementarity slackness condition holds, and so the proposed algorithm reduces finally to an unconstrained minimization method in a finite number of iterations, allowing a fast asymptotic rate of convergence. Numerical experiments show that the method is efficient

On Solving Lq-Penalized Regressions

Directory of Open Access Journals (Sweden)

Tracy Zhou Wu

2007-01-01

Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Development of a New RP-UPLC Method for the Determination of ...

African Journals Online (AJOL)

Erah

Results: The developed method was linear for rabeprazole sodium from 0.03 - 30 µg/ml ... regression obtained was > 0.999. ... cost-effective for routine analysis in the pharmaceutical industry. ... a simple UPLC method for the determination.

Simple and rapid analytical method for detection of amino acids in blood using blood spot on filter paper, fast-GC/MS and isotope dilution technique.

Science.gov (United States)

Kawana, Shuichi; Nakagawa, Katsuhiro; Hasegawa, Yuki; Yamaguchi, Seiji

2010-11-15

A simple and rapid method for quantitative analysis of amino acids, including valine (Val), leucine (Leu), isoleucine (Ile), methionine (Met) and phenylalanine (Phe), in whole blood has been developed using GC/MS. In this method, whole blood was collected using a filter paper technique, and a 1/8 in. blood spot punch was used for sample preparation. Amino acids were extracted from the sample, and the extracts were purified using cation-exchange resins. The isotope dilution method using ²H₈-Val, ²H₃-Leu, ²H₃-Met and ²H₅-Phe as internal standards was applied. Following propyl chloroformate derivatization, the derivatives were analyzed using fast-GC/MS. The extraction recoveries using these techniques ranged from 69.8% to 87.9%, and analysis time for each sample was approximately 26 min. Calibration curves at concentrations from 0.0 to 1666.7 μmol/l for Val, Leu, Ile and Phe and from 0.0 to 333.3 μmol/l for Met showed good linearity with regression coefficients=1. The method detection limits for Val, Leu, Ile, Met and Phe were 24.2, 16.7, 8.7, 1.5 and 12.9 μmol/l, respectively. This method was applied to blood spot samples obtained from patients with phenylketonuria (PKU), maple syrup urine disease (MSUD), hypermethionine and neonatal intrahepatic cholestasis caused by citrin deficiency (NICCD), and the analysis results showed that the concentrations of amino acids that characterize these diseases were increased. These results indicate that this method provides a simple and rapid procedure for precise determination of amino acids in whole blood. Copyright © 2010 Elsevier B.V. All rights reserved.

Analysis of some methods for reduced rank Gaussian process regression

DEFF Research Database (Denmark)

Quinonero-Candela, J.; Rasmussen, Carl Edward

2005-01-01

While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent...... proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank...... Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning...

Conditional Monte Carlo randomization tests for regression models.

Science.gov (United States)

Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

2014-08-15

We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

Laser-induced Breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

International Nuclear Information System (INIS)

Yang, Jianhong; Yi, Cancan; Xu, Jinwu; Ma, Xianghong

2015-01-01

A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine. - Highlights: • Both training and testing samples are considered for analytical lines selection. • The analytical lines are auto-selected based on the built-in characteristics of spectral lines. • The new method can achieve better prediction accuracy and modeling robustness. • Model predictions are given with confidence interval of probabilistic distribution

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Capacitance Regression Modelling Analysis on Latex from Selected Rubber Tree Clones

International Nuclear Information System (INIS)

Rosli, A D; Baharudin, R; Hashim, H; Khairuzzaman, N A; Mohd Sampian, A F; Abdullah, N E; Kamaru'zzaman, M; Sulaiman, M S

2015-01-01

This paper investigates the capacitance regression modelling performance of latex for various rubber tree clones, namely clone 2002, 2008, 2014 and 3001. Conventionally, the rubber tree clones identification are based on observation towards tree features such as shape of leaf, trunk, branching habit and pattern of seeds texture. The former method requires expert persons and very time-consuming. Currently, there is no sensing device based on electrical properties that can be employed to measure different clones from latex samples. Hence, with a hypothesis that the dielectric constant of each clone varies, this paper discusses the development of a capacitance sensor via Capacitance Comparison Bridge (known as capacitance sensor) to measure an output voltage of different latex samples. The proposed sensor is initially tested with 30ml of latex sample prior to gradually addition of dilution water. The output voltage and capacitance obtained from the test are recorded and analyzed using Simple Linear Regression (SLR) model. This work outcome infers that latex clone of 2002 has produced the highest and reliable linear regression line with determination coefficient of 91.24%. In addition, the study also found that the capacitive elements in latex samples deteriorate if it is diluted with higher volume of water. (paper)

Differentiating regressed melanoma from regressed lichenoid keratosis.

Science.gov (United States)

Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

2017-04-01

Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A new and simple gravimetric method for determination of uranium

International Nuclear Information System (INIS)

Saxena, A.K.

1994-01-01

A new and simple gravimetric method for determining uranium has been described. Using a known quantity of uranyl nitrate as the test solution, an alcoholic solution of 2-amino-2-methyl 1:3 propanediol (AMP) was added slowly. A yellow precipitate was obtained which was filtered through ashless filter paper, washed with alcohol, dried and ignited at 800 degC for 4h. It gave a black powder as a product which was shown by X-ray diffraction to be U 3 O 8 . The percentage error was found in the range -0.09 to +0.89. (author). 8 refs., 1 tab

A Comparative Investigation of the Combined Effects of Pre-Processing, Wavelength Selection, and Regression Methods on Near-Infrared Calibration Model Performance.

Science.gov (United States)

Wan, Jian; Chen, Yi-Chieh; Morris, A Julian; Thennadil, Suresh N

2017-07-01

Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant

Simple analytical methods for computing the gravity-wave contribution to the cosmic background radiation anisotropy

International Nuclear Information System (INIS)

Wang, Y.

1996-01-01

We present two simple analytical methods for computing the gravity-wave contribution to the cosmic background radiation (CBR) anisotropy in inflationary models; one method uses a time-dependent transfer function, the other methods uses an approximate gravity-mode function which is a simple combination of the lowest order spherical Bessel functions. We compare the CBR anisotropy tensor multipole spectrum computed using our methods with the previous result of the highly accurate numerical method, the open-quote open-quote Boltzmann close-quote close-quote method. Our time-dependent transfer function is more accurate than the time-independent transfer function found by Turner, White, and Lindsey; however, we find that the transfer function method is only good for l approx-lt 120. Using our approximate gravity-wave mode function, we obtain much better accuracy; the tensor multipole spectrum we find differs by less than 2% for l approx-lt 50, less than 10% for l approx-lt 120, and less than 20% for l≤300 from the open-quote open-quote Boltzmann close-quote close-quote result. Our approximate graviton mode function should be quite useful in studying tensor perturbations from inflationary models. copyright 1996 The American Physical Society

Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

Science.gov (United States)

Choi, Jae-Seok; Kim, Munchurl

2017-03-01

Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower

A simple method to design non-collision relative orbits for close spacecraft formation flying

Science.gov (United States)

Jiang, Wei; Li, JunFeng; Jiang, FangHua; Bernelli-Zazzera, Franco

2018-05-01

A set of linearized relative motion equations of spacecraft flying on unperturbed elliptical orbits are specialized for particular cases, where the leader orbit is circular or equatorial. Based on these extended equations, we are able to analyze the relative motion regulation between a pair of spacecraft flying on arbitrary unperturbed orbits with the same semi-major axis in close formation. Given the initial orbital elements of the leader, this paper presents a simple way to design initial relative orbital elements of close spacecraft with the same semi-major axis, thus preventing collision under non-perturbed conditions. Considering the mean influence of J 2 perturbation, namely secular J 2 perturbation, we derive the mean derivatives of orbital element differences, and then expand them to first order. Thus the first order expansion of orbital element differences can be added to the relative motion equations for further analysis. For a pair of spacecraft that will never collide under non-perturbed situations, we present a simple method to determine whether a collision will occur when J 2 perturbation is considered. Examples are given to prove the validity of the extended relative motion equations and to illustrate how the methods presented can be used. The simple method for designing initial relative orbital elements proposed here could be helpful to the preliminary design of the relative orbital elements between spacecraft in a close formation, when collision avoidance is necessary.

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.

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.

Face Hallucination with Linear Regression Model in Semi-Orthogonal Multilinear PCA Method

Science.gov (United States)

Asavaskulkiet, Krissada

2018-04-01

In this paper, we propose a new face hallucination technique, face images reconstruction in HSV color space with a semi-orthogonal multilinear principal component analysis method. This novel hallucination technique can perform directly from tensors via tensor-to-vector projection by imposing the orthogonality constraint in only one mode. In our experiments, we use facial images from FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color faces. The experimental results assure clearly demonstrated that we can generate photorealistic color face images by using the SO-MPCA subspace with a linear regression model.

Simple and effective method for nuclear tellurium isomers separation from antimony cyclotron targets

International Nuclear Information System (INIS)

Bondarevskij, S.I.; Eremin, V.V.

1999-01-01

Simple and effective method of generation of tellurium nuclear isomers from irradiated on cyclotron metallic antimony is suggested. Basically this method consists in consideration of the big difference in volatilities of metallic forms of antimony, tin and tellurium. Heating of the tin-antimony alloy at 1200 K permits to separate about 90 % of produced quantity of 121m Te and 123m Te (in this case impurity of antimony radionuclides is not more than 1 % on activity) [ru

A new method to study simple shear processing of wheat gluten-starch mixtures

NARCIS (Netherlands)

Peighambardoust, S.H.; Goot, A.J. van der; Hamer, R.J.; Boom, R.M.

2004-01-01

This article introduces a new method that uses a shearing device to study the effect of simple shear on the overall properties of pasta-like products made from commercial wheat gluten-starch (GS) blends. The shear-processed GS samples had a lower cooking loss (CL) and a higher swelling index (SI)

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...

Selecting a Regression Saturated by Indicators

DEFF Research Database (Denmark)

Hendry, David F.; Johansen, Søren; Santos, Carlos

We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest....

Selecting a Regression Saturated by Indicators

DEFF Research Database (Denmark)

Hendry, David F.; Johansen, Søren; Santos, Carlos

We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest...

A simple method for validation and verification of pipettes mounted on automated liquid handlers

DEFF Research Database (Denmark)

Stangegaard, Michael; Hansen, Anders Johannes; Frøslev, Tobias G

2011-01-01

We have implemented a simple, inexpensive, and fast procedure for validation and verification of the performance of pipettes mounted on automated liquid handlers (ALHs) as necessary for laboratories accredited under ISO 17025. A six- or seven-step serial dilution of OrangeG was prepared in quadru......We have implemented a simple, inexpensive, and fast procedure for validation and verification of the performance of pipettes mounted on automated liquid handlers (ALHs) as necessary for laboratories accredited under ISO 17025. A six- or seven-step serial dilution of OrangeG was prepared...... are freely available. In conclusion, we have set up a simple, inexpensive, and fast solution for the continuous validation of ALHs used for accredited work according to the ISO 17025 standard. The method is easy to use for aqueous solutions but requires a spectrophotometer that can read microtiter plates....

cp-R, an interface the R programming language for clinical laboratory method comparisons.

Science.gov (United States)

Holmes, Daniel T

2015-02-01

Clinical scientists frequently need to compare two different bioanalytical methods as part of assay validation/monitoring. As a matter necessity, regression methods for quantitative comparison in clinical chemistry, hematology and other clinical laboratory disciplines must allow for error in both the x and y variables. Traditionally the methods popularized by 1) Deming and 2) Passing and Bablok have been recommended. While commercial tools exist, no simple open source tool is available. The purpose of this work was to develop and entirely open-source GUI-driven program for bioanalytical method comparisons capable of performing these regression methods and able to produce highly customized graphical output. The GUI is written in python and PyQt4 with R scripts performing regression and graphical functions. The program can be run from source code or as a pre-compiled binary executable. The software performs three forms of regression and offers weighting where applicable. Confidence bands of the regression are calculated using bootstrapping for Deming and Passing Bablok methods. Users can customize regression plots according to the tools available in R and can produced output in any of: jpg, png, tiff, bmp at any desired resolution or ps and pdf vector formats. Bland Altman plots and some regression diagnostic plots are also generated. Correctness of regression parameter estimates was confirmed against existing R packages. The program allows for rapid and highly customizable graphical output capable of conforming to the publication requirements of any clinical chemistry journal. Quick method comparisons can also be performed and cut and paste into spreadsheet or word processing applications. We present a simple and intuitive open source tool for quantitative method comparison in a clinical laboratory environment. Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

Science.gov (United States)

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

Comparison of Linear and Non-linear Regression Analysis to Determine Pulmonary Pressure in Hyperthyroidism.

Science.gov (United States)

Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan

2017-01-01

This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second

A method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1971-01-01

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

Determination of Some Cephalosporins in Pharmaceutical Formulations by a Simple and Sensitive Spectrofluorimetric Method

Directory of Open Access Journals (Sweden)

Ali Abdollahi, Ahad Bavili-Tabrizi

2016-03-01

Full Text Available Background: Cephalosporins are among the safest and the most effective broad-spectrum bactericidal antimicrobial agents which have been prescribed by the clinician as antibiotics. Thus, the developing of simple, sensitive and rapid analytical methods for their determination can be attractive and desirable. Methods: A simple, rapid and sensitive spectrofluorimetric method was developed for the determination of cefixime, cefalexin and ceftriaxone in pharmaceutical formulations. Proposed method is based on the oxidation of these cephalosporins with cerium (IV to produce cerium (III, and its fluorescence was monitored at 356 ± 3 nm after excitation at 254 ± 3 nm. Results: The variables effecting oxidation of each cephalosporin with cerum (IV were studied and optimized. Under the experimental conditions used, the calibration graphs were linear over the range 0.1-4 µg/mL. The limit of detection and limit of quantification were in the range 0.031-0.054 and 0.102-0.172 µg/mL, respectively. Intra- and inter-day assay precisions, expressed as the relative standard deviation (RSD, were lower than 5.6 and 6.8%, respectively. Conclusion: The proposed method was applied to the determination of studied cephalosporins in pharmaceutical formulations by good recoveries in the range 91-110%.

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.

A simple method of shower localization and identification in laterally segmented calorimeters

International Nuclear Information System (INIS)

Awes, T.C.; Obenshain, F.E.; Plasil, F.; Saini, S.; Young, G.R.; Sorensen, S.P.

1992-01-01

A method is proposed to calculate the first and second moments of the spatial distribution of the energy of electromagnetic and hadronic showers measured in laterally segmented colorimeters. The technique uses a logarithmic weighting of energy fraction observed in the individual detector cells. It is fast and simple requiring no fitting or complicated corrections for the position or angle of incidence. The method is demonstrated with GEANT simulations of a BGO detector array. The position resolution results and the e/π separation results are found to be equal or superior to those obtained with more complicated techniques. (orig.)

Surgery for left ventricular aneurysm: early and late survival after simple linear repair and endoventricular patch plasty.

Science.gov (United States)

Lundblad, Runar; Abdelnoor, Michel; Svennevig, Jan Ludvig

2004-09-01

Simple linear resection and endoventricular patch plasty are alternative techniques to repair postinfarction left ventricular aneurysm. The aim of the study was to compare these 2 methods with regard to early mortality and long-term survival. We retrospectively reviewed 159 patients undergoing operations between 1989 and 2003. The epidemiologic design was of an exposed (simple linear repair, n = 74) versus nonexposed (endoventricular patch plasty, n = 85) cohort with 2 endpoints: early mortality and long-term survival. The crude effect of aneurysm repair technique versus endpoint was estimated by odds ratio, rate ratio, or relative risk and their 95% confidence intervals. Stratification analysis by using the Mantel-Haenszel method was done to quantify confounders and pinpoint effect modifiers. Adjustment for multiconfounders was performed by using logistic regression and Cox regression analysis. Survival curves were analyzed with the Breslow test and the log-rank test. Early mortality was 8.2% for all patients, 13.5% after linear repair and 3.5% after endoventricular patch plasty. When adjusted for multiconfounders, the risk of early mortality was significantly higher after simple linear repair than after endoventricular patch plasty (odds ratio, 4.4; 95% confidence interval, 1.1-17.8). Mean follow-up was 5.8 +/- 3.8 years (range, 0-14.0 years). Overall 5-year cumulative survival was 78%, 70.1% after linear repair and 91.4% after endoventricular patch plasty. The risk of total mortality was significantly higher after linear repair than after endoventricular patch plasty when controlled for multiconfounders (relative risk, 4.5; 95% confidence interval, 2.0-9.7). Linear repair dominated early in the series and patch plasty dominated later, giving a possible learning-curve bias in favor of patch plasty that could not be adjusted for in the regression analysis. Postinfarction left ventricular aneurysm can be repaired with satisfactory early and late results. Surgical

Statistical learning method in regression analysis of simulated positron spectral data

International Nuclear Information System (INIS)

Avdic, S. Dz.

2005-01-01

Positron lifetime spectroscopy is a non-destructive tool for detection of radiation induced defects in nuclear reactor materials. This work concerns the applicability of the support vector machines method for the input data compression in the neural network analysis of positron lifetime spectra. It has been demonstrated that the SVM technique can be successfully applied to regression analysis of positron spectra. A substantial data compression of about 50 % and 8 % of the whole training set with two and three spectral components respectively has been achieved including a high accuracy of the spectra approximation. However, some parameters in the SVM approach such as the insensitivity zone e and the penalty parameter C have to be chosen carefully to obtain a good performance. (author)

The crux of the method: assumptions in ordinary least squares and logistic regression.

Science.gov (United States)

Long, Rebecca G

2008-10-01

Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.

Regression and direct methods do not give different estimates of digestible and metabolizable energy values of barley, sorghum, and wheat for pigs.

Science.gov (United States)

Bolarinwa, O A; Adeola, O

2016-02-01

Direct or indirect methods can be used to determine the DE and ME of feed ingredients for pigs. In situations when only the indirect approach is suitable, the regression method presents a robust indirect approach. Three experiments were conducted to compare the direct and regression methods for determining the DE and ME values of barley, sorghum, and wheat for pigs. In each experiment, 24 barrows with an average initial BW of 31, 32, and 33 kg were assigned to 4 diets in a randomized complete block design. The 4 diets consisted of 969 g barley, sorghum, or wheat/kg plus minerals and vitamins for the direct method; a corn-soybean meal reference diet (RD); the RD + 300 g barley, sorghum, or wheat/kg; and the RD + 600 g barley, sorghum, or wheat/kg. The 3 corn-soybean meal diets were used for the regression method. Each diet was fed to 6 barrows in individual metabolism crates for a 5-d acclimation followed by a 5-d period of total but separate collection of feces and urine in each experiment. Graded substitution of barley or wheat, but not sorghum, into the RD linearly reduced ( direct method-derived DE and ME for barley were 3,669 and 3,593 kcal/kg DM, respectively. The regressions of barley contribution to DE and ME in kilocalories against the quantity of barley DMI in kilograms generated 3,746 kcal DE/kg DM and 3,647 kcal ME/kg DM. The DE and ME for sorghum by the direct method were 4,097 and 4,042 kcal/kg DM, respectively; the corresponding regression-derived estimates were 4,145 and 4,066 kcal/kg DM. Using the direct method, energy values for wheat were 3,953 kcal DE/kg DM and 3,889 kcal ME/kg DM. The regressions of wheat contribution to DE and ME in kilocalories against the quantity of wheat DMI in kilograms generated 3,960 kcal DE/kg DM and 3,874 kcal ME/kg DM. The DE and ME of barley using the direct method were not different (0.3 direct method-derived DE and ME of sorghum were not different (0.5 direct method- and regression method-derived DE (3,953 and 3

Learning a Nonnegative Sparse Graph for Linear Regression.

Science.gov (United States)

Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

2015-09-01

Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.