Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,
This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)
General Nature of Multicollinearity in Multiple Regression Analysis.
Liu, Richard
1981-01-01
Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)
Research and analyze of physical health using multiple regression analysis
T. S. Kyi
2014-01-01
Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.
MULGRES: a computer program for stepwise multiple regression analysis
A. Jeff Martin
1971-01-01
MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.
231 Using Multiple Regression Analysis in Modelling the Role of ...
User
of Internal Revenue, Tourism Bureau and hotel records. The multiple regression .... additional guest facilities such as restaurant, a swimming pool or child care and social function ... and provide good quality service to the public. Conclusion.
Weibull and lognormal Taguchi analysis using multiple linear regression
Piña-Monarrez, Manuel R.; Ortiz-Yañez, Jesús F.
2015-01-01
The paper provides to reliability practitioners with a method (1) to estimate the robust Weibull family when the Taguchi method (TM) is applied, (2) to estimate the normal operational Weibull family in an accelerated life testing (ALT) analysis to give confidence to the extrapolation and (3) to perform the ANOVA analysis to both the robust and the normal operational Weibull family. On the other hand, because the Weibull distribution neither has the normal additive property nor has a direct relationship with the normal parameters (µ, σ), in this paper, the issues of estimating a Weibull family by using a design of experiment (DOE) are first addressed by using an L_9 (3"4) orthogonal array (OA) in both the TM and in the Weibull proportional hazard model approach (WPHM). Then, by using the Weibull/Gumbel and the lognormal/normal relationships and multiple linear regression, the direct relationships between the Weibull and the lifetime parameters are derived and used to formulate the proposed method. Moreover, since the derived direct relationships always hold, the method is generalized to the lognormal and ALT analysis. Finally, the method’s efficiency is shown through its application to the used OA and to a set of ALT data. - Highlights: • It gives the statistical relations and steps to use the Taguchi Method (TM) to analyze Weibull data. • It gives the steps to determine the unknown Weibull family to both the robust TM setting and the normal ALT level. • It gives a method to determine the expected lifetimes and to perform its ANOVA analysis in TM and ALT analysis. • It gives a method to give confidence to the extrapolation in an ALT analysis by using the Weibull family of the normal level.
Multiple regression for physiological data analysis: the problem of multicollinearity.
Slinker, B K; Glantz, S A
1985-07-01
Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.
A comparative study of multiple regression analysis and back ...
Abhijit Sarkar
artificial neural network (ANN) models to predict weld bead geometry and HAZ width in submerged arc welding ... Keywords. Submerged arc welding (SAW); multi-regression analysis (MRA); artificial neural network ..... Degree of freedom.
Quantitative electron microscope autoradiography: application of multiple linear regression analysis
Markov, D.V.
1986-01-01
A new method for the analysis of high resolution EM autoradiographs is described. It identifies labelled cell organelle profiles in sections on a strictly statistical basis and provides accurate estimates for their radioactivity without the need to make any assumptions about their size, shape and spatial arrangement. (author)
Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha
2012-05-01
Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
An improved multiple linear regression and data analysis computer program package
Sidik, S. M.
1972-01-01
NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.
Analysis of γ spectra in airborne radioactivity measurements using multiple linear regressions
Bao Min; Shi Quanlin; Zhang Jiamei
2004-01-01
This paper describes the net peak counts calculating of nuclide 137 Cs at 662 keV of γ spectra in airborne radioactivity measurements using multiple linear regressions. Mathematic model is founded by analyzing every factor that has contribution to Cs peak counts in spectra, and multiple linear regression function is established. Calculating process adopts stepwise regression, and the indistinctive factors are eliminated by F check. The regression results and its uncertainty are calculated using Least Square Estimation, then the Cs peak net counts and its uncertainty can be gotten. The analysis results for experimental spectrum are displayed. The influence of energy shift and energy resolution on the analyzing result is discussed. In comparison with the stripping spectra method, multiple linear regression method needn't stripping radios, and the calculating result has relation with the counts in Cs peak only, and the calculating uncertainty is reduced. (authors)
Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki
2014-12-01
This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.
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.
Khalil, Mohamed H; Shebl, Mostafa K; Kosba, Mohamed A; El-Sabrout, Karim; Zaki, Nesma
2016-08-01
This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens' eggs. Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.
A multiple regression analysis for accurate background subtraction in 99Tcm-DTPA renography
Middleton, G.W.; Thomson, W.H.; Davies, I.H.; Morgan, A.
1989-01-01
A technique for accurate background subtraction in 99 Tc m -DTPA renography is described. The technique is based on a multiple regression analysis of the renal curves and separate heart and soft tissue curves which together represent background activity. It is compared, in over 100 renograms, with a previously described linear regression technique. Results show that the method provides accurate background subtraction, even in very poorly functioning kidneys, thus enabling relative renal filtration and excretion to be accurately estimated. (author)
Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki
2017-05-01
This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
Application of range-test in multiple linear regression analysis in ...
Application of range-test in multiple linear regression analysis in the presence of outliers is studied in this paper. First, the plot of the explanatory variables (i.e. Administration, Social/Commercial, Economic services and Transfer) on the dependent variable (i.e. GDP) was done to identify the statistical trend over the years.
Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku
2013-09-01
Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p regression analysis showed a greater likelihood for minimum frontal breadth (p regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.
Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A
2016-01-01
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
Mamikonyan, S.V.; Berezkin, V.V.; Lyubimova, S.V.; Svetajlo, Yu.N.; Shchekin, K.I.
1978-01-01
A method to derive multiple regression equations for X-ray radiometric analysis is described. Te method is realized in the form of the REGRA program in an algorithmic language. The subprograms included in the program are describe. In analyzing cement for Mg, Al, Si, Ca and Fe contents as an example, the obtainment of working equations in the course of calculations by the program is shown to simpliy the realization of computing devices in instruments for X-ray radiometric analysis
Laurens, L M L; Wolfrum, E J
2013-12-18
One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.
BUDIMAN
2012-01-01
Full Text Available Budiman, Arisoesilaningsih E. 2012. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis. Biodiversitas 13: 18-22. The aims of this research was to determine the multiple regression models of vegetative and corm growth of Amorphophallus muelleri Blume in some age variations and habitat conditions of agroforestry in East Java. Descriptive exploratory research method was conducted by systematic random sampling at five agroforestries on four plantations in East Java: Saradan, Bojonegoro, Nganjuk and Blitar. In each agroforestry, we observed A. muelleri vegetative and corm growth on four growing age (1, 2, 3 and 4 years old respectively as well as environmental variables such as altitude, vegetation, climate and soil conditions. Data were analyzed using descriptive statistics to compare A. muelleri habitat in five agroforestries. Meanwhile, the influence and contribution of each environmental variable to the growth of A. muelleri vegetative and corm were determined using multiple regression analysis of SPSS 17.0. The multiple regression models of A. muelleri vegetative and corm growth were generated based on some characteristics of agroforestries and age showed high validity with R2 = 88-99%. Regression model showed that age, monthly temperatures, percentage of radiation and soil calcium (Ca content either simultaneously or partially determined the growth of A. muelleri vegetative and corm. Based on these models, the A. muelleri corm reached the optimal growth after four years of cultivation and they will be ready to be harvested. Additionally, the soil Ca content should reach 25.3 me.hg-1 as Sugihwaras agroforestry, with the maximal radiation of 60%.
Multiple regression analysis of Jominy hardenability data for boron treated steels
Komenda, J.; Sandstroem, R.; Tukiainen, M.
1997-01-01
The relations between chemical composition and their hardenability of boron treated steels have been investigated using a multiple regression analysis method. A linear model of regression was chosen. The free boron content that is effective for the hardenability was calculated using a model proposed by Jansson. The regression analysis for 1261 steel heats provided equations that were statistically significant at the 95% level. All heats met the specification according to the nordic countries producers classification. The variation in chemical composition explained typically 80 to 90% of the variation in the hardenability. In the regression analysis elements which did not significantly contribute to the calculated hardness according to the F test were eliminated. Carbon, silicon, manganese, phosphorus and chromium were of importance at all Jominy distances, nickel, vanadium, boron and nitrogen at distances above 6 mm. After the regression analysis it was demonstrated that very few outliers were present in the data set, i.e. data points outside four times the standard deviation. The model has successfully been used in industrial practice replacing some of the necessary Jominy tests. (orig.)
MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY
Chayalakshmi C.L
2018-01-01
MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY ABSTRACT Calculation of boiler efficiency is essential if its parameters need to be controlled for either maintaining or enhancing its efficiency. But determination of boiler efficiency using conventional method is time consuming and very expensive. Hence, it is not recommended to find boiler efficiency frequently. The work presented in this paper deals with establishing the statistical mo...
Multiple Regression Analysis of Unconfined Compression Strength of Mine Tailings Matrices
Mahmood Ali A.
2017-01-01
Full Text Available As part of a novel approach of sustainable development of mine tailings, experimental and numerical analysis is carried out on newly formulated tailings matrices. Several physical characteristic tests are carried out including the unconfined compression strength test to ascertain the integrity of these matrices when subjected to loading. The current paper attempts a multiple regression analysis of the unconfined compressive strength test results of these matrices to investigate the most pertinent factors affecting their strength. Results of this analysis showed that the suggested equation is reasonably applicable to the range of binder combinations used.
[Multiple linear regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis].
Ma, Yu-Feng; Wang, Qing-Fu; Chen, Zhao-Jun; Du, Chun-Lin; Li, Jun-Hai; Huang, Hu; Shi, Zong-Ting; Yin, Yue-Shan; Zhang, Lei; A-Di, Li-Jiang; Dong, Shi-Yu; Wu, Ji
2012-05-01
To perform Multiple Linear Regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis, and to analyze their relationship with clinical and biomechanical concepts. From March 2011 to July 2011, 140 patients (250 knees) were reviewed, including 132 knees in the left and 118 knees in the right; ranging in age from 40 to 71 years, with an average of 54.68 years. The MB-RULER measurement software was applied to measure femoral angle, tibial angle, femorotibial angle, joint gap angle from antero-posterir and lateral position of X-rays. The WOMAC scores were also collected. Then multiple regression equations was applied for the linear regression analysis of correlation between the X-ray measurement and WOMAC scores. There was statistical significance in the regression equation of AP X-rays value and WOMAC scores (Pregression equation of lateral X-ray value and WOMAC scores (P>0.05). 1) X-ray measurement of knee joint can reflect the WOMAC scores to a certain extent. 2) It is necessary to measure the X-ray mechanical axis of knee, which is important for diagnosis and treatment of osteoarthritis. 3) The correlation between tibial angle,joint gap angle on antero-posterior X-ray and WOMAC scores is significant, which can be used to assess the functional recovery of patients before and after treatment.
COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS
K. Seetharaman
2015-08-01
Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.
Freund, Rudolf J; Sa, Ping
2006-01-01
The book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design
Abstract. The predictive analysis based on quantitative structure activity relationships (QSAR) on benzim- ... could lead to treatment of obesity, diabetes and related conditions. ..... After discussing the physical and chemical mean- ing of the ...
Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro
2017-09-01
To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.
ANALYSIS OF THE FINANCIAL PERFORMANCES OF THE FIRM, BY USING THE MULTIPLE REGRESSION MODEL
Constantin Anghelache
2011-11-01
Full Text Available The information achieved through the use of simple linear regression are not always enough to characterize the evolution of an economic phenomenon and, furthermore, to identify its possible future evolution. To remedy these drawbacks, the special literature includes multiple regression models, in which the evolution of the dependant variable is defined depending on two or more factorial variables.
Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.
2013-06-01
This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.
Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei
2014-10-01
Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.
Bakker, D.P.; Busscher, H.J.; Zanten, J. van; Vries, J. de; Klijnstra, J.W.; Mei, H.C. van der
2004-01-01
Many studies have shown relationships of substratum hydrophobicity, charge or roughness with bacterial adhesion, although bacterial adhesion is governed by interplay of different physico-chemical properties and multiple regression analysis would be more suitable to reveal mechanisms of bacterial
Bakker, Dewi P; Busscher, Henk J; van Zanten, Joyce; de Vries, Jacob; Klijnstra, Job W; van der Mei, Henny C
Many studies have shown relationships of substratum hydrophobicity, charge or roughness with bacterial adhesion, although bacterial adhesion is governed by interplay of different physico-chemical properties and multiple regression analysis would be more suitable to reveal mechanisms of bacterial
Multiple Regression Analysis of mRNA-miRNA Associations in Colorectal Cancer Pathway
Wang, Fengfeng; Wong, S. C. Cesar; Chan, Lawrence W. C.; Cho, William C. S.; Yip, S. P.; Yung, Benjamin Y. M.
2014-01-01
Background. MicroRNA (miRNA) is a short and endogenous RNA molecule that regulates posttranscriptional gene expression. It is an important factor for tumorigenesis of colorectal cancer (CRC), and a potential biomarker for diagnosis, prognosis, and therapy of CRC. Our objective is to identify the related miRNAs and their associations with genes frequently involved in CRC microsatellite instability (MSI) and chromosomal instability (CIN) signaling pathways. Results. A regression model was adopted to identify the significantly associated miRNAs targeting a set of candidate genes frequently involved in colorectal cancer MSI and CIN pathways. Multiple linear regression analysis was used to construct the model and find the significant mRNA-miRNA associations. We identified three significantly associated mRNA-miRNA pairs: BCL2 was positively associated with miR-16 and SMAD4 was positively associated with miR-567 in the CRC tissue, while MSH6 was positively associated with miR-142-5p in the normal tissue. As for the whole model, BCL2 and SMAD4 models were not significant, and MSH6 model was significant. The significant associations were different in the normal and the CRC tissues. Conclusion. Our results have laid down a solid foundation in exploration of novel CRC mechanisms, and identification of miRNA roles as oncomirs or tumor suppressor mirs in CRC. PMID:24895601
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.
Greensmith, David J
2014-01-01
Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. Copyright © 2013 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.
Zhang, Xiaodong; Zhao, Yinxia; Hu, Shaoyong; Hao, Shuai; Yan, Jiewen; Zhang, Lingyan; Zhao, Jing; Li, Shaolin
2015-09-01
To investigate the correlation between the lumbar vertebra bone mineral density (BMD) and age, gender, height, weight, body mass index, waistline, hipline, bone marrow and abdomen fat, and to explore the key factor affecting the BMD. A total of 72 cases were randomly recruited. All the subjects underwent a spectroscopic examination of the third lumber vertebra with single-voxel method in 1.5T MR. Lipid fractions (FF%) were measured. Quantitative CT were also performed to get the BMD of L3 and the corresponding abdomen subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). The statistical analysis were performed by SPSS 19.0. Multiple linear regression showed except the age and FF% showed significant difference (P0.05). The correlation of age and FF% with BMD was statistically negatively significant (r=-0.830, -0.521, P<0.05). The ROC curve analysis showed that the sensitivety and specificity of predicting osteoporosis were 81.8% and 86.9%, with a threshold of 58.5 years old. And it showed that the sensitivety and specificity of predicting osteoporosis were 90.9% and 55.7%, with a threshold of 52.8% for FF%. The lumbar vertebra BMD was significantly and negatively correlated with age and bone marrow FF%, but it was not significantly correlated with gender, height, weight, BMI, waistline, hipline, SAT and VAT. And age was the critical factor.
Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio
2016-10-01
We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms ( 13 C 2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C 2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C 2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
C. Makendran
2015-01-01
Full Text Available Prediction models for low volume village roads in India are developed to evaluate the progression of different types of distress such as roughness, cracking, and potholes. Even though the Government of India is investing huge quantum of money on road construction every year, poor control over the quality of road construction and its subsequent maintenance is leading to the faster road deterioration. In this regard, it is essential that scientific maintenance procedures are to be evolved on the basis of performance of low volume flexible pavements. Considering the above, an attempt has been made in this research endeavor to develop prediction models to understand the progression of roughness, cracking, and potholes in flexible pavements exposed to least or nil routine maintenance. Distress data were collected from the low volume rural roads covering about 173 stretches spread across Tamil Nadu state in India. Based on the above collected data, distress prediction models have been developed using multiple linear regression analysis. Further, the models have been validated using independent field data. It can be concluded that the models developed in this study can serve as useful tools for the practicing engineers maintaining flexible pavements on low volume roads.
Zhan, Xinhua; Liang, Xiao; Xu, Guohua; Zhou, Lixiang
2013-01-01
Polycyclic aromatic hydrocarbons (PAHs) are contaminants that reside mainly in surface soils. Dietary intake of plant-based foods can make a major contribution to total PAH exposure. Little information is available on the relationship between root morphology and plant uptake of PAHs. An understanding of plant root morphologic and compositional factors that affect root uptake of contaminants is important and can inform both agricultural (chemical contamination of crops) and engineering (phytoremediation) applications. Five crop plant species are grown hydroponically in solutions containing the PAH phenanthrene. Measurements are taken for 1) phenanthrene uptake, 2) root morphology – specific surface area, volume, surface area, tip number and total root length and 3) root tissue composition – water, lipid, protein and carbohydrate content. These factors are compared through Pearson's correlation and multiple linear regression analysis. The major factors which promote phenanthrene uptake are specific surface area and lipid content. -- Highlights: •There is no correlation between phenanthrene uptake and total root length, and water. •Specific surface area and lipid are the most crucial factors for phenanthrene uptake. •The contribution of specific surface area is greater than that of lipid. -- The contribution of specific surface area is greater than that of lipid in the two most important root morphological and compositional factors affecting phenanthrene uptake
Carew, John F.; Finch, Stephen J.; Lois, Lambros
2003-01-01
The calculated >1-MeV pressure vessel fluence is used to determine the fracture toughness and integrity of the reactor pressure vessel. It is therefore of the utmost importance to ensure that the fluence prediction is accurate and unbiased. In practice, this assurance is provided by comparing the predictions of the calculational methodology with an extensive set of accurate benchmarks. A benchmarking database is used to provide an estimate of the overall average measurement-to-calculation (M/C) bias in the calculations ( ). This average is used as an ad-hoc multiplicative adjustment to the calculations to correct for the observed calculational bias. However, this average only provides a well-defined and valid adjustment of the fluence if the M/C data are homogeneous; i.e., the data are statistically independent and there is no correlation between subsets of M/C data.Typically, the identification of correlations between the errors in the database M/C values is difficult because the correlation is of the same magnitude as the random errors in the M/C data and varies substantially over the database. In this paper, an evaluation of a reactor dosimetry benchmark database is performed to determine the statistical validity of the adjustment to the calculated pressure vessel fluence. Physical mechanisms that could potentially introduce a correlation between the subsets of M/C ratios are identified and included in a multiple regression analysis of the M/C data. Rigorous statistical criteria are used to evaluate the homogeneity of the M/C data and determine the validity of the adjustment.For the database evaluated, the M/C data are found to be strongly correlated with dosimeter response threshold energy and dosimeter location (e.g., cavity versus in-vessel). It is shown that because of the inhomogeneity in the M/C data, for this database, the benchmark data do not provide a valid basis for adjusting the pressure vessel fluence.The statistical criteria and methods employed in
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Multiple Logistic Regression Analysis of Cigarette Use among High School Students
Adwere-Boamah, Joseph
2011-01-01
A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…
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.
Error analysis of dimensionless scaling experiments with multiple points using linear regression
Guercan, Oe.D.; Vermare, L.; Hennequin, P.; Bourdelle, C.
2010-01-01
A general method of error estimation in the case of multiple point dimensionless scaling experiments, using linear regression and standard error propagation, is proposed. The method reduces to the previous result of Cordey (2009 Nucl. Fusion 49 052001) in the case of a two-point scan. On the other hand, if the points follow a linear trend, it explains how the estimated error decreases as more points are added to the scan. Based on the analytical expression that is derived, it is argued that for a low number of points, adding points to the ends of the scanned range, rather than the middle, results in a smaller error estimate. (letter)
Lee, Seung-Yong; Lee, Sang-In; Hwang, Byoung-chul
2016-01-01
In this study, the effect of microstructural factors on the impact toughness of hypoeutectoid steels with ferrite-pearlite structure was quantitatively investigated using multiple regression analysis. Microstructural analysis results showed that the pearlite fraction increased with increasing austenitizing temperature and decreasing transformation temperature which substantially decreased the pearlite interlamellar spacing and cementite thickness depending on carbon content. The impact toughness of hypoeutectoid steels usually increased as interlamellar spacing or cementite thickness decreased, although the impact toughness was largely associated with pearlite fraction. Based on these results, multiple regression analysis was performed to understand the individual effect of pearlite fraction, interlamellar spacing, and cementite thickness on the impact toughness. The regression analysis results revealed that pearlite fraction significantly affected impact toughness at room temperature, while cementite thickness did at low temperature.
Greensmith, David J.
2014-01-01
Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908
Investigations upon the indefinite rolls quality assurance in multiple regression analysis
Kiss, I.
2012-04-01
Full Text Available The rolling rolls quality has been enhanced mainly due to the improvements of the chemical compositions of rolls materials. The realization of an optimal chemical composition can constitute a technical efficient mode to assure the exploitation properties, the material from which the rolling mills rolls are manufactured having a higher importance in this sense. This paper continues to present the scientifically results of our experimental research in the area of the rolling rolls. The basic research contains concrete elements of immediate practical utilities in the metallurgical enterprises, for the quality improvements of rolls, having in last as the aim the durability growth and the safety in exploitation. This paper presents an analysis of the chemical composition, the influences upon the mechanical properties of the indefinite cast iron rolls. We present some mathematical correlations and graphical interpretations between the hardness (on the working surface and on necks and the chemical composition. Using the double and triple correlations which is really helpful in the foundry practice, as it allows us to determine variation boundaries for the chemical composition, in view the obtaining the optimal values of the hardness. We suggest a mathematical interpretation of the influence of the chemical composition over the hardness of these indefinite rolling rolls. In this sense we use the multiple regression analysis which can be an important statistical tool for the investigation of relationships between variables. The enunciation of some mathematically modeling results can be described through a number of multi-component equations determined for the spaces with 3 and 4 dimensions. Also, the regression surfaces, curves of levels and volumes of variations can be represented and interpreted by technologists considering these as correlation diagrams between the analyzed variables. In this sense, these researches results can be used in the engineers
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Lorenzo-Seva, Urbano; Ferrando, Pere J
2011-03-01
We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
Integrative analysis of multiple diverse omics datasets by sparse group multitask regression
Dongdong eLin
2014-10-01
Full Text Available A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: 1 treat the biomarker identification in each single study as a task and then combine them by multitask learning; 2 group variables from all studies for identifying significant genes; 3 enforce sparse constraint on groups of variables to overcome the ‘small sample, but large variables’ problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed
Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana
2017-02-01
The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism
Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana
2017-01-01
The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200–240 g for 28 days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15 mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000 mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight
Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat
2015-04-01
Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.
Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai
2017-08-01
Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried
2018-03-01
This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.
Oguntunde, Philip G; Lischeid, Gunnar; Dietrich, Ottfried
2018-03-01
This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.
Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.
2017-07-01
This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.
Eekhout, I.; Wiel, M.A. van de; Heymans, M.W.
2017-01-01
Background. Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels
Olaya-Abril, Alfonso; Parras-Alcántara, Luis; Lozano-García, Beatriz; Obregón-Romero, Rafael
2017-08-15
Over time, the interest on soil studies has increased due to its role in carbon sequestration in terrestrial ecosystems, which could contribute to decreasing atmospheric CO 2 rates. In many studies, independent variables were related to soil organic carbon (SOC) alone, however, the contribution degree of each variable with the experimentally determined SOC content were not considered. In this study, samples from 612 soil profiles were obtained in a natural protected (Red Natura 2000) of Sierra Morena (Mediterranean area, South Spain), considering only the topsoil 0-25cm, for better comparison between results. 24 independent variables were used to define it relationship with SOC content. Subsequently, using a multiple linear regression analysis, the effects of these variables on the SOC correlation was considered. Finally, the best parameters determined with the regression analysis were used in a climatic change scenario. The model indicated that SOC in a future scenario of climate change depends on average temperature of coldest quarter (41.9%), average temperature of warmest quarter (34.5%), annual precipitation (22.2%) and annual average temperature (1.3%). When the current and future situations were compared, the SOC content in the study area was reduced a 35.4%, and a trend towards migration to higher latitude and altitude was observed. Copyright © 2017 Elsevier B.V. All rights reserved.
Brown, Angus M
2006-04-01
The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.
Barrett, C. A.
1985-01-01
Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.
Schierle, C.; Otto, M.
1992-01-01
A two layer perceptron with backpropagation of error is used for quantitative analysis in ICP-AES. The network was trained by emission spectra of two interfering lines of Cd and As and the concentrations of both elements were subsequently estimated from mixture spectra. The spectra of the Cd and As lines were also used to perform multiple linear regression (MLR) via the calculation of the pseudoinverse S + of the sensitivity matrix S. In the present paper it is shown that there exist close relations between the operation of the perceptron and the MLR procedure. These are most clearly apparent in the correlation between the weights of the backpropagation network and the elements of the pseudoinverse. Using MLR, the confidence intervals over the predictions are exploited to correct for the optical device of the wavelength shift. (orig.)
Reddy, M Srinivasa; Basha, Shaik; Joshi, H V; Sravan Kumar, V G; Jha, B; Ghosh, P K
2005-01-01
Alang-Sosiya is the largest ship-scrapping yard in the world, established in 1982. Every year an average of 171 ships having a mean weight of 2.10 x 10(6)(+/-7.82 x 10(5)) of light dead weight tonnage (LDT) being scrapped. Apart from scrapped metals, this yard generates a massive amount of combustible solid waste in the form of waste wood, plastic, insulation material, paper, glass wool, thermocol pieces (polyurethane foam material), sponge, oiled rope, cotton waste, rubber, etc. In this study multiple regression analysis was used to develop predictive models for energy content of combustible ship-scrapping solid wastes. The scope of work comprised qualitative and quantitative estimation of solid waste samples and performing a sequential selection procedure for isolating variables. Three regression models were developed to correlate the energy content (net calorific values (LHV)) with variables derived from material composition, proximate and ultimate analyses. The performance of these models for this particular waste complies well with the equations developed by other researchers (Dulong, Steuer, Scheurer-Kestner and Bento's) for estimating energy content of municipal solid waste.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
Denli, H. H.; Durmus, B.
2016-12-01
The purpose of this study is to examine the factors which may affect the apartment prices with multiple linear regression analysis models and visualize the results by value maps. The study is focused on a county of Istanbul - Turkey. Totally 390 apartments around the county Umraniye are evaluated due to their physical and locational conditions. The identification of factors affecting the price of apartments in the county with a population of approximately 600k is expected to provide a significant contribution to the apartment market.Physical factors are selected as the age, number of rooms, size, floor numbers of the building and the floor that the apartment is positioned in. Positional factors are selected as the distances to the nearest hospital, school, park and police station. Totally ten physical and locational parameters are examined by regression analysis.After the regression analysis has been performed, value maps are composed from the parameters age, price and price per square meters. The most significant of the composed maps is the price per square meters map. Results show that the location of the apartment has the most influence to the square meter price information of the apartment. A different practice is developed from the composed maps by searching the ability of using price per square meters map in urban transformation practices. By marking the buildings older than 15 years in the price per square meters map, a different and new interpretation has been made to determine the buildings, to which should be given priority during an urban transformation in the county.This county is very close to the North Anatolian Fault zone and is under the threat of earthquakes. By marking the apartments older than 15 years on the price per square meters map, both older and expensive square meters apartments list can be gathered. By the help of this list, the priority could be given to the selected higher valued old apartments to support the economy of the country
Shrivastava, Prashant Kumar; Pandey, Arun Kumar
2018-06-01
Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.
Ingunn Fride Tvete
Full Text Available Rheumatoid arthritis patients have been treated with disease modifying anti-rheumatic drugs (DMARDs and the newer biologic drugs. We sought to compare and rank the biologics with respect to efficacy. We performed a literature search identifying 54 publications encompassing 9 biologics. We conducted a multiple treatment comparison regression analysis letting the number experiencing a 50% improvement on the ACR score be dependent upon dose level and disease duration for assessing the comparable relative effect between biologics and placebo or DMARD. The analysis embraced all treatment and comparator arms over all publications. Hence, all measured effects of any biologic agent contributed to the comparison of all biologic agents relative to each other either given alone or combined with DMARD. We found the drug effect to be dependent on dose level, but not on disease duration, and the impact of a high versus low dose level was the same for all drugs (higher doses indicated a higher frequency of ACR50 scores. The ranking of the drugs when given without DMARD was certolizumab (ranked highest, etanercept, tocilizumab/ abatacept and adalimumab. The ranking of the drugs when given with DMARD was certolizumab (ranked highest, tocilizumab, anakinra/rituximab, golimumab/ infliximab/ abatacept, adalimumab/ etanercept [corrected]. Still, all drugs were effective. All biologic agents were effective compared to placebo, with certolizumab the most effective and adalimumab (without DMARD treatment and adalimumab/ etanercept (combined with DMARD treatment the least effective. The drugs were in general more effective, except for etanercept, when given together with DMARDs.
Keith, Timothy Z
2014-01-01
Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.
Li, Kai; Rüdiger, Heinz; Haase, Rocco; Ziemssen, Tjalf
2018-01-01
Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool. Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS. Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations. Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.
Boulet, Sebastien; Boudot, Elsa; Houel, Nicolas
2016-05-03
Back pain is a common reason for consultation in primary healthcare clinical practice, and has effects on daily activities and posture. Relationships between the whole spine and upright posture, however, remain unknown. The aim of this study was to identify the relationship between each spinal curve and centre of pressure position as well as velocity for healthy subjects. Twenty-one male subjects performed quiet stance in natural position. Each upright posture was then recorded using an optoelectronics system (Vicon Nexus) synchronized with two force plates. At each moment, polynomial interpolations of markers attached on the spine segment were used to compute cervical lordosis, thoracic kyphosis and lumbar lordosis angle curves. Mean of centre of pressure position and velocity was then computed. Multiple stepwise linear regression analysis showed that the position and velocity of centre of pressure associated with each part of the spinal curves were defined as best predictors of the lumbar lordosis angle (R(2)=0.45; p=1.65*10-10) and the thoracic kyphosis angle (R(2)=0.54; p=4.89*10-13) of healthy subjects in quiet stance. This study showed the relationships between each of cervical, thoracic, lumbar curvatures, and centre of pressure's fluctuation during free quiet standing using non-invasive full spinal curve exploration. Copyright © 2016 Elsevier Ltd. All rights reserved.
Shabri, Ani; Samsudin, Ruhaidah
2014-01-01
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666
Ani Shabri
2014-01-01
Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
Shabri, Ani; Samsudin, Ruhaidah
2014-01-01
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru
2017-09-01
Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.
Computing multiple-output regression quantile regions
Paindaveine, D.; Šiman, Miroslav
2012-01-01
Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf
Denli, H. H.; Koc, Z.
2015-12-01
Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.
Zhang, Yanyan; Ma, Haile; Wang, Bei; Qu, Wenjuan; Wali, Asif; Zhou, Cunshan
2016-08-01
Ultrasound pretreatment of wheat gluten (WG) before enzymolysis can improve the angiotensin converting enzyme (ACE) inhibitory activity of the hydrolysates by alerting the structure of substrate proteins. Establishment of a relationship between the structure of WG and ACE inhibitory activity of the hydrolysates to judge the end point of the ultrasonic pretreatment is vital. The results of stepwise multiple linear regression (MLR) showed that the contents of free sulfhydryl, α-helix, disulfide bond, surface hydrophobicity and random coil were significantly correlated to ACE Inhibitory activity of the hydrolysate, with the standard partial regression coefficients were 3.729, -0.676, -0.252, 0.022 and 0.156, respectively. The R(2) of this model was 0.970. External validation showed that the stepwise MLR model could well predict the ACE inhibitory activity of hydrolysate based on the content of free sulfhydryl, α-helix, disulfide bond, surface hydrophobicity and random coil of WG before hydrolysis. A stepwise multiple linear regression model describing the quantitative relationships between the structure of WG and the ACE Inhibitory activity of the hydrolysates was established. This model can be used to predict the endpoint of the ultrasonic pretreatment. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.
Suresh, Arumuganainar; Choi, Hong Lim
2011-10-01
Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (Phydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.
The M Word: Multicollinearity in Multiple Regression.
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Multiple Linear Regression: A Realistic Reflector.
Nutt, A. T.; Batsell, R. R.
Examples of the use of Multiple Linear Regression (MLR) techniques are presented. This is done to show how MLR aids data processing and decision-making by providing the decision-maker with freedom in phrasing questions and by accurately reflecting the data on hand. A brief overview of the rationale underlying MLR is given, some basic definitions…
On directional multiple-output quantile regression
Paindaveine, D.; Šiman, Miroslav
2011-01-01
Roč. 102, č. 2 (2011), s. 193-212 ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf
Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D
2017-11-01
Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.
Guangren Shi; Xingxi Zhou; Guangya Zhang; Xiaofeng Shi; Honghui Li [Research Institute of Petroleum Exploration and Development, Beijing (China)
2004-03-01
Artificial neural network analysis is found to be far superior to multiple regression when applied to the evaluation of trap quality in the Northern Kuqa Depression, a gas-rich depression of Tarim Basin in western China. This is because this technique can correlate the complex and non-linear relationship between trap quality and related geological factors, whereas multiple regression can only describe a linear relationship. However, multiple regression can work as an auxiliary tool, as it is suited to high-speed calculations and can indicate the degree of dependence between the trap quality and its related geological factors which artificial neural network analysis cannot. For illustration, we have investigated 30 traps in the Northern Kuqa Depression. For each of the traps, the values of 14 selected geological factors were all known. While geologists were also able to assign individual trap quality values to 27 traps, they were less certain about the values for the other three traps. Multiple regression and artificial neural network analysis were, therefore, respectively used to ascertain these values. Data for the 27 traps were used as known sample data, while the three traps were used as prediction candidates. Predictions from artificial neural network analysis are found to agree with exploration results: where simulation predicted high trap quality, commercial quality flows were afterwards found, and where low trap quality is indicated, no such discoveries have yet been made. On the other hand, multiple regression results indicate the order of dependence of the trap quality on geological factors, which reconciles with what geologists have commonly recognized. We can conclude, therefore, that the application of artificial neural network analysis with the aid of multiple regression to trap evaluation in the Northern Kuqa Depression has been quite successful. To ensure the precision of the above mentioned geological factors and their related parameters for each
Hukharnsusatrue, A.
2005-11-01
Full Text Available The objective of this research is to compare multiple regression coefficients estimating methods with existence of multicollinearity among independent variables. The estimation methods are Ordinary Least Squares method (OLS, Restricted Least Squares method (RLS, Restricted Ridge Regression method (RRR and Restricted Liu method (RL when restrictions are true and restrictions are not true. The study used the Monte Carlo Simulation method. The experiment was repeated 1,000 times under each situation. The analyzed results of the data are demonstrated as follows. CASE 1: The restrictions are true. In all cases, RRR and RL methods have a smaller Average Mean Square Error (AMSE than OLS and RLS method, respectively. RRR method provides the smallest AMSE when the level of correlations is high and also provides the smallest AMSE for all level of correlations and all sample sizes when standard deviation is equal to 5. However, RL method provides the smallest AMSE when the level of correlations is low and middle, except in the case of standard deviation equal to 3, small sample sizes, RRR method provides the smallest AMSE.The AMSE varies with, most to least, respectively, level of correlations, standard deviation and number of independent variables but inversely with to sample size.CASE 2: The restrictions are not true.In all cases, RRR method provides the smallest AMSE, except in the case of standard deviation equal to 1 and error of restrictions equal to 5%, OLS method provides the smallest AMSE when the level of correlations is low or median and there is a large sample size, but the small sample sizes, RL method provides the smallest AMSE. In addition, when error of restrictions is increased, OLS method provides the smallest AMSE for all level, of correlations and all sample sizes, except when the level of correlations is high and sample sizes small. Moreover, the case OLS method provides the smallest AMSE, the most RLS method has a smaller AMSE than
Kumar, Ajay; Ravi, P.M.; Guneshwar, S.L.; Rout, Sabyasachi; Mishra, Manish K.; Pulhani, Vandana; Tripathi, R.M.
2018-01-01
Numerous common methods (batch laboratory, the column laboratory, field-batch method, field modeling and K 0c method) are used frequently for determination of K d values. Recently, multiple regression models are considered as new best estimates for predicting the K d of radionuclides in the environment. It is also well known fact that the K d value is highly influenced by physico-chemical properties of sediment. Due to the significant variability in influencing parameters, the measured K d values can range over several orders of magnitude under different environmental conditions. The aim of this study is to develop a predictive model for K d values of 137 Cs and 60 Co based on the sediment properties using multiple linear regression analysis
Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos
2017-06-01
We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Fölsche, C; Staufenbiel, R
2014-01-01
The relationship between milk yield and both fertility and general animal health in dairy herds is discussed from opposing viewpoints. The hypothesis (1) that raising the herd milk yield would decrease fertility results, the number of milk cells as an indicator for udder health and the replacement rate as a global indicator for animal health as well as increasing the occurrence of specific diseases as a herd problem was compared to the opposing hypotheses that there is no relationship (2) or that there is a differentiated and changing relationship (3). A total of 743 herd examinations, considered independent, were performed in 489 herds between 1995 and 2010. The milk yield, fertility rate, milk cell count, replacement rate, categorized herd problems and management information were recorded. The relationship between the milk yield and both the fertility data and animal health was evaluated using simple and multiple regression analyses. The period between calving and the first service displayed no significant relationship to the herd milk yield. Simple regression analysis showed that the period between calving and gestation, the calving interval and the insemination number were significantly positively associated with the herd milk yield. This positive correlation was lost in multiple regression analysis. The milk cell count and replacement rate using both the simple and multiple regression analyses displayed a significant negative relationship to the milk yield. The alternative hypothesis (3) was confirmed. A higher milk yield has no negative influence on the milk cell count and the replacement rate in terms of the udder and general health. When parameterizing the fertility, the herd milk yield should be considered. Extending the resting time may increase the milk yield while preventing a decline in the insemination index.
Entrepreneurial intention modeling using hierarchical multiple regression
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
Fang, Yu-Hua; Kao, Tsair; Liu, Ren-Shyan; Wu, Liang-Chih
2004-01-01
A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13±8.85 and 16.60±9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student's t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification. (orig.)
Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi
2013-09-01
Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore
Suppression Situations in Multiple Linear Regression
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…
Demirturk Kocasarac, Husniye; Sinanoglu, Alper; Noujeim, Marcel; Helvacioglu Yigit, Dilek; Baydemir, Canan
2016-05-01
For forensic age estimation, radiographic assessment of third molar mineralization is important between 14 and 21 years which coincides with the legal age in most countries. The spheno-occipital synchondrosis (SOS) is an important growth site during development, and its use for age estimation is beneficial when combined with other markers. In this study, we aimed to develop a regression model to estimate and narrow the age range based on the radiologic assessment of third molar and SOS in a Turkish subpopulation. Panoramic radiographs and cone beam CT scans of 349 subjects (182 males, 167 females) with age between 8 and 25 were evaluated. Four-stage system was used to evaluate the fusion degree of SOS, and Demirjian's eight stages of development for calcification for third molars. The Pearson correlation indicated a strong positive relationship between age and third molar calcification for both sexes (r = 0.850 for females, r = 0.839 for males, P < 0.001) and also between age and SOS fusion for females (r = 0.814), but a moderate relationship was found for males (r = 0.599), P < 0.001). Based on the results obtained, an age determination formula using these scores was established.
Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.
2017-05-01
The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Yano, Kentaro; Mita, Suzune; Morimoto, Kaori; Haraguchi, Tamami; Arakawa, Hiroshi; Yoshida, Miyako; Yamashita, Fumiyoshi; Uchida, Takahiro; Ogihara, Takuo
2015-09-01
P-glycoprotein (P-gp) regulates absorption of many drugs in the gastrointestinal tract and their accumulation in tumor tissues, but the basis of substrate recognition by P-gp remains unclear. Bitter-tasting phenylthiocarbamide, which stimulates taste receptor 2 member 38 (T2R38), increases P-gp activity and is a substrate of P-gp. This led us to hypothesize that bitterness intensity might be a predictor of P-gp-inhibitor/substrate status. Here, we measured the bitterness intensity of a panel of P-gp substrates and nonsubstrates with various taste sensors, and used multiple linear regression analysis to examine the relationship between P-gp-inhibitor/substrate status and various physical properties, including intensity of bitter taste measured with the taste sensor. We calculated the first principal component analysis score (PC1) as the representative value of bitterness, as all taste sensor's outputs shared significant correlation. The P-gp substrates showed remarkably greater mean bitterness intensity than non-P-gp substrates. We found that Km value of P-gp substrates were correlated with molecular weight, log P, and PC1 value, and the coefficient of determination (R(2) ) of the linear regression equation was 0.63. This relationship might be useful as an aid to predict P-gp substrate status at an early stage of drug discovery. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.
Polylinear regression analysis in radiochemistry
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Polynomial regression analysis and significance test of the regression function
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
A test for the parameters of multiple linear regression models ...
A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...
Credit Scoring Problem Based on Regression Analysis
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....
Zhang, J.; Humphreys, M.; Cooper, G.; Davidson, J.; Macpherson, C.
2015-12-01
We present a new multiple regression (MR) analysis of published amphibole-melt trace element partitioning data, with the aim of retrieving robust relationships between amphibole crystal-chemical compositions and trace element partition coefficients (D). We examined experimental data for calcic amphiboles of kaersutite, pargasite, tschermakite (Tsch), magnesiohornblende (MgHbl) and magnesiohastingsite (MgHst) compositions crystallized from basanitic-rhyolitic melts (n = 150). The MR analysis demonstrates the varying significance of amphibole major element components assigned to different crystallographic sites (T, M1-3, M4, A) as independent variables in controlling D, and it allows us to retrieve statistically significant relationships for REE, Y, Rb, Sr, Pb, Ti, Zr, Nb (n > 25, R2 > 0.6, p-value Ridolfi & Renzulli 2012) with lower Rb and Sr and higher Pb, relative to a hot, andesitic-dacitic melt (950-1,000±50 ºC; 60-70±5 wt % SiO2) where MgHst are crystallized. REE and Nb contents are similar in both types of melts despite higher REE and Nb in MgHbl-Tsch. Therefore, the REE compositional disparity between MgHst and MgHbl-Tsch is driven by the difference in the DREE, rather than the melt REE concentrations.
Artur Wnorowski
2017-06-01
Full Text Available Tree saps are nourishing biological media commonly used for beverage and syrup production. Although the nutritional aspect of tree saps is widely acknowledged, the exact relationship between the sap composition, origin, and effect on the metabolic rate of human cells is still elusive. Thus, we collected saps from seven different tree species and conducted composition-activity analysis. Saps from trees of Betulaceae, but not from Salicaceae, Sapindaceae, nor Juglandaceae families, were increasing the metabolic rate of HepG2 cells, as measured using tetrazolium-based assay. Content of glucose, fructose, sucrose, chlorides, nitrates, sulphates, fumarates, malates, and succinates in sap samples varied across different tree species. Grade correspondence analysis clustered trees based on the saps’ chemical footprint indicating its usability in chemotaxonomy. Multiple regression modeling showed that glucose and fumarate present in saps from silver birch (Betula pendula Roth., black alder (Alnus glutinosa Gaertn., and European hornbeam (Carpinus betulus L. are positively affecting the metabolic activity of HepG2 cells.
Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo
2011-03-04
Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.
An Additive-Multiplicative Cox-Aalen Regression Model
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Iserbyt, Peter; Schouppe, Gilles; Charlier, Nathalie
2015-04-01
Research investigating lifeguards' performance of Basic Life Support (BLS) with Automated External Defibrillator (AED) is limited. Assessing simulated BLS/AED performance in Flemish lifeguards and identifying factors affecting this performance. Six hundred and sixteen (217 female and 399 male) certified Flemish lifeguards (aged 16-71 years) performed BLS with an AED on a Laerdal ResusciAnne manikin simulating an adult victim of drowning. Stepwise multiple linear regression analysis was conducted with BLS/AED performance as outcome variable and demographic data as explanatory variables. Mean BLS/AED performance for all lifeguards was 66.5%. Compression rate and depth adhered closely to ERC 2010 guidelines. Ventilation volume and flow rate exceeded the guidelines. A significant regression model, F(6, 415)=25.61, p<.001, ES=.38, explained 27% of the variance in BLS performance (R2=.27). Significant predictors were age (beta=-.31, p<.001), years of certification (beta=-.41, p<.001), time on duty per year (beta=-.25, p<.001), practising BLS skills (beta=.11, p=.011), and being a professional lifeguard (beta=-.13, p=.029). 71% of lifeguards reported not practising BLS/AED. Being young, recently certified, few days of employment per year, practising BLS skills and not being a professional lifeguard are factors associated with higher BLS/AED performance. Measures should be taken to prevent BLS/AED performances from decaying with age and longer certification. Refresher courses could include a formal skills test and lifeguards should be encouraged to practise their BLS/AED skills. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Dong, Jingtao; Lu, Rongsheng
2018-04-30
The simultaneous determination of t, n(λ), and κ(λ) of thin films can be a tough task for the high correlation of fit parameters. The strong assumptions about the type of dispersion relation are commonly used as a consequence to alleviate correlation concerns by reducing the free parameters before the nonlinear regression analysis. Here we present an angle-resolved spectral reflectometry for the simultaneous determination of weakly absorbing thin film parameters, where a reflectance interferogram is recorded in both angular and spectral domains in a single-shot measurement for the point of the sample being illuminated. The variations of the phase recovered from the interferogram as functions of t, n, and κ reveals that the unwrapped phase is monotonically related to t, n, and κ, thereby allowing the problem of correlation to be alleviated by multiple linear regression. After removing the 2π ambiguity of the unwrapped phase, the merit function based on the absolute unwrapped phase performs a 3D data cube with variables of t, n and κ at each wavelength. The unique solution of t, n, and κ can then be directly determined from the extremum of the 3D data cube at each wavelength with no need of dispersion relation. A sample of GaN thin film grown on a polished sapphire substrate is tested. The experimental data of t and [n(λ), κ(λ)] are confirmed by the scanning electron microscopy and the comparison with the results of other related works, respectively. The consistency of the results shows the proposed method provides a useful tool for the determination of the thickness and optical constants of weakly absorbing thin films.
Croft, Arthur C; Milam, Bryce; Meylor, Jade; Manning, Richard
2016-06-01
Because of previously published recommendations to modify the Neck Disability Index (NDI), we evaluated the responsiveness and dimensionality of the NDI within a population of adult whiplash-injured subjects. The purpose of the present study was to evaluate the responsiveness and dimensionality of the NDI within a population of adult whiplash-injured subjects. Subjects who had sustained whiplash injuries of grade 2 or higher completed an NDI questionnaire. There were 123 subjects (55% female, of which 36% had recovered and 64% had chronic symptoms. NDI subscales were analyzed using confirmatory factor analysis, considering only the subscales and, secondly, using sex as an 11th variable. The subscales were also tested with multiple linear regression modeling using the total score as a target variable. When considering only the 10 NDI subscales, only a single factor emerged, with an eigenvalue of 5.4, explaining 53.7% of the total variance. Strong correlation (> .55) (P factor model of the NDI is not justified based on our results, and in this population of whiplash subjects, the NDI was unidimensional, demonstrating high internal consistency and supporting the original validation study of Vernon and Mior.
Nakagawa, S [Maizuru National College of Technology, Kyoto (Japan); Kenmoku, Y; Sakakibara, T [Toyohashi University of Technology, Aichi (Japan); Kawamoto, T [Shizuoka University, Shizuoka (Japan). Faculty of Engineering
1996-10-27
Study is under way for a more accurate solar radiation quantity prediction for the enhancement of solar energy utilization efficiency. Utilizing the technique of roughly estimating the day`s clearness index from forecast weather, the forecast weather (constituted of weather conditions such as `clear,` `cloudy,` etc., and adverbs or adjectives such as `afterward,` `temporary,` and `intermittent`) has been quantified relative to the clearness index. This index is named the `weather index` for the purpose of this article. The error high in rate in the weather index relates to cloudy days, which means a weather index falling in 0.2-0.5. It has also been found that there is a high correlation between the clearness index and the north-south wind direction component. A multiple regression analysis has been carried out, under the circumstances, for the estimation of clearness index from the maximum temperature and the north-south wind direction component. As compared with estimation of the clearness index on the basis only of the weather index, estimation using the weather index and maximum temperature achieves a 3% improvement throughout the year. It has also been learned that estimation by use of the weather index and north-south wind direction component enables a 2% improvement for summer and a 5% or higher improvement for winter. 2 refs., 6 figs., 4 tabs.
Nakamura, Kengo; Yasutaka, Tetsuo; Kuwatani, Tatsu; Komai, Takeshi
2017-11-01
In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models. Copyright © 2017 Elsevier Ltd. All rights reserved.
Regression analysis with categorized regression calibrated exposure: some interesting findings
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
A multiple regression method for genomewide association studies ...
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.
Caravaggi, Paolo; Leardini, Alberto; Giacomozzi, Claudia
2016-10-03
Plantar load can be considered as a measure of the foot ability to transmit forces at the foot/ground, or foot/footwear interface during ambulatory activities via the lower limb kinematic chain. While morphological and functional measures have been shown to be correlated with plantar load, no exhaustive data are currently available on the possible relationships between range of motion of foot joints and plantar load regional parameters. Joints' kinematics from a validated multi-segmental foot model were recorded together with plantar pressure parameters in 21 normal-arched healthy subjects during three barefoot walking trials. Plantar pressure maps were divided into six anatomically-based regions of interest associated to corresponding foot segments. A stepwise multiple regression analysis was performed to determine the relationships between pressure-based parameters, joints range of motion and normalized walking speed (speed/subject height). Sagittal- and frontal-plane joint motion were those most correlated to plantar load. Foot joints' range of motion and normalized walking speed explained between 6% and 43% of the model variance (adjusted R 2 ) for pressure-based parameters. In general, those joints' presenting lower mobility during stance were associated to lower vertical force at forefoot and to larger mean and peak pressure at hindfoot and forefoot. Normalized walking speed was always positively correlated to mean and peak pressure at hindfoot and forefoot. While a large variance in plantar pressure data is still not accounted for by the present models, this study provides statistical corroboration of the close relationship between joint mobility and plantar pressure during stance in the normal healthy foot. Copyright © 2016 Elsevier Ltd. All rights reserved.
Principal component regression analysis with SPSS.
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.
Regression Analysis by Example. 5th Edition
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…
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Gaussian process regression analysis for functional data
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
Elliptical multiple-output quantile regression and convex optimization
Hallin, M.; Šiman, Miroslav
2016-01-01
Roč. 109, č. 1 (2016), s. 232-237 ISSN 0167-7152 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : quantile regression * elliptical quantile * multivariate quantile * multiple-output regression Subject RIV: BA - General Mathematics Impact factor: 0.540, year: 2016 http://library.utia.cas.cz/separaty/2016/SI/siman-0458243.pdf
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
Multiple Response Regression for Gaussian Mixture Models with Known Labels.
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.
Whitlock, C. H., III
1977-01-01
Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.
Marami Milani, Mohammad Reza; Hense, Andreas; Rahmani, Elham; Ploeger, Angelika
2016-07-23
This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new ), and respiratory rate predictor RRP) with three main components of cow's milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p -value < 0.001 and R ² (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation ( p -value < 0.001) with R ² (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.
Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression
Beckstead, Jason W.
2012-01-01
The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…
Using multiple linear regression techniques to quantify carbon ...
Fallow ecosystems provide a significant carbon stock that can be quantified for inclusion in the accounts of global carbon budgets. Process and statistical models of productivity, though useful, are often technically rigid as the conditions for their application are not easy to satisfy. Multiple regression techniques have been ...
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance
Nathans, Laura L.; Oswald, Frederick L.; Nimon, Kim
2012-01-01
Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what…
Direction of Effects in Multiple Linear Regression Models.
Wiedermann, Wolfgang; von Eye, Alexander
2015-01-01
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.
Li, Xiao Ju; Yao, Kun; Dai, Jun Yu; Song, Yun Long
2018-05-01
The underground space, also known as the “fourth dimension” of the city, reflects the efficient use of urban development intensive. Urban traffic link tunnel is a typical underground limited-length space. Due to the geographical location, the special structure of space and the curvature of the tunnel, high-temperature smoke can easily form the phenomenon of “smoke turning” and the fire risk is extremely high. This paper takes an urban traffic link tunnel as an example to focus on the relationship between curvature and the temperature near the fire source, and use the pyrosim built different curvature fire model to analyze the influence of curvature on the temperature of the fire, then using SPSS Multivariate regression analysis simulate curvature of the tunnel and fire temperature data. Finally, a prediction model of urban traffic link tunnel curvature on fire temperature was proposed. The regression model analysis and test show that the curvature is negatively correlated with the tunnel temperature. This model is feasible and can provide a theoretical reference for the urban traffic link tunnel fire protection design and the preparation of the evacuation plan. And also, it provides some reference for other related curved tunnel curvature design and smoke control measures.
Simple and multiple linear regression: sample size considerations.
Hanley, James A
2016-11-01
The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright Â© 2016 Elsevier Inc. All rights reserved.
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Time-localized wavelet multiple regression and correlation
Fernández-Macho, Javier
2018-02-01
This paper extends wavelet methodology to handle comovement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multiscale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard pairwise wavelet correlations with rolling windows. Also, the spectral properties of weight functions are investigated and it is argued that some common time windows, such as the usual rectangular rolling window, are not satisfactory on these grounds. The method is illustrated with a multiscale analysis of the comovements of Eurozone stock markets during this century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along time and across timescales featuring an acute divide across timescales at about the quarterly scale. At longer scales, evidence from the long-term correlation structure can be interpreted as stable perfect integration among Euro stock markets. On the other hand, at intramonth and intraweek scales, the short-term correlation structure has been clearly evolving along time, experiencing a sharp increase during financial crises which may be interpreted as evidence of financial 'contagion'.
Yi Liang
2016-11-01
Full Text Available The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD with induced ordered weighted harmonic averaging operator (IOWHA to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM forecasting model and multiple regression (MR model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.
Hung, J.; Chaitman, B.R.; Lam, J.; Lesperance, J.; Dupras, G.; Fines, P.; Cherkaoui, O.; Robert, P.; Bourassa, M.G.
1985-01-01
The incremental diagnostic yield of clinical data, exercise ECG, stress thallium scintigraphy, and cardiac fluoroscopy to predict coronary and multivessel disease was assessed in 171 symptomatic men by means of multiple logistic regression analyses. When clinical variables alone were analyzed, chest pain type and age were predictive of coronary disease, whereas chest pain type, age, a family history of premature coronary disease before age 55 years, and abnormal ST-T wave changes on the rest ECG were predictive of multivessel disease. The percentage of patients correctly classified by cardiac fluoroscopy (presence or absence of coronary artery calcification), exercise ECG, and thallium scintigraphy was 9%, 25%, and 50%, respectively, greater than for clinical variables, when the presence or absence of coronary disease was the outcome, and 13%, 25%, and 29%, respectively, when multivessel disease was studied; 5% of patients were misclassified. When the 37 clinical and noninvasive test variables were analyzed jointly, the most significant variable predictive of coronary disease was an abnormal thallium scan and for multivessel disease, the amount of exercise performed. The data from this study provide a quantitative model and confirm previous reports that optimal diagnostic efficacy is obtained when noninvasive tests are ordered sequentially. In symptomatic men, cardiac fluoroscopy is a relatively ineffective test when compared to exercise ECG and thallium scintigraphy
Applied regression analysis a research tool
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 rotor optimization using regression analysis
Giansante, N.
1984-01-01
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
Smith, Paul F; Ganesh, Siva; Liu, Ping
2013-10-30
Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.
A Solution to Separation and Multicollinearity in Multiple Logistic Regression.
Shen, Jianzhao; Gao, Sujuan
2008-10-01
In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.
Two SPSS programs for interpreting multiple regression results.
Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo
2010-02-01
When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.
A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield
Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan
2018-04-01
In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.
Interpret with caution: multicollinearity in multiple regression of cognitive data.
Morrison, Catriona M
2003-08-01
Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.
General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning.
Chung, Wooyong; Kim, Jisu; Lee, Heejin; Kim, Euntai
2015-11-01
Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly investigated. Further, the proposed MSVRs are extended into the multiple kernel learning and their training is implemented by the off-the-shelf convex optimization tools. The proposed MSVRs are applied to benchmark problems and their performances are compared with those of the previous methods in the experimental section.
Li, Spencer D.
2011-01-01
Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…
Two Paradoxes in Linear Regression Analysis
FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong
2016-01-01
Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214
Regression analysis using dependent Polya trees.
Schörgendorfer, Angela; Branscum, Adam J
2013-11-30
Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.
Method for nonlinear exponential regression analysis
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.
Al-Hadeethi, Farqad; Al-Nimr, Moh'd; Al-Safadi, Mohammad
2015-01-01
The performance of PEM (proton exchange membrane) fuel cell was experimentally investigated at three temperatures (30, 50 and 70 °C), four flow rates (5, 10, 15 and 20 ml/min) and two flow patterns (co-current and counter current) in order to generate two correlations using multiple regression analysis with respect to ANOVA. Results revealed that increasing the temperature for co-current and counter current flow patterns will increase both hydrogen and oxygen diffusivities, water management and membrane conductivity. The derived mathematical correlations and three dimensional mapping (i.e. surface response) for the co-current and countercurrent flow patterns showed that there is a clear interaction among the various variables (temperatures and flow rates). - Highlights: • Generating mathematical correlations using multiple regression analysis with respect to ANOVA for the performance of the PEM fuel cell. • Using the 3D mapping to diagnose the optimum performance of the PEM fuel cell at the given operating conditions. • Results revealed that increasing the flow rate had direct influence on the consumption of oxygen. • Results assured that increasing the temperature in co-current and counter current flow patterns increases the performance of PEM fuel cell.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
Overcoming multicollinearity in multiple regression using correlation coefficient
Zainodin, H. J.; Yap, S. J.
2013-09-01
Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.
Modeling Pan Evaporation for Kuwait by Multiple Linear Regression
Almedeij, Jaber
2012-01-01
Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984
He, Dan; Kuhn, David; Parida, Laxmi
2016-06-15
Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.
On logistic regression analysis of dichotomized responses.
Lu, Kaifeng
2017-01-01
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Wakhid S. Ciptono
2010-05-01
mediating roles of the contextual factors of world-class performance in operations (i.e., world-class company practices or WCC, operational excellence practices or OE, company nonfinancial performance or CNFP, and the company financial performance would enable the company to facilitate the sustainability of TQM implementation model. This empirical study aims to assess how TQM—a holistic management philosophy initially developed by W. Edward Deming, which integrates improvement strategy, management practices, and organizational performance—is specifically implemented in the oil and gas companies operating in Indonesia. Relevant literature on the TQM, the world-class performance in operations (world-class company and operational performance, the company performance (financial and non-financial performances, and the amendments of the Law of the Republic of Indonesia concerning the oil and gas industry, and related research on how the oil and gas industry in Indonesia develops sustainable competitive advantage and sustainable development programs are reviewed in details in our study. The findings from data analysis provide evidence that there is a strong positive relationship between the critical factors of quality management practices and the company financial performance mediated by the three mediating variables, i.e., world-class company practices, operational excellence practices, and company non-financial performance.
Robust Mediation Analysis Based on Median Regression
Yuan, Ying; MacKinnon, David P.
2014-01-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Functional data analysis of generalized regression quantiles
Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl
2013-01-01
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Local bilinear multiple-output quantile/depth regression
Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav
2015-01-01
Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf
Ling, Ru; Liu, Jiawang
2011-12-01
To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright
A method for nonlinear exponential regression analysis
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.
Regression analysis for the social sciences
Gordon, Rachel A
2010-01-01
The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.
Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients
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.
Management of Industrial Performance Indicators: Regression Analysis and Simulation
Walter Roberto Hernandez Vergara
2017-11-01
Full Text Available Stochastic methods can be used in problem solving and explanation of natural phenomena through the application of statistical procedures. The article aims to associate the regression analysis and systems simulation, in order to facilitate the practical understanding of data analysis. The algorithms were developed in Microsoft Office Excel software, using statistical techniques such as regression theory, ANOVA and Cholesky Factorization, which made it possible to create models of single and multiple systems with up to five independent variables. For the analysis of these models, the Monte Carlo simulation and analysis of industrial performance indicators were used, resulting in numerical indices that aim to improve the goals’ management for compliance indicators, by identifying systems’ instability, correlation and anomalies. The analytical models presented in the survey indicated satisfactory results with numerous possibilities for industrial and academic applications, as well as the potential for deployment in new analytical techniques.
A Powerful Test for Comparing Multiple Regression Functions.
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).
Multiple regression models for energy use in air-conditioned office buildings in different climates
Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.
2010-01-01
An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.
Frame, Katherine Chiyoko [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-06-28
Neutron multiplicity measurements are widely used for nondestructive assay (NDA) of special nuclear material (SNM). When combined with isotopic composition information, neutron multiplicity analysis can be used to estimate the spontaneous fission rate and leakage multiplication of SNM. When combined with isotopic information, the total mass of fissile material can also be determined. This presentation provides an overview of this technique.
Regression analysis for the social sciences
Gordon, Rachel A
2015-01-01
Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
Bielecki, John
2003-01-01
.... Previous research has demonstrated the use of a two-step logistic and multiple regression methodology to predicting cost growth produces desirable results versus traditional single-step regression...
Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression
Maokuan Zheng
2017-01-01
Full Text Available The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2. The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.
Interpreting Multiple Logistic Regression Coefficients in Prospective Observational Studies
1982-11-01
prompted close examination of the issue at a workshop on hypertriglyceridemia where some of the cautions and perspectives given in this paper were...characteristics. If this is not the interest, then to isolate and-understand the effect of a characteris- tic on CHD when it could be one of several interacting...also easily extended to the case when several independent variables are modeled in a multiple logistic equation. In this instance, if xlx 2,..., x are
Repeated Results Analysis for Middleware Regression Benchmarking
Bulej, Lubomír; Kalibera, T.; Tůma, P.
2005-01-01
Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005
Common pitfalls in statistical analysis: Linear regression analysis
Rakesh Aggarwal
2017-01-01
Full Text Available In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.
Research on the multiple linear regression in non-invasive blood glucose measurement.
Zhu, Jianming; Chen, Zhencheng
2015-01-01
A non-invasive blood glucose measurement sensor and the data process algorithm based on the metabolic energy conservation (MEC) method are presented in this paper. The physiological parameters of human fingertip can be measured by various sensing modalities, and blood glucose value can be evaluated with the physiological parameters by the multiple linear regression analysis. Five methods such as enter, remove, forward, backward and stepwise in multiple linear regression were compared, and the backward method had the best performance. The best correlation coefficient was 0.876 with the standard error of the estimate 0.534, and the significance was 0.012 (sig. regression equation was valid. The Clarke error grid analysis was performed to compare the MEC method with the hexokinase method, using 200 data points. The correlation coefficient R was 0.867 and all of the points were located in Zone A and Zone B, which shows the MEC method provides a feasible and valid way for non-invasive blood glucose measurement.
Using Multiple Linear Regression Techniques to Quantify Carbon ...
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Process and statistical models of productivity, though useful, are often ... The carbon balance of terrestrial ecosystems is uncertain, in part due to discrepancies and errors in .... The ecological data were collected through field work to include both .... Computer-aided Multivariate Analysis, Life Learning Publications, Belmont,.
Yoo, Yun Joo; Sun, Lei; Poirier, Julia G; Paterson, Andrew D; Bull, Shelley B
2017-02-01
By jointly analyzing multiple variants within a gene, instead of one at a time, gene-based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster-specific effects in a quadratic sum of squares and cross-products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well-powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P-value, variance-component, and principal-component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene-specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome-wide analysis. The cluster construction of the MLC test statistics helps reveal within-gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations. © 2016 The Authors Genetic Epidemiology Published by Wiley Periodicals, Inc.
Multiple predictor smoothing methods for sensitivity analysis
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
Multiple predictor smoothing methods for sensitivity analysis.
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.
Multiattribute shopping models and ridge regression analysis
Timmermans, H.J.P.
1981-01-01
Policy decisions regarding retailing facilities essentially involve multiple attributes of shopping centres. If mathematical shopping models are to contribute to these decision processes, their structure should reflect the multiattribute character of retailing planning. Examination of existing
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Ng, Kar Yong; Awang, Norhashidah
2018-01-06
Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.
Methods of Detecting Outliers in A Regression Analysis Model ...
PROF. O. E. OSUAGWU
2013-06-01
Jun 1, 2013 ... especially true in observational studies .... Simple linear regression and multiple ... The simple linear ..... Grubbs,F.E (1950): Sample Criteria for Testing Outlying observations: Annals of ... In experimental design, the Relative.
As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...
Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression
Abdul Jameel, Abdul Gani; Naser, Nimal; Emwas, Abdul-Hamid M.; Dooley, Stephen; Sarathy, Mani
2016-01-01
An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN
Adi Syahputra
2014-03-01
Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.
Tightness of M-estimators for multiple linear regression in time series
Johansen, Søren; Nielsen, Bent
We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...
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
A note on the use of multiple linear regression in molecular ecology.
Frasier, Timothy R
2016-03-01
Multiple linear regression analyses (also often referred to as generalized linear models--GLMs, or generalized linear mixed models--GLMMs) are widely used in the analysis of data in molecular ecology, often to assess the relative effects of genetic characteristics on individual fitness or traits, or how environmental characteristics influence patterns of genetic differentiation. However, the coefficients resulting from multiple regression analyses are sometimes misinterpreted, which can lead to incorrect interpretations and conclusions within individual studies, and can propagate to wider-spread errors in the general understanding of a topic. The primary issue revolves around the interpretation of coefficients for independent variables when interaction terms are also included in the analyses. In this scenario, the coefficients associated with each independent variable are often interpreted as the independent effect of each predictor variable on the predicted variable. However, this interpretation is incorrect. The correct interpretation is that these coefficients represent the effect of each predictor variable on the predicted variable when all other predictor variables are zero. This difference may sound subtle, but the ramifications cannot be overstated. Here, my goals are to raise awareness of this issue, to demonstrate and emphasize the problems that can result and to provide alternative approaches for obtaining the desired information. © 2015 John Wiley & Sons Ltd.
Grotti, Marco; Abelmoschi, Maria Luisa; Soggia, Francesco; Tiberiade, Christian; Frache, Roberto
2000-12-01
The multivariate effects of Na, K, Mg and Ca as nitrates on the electrothermal atomisation of manganese, cadmium and iron were studied by multiple linear regression modelling. Since the models proved to efficiently predict the effects of the considered matrix elements in a wide range of concentrations, they were applied to correct the interferences occurring in the determination of trace elements in seawater after pre-concentration of the analytes. In order to obtain a statistically significant number of samples, a large volume of the certified seawater reference materials CASS-3 and NASS-3 was treated with Chelex-100 resin; then, the chelating resin was separated from the solution, divided into several sub-samples, each of them was eluted with nitric acid and analysed by electrothermal atomic absorption spectrometry (for trace element determinations) and inductively coupled plasma optical emission spectrometry (for matrix element determinations). To minimise any other systematic error besides that due to matrix effects, accuracy of the pre-concentration step and contamination levels of the procedure were checked by inductively coupled plasma mass spectrometric measurements. Analytical results obtained by applying the multiple linear regression models were compared with those obtained with other calibration methods, such as external calibration using acid-based standards, external calibration using matrix-matched standards and the analyte addition technique. Empirical models proved to efficiently reduce interferences occurring in the analysis of real samples, allowing an improvement of accuracy better than for other calibration methods.
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.
Multiple regression technique for Pth degree polynominals with and without linear cross products
Davis, J. W.
1973-01-01
A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.
Bonellie, Sandra R
2012-10-01
To illustrate the use of regression and logistic regression models to investigate changes over time in size of babies particularly in relation to social deprivation, age of the mother and smoking. Mean birthweight has been found to be increasing in many countries in recent years, but there are still a group of babies who are born with low birthweights. Population-based retrospective cohort study. Multiple linear regression and logistic regression models are used to analyse data on term 'singleton births' from Scottish hospitals between 1994-2003. Mothers who smoke are shown to give birth to lighter babies on average, a difference of approximately 0.57 Standard deviations lower (95% confidence interval. 0.55-0.58) when adjusted for sex and parity. These mothers are also more likely to have babies that are low birthweight (odds ratio 3.46, 95% confidence interval 3.30-3.63) compared with non-smokers. Low birthweight is 30% more likely where the mother lives in the most deprived areas compared with the least deprived, (odds ratio 1.30, 95% confidence interval 1.21-1.40). Smoking during pregnancy is shown to have a detrimental effect on the size of infants at birth. This effect explains some, though not all, of the observed socioeconomic birthweight. It also explains much of the observed birthweight differences by the age of the mother. Identifying mothers at greater risk of having a low birthweight baby as important implications for the care and advice this group receives. © 2012 Blackwell Publishing Ltd.
Regression analysis of sparse asynchronous longitudinal data.
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.
Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said
2014-09-01
In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.
Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant
Chan, Yea-Kuang; Tsai, Yu-Ching
2017-01-01
The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.
Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant
Chan, Yea-Kuang; Tsai, Yu-Ching [Institute of Nuclear Energy Research, Taoyuan City, Taiwan (China). Nuclear Engineering Division
2017-03-15
The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.
Regression analysis as a design optimization tool
Perley, R.
1984-01-01
The optimization concepts are described in relation to an overall design process as opposed to a detailed, part-design process where the requirements are firmly stated, the optimization criteria are well established, and a design is known to be feasible. The overall design process starts with the stated requirements. Some of the design criteria are derived directly from the requirements, but others are affected by the design concept. It is these design criteria that define the performance index, or objective function, that is to be minimized within some constraints. In general, there will be multiple objectives, some mutually exclusive, with no clear statement of their relative importance. The optimization loop that is given adjusts the design variables and analyzes the resulting design, in an iterative fashion, until the objective function is minimized within the constraints. This provides a solution, but it is only the beginning. In effect, the problem definition evolves as information is derived from the results. It becomes a learning process as we determine what the physics of the system can deliver in relation to the desirable system characteristics. As with any learning process, an interactive capability is a real attriubute for investigating the many alternatives that will be suggested as learning progresses.
Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon
2015-01-01
Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.
Ansorge, Heather L; Adams, Sheila; Jawad, Abbas F; Birk, David E; Soslowsky, Louis J
2012-04-30
During neonatal development, tendons undergo a well orchestrated process whereby extensive structural and compositional changes occur in synchrony to produce a normal tissue. Conversely, during the repair response to injury, structural and compositional changes occur, but a mechanically inferior tendon is produced. As a result, developmental processes have been postulated as a potential paradigm for elucidation of mechanistic insight required to develop treatment modalities to improve adult tissue healing. The objective of this study was to compare and contrast normal development with injury during early and late developmental healing. Using backwards multiple linear regressions, quantitative and objective information was obtained into the structure-function relationships in tendon. Specifically, proteoglycans were shown to be significant predictors of modulus during early developmental healing but not during late developmental healing or normal development. Multiple independent parameters predicted percent relaxation during normal development, however, only biglycan and fibril diameter parameters predicted percent relaxation during early developmental healing. Lastly, multiple differential predictors were observed between early development and early developmental healing; however, no differential predictors were observed between late development and late developmental healing. This study presents a model through which objective analysis of how compositional and structural parameters that affect the development of mechanical parameters can be quantitatively measured. In addition, information from this study can be used to develop new treatment and therapies through which improved adult tendon healing can be obtained. Copyright © 2012 Elsevier Ltd. All rights reserved.
Preface to Berk's "Regression Analysis: A Constructive Critique"
de Leeuw, Jan
2003-01-01
It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...
Comparing the index-flood and multiple-regression methods using L-moments
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
Bhowmik, K.R.; Islam, S.
2016-01-01
Logistic regression (LR) analysis is the most common statistical methodology to find out the determinants of childhood mortality. However, the significant predictors cannot be ranked according to their influence on the response variable. Multiple classification (MC) analysis can be applied to identify the significant predictors with a priority index which helps to rank the predictors. The main objective of the study is to find the socio-demographic determinants of childhood mortality at neonatal, post-neonatal, and post-infant period by fitting LR model as well as to rank those through MC analysis. The study is conducted using the data of Bangladesh Demographic and Health Survey 2007 where birth and death information of children were collected from their mothers. Three dichotomous response variables are constructed from children age at death to fit the LR and MC models. Socio-economic and demographic variables significantly associated with the response variables separately are considered in LR and MC analyses. Both the LR and MC models identified the same significant predictors for specific childhood mortality. For both the neonatal and child mortality, biological factors of children, regional settings, and parents socio-economic status are found as 1st, 2nd, and 3rd significant groups of predictors respectively. Mother education and household environment are detected as major significant predictors of post-neonatal mortality. This study shows that MC analysis with or without LR analysis can be applied to detect determinants with rank which help the policy makers taking initiatives on a priority basis. (author)
Simulation Experiments in Practice: Statistical Design and Regression Analysis
Kleijnen, J.P.C.
2007-01-01
In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...
Tools to support interpreting multiple regression in the face of multicollinearity.
Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K
2012-01-01
While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.
Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.
Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A
2017-02-01
In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).
Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari
2018-01-01
Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg
application of multilinear regression analysis in modeling of soil
Windows User
Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...
Inferring gene expression dynamics via functional regression analysis
Leng Xiaoyan
2008-01-01
Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.
Željko V. Račić
2010-12-01
Full Text Available This paper aims to present the specifics of the application of multiple linear regression model. The economic (financial crisis is analyzed in terms of gross domestic product which is in a function of the foreign trade balance (on one hand and the credit cards, i.e. indebtedness of the population on this basis (on the other hand, in the USA (from 1999. to 2008. We used the extended application model which shows how the analyst should run the whole development process of regression model. This process began with simple statistical features and the application of regression procedures, and ended with residual analysis, intended for the study of compatibility of data and model settings. This paper also analyzes the values of some standard statistics used in the selection of appropriate regression model. Testing of the model is carried out with the use of the Statistics PASW 17 program.
A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity
Martin, David
2008-01-01
This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…
A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants
Cooper, Paul D.
2010-01-01
A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…
Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...
Calculation of U, Ra, Th and K contents in uranium ore by multiple linear regression method
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
Clinical trials: odds ratios and multiple regression models--why and how to assess them
Sobh, Mohamad; Cleophas, Ton J.; Hadj-Chaib, Amel; Zwinderman, Aeilko H.
2008-01-01
Odds ratios (ORs), unlike chi2 tests, provide direct insight into the strength of the relationship between treatment modalities and treatment effects. Multiple regression models can reduce the data spread due to certain patient characteristics and thus improve the precision of the treatment
The evolution of GDP in USA using cyclic regression analysis
Catalin Angelo IOAN; Gina IOAN
2013-01-01
Based on the four major types of economic cycles (Kondratieff, Juglar, Kitchin, Kuznet), the paper aims to determine their actual length (for the U.S. economy) using cyclic regressions based on Fourier analysis.
On two flexible methods of 2-dimensional regression analysis
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
Li, Yanming; Nan, Bin; Zhu, Ji
2015-06-01
We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.
Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.
2017-12-01
The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.
Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis
Nielsen, Allan Aasbjerg
2007-01-01
This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...
Determinants of orphan drugs prices in France: a regression analysis.
Korchagina, Daria; Millier, Aurelie; Vataire, Anne-Lise; Aballea, Samuel; Falissard, Bruno; Toumi, Mondher
2017-04-21
The introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis. All drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates. A total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation. The
Qiu, Shibin; Lane, Terran
2009-01-01
The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.
1981-09-01
corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John
Sintering equation: determination of its coefficients by experiments - using multiple regression
Windelberg, D.
1999-01-01
Sintering is a method for volume-compression (or volume-contraction) of powdered or grained material applying high temperature (less than the melting point of the material). Maekipirtti tried to find an equation which describes the process of sintering by its main parameters sintering time, sintering temperature and volume contracting. Such equation is called a sintering equation. It also contains some coefficients which characterise the behaviour of the material during the process of sintering. These coefficients have to be determined by experiments. Here we show that some linear regressions will produce wrong coefficients, but multiple regression results in an useful sintering equation. (orig.)
Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.
2012-01-01
Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…
Multiple regression as a preventive tool for determining the risk of Legionella spp.
Enrique Gea-Izquierdo
2012-04-01
Full Text Available To determine the interrelationship between health & hygiene conditions for prevention of legionellosis, the compositionof materials used in water distribution systems, the water origin and Legionella pneumophila risk. Material and methods. Include adescriptive study and multiple regression analysis on a sample of golf course sprinkler irrigation systems (n=31 pertaining to hotelslocated on the Costa del Sol (Malaga, Spain. The study was carried out in 2009. Results. Presented a significant lineal relation, withall the independent variables contributing significantly (p<0.05 to the model’s fit. The relationship between water type and the risk ofLegionella, as well as the material composition and the latter, is lineal and positive. In contrast, the relationship between health-hygieneconditions and Legionella risk is lineal and negative. Conclusion. The characterization of Legionella pneumophila concentration, asdefined by the risk in water and through use of the predictive method, can contribute to the consideration of new influence variables inthe development of the agent, resulting in improved control and prevention of the disease.
Forecasting on the total volumes of Malaysia's imports and exports by multiple linear regression
Beh, W. L.; Yong, M. K. Au
2017-04-01
This study is to give an insight on the doubt of the important of macroeconomic variables that affecting the total volumes of Malaysia's imports and exports by using multiple linear regression (MLR) analysis. The time frame for this study will be determined by using quarterly data of the total volumes of Malaysia's imports and exports covering the period between 2000-2015. The macroeconomic variables will be limited to eleven variables which are the exchange rate of US Dollar with Malaysia Ringgit (USD-MYR), exchange rate of China Yuan with Malaysia Ringgit (RMB-MYR), exchange rate of European Euro with Malaysia Ringgit (EUR-MYR), exchange rate of Singapore Dollar with Malaysia Ringgit (SGD-MYR), crude oil prices, gold prices, producer price index (PPI), interest rate, consumer price index (CPI), industrial production index (IPI) and gross domestic product (GDP). This study has applied the Johansen Co-integration test to investigate the relationship among the total volumes to Malaysia's imports and exports. The result shows that crude oil prices, RMB-MYR, EUR-MYR and IPI play important roles in the total volumes of Malaysia's imports. Meanwhile crude oil price, USD-MYR and GDP play important roles in the total volumes of Malaysia's exports.
On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression.
Pek, Jolynn; Chalmers, R Philip; Monette, Georges
2016-01-01
When statistical models are employed to provide a parsimonious description of empirical relationships, the extent to which strong conclusions can be drawn rests on quantifying the uncertainty in parameter estimates. In multiple linear regression (MLR), regression weights carry two kinds of uncertainty represented by confidence sets (CSs) and exchangeable weights (EWs). Confidence sets quantify uncertainty in estimation whereas the set of EWs quantify uncertainty in the substantive interpretation of regression weights. As CSs and EWs share certain commonalities, we clarify the relationship between these two kinds of uncertainty about regression weights. We introduce a general framework describing how CSs and the set of EWs for regression weights are estimated from the likelihood-based and Wald-type approach, and establish the analytical relationship between CSs and sets of EWs. With empirical examples on posttraumatic growth of caregivers (Cadell et al., 2014; Schneider, Steele, Cadell & Hemsworth, 2011) and on graduate grade point average (Kuncel, Hezlett & Ones, 2001), we illustrate the usefulness of CSs and EWs for drawing strong scientific conclusions. We discuss the importance of considering both CSs and EWs as part of the scientific process, and provide an Online Appendix with R code for estimating Wald-type CSs and EWs for k regression weights.
Mediation Analysis with Multiple Mediators
VanderWeele, T.J.; Vansteelandt, S.
2014-01-01
Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects throu...
Linear regression and sensitivity analysis in nuclear reactor design
Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.
2015-01-01
Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data
Optimal choice of basis functions in the linear regression analysis
Khotinskij, A.M.
1988-01-01
Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs
Predicting Dropouts of University Freshmen: A Logit Regression Analysis.
Lam, Y. L. Jack
1984-01-01
Stepwise discriminant analysis coupled with logit regression analysis of freshmen data from Brandon University (Manitoba) indicated that six tested variables drawn from research on university dropouts were useful in predicting attrition: student status, residence, financial sources, distance from home town, goal fulfillment, and satisfaction with…
Simulation Experiments in Practice : Statistical Design and Regression Analysis
Kleijnen, J.P.C.
2007-01-01
In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic
Mass estimation of loose parts in nuclear power plant based on multiple regression
He, Yuanfeng; Cao, Yanlong; Yang, Jiangxin; Gan, Chunbiao
2012-01-01
According to the application of the Hilbert–Huang transform to the non-stationary signal and the relation between the mass of loose parts in nuclear power plant and corresponding frequency content, a new method for loose part mass estimation based on the marginal Hilbert–Huang spectrum (MHS) and multiple regression is proposed in this paper. The frequency spectrum of a loose part in a nuclear power plant can be expressed by the MHS. The multiple regression model that is constructed by the MHS feature of the impact signals for mass estimation is used to predict the unknown masses of a loose part. A simulated experiment verified that the method is feasible and the errors of the results are acceptable. (paper)
Single image super-resolution using locally adaptive multiple linear regression.
Yu, Soohwan; Kang, Wonseok; Ko, Seungyong; Paik, Joonki
2015-12-01
This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.
User's Guide to the Weighted-Multiple-Linear Regression Program (WREG version 1.0)
Eng, Ken; Chen, Yin-Yu; Kiang, Julie.E.
2009-01-01
Streamflow is not measured at every location in a stream network. Yet hydrologists, State and local agencies, and the general public still seek to know streamflow characteristics, such as mean annual flow or flood flows with different exceedance probabilities, at ungaged basins. The goals of this guide are to introduce and familiarize the user with the weighted multiple-linear regression (WREG) program, and to also provide the theoretical background for program features. The program is intended to be used to develop a regional estimation equation for streamflow characteristics that can be applied at an ungaged basin, or to improve the corresponding estimate at continuous-record streamflow gages with short records. The regional estimation equation results from a multiple-linear regression that relates the observable basin characteristics, such as drainage area, to streamflow characteristics.
Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal
2005-09-01
To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.
Background stratified Poisson regression analysis of cohort data.
Richardson, David B; Langholz, Bryan
2012-03-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.
Background stratified Poisson regression analysis of cohort data
Richardson, David B.; Langholz, Bryan
2012-01-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)
Association analysis of multiple traits by an approach of combining ...
Lili Chen
diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic ... genthaler and Thilly 2007), the combined multivariate and ... Because of using reverse regression model, our.
Yoonsu Shin
2016-01-01
Full Text Available In the 5G era, the operational cost of mobile wireless networks will significantly increase. Further, massive network capacity and zero latency will be needed because everything will be connected to mobile networks. Thus, self-organizing networks (SON are needed, which expedite automatic operation of mobile wireless networks, but have challenges to satisfy the 5G requirements. Therefore, researchers have proposed a framework to empower SON using big data. The recent framework of a big data-empowered SON analyzes the relationship between key performance indicators (KPIs and related network parameters (NPs using machine-learning tools, and it develops regression models using a Gaussian process with those parameters. The problem, however, is that the methods of finding the NPs related to the KPIs differ individually. Moreover, the Gaussian process regression model cannot determine the relationship between a KPI and its various related NPs. In this paper, to solve these problems, we proposed multivariate multiple regression models to determine the relationship between various KPIs and NPs. If we assume one KPI and multiple NPs as one set, the proposed models help us process multiple sets at one time. Also, we can find out whether some KPIs are conflicting or not. We implement the proposed models using MapReduce.
Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.
Ohring, G.
1972-01-01
Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.
NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel
2017-08-01
Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.
hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression.
Wang, Pu; Ge, Ruiquan; Xiao, Xuan; Zhou, Manli; Zhou, Fengfeng
2017-01-01
Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label, and the multi-label classification problem remains to be a challenge for biomedical researchers. This study proposes a novel multi-label learning algorithm, hMuLab, by integrating both feature-based and neighbor-based similarity scores. The multiple linear regression modeling techniques make hMuLab capable of producing multiple label assignments for a query sample. The comparison results over six commonly-used multi-label performance measurements suggest that hMuLab performs accurately and stably for the biomedical datasets, and may serve as a complement to the existing literature.
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin
2015-04-03
© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy
Merlo, Juan; Wagner, Philippe; Ghith, Nermin
2016-01-01
BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting...
Application of multilinear regression analysis in modeling of soil ...
The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, ...
Regression Analysis: Instructional Resource for Cost/Managerial Accounting
Stout, David E.
2015-01-01
This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…
Real-time regression analysis with deep convolutional neural networks
Huerta, E. A.; George, Daniel; Zhao, Zhizhen; Allen, Gabrielle
2018-01-01
We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.
Faridah Hani Mohamed Salleh
2017-01-01
Full Text Available Gene regulatory network (GRN reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C as a direct interaction (A → C. Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Wang, J; Wang, F; Liu, Y; Xu, J; Lin, H; Jia, B; Zuo, W; Jiang, Y; Hu, L; Lin, F
2016-01-01
Overweight individuals are at higher risk for developing type II diabetes than the general population. We conducted this study to analyze the correlation between blood glucose and biochemical parameters, and developed a blood glucose prediction model tailored to overweight patients. A total of 346 overweight Chinese people patients ages 18-81 years were involved in this study. Their levels of fasting glucose (fs-GLU), blood lipids, and hepatic and renal functions were measured and analyzed by multiple linear regression (MLR). Based the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tansig as the transfer function of the hidden layers nodes, and purelin for the output layer nodes, with training goal of 0.5×10(-5). There was significant correlation between fs-GLU with age, BMI, and blood biochemical indexes (P<0.05). The results of MLR analysis indicated that age, fasting alanine transaminase (fs-ALT), blood urea nitrogen (fs-BUN), total protein (fs-TP), uric acid (fs-BUN), and BMI are 6 independent variables related to fs-GLU. Based on these parameters, the BP-ANN model was performed well and reached high prediction accuracy when training 1 000 epoch (R=0.9987). The level of fs-GLU was predictable using the proposed BP-ANN model based on 6 related parameters (age, fs-ALT, fs-BUN, fs-TP, fs-UA and BMI) in overweight patients. © Georg Thieme Verlag KG Stuttgart · New York.
Salleh, Faridah Hani Mohamed; Zainudin, Suhaila; Arif, Shereena M
2017-01-01
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa
2008-01-01
This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.
Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf
2015-10-01
The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200-400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset. © The Author 2014. Published by Oxford University Press.
Early cost estimating for road construction projects using multiple regression techniques
Ibrahim Mahamid
2011-12-01
Full Text Available The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% and ±50%.
Reddy, T.A. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States)); Claridge, D.E. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States))
1994-01-01
Multiple regression modeling of monitored building energy use data is often faulted as a reliable means of predicting energy use on the grounds that multicollinearity between the regressor variables can lead both to improper interpretation of the relative importance of the various physical regressor parameters and to a model with unstable regressor coefficients. Principal component analysis (PCA) has the potential to overcome such drawbacks. While a few case studies have already attempted to apply this technique to building energy data, the objectives of this study were to make a broader evaluation of PCA and multiple regression analysis (MRA) and to establish guidelines under which one approach is preferable to the other. Four geographic locations in the US with different climatic conditions were selected and synthetic data sequence representative of daily energy use in large institutional buildings were generated in each location using a linear model with outdoor temperature, outdoor specific humidity and solar radiation as the three regression variables. MRA and PCA approaches were then applied to these data sets and their relative performances were compared. Conditions under which PCA seems to perform better than MRA were identified and preliminary recommendations on the use of either modeling approach formulated. (orig.)
Harrell , Jr , Frank E
2015-01-01
This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically...
Parisi Kern, Andrea; Ferreira Dias, Michele; Piva Kulakowski, Marlova; Paulo Gomes, Luciana
2015-05-01
Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable. Copyright © 2015 Elsevier Ltd. All rights reserved.
Screening for ketosis using multiple logistic regression based on milk yield and composition.
Kayano, Mitsunori; Kataoka, Tomoko
2015-11-01
Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (Pketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (Pketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively.
Application of Soft Computing Techniques and Multiple Regression Models for CBR prediction of Soils
Fatimah Khaleel Ibrahim
2017-08-01
Full Text Available The techniques of soft computing technique such as Artificial Neutral Network (ANN have improved the predicting capability and have actually discovered application in Geotechnical engineering. The aim of this research is to utilize the soft computing technique and Multiple Regression Models (MLR for forecasting the California bearing ratio CBR( of soil from its index properties. The indicator of CBR for soil could be predicted from various soils characterizing parameters with the assist of MLR and ANN methods. The data base that collected from the laboratory by conducting tests on 86 soil samples that gathered from different projects in Basrah districts. Data gained from the experimental result were used in the regression models and soft computing techniques by using artificial neural network. The liquid limit, plastic index , modified compaction test and the CBR test have been determined. In this work, different ANN and MLR models were formulated with the different collection of inputs to be able to recognize their significance in the prediction of CBR. The strengths of the models that were developed been examined in terms of regression coefficient (R2, relative error (RE% and mean square error (MSE values. From the results of this paper, it absolutely was noticed that all the proposed ANN models perform better than that of MLR model. In a specific ANN model with all input parameters reveals better outcomes than other ANN models.
Exploratory regression analysis: a tool for selecting models and determining predictor importance.
Braun, Michael T; Oswald, Frederick L
2011-06-01
Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.
The Regression Analysis of Individual Financial Performance: Evidence from Croatia
Bahovec, Vlasta; Barbić, Dajana; Palić, Irena
2017-01-01
Background: A large body of empirical literature indicates that gender and financial literacy are significant determinants of individual financial performance. Objectives: The purpose of this paper is to recognize the impact of the variable financial literacy and the variable gender on the variation of the financial performance using the regression analysis. Methods/Approach: The survey was conducted using the systematically chosen random sample of Croatian financial consumers. The cross sect...
Regression analysis of a chemical reaction fouling model
Vasak, F.; Epstein, N.
1996-01-01
A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs
Majumdar, Arunabha; Witte, John S; Ghosh, Saurabh
2015-12-01
Binary phenotypes commonly arise due to multiple underlying quantitative precursors and genetic variants may impact multiple traits in a pleiotropic manner. Hence, simultaneously analyzing such correlated traits may be more powerful than analyzing individual traits. Various genotype-level methods, e.g., MultiPhen (O'Reilly et al. []), have been developed to identify genetic factors underlying a multivariate phenotype. For univariate phenotypes, the usefulness and applicability of allele-level tests have been investigated. The test of allele frequency difference among cases and controls is commonly used for mapping case-control association. However, allelic methods for multivariate association mapping have not been studied much. In this article, we explore two allelic tests of multivariate association: one using a Binomial regression model based on inverted regression of genotype on phenotype (Binomial regression-based Association of Multivariate Phenotypes [BAMP]), and the other employing the Mahalanobis distance between two sample means of the multivariate phenotype vector for two alleles at a single-nucleotide polymorphism (Distance-based Association of Multivariate Phenotypes [DAMP]). These methods can incorporate both discrete and continuous phenotypes. Some theoretical properties for BAMP are studied. Using simulations, the power of the methods for detecting multivariate association is compared with the genotype-level test MultiPhen's. The allelic tests yield marginally higher power than MultiPhen for multivariate phenotypes. For one/two binary traits under recessive mode of inheritance, allelic tests are found to be substantially more powerful. All three tests are applied to two different real data and the results offer some support for the simulation study. We propose a hybrid approach for testing multivariate association that implements MultiPhen when Hardy-Weinberg Equilibrium (HWE) is violated and BAMP otherwise, because the allelic approaches assume HWE
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra
Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D
2015-05-01
Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical
Multiple predictor smoothing methods for sensitivity analysis: Description of techniques
Storlie, Curtis B.; Helton, Jon C.
2008-01-01
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: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, 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: Example results
Storlie, Curtis B.; Helton, Jon C.
2008-01-01
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 in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, 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
On macroeconomic values investigation using fuzzy linear regression analysis
Richard Pospíšil
2017-06-01
Full Text Available The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts.
Regression analysis of radiological parameters in nuclear power plants
Bhargava, Pradeep; Verma, R.K.; Joshi, M.L.
2003-01-01
Indian Pressurized Heavy Water Reactors (PHWRs) have now attained maturity in their operations. Indian PHWR operation started in the year 1972. At present there are 12 operating PHWRs collectively producing nearly 2400 MWe. Sufficient radiological data are available for analysis to draw inferences which may be utilised for better understanding of radiological parameters influencing the collective internal dose. Tritium is the main contributor to the occupational internal dose originating in PHWRs. An attempt has been made to establish the relationship between radiological parameters, which may be useful to draw inferences about the internal dose. Regression analysis have been done to find out the relationship, if it exist, among the following variables: A. Specific tritium activity of heavy water (Moderator and PHT) and tritium concentration in air at various work locations. B. Internal collective occupational dose and tritium release to environment through air route. C. Specific tritium activity of heavy water (Moderator and PHT) and collective internal occupational dose. For this purpose multivariate regression analysis has been carried out. D. Tritium concentration in air at various work location and tritium release to environment through air route. For this purpose multivariate regression analysis has been carried out. This analysis reveals that collective internal dose has got very good correlation with the tritium activity release to the environment through air route. Whereas no correlation has been found between specific tritium activity in the heavy water systems and collective internal occupational dose. The good correlation has been found in case D and F test reveals that it is not by chance. (author)
Forecasting urban water demand: A meta-regression analysis.
Sebri, Maamar
2016-12-01
Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.
Spontaneous regression of multiple pulmonary metastatic nodules of hepatocarcinoma: a case report
Bahk, Yong Whee; Park, Seog Hee; Kim, Sun Moo [St. Mary' s Hospital, Catholic Medical College, Seoul (Korea, Republic of)
1981-09-15
Although are spontaneous regression of either primary or metastatic malignant tumor in the absence of or inadequate therapy has been well documented. Since the earliest day of this century various malignant tumors have been reported to spontaneously disappear or to be arrested of their growth, but the cases of hepatocarcinoma has been very rare. From the literature, we were able to find out 5 previously reported cases of hepatocarcinoma which showed spontaneous regression at the primary site. Recently we have seen a case of multiple pulmonary metastatic nodules of hepatocarcinoma which completely regressed spontaneously and this forms the basis of the present case report. The patient was 55-year-old male admitted to St. Mary's Hospital, Catholic Medical College because of a hard palpable mass in the epigastrium on April 26, 1978. The admission PA chest roentgenogram revealed multiple small nodular densities scattered throughout both lung field especially in lower zones and toward the peripheral portion. A hepatoscintigram revealed a large cold area involving the left lobe and inermediate zone of the liver. Alfa-fetoprotein and hepatitis B serum antigen test were positive whereas many other standard liver function tests turned out to be negative. A needle biopsy of the tumor revealed well differentiated hepatocellular carcinoma. The patient was put under chemotherapy which consisted of 5 FU 500 mg intravenously for 6 days from April 28 to May 3, 1978. The patient was discharged after this single course of 5 FU treatment and was on a herb medicine, the nature and quantity of which obscure. No other specific treatment was given. The second admission took place on Dec. 3, 1980 because of irregularity in bowel habits and dyspepsia. A follow up PA chest roentgenogram obtained on the second admission revealed complete disappearance of previously noted multiple pulmonary nodular lesions (Fig. 3). Follow up liver scan revealed persistence of the cold area in the left lobe
Spontaneous regression of multiple pulmonary metastatic nodules of hepatocarcinoma: a case report
Bahk, Yong Whee; Park, Seog Hee; Kim, Sun Moo
1981-01-01
Although are spontaneous regression of either primary or metastatic malignant tumor in the absence of or inadequate therapy has been well documented. Since the earliest day of this century various malignant tumors have been reported to spontaneously disappear or to be arrested of their growth, but the cases of hepatocarcinoma has been very rare. From the literature, we were able to find out 5 previously reported cases of hepatocarcinoma which showed spontaneous regression at the primary site. Recently we have seen a case of multiple pulmonary metastatic nodules of hepatocarcinoma which completely regressed spontaneously and this forms the basis of the present case report. The patient was 55-year-old male admitted to St. Mary's Hospital, Catholic Medical College because of a hard palpable mass in the epigastrium on April 26, 1978. The admission PA chest roentgenogram revealed multiple small nodular densities scattered throughout both lung field especially in lower zones and toward the peripheral portion. A hepatoscintigram revealed a large cold area involving the left lobe and inermediate zone of the liver. Alfa-fetoprotein and hepatitis B serum antigen test were positive whereas many other standard liver function tests turned out to be negative. A needle biopsy of the tumor revealed well differentiated hepatocellular carcinoma. The patient was put under chemotherapy which consisted of 5 FU 500 mg intravenously for 6 days from April 28 to May 3, 1978. The patient was discharged after this single course of 5 FU treatment and was on a herb medicine, the nature and quantity of which obscure. No other specific treatment was given. The second admission took place on Dec. 3, 1980 because of irregularity in bowel habits and dyspepsia. A follow up PA chest roentgenogram obtained on the second admission revealed complete disappearance of previously noted multiple pulmonary nodular lesions (Fig. 3). Follow up liver scan revealed persistence of the cold area in the left lobe
Avval Zhila Mohajeri
2015-01-01
Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.
Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression
Abdul Jameel, Abdul Gani
2016-09-14
An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.
Abad, Cesar C C; Barros, Ronaldo V; Bertuzzi, Romulo; Gagliardi, João F L; Lima-Silva, Adriano E; Lambert, Mike I; Pires, Flavio O
2016-06-01
The aim of this study was to verify the power of VO 2max , peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO 2max and PTV; 2) a constant submaximal run at 12 km·h -1 on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO 2max , PTV and RE) and adjusted variables (VO 2max 0.72 , PTV 0.72 and RE 0.60 ) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO 2max . Significant correlations (p 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV 0.72 and RE 0.60 and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation.
Confidence intervals for distinguishing ordinal and disordinal interactions in multiple regression.
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).
Mathur, Praveen; Sharma, Sarita; Soni, Bhupendra
2010-01-01
In the present work, an attempt is made to formulate multiple regression equations using all possible regressions method for groundwater quality assessment of Ajmer-Pushkar railway line region in pre- and post-monsoon seasons. Correlation studies revealed the existence of linear relationships (r 0.7) for electrical conductivity (EC), total hardness (TH) and total dissolved solids (TDS) with other water quality parameters. The highest correlation was found between EC and TDS (r = 0.973). EC showed highly significant positive correlation with Na, K, Cl, TDS and total solids (TS). TH showed highest correlation with Ca and Mg. TDS showed significant correlation with Na, K, SO4, PO4 and Cl. The study indicated that most of the contamination present was water soluble or ionic in nature. Mg was present as MgCl2; K mainly as KCl and K2SO4, and Na was present as the salts of Cl, SO4 and PO4. On the other hand, F and NO3 showed no significant correlations. The r2 values and F values (at 95% confidence limit, alpha = 0.05) for the modelled equations indicated high degree of linearity among independent and dependent variables. Also the error % between calculated and experimental values was contained within +/- 15% limit.
Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.
2018-05-01
Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.
Linear regression analysis: part 14 of a series on evaluation of scientific publications.
Schneider, Astrid; Hommel, Gerhard; Blettner, Maria
2010-11-01
Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.
Prybutok, V.R.
1995-01-01
Risk associated with power generation must be identified to make intelligent choices between alternate power technologies. Radionuclide air stack emissions for a single coal plant and a single nuclear plant are used to compute the single plant leukemia incidence risk and total industry leukemia incidence risk. Leukemia incidence is the response variable as a function of radionuclide bone dose for the six proposed dose response curves considered. During normal operation a coal plant has higher radionuclide emissions than a nuclear plant and the coal industry has a higher leukaemia incidence risk than the nuclear industry, unless a nuclear accident occurs. Variation of nuclear accident size allows quantification of the impact of accidents on the total industry leukemia incidence risk comparison. The leukemia incidence risk is quantified as the number of accidents of a given size for the nuclear industry leukemia incidence risk to equal the coal industry leukemia incidence risk. The general linear model is used to develop equations that relate the accident frequency required for equal industry risks to the magnitude of the nuclear emission. Exploratory data analysis revealed that the relationship between the natural log of accident number versus the natural log of accident size is linear. (Author)
Finding determinants of audit delay by pooled OLS regression analysis
Vuko, Tina; Čular, Marko
2014-01-01
The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days) from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011). We use pooled OLS regression analysis, mode...
Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.
2013-01-01
This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)
Balachandran, K.K.; Jayalakshmy, K.V.; Laluraj, C.M.; Nair, M.; Joseph, T.; Sheeba, P.
The interaction effects of abiotic processes in the production of phytoplankton in a coastal marine region off Cochin are evaluated using multiple regression models. The study shows that chlorophyll production is not limited by nutrients...
Model selection with multiple regression on distance matrices leads to incorrect inferences.
Ryan P Franckowiak
Full Text Available In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC, its small-sample correction (AICc, and the Bayesian information criterion (BIC to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.
Regression analysis: An evaluation of the inuences behindthe pricing of beer
Eriksson, Sara; Häggmark, Jonas
2017-01-01
This bachelor thesis in applied mathematics is an analysis of which factors affect the pricing of beer at the Swedish market. A multiple linear regression model is created with the statistical programming language R through a study of the influences for several explanatory variables. For example these variables include country of origin, beer style, volume sold and a Bayesian weighted mean rating from RateBeer, a popular website for beer enthusiasts. The main goal of the project is to find si...
Bayesian Analysis for Penalized Spline Regression Using WinBUGS
Ciprian M. Crainiceanu
2005-09-01
Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.
Pressure Points in Reading Comprehension: A Quantile Multiple Regression Analysis
Logan, Jessica
2017-01-01
The goal of this study was to examine how selected pressure points or areas of vulnerability are related to individual differences in reading comprehension and whether the importance of these pressure points varies as a function of the level of children's reading comprehension. A sample of 245 third-grade children were given an assessment battery…
Buck, J.; Underhill, P.R.; Mokros, S.G.; Morelli, J.; Krause, T.W.; Babbar, V.K.; Lepine, B.
2015-01-01
Nuclear steam generator (SG) support structure degradation and fouling can result in damage to SG tubes and loss of SG efficiency. Conventional eddy current technology is extensively used to detect cracks, frets at supports and other flaws, but has limited capabilities in the presence of multiple degradation modes or fouling. Pulsed eddy current (PEC) combined with principal components analysis (PCA) and multiple linear regression models was examined for the inspection of support structure degradation and SG tube off-centering with the goal of extending results to include additional degradation modes. (author)
Kolasa-Wiecek, Alicja
2015-04-01
The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R2=0.90. For N2O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2O emission is the peat and wood fuel consumption. Copyright © 2015. Published by Elsevier B.V.
Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H
2018-01-01
Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.
Xu Li; Liang Changhong; Xiao Yuanqiu; Zhang Zhonglin
2010-01-01
Objective: To analyze the correlations between liver lipid level determined by liver 3.0 T 1 H-MRS in vivo and influencing factors using multiple linear stepwise regression. Methods: The prospective study of liver 1 H-MRS was performed with 3.0 T system and eight-channel torso phased-array coils using PRESS sequence. Forty-four volunteers were enrolled in this study. Liver spectra were collected with a TR of 1500 ms, TE of 30 ms, volume of interest of 2 cm×2 cm×2 cm, NSA of 64 times. The acquired raw proton MRS data were processed by using a software program SAGE. For each MRS measurement, using water as the internal reference, the amplitude of the lipid signal was normalized to the sum of the signal from lipid and water to obtain percentage lipid within the liver. The statistical description of height, weight, age and BMI, Line width and water suppression were recorded, and Pearson analysis was applied to test their relationships. Multiple linear stepwise regression was used to set the statistical model for the prediction of Liver lipid content. Results: Age (39.1±12.6) years, body weight (64.4±10.4) kg, BMI (23.3±3.1) kg/m 2 , linewidth (18.9±4.4) and the water suppression (90.7±6.5)% had significant correlation with liver lipid content (0.00 to 0.96%, median 0.02%), r were 0.11, 0.44, 0.40, 0.52, -0.73 respectively (P<0.05). But only age, BMI, line width, and the water suppression entered into the multiple linear regression equation. Liver lipid content prediction equation was as follows: Y= 1.395 - (0.021×water suppression) + (0.022×BMI) + (0.014×line width) - (0.004×age), and the coefficient of determination was 0. 613, corrected coefficient of determination was 0.59. Conclusion: The regression model fitted well, since the variables of age, BMI, width, and water suppression can explain about 60% of liver lipid content changes. (authors)
Mediation Analysis with Multiple Mediators.
VanderWeele, T J; Vansteelandt, S
2014-01-01
Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators.
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu
2015-01-01
predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths
Finding determinants of audit delay by pooled OLS regression analysis
Tina Vuko
2014-03-01
Full Text Available The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011. We use pooled OLS regression analysis, modelling audit delay as a function of the following explanatory variables: audit firm type, audit opinion, profitability, leverage, inventory and receivables to total assets, absolute value of total accruals, company size and audit committee existence. Our results indicate that audit committee existence, profitability and leverage are statistically significant determinants of audit delay in Croatia.
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)
2012-02-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Giuseppe Perinetti
2016-01-01
Full Text Available The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males were included in the study (mean age, 12.3±1.7 years; range, 7.6–16.7 years. These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles with age of attainment of the corresponding CVM stage (in months. Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, −0.7. These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases.
Marjan Čeh
2018-05-01
Full Text Available The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1 the non-linear nature of the prediction assignment task; (2 input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3 the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE, coefficient of dispersion (COD revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.
An Application of Robust Method in Multiple Linear Regression Model toward Credit Card Debt
Amira Azmi, Nur; Saifullah Rusiman, Mohd; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Salleh, Rohayu Mohd; Hamzah, Nur Shamsidah Amir
2018-04-01
Credit card is a convenient alternative replaced cash or cheque, and it is essential component for electronic and internet commerce. In this study, the researchers attempt to determine the relationship and significance variables between credit card debt and demographic variables such as age, household income, education level, years with current employer, years at current address, debt to income ratio and other debt. The provided data covers 850 customers information. There are three methods that applied to the credit card debt data which are multiple linear regression (MLR) models, MLR models with least quartile difference (LQD) method and MLR models with mean absolute deviation method. After comparing among three methods, it is found that MLR model with LQD method became the best model with the lowest value of mean square error (MSE). According to the final model, it shows that the years with current employer, years at current address, household income in thousands and debt to income ratio are positively associated with the amount of credit debt. Meanwhile variables for age, level of education and other debt are negatively associated with amount of credit debt. This study may serve as a reference for the bank company by using robust methods, so that they could better understand their options and choice that is best aligned with their goals for inference regarding to the credit card debt.
Liu, Ke; Chen, Xiaojing; Li, Limin; Chen, Huiling; Ruan, Xiukai; Liu, Wenbin
2015-02-09
The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. Copyright © 2014 Elsevier B.V. All rights reserved.
EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression.
Lian, Yao; Ge, Meng; Pan, Xian-Ming
2014-12-19
B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task. In this work, based on the antigen's primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728. We have presented a reliable method for the identification of linear B cell epitope using antigen's primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/ .
Rosso, Luigi; Riatti, Riccardo
2016-01-01
The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6–16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, −0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases. PMID:27995136
Determining Balıkesir’s Energy Potential Using a Regression Analysis Computer Program
Bedri Yüksel
2014-01-01
Full Text Available Solar power and wind energy are used concurrently during specific periods, while at other times only the more efficient is used, and hybrid systems make this possible. When establishing a hybrid system, the extent to which these two energy sources support each other needs to be taken into account. This paper is a study of the effects of wind speed, insolation levels, and the meteorological parameters of temperature and humidity on the energy potential in Balıkesir, in the Marmara region of Turkey. The relationship between the parameters was studied using a multiple linear regression method. Using a designed-for-purpose computer program, two different regression equations were derived, with wind speed being the dependent variable in the first and insolation levels in the second. The regression equations yielded accurate results. The computer program allowed for the rapid calculation of different acceptance rates. The results of the statistical analysis proved the reliability of the equations. An estimate of identified meteorological parameters and unknown parameters could be produced with a specified precision by using the regression analysis method. The regression equations also worked for the evaluation of energy potential.
Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T
2016-02-01
The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.
Krasikova, Dina V; Le, Huy; Bachura, Eric
2018-01-22
To address a long-standing concern regarding a gap between organizational science and practice, scholars called for more intuitive and meaningful ways of communicating research results to users of academic research. In this article, we develop a common language effect size index (CLβ) that can help translate research results to practice. We demonstrate how CLβ can be computed and used to interpret the effects of continuous and categorical predictors in multiple linear regression models. We also elaborate on how the proposed CLβ index is computed and used to interpret interactions and nonlinear effects in regression models. In addition, we test the robustness of the proposed index to violations of normality and provide means for computing standard errors and constructing confidence intervals around its estimates. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.
1985-01-01
Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque
Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.
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
Mixed kernel function support vector regression for global sensitivity analysis
Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng
2017-11-01
Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.
Framing an Nuclear Emergency Plan using Qualitative Regression Analysis
Amy Hamijah Abdul Hamid; Ibrahim, M.Z.A.; Deris, S.R.
2014-01-01
Since the arising on safety maintenance issues due to post-Fukushima disaster, as well as, lack of literatures on disaster scenario investigation and theory development. This study is dealing with the initiation difficulty on the research purpose which is related to content and problem setting of the phenomenon. Therefore, the research design of this study refers to inductive approach which is interpreted and codified qualitatively according to primary findings and written reports. These data need to be classified inductively into thematic analysis as to develop conceptual framework related to several theoretical lenses. Moreover, the framing of the expected framework of the respective emergency plan as the improvised business process models are abundant of unstructured data abstraction and simplification. The structural methods of Qualitative Regression Analysis (QRA) and Work System snapshot applied to form the data into the proposed model conceptualization using rigorous analyses. These methods were helpful in organising and summarizing the snapshot into an ' as-is ' work system that being recommended as ' to-be' w ork system towards business process modelling. We conclude that these methods are useful to develop comprehensive and structured research framework for future enhancement in business process simulation. (author)
Online Statistical Modeling (Regression Analysis) for Independent Responses
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Regression analysis for LED color detection of visual-MIMO system
Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo
2018-04-01
Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.
Robust Regression and its Application in Financial Data Analysis
Mansoor Momeni; Mahmoud Dehghan Nayeri; Ali Faal Ghayoumi; Hoda Ghorbani
2010-01-01
This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from th...
Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y
2008-02-18
The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.
Modeling of Soil Aggregate Stability using Support Vector Machines and Multiple Linear Regression
Ali Asghar Besalatpour
2016-02-01
Full Text Available Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010. Various indicators have been proposed to characterize and quantify soil aggregate stability, for example percentage of water-stable aggregates (WSA, mean weight diameter (MWD, geometric mean diameter (GMD of aggregates, and water-dispersible clay (WDC content (Calero et al., 2008. Unfortunately, the experimental methods available to determine these indicators are laborious, time-consuming and difficult to standardize (Canasveras et al., 2010. Therefore, it would be advantageous if aggregate stability could be predicted indirectly from more easily available data (Besalatpour et al., 2014. The main objective of this study is to investigate the potential use of support vector machines (SVMs method for estimating soil aggregate stability (as quantified by GMD as compared to multiple linear regression approach. Materials and Methods: The study area was part of the Bazoft watershed (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E, which is located in the Northern part of the Karun river basin in central Iran. A total of 160 soil samples were collected from the top 5 cm of soil surface. Some easily available characteristics including topographic, vegetation, and soil properties were used as inputs. Soil organic matter (SOM content was determined by the Walkley-Black method (Nelson & Sommers, 1986. Particle size distribution in the soil samples (clay, silt, sand, fine sand, and very fine sand were measured using the procedure described by Gee & Bauder (1986 and calcium carbonate equivalent (CCE content was determined by the back-titration method (Nelson, 1982. The modified Kemper & Rosenau (1986 method was used to determine wet-aggregate stability (GMD. The topographic attributes of elevation, slope, and aspect were characterized using a 20-m
Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.
Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi
2017-09-20
Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Statistical analysis of sediment toxicity by additive monotone regression splines
Boer, de W.J.; Besten, den P.J.; Braak, ter C.J.F.
2002-01-01
Modeling nonlinearity and thresholds in dose-effect relations is a major challenge, particularly in noisy data sets. Here we show the utility of nonlinear regression with additive monotone regression splines. These splines lead almost automatically to the estimation of thresholds. We applied this
A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis
Zhiming Song
2015-01-01
Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.
Regression and kriging analysis for grid power factor estimation
Rajesh Guntaka
2014-12-01
Full Text Available The measurement of power factor (PF in electrical utility grids is a mainstay of load balancing and is also a critical element of transmission and distribution efficiency. The measurement of PF dates back to the earliest periods of electrical power distribution to public grids. In the wide-area distribution grid, measurement of current waveforms is trivial and may be accomplished at any point in the grid using a current tap transformer. However, voltage measurement requires reference to ground and so is more problematic and measurements are normally constrained to points that have ready and easy access to a ground source. We present two mathematical analysis methods based on kriging and linear least square estimation (LLSE (regression to derive PF at nodes with unknown voltages that are within a perimeter of sample nodes with ground reference across a selected power grid. Our results indicate an error average of 1.884% that is within acceptable tolerances for PF measurements that are used in load balancing tasks.
A simplified procedure of linear regression in a preliminary analysis
Silvia Facchinetti
2013-05-01
Full Text Available The analysis of a statistical large data-set can be led by the study of a particularly interesting variable Y – regressed – and an explicative variable X, chosen among the remained variables, conjointly observed. The study gives a simplified procedure to obtain the functional link of the variables y=y(x by a partition of the data-set into m subsets, in which the observations are synthesized by location indices (mean or median of X and Y. Polynomial models for y(x of order r are considered to verify the characteristics of the given procedure, in particular we assume r= 1 and 2. The distributions of the parameter estimators are obtained by simulation, when the fitting is done for m= r + 1. Comparisons of the results, in terms of distribution and efficiency, are made with the results obtained by the ordinary least square methods. The study also gives some considerations on the consistency of the estimated parameters obtained by the given procedure.
Multiple logistic regression model of signalling practices of drivers on urban highways
Puan, Othman Che; Ibrahim, Muttaka Na'iya; Zakaria, Rozana
2015-05-01
Giving signal is a way of informing other road users, especially to the conflicting drivers, the intention of a driver to change his/her movement course. Other users are exposed to hazard situation and risks of accident if the driver who changes his/her course failed to give signal as required. This paper describes the application of logistic regression model for the analysis of driver's signalling practices on multilane highways based on possible factors affecting driver's decision such as driver's gender, vehicle's type, vehicle's speed and traffic flow intensity. Data pertaining to the analysis of such factors were collected manually. More than 2000 drivers who have performed a lane changing manoeuvre while driving on two sections of multilane highways were observed. Finding from the study shows that relatively a large proportion of drivers failed to give any signals when changing lane. The result of the analysis indicates that although the proportion of the drivers who failed to provide signal prior to lane changing manoeuvre is high, the degree of compliances of the female drivers is better than the male drivers. A binary logistic model was developed to represent the probability of a driver to provide signal indication prior to lane changing manoeuvre. The model indicates that driver's gender, type of vehicle's driven, speed of vehicle and traffic volume influence the driver's decision to provide a signal indication prior to a lane changing manoeuvre on a multilane urban highway. In terms of types of vehicles driven, about 97% of motorcyclists failed to comply with the signal indication requirement. The proportion of non-compliance drivers under stable traffic flow conditions is much higher than when the flow is relatively heavy. This is consistent with the data which indicates a high degree of non-compliances when the average speed of the traffic stream is relatively high.
Ma, Jing; Yu, Jiong; Hao, Guangshu; Wang, Dan; Sun, Yanni; Lu, Jianxin; Cao, Hongcui; Lin, Feiyan
2017-02-20
The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.
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.
An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis
Wen-Tsao Pan
2016-01-01
Full Text Available Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.
Bias due to two-stage residual-outcome regression analysis in genetic association studies.
Demissie, Serkalem; Cupples, L Adrienne
2011-11-01
Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.
Kim, T. W.; Park, G. H.
2014-12-01
Seasonal variation of aragonite saturation state (Ωarag) in the North Pacific Ocean (NPO) was investigated, using multiple linear regression (MLR) models produced from the PACIFICA (Pacific Ocean interior carbon) dataset. Data within depth ranges of 50-1200m were used to derive MLR models, and three parameters (potential temperature, nitrate, and apparent oxygen utilization (AOU)) were chosen as predictor variables because these parameters are associated with vertical mixing, DIC (dissolved inorganic carbon) removal and release which all affect Ωarag in water column directly or indirectly. The PACIFICA dataset was divided into 5° × 5° grids, and a MLR model was produced in each grid, giving total 145 independent MLR models over the NPO. Mean RMSE (root mean square error) and r2 (coefficient of determination) of all derived MLR models were approximately 0.09 and 0.96, respectively. Then the obtained MLR coefficients for each of predictor variables and an intercept were interpolated over the study area, thereby making possible to allocate MLR coefficients to data-sparse ocean regions. Predictability from the interpolated coefficients was evaluated using Hawaiian time-series data, and as a result mean residual between measured and predicted Ωarag values was approximately 0.08, which is less than the mean RMSE of our MLR models. The interpolated MLR coefficients were combined with seasonal climatology of World Ocean Atlas 2013 (1° × 1°) to produce seasonal Ωarag distributions over various depths. Large seasonal variability in Ωarag was manifested in the mid-latitude Western NPO (24-40°N, 130-180°E) and low-latitude Eastern NPO (0-12°N, 115-150°W). In the Western NPO, seasonal fluctuations of water column stratification appeared to be responsible for the seasonal variation in Ωarag (~ 0.5 at 50 m) because it closely followed temperature variations in a layer of 0-75 m. In contrast, remineralization of organic matter was the main cause for the seasonal
Analysis of quantile regression as alternative to ordinary least squares
Ibrahim Abdullahi; Abubakar Yahaya
2015-01-01
In this article, an alternative to ordinary least squares (OLS) regression based on analytical solution in the Statgraphics software is considered, and this alternative is no other than quantile regression (QR) model. We also present goodness of fit statistic as well as approximate distributions of the associated test statistics for the parameters. Furthermore, we suggest a goodness of fit statistic called the least absolute deviation (LAD) coefficient of determination. The procedure is well ...
Janssen, I.; Stebbings, J.H.
1990-01-01
In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs
Nasution, Inggrita Gusti Sari; Muchtar, Yasmin Chairunnisa
2013-01-01
This research is to study the factors which influence the business success of small business ‘processed rotan’. The data employed in the study are primary data within the period of July to August 2013, 30 research observations through census method. Method of analysis used in the study is multiple linear regressions. The results of analysis showed that the factors of labor, innovation and promotion have positive and significant influence on the business success of small busine...
Le, Huy; Marcus, Justin
2012-01-01
This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…
Choi, Kilchan
2011-01-01
This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…
Wang, Xuntao; Feng, Jianhu; Wang, Hu; Hong, Shidi; Zheng, Supei
2018-03-01
A three-dimensional finite element box girder bridge and its asphalt concrete deck pavement were established by ANSYS software, and the interlayer bonding condition of asphalt concrete deck pavement was assumed to be contact bonding condition. Orthogonal experimental design is used to arrange the testing plans of material parameters, and an evaluation of the effect of different material parameters in the mechanical response of asphalt concrete surface layer was conducted by multiple linear regression model and using the results from the finite element analysis. Results indicated that stress regression equations can well predict the stress of the asphalt concrete surface layer, and elastic modulus of waterproof layer has a significant influence on stress values of asphalt concrete surface layer.
Gopal Rajendiran
2014-01-01
Full Text Available The purpose of this research work is to build a multiple linear regression model for the characteristics of multicylinder diesel engine using multicomponent blends (diesel- pungamia methyl ester-ethanol as fuel. Nine blends were tested by varying diesel (100 to 10% by Vol., biodiesel (80 to 10% by vol. and keeping ethanol as 10% constant. The brake thermal efficiency, smoke, oxides of nitrogen, carbon dioxide, maximum cylinder pressure, angle of maximum pressure, angle of 5% and 90% mass burning were predicted based on load, speed, diesel and biodiesel percentage. To validate this regression model another multi component fuel comprising diesel-palm methyl ester-ethanol was used in same engine. Statistical analysis was carried out between predicted and experimental data for both fuel. The performance, emission and combustion characteristics of multi cylinder diesel engine using similar fuel blends can be predicted without any expenses for experimentation.
Analysis of some methods for reduced rank Gaussian process regression
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...
Intermediate and advanced topics in multilevel logistic regression analysis.
Austin, Peter C; Merlo, Juan
2017-09-10
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan
2018-02-01
The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.
Grades, Gender, and Encouragement: A Regression Discontinuity Analysis
Owen, Ann L.
2010-01-01
The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…
More, Anand Govind; Gupta, Sunil Kumar
2018-03-24
Bioelectrochemical system (BES) is a novel, self-sustaining metal removal technology functioning on the utilization of chemical energy of organic matter with the help of microorganisms. Experimental trials of two chambered BES reactor were conducted with varying substrate concentration using sodium acetate (500 mg/L to 2000 mg/L COD) and different initial chromium concentration (Cr i ) (10-100 mg/L) at different cathode pH (pH 1-7). In the current study mathematical models based on multiple linear regression (MLR) and non-linear regression (NLR) approach were developed using laboratory experimental data for determining chromium removal efficiency (CRE) in the cathode chamber of BES. Substrate concentration, rate of substrate consumption, Cr i , pH, temperature and hydraulic retention time (HRT) were the operating process parameters of the reactor considered for development of the proposed models. MLR showed a better correlation coefficient (0.972) as compared to NLR (0.952). Validation of the models using t-test analysis revealed unbiasedness of both the models, with t critical value (2.04) greater than t-calculated values for MLR (-0.708) and NLR (-0.86). The root-mean-square error (RMSE) for MLR and NLR were 5.06 % and 7.45 %, respectively. Comparison between both models suggested MLR to be best suited model for predicting the chromium removal behavior using the BES technology to specify a set of operating conditions for BES. Modelling the behavior of CRE will be helpful for scale up of BES technology at industrial level. Copyright © 2018 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.
Design and analysis of experiments classical and regression approaches with SAS
Onyiah, Leonard C
2008-01-01
Introductory Statistical Inference and Regression Analysis Elementary Statistical Inference Regression Analysis Experiments, the Completely Randomized Design (CRD)-Classical and Regression Approaches Experiments Experiments to Compare Treatments Some Basic Ideas Requirements of a Good Experiment One-Way Experimental Layout or the CRD: Design and Analysis Analysis of Experimental Data (Fixed Effects Model) Expected Values for the Sums of Squares The Analysis of Variance (ANOVA) Table Follow-Up Analysis to Check fo
Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul
2011-01-01
We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…
The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.
Fanning, Fred; Newman, Isadore
Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…
REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.
SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.
Functional Multiple-Set Canonical Correlation Analysis
Hwang, Heungsun; Jung, Kwanghee; Takane, Yoshio; Woodward, Todd S.
2012-01-01
We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the…
Multiple Additive Regression Trees a Methodology for Predictive Data Mining for Fraud Detection
da
2002-01-01
...) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits...
Marginal regression analysis of recurrent events with coarsened censoring times.
Hu, X Joan; Rosychuk, Rhonda J
2016-12-01
Motivated by an ongoing pediatric mental health care (PMHC) study, this article presents weakly structured methods for analyzing doubly censored recurrent event data where only coarsened information on censoring is available. The study extracted administrative records of emergency department visits from provincial health administrative databases. The available information of each individual subject is limited to a subject-specific time window determined up to concealed data. To evaluate time-dependent effect of exposures, we adapt the local linear estimation with right censored survival times under the Cox regression model with time-varying coefficients (cf. Cai and Sun, Scandinavian Journal of Statistics 2003, 30, 93-111). We establish the pointwise consistency and asymptotic normality of the regression parameter estimator, and examine its performance by simulation. The PMHC study illustrates the proposed approach throughout the article. © 2016, The International Biometric Society.
Regression analysis of censored data using pseudo-observations
Parner, Erik T.; Andersen, Per Kragh
2010-01-01
We draw upon a series of articles in which a method based on pseu- dovalues is proposed for direct regression modeling of the survival function, the restricted mean, and the cumulative incidence function in competing risks with right-censored data. The models, once the pseudovalues have been...... computed, can be fit using standard generalized estimating equation software. Here we present Stata procedures for computing these pseudo-observations. An example from a bone marrow transplantation study is used to illustrate the method....
Application of regression analysis to creep of space shuttle materials
Rummler, D.R.
1975-01-01
Metallic heat shields for Space Shuttle thermal protection systems must operate for many flight cycles at high temperatures in low-pressure air and use thin-gage (less than or equal to 0.65 mm) sheet. Available creep data for thin sheet under those conditions are inadequate. To assess the effects of oxygen partial pressure and sheet thickness on creep behavior and to develop constitutive creep equations for small sets of data, regression techniques are applied and discussed
Model performance analysis and model validation in logistic regression
Rosa Arboretti Giancristofaro
2007-10-01
Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.
BRGLM, Interactive Linear Regression Analysis by Least Square Fit
Ringland, J.T.; Bohrer, R.E.; Sherman, M.E.
1985-01-01
1 - Description of program or function: BRGLM is an interactive program written to fit general linear regression models by least squares and to provide a variety of statistical diagnostic information about the fit. Stepwise and all-subsets regression can be carried out also. There are facilities for interactive data management (e.g. setting missing value flags, data transformations) and tools for constructing design matrices for the more commonly-used models such as factorials, cubic Splines, and auto-regressions. 2 - Method of solution: The least squares computations are based on the orthogonal (QR) decomposition of the design matrix obtained using the modified Gram-Schmidt algorithm. 3 - Restrictions on the complexity of the problem: The current release of BRGLM allows maxima of 1000 observations, 99 variables, and 3000 words of main memory workspace. For a problem with N observations and P variables, the number of words of main memory storage required is MAX(N*(P+6), N*P+P*P+3*N, and 3*P*P+6*N). Any linear model may be fit although the in-memory workspace will have to be increased for larger problems
Use of generalized regression models for the analysis of stress-rupture data
Booker, M.K.
1978-01-01
The design of components for operation in an elevated-temperature environment often requires a detailed consideration of the creep and creep-rupture properties of the construction materials involved. Techniques for the analysis and extrapolation of creep data have been widely discussed. The paper presents a generalized regression approach to the analysis of such data. This approach has been applied to multiple heat data sets for types 304 and 316 austenitic stainless steel, ferritic 2 1 / 4 Cr-1 Mo steel, and the high-nickel austenitic alloy 800H. Analyses of data for single heats of several materials are also presented. All results appear good. The techniques presented represent a simple yet flexible and powerful means for the analysis and extrapolation of creep and creep-rupture data
Rafiei, Hamid; Khanzadeh, Marziyeh; Mozaffari, Shahla; Bostanifar, Mohammad Hassan; Avval, Zhila Mohajeri; Aalizadeh, Reza; Pourbasheer, Eslam
2016-01-01
Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors . A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r(2), concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained.
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
Jin-Peng Liu
2017-07-01
Full Text Available Under the background of a new round of power market reform, realizing the goals of energy saving and emission reduction, reducing the coal consumption and ensuring the sustainable development are the key issues for thermal power industry. With the biggest economy and energy consumption scales in the world, China should promote the energy efficiency of thermal power industry to solve these problems. Therefore, from multiple perspectives, the factors influential to the energy efficiency of thermal power industry were identified. Based on the economic, social and environmental factors, a combination model with Data Envelopment Analysis (DEA and Malmquist index was constructed to evaluate the total-factor energy efficiency (TFEE in thermal power industry. With the empirical studies from national and provincial levels, the TFEE index can be factorized into the technical efficiency index (TECH, the technical progress index (TPCH, the pure efficiency index (PECH and the scale efficiency index (SECH. The analysis showed that the TFEE was mainly determined by TECH and PECH. Meanwhile, by panel data regression model, unit coal consumption, talents and government supervision were selected as important indexes to have positive effects on TFEE in thermal power industry. In addition, the negative indexes, such as energy price and installed capacity, were also analyzed to control their undesired effects. Finally, considering the analysis results, measures for improving energy efficiency of thermal power industry were discussed widely, such as strengthening technology research and design (R&D, enforcing pollutant and emission reduction, distributing capital and labor rationally and improving the government supervision. Relative study results and suggestions can provide references for Chinese government and enterprises to enhance the energy efficiency level.
Inverse estimation of multiple muscle activations based on linear logistic regression.
Sekiya, Masashi; Tsuji, Toshiaki
2017-07-01
This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.
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.
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.
Soft Sensor Modeling Based on Multiple Gaussian Process Regression and Fuzzy C-mean Clustering
Xianglin ZHU
2014-06-01
Full Text Available In order to overcome the difficulties of online measurement of some crucial biochemical variables in fermentation processes, a new soft sensor modeling method is presented based on the Gaussian process regression and fuzzy C-mean clustering. With the consideration that the typical fermentation process can be distributed into 4 phases including lag phase, exponential growth phase, stable phase and dead phase, the training samples are classified into 4 subcategories by using fuzzy C- mean clustering algorithm. For each sub-category, the samples are trained using the Gaussian process regression and the corresponding soft-sensing sub-model is established respectively. For a new sample, the membership between this sample and sub-models are computed based on the Euclidean distance, and then the prediction output of soft sensor is obtained using the weighting sum. Taking the Lysine fermentation as example, the simulation and experiment are carried out and the corresponding results show that the presented method achieves better fitting and generalization ability than radial basis function neutral network and single Gaussian process regression model.
Temporal trends in sperm count: a systematic review and meta-regression analysis.
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
Additive Intensity Regression Models in Corporate Default Analysis
Lando, David; Medhat, Mamdouh; Nielsen, Mads Stenbo
2013-01-01
We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, nonfinancial U.S. firms. The setting allows for estimating and testing the significance of time-varying effects. We use a variety of mo...
Renfu Jia
2016-01-01
Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.
Kleijnen, J.P.C.
1995-01-01
This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for
Semiparametric regression analysis of interval-censored competing risks data.
Mao, Lu; Lin, Dan-Yu; Zeng, Donglin
2017-09-01
Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes. © 2017, The International Biometric Society.
Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.
Bulcock, J. W.
The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…
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.)
Multivariate linear regression of high-dimensional fMRI data with multiple target variables.
Valente, Giancarlo; Castellanos, Agustin Lage; Vanacore, Gianluca; Formisano, Elia
2014-05-01
Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets. Copyright © 2013 Wiley Periodicals, Inc.
Cohen, B.L.
1992-01-01
A plot of lung-cancer rates versus radon exposures in 965 US counties, or in all US states, has a strong negative slope, b, in sharp contrast to the strong positive slope predicted by linear/no-threshold theory. The discrepancy between these slopes exceeds 20 standard deviations (SD). Including smoking frequency in the analysis substantially improves fits to a linear relationship but has little effect on the discrepancy in b, because correlations between smoking frequency and radon levels are quite weak. Including 17 socioeconomic variables (SEV) in multiple regression analysis reduces the discrepancy to 15 SD. Data were divided into segments by stratifying on each SEV in turn, and on geography, and on both simultaneously, giving over 300 data sets to be analyzed individually, but negative slopes predominated. The slope is negative whether one considers only the most urban counties or only the most rural; only the richest or only the poorest; only the richest in the South Atlantic region or only the poorest in that region, etc., etc.,; and for all the strata in between. Since this is an ecological study, the well-known problems with ecological studies were investigated and found not to be applicable here. The open-quotes ecological fallacyclose quotes was shown not to apply in testing a linear/no-threshold theory, and the vulnerability to confounding is greatly reduced when confounding factors are only weakly correlated with radon levels, as is generally the case here. All confounding factors known to correlate with radon and with lung cancer were investigated quantitatively and found to have little effect on the discrepancy
A REVIEW ON THE USE OF REGRESSION ANALYSIS IN STUDIES OF AUDIT QUALITY
Agung Dodit Muliawan
2015-07-01
Full Text Available This study aimed to review how regression analysis has been used in studies of abstract phenomenon, such as audit quality, an importance concept in the auditing practice (Schroeder et al., 1986, yet is not well defined. The articles reviewed were the research articles that include audit quality as research variable, either as dependent or independent variables. The articles were purposefully selected to represent balance combination between audit specific and more general accounting journals and between Anglo Saxon and Anglo American journals. The articles were published between 1983-2011 and from the A/A class journal based on ERA 2010’s classifications. The study found that most of the articles reviewed used multiple regression analysis and treated audit quality as dependent variable and measured it by using a proxy. This study also highlights the size of data sample used and the lack of discussions about the assumptions of the statistical analysis used in most of the articles reviewed. This study concluded that the effectiveness and validity of multiple regressions do not only depends on its application by the researchers but also on how the researchers communicate their findings to the audience. KEYWORDS Audit quality, regression analysis ABSTRAK Kajian ini bertujuan untuk mereviu bagaimana analisa regresi digunakan dalam suatu fenomena abstrak seperti kualitas audit, suatu konsep yang penting dalam praktik audit (Schroeder et al., 1986 namun belum terdefinisi dengan jelas. Artikel yang direviu dalam kajian ini adalah artikel penelitian yang memasukkan kualitas audit sebagai variabel penelitian, baik sebagai variabel independen maupun dependen. Artikel-artikel tersebut dipilih dengan cara purposif sampling untuk mendapatkan keterwakilan yang seimbang antara artikel jurnal khusus audit dan akuntansi secara umum, serta mewakili jurnal Anglo Saxon dan Anglo American. Artikel yang direviu diterbitkan pada periode 1983-2011 oleh jurnal yang
Oil and gas pipeline construction cost analysis and developing regression models for cost estimation
Thaduri, Ravi Kiran
In this study, cost data for 180 pipelines and 136 compressor stations have been analyzed. On the basis of the distribution analysis, regression models have been developed. Material, Labor, ROW and miscellaneous costs make up the total cost of a pipeline construction. The pipelines are analyzed based on different pipeline lengths, diameter, location, pipeline volume and year of completion. In a pipeline construction, labor costs dominate the total costs with a share of about 40%. Multiple non-linear regression models are developed to estimate the component costs of pipelines for various cross-sectional areas, lengths and locations. The Compressor stations are analyzed based on the capacity, year of completion and location. Unlike the pipeline costs, material costs dominate the total costs in the construction of compressor station, with an average share of about 50.6%. Land costs have very little influence on the total costs. Similar regression models are developed to estimate the component costs of compressor station for various capacities and locations.
Hardt Jochen
2012-12-01
Full Text Available Abstract Background Multiple imputation is becoming increasingly popular. Theoretical considerations as well as simulation studies have shown that the inclusion of auxiliary variables is generally of benefit. Methods A simulation study of a linear regression with a response Y and two predictors X1 and X2 was performed on data with n = 50, 100 and 200 using complete cases or multiple imputation with 0, 10, 20, 40 and 80 auxiliary variables. Mechanisms of missingness were either 100% MCAR or 50% MAR + 50% MCAR. Auxiliary variables had low (r=.10 vs. moderate correlations (r=.50 with X’s and Y. Results The inclusion of auxiliary variables can improve a multiple imputation model. However, inclusion of too many variables leads to downward bias of regression coefficients and decreases precision. When the correlations are low, inclusion of auxiliary variables is not useful. Conclusion More research on auxiliary variables in multiple imputation should be performed. A preliminary rule of thumb could be that the ratio of variables to cases with complete data should not go below 1 : 3.
Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting
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.
Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation
Chunqing Li
2012-01-01
Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.
Simulation Experiments in Practice : Statistical Design and Regression Analysis
Kleijnen, J.P.C.
2007-01-01
In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is
Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...
Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis
Yoon Song Ha
2018-01-01
Full Text Available A relationship between human personality and preferred locations have been a long conjecture for human mobility research. In this paper, we analyzed the relationship between personality and visiting place with Poisson Regression. Poisson Regression can analyze correlation between countable dependent variable and independent variable. For this analysis, 33 volunteers provided their personality data and 49 location categories data are used. Raw location data is preprocessed to be normalized into rates of visit and outlier data is prunned. For the regression analysis, independent variables are personality data and dependent variables are preprocessed location data. Several meaningful results are found. For example, persons with high tendency of frequent visiting to university laboratory has personality with high conscientiousness and low openness. As well, other meaningful location categories are presented in this paper.
Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.
Ferrari, Alberto
2017-01-01
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.
Quantile regression analysis of body mass and wages.
Johar, Meliyanni; Katayama, Hajime
2012-05-01
Using the National Longitudinal Survey of Youth 1979, we explore the relationship between body mass and wages. We use quantile regression to provide a broad description of the relationship across the wage distribution. We also allow the relationship to vary by the degree of social skills involved in different jobs. Our results find that for female workers body mass and wages are negatively correlated at all points in their wage distribution. The strength of the relationship is larger at higher-wage levels. For male workers, the relationship is relatively constant across wage distribution but heterogeneous across ethnic groups. When controlling for the endogeneity of body mass, we find that additional body mass has a negative causal impact on the wages of white females earning more than the median wages and of white males around the median wages. Among these workers, the wage penalties are larger for those employed in jobs that require extensive social skills. These findings may suggest that labor markets reward white workers for good physical shape differently, depending on the level of wages and the type of job a worker has. Copyright © 2011 John Wiley & Sons, Ltd.
Østergaard, Søren; Ettema, Jehan Frans; Hjortø, Line
Multiple regression and model building with mediator variables was addressed to avoid double counting when economic values are estimated from data simulated with herd simulation modeling (using the SimHerd model). The simulated incidence of metritis was analyzed statistically as the independent v...... in multiparous cows. The merit of using this approach was demonstrated since the economic value of metritis was estimated to be 81% higher when no mediator variables were included in the multiple regression analysis......Multiple regression and model building with mediator variables was addressed to avoid double counting when economic values are estimated from data simulated with herd simulation modeling (using the SimHerd model). The simulated incidence of metritis was analyzed statistically as the independent...... variable, while using the traits representing the direct effects of metritis on yield, fertility and occurrence of other diseases as mediator variables. The economic value of metritis was estimated to be €78 per 100 cow-years for each 1% increase of metritis in the period of 1-100 days in milk...
Parsons, Vickie s.
2009-01-01
The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.
CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions
Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.
Statistical 21-cm Signal Separation via Gaussian Process Regression Analysis
Mertens, F. G.; Ghosh, A.; Koopmans, L. V. E.
2018-05-01
Detecting and characterizing the Epoch of Reionization and Cosmic Dawn via the redshifted 21-cm hyperfine line of neutral hydrogen will revolutionize the study of the formation of the first stars, galaxies, black holes and intergalactic gas in the infant Universe. The wealth of information encoded in this signal is, however, buried under foregrounds that are many orders of magnitude brighter. These must be removed accurately and precisely in order to reveal the feeble 21-cm signal. This requires not only the modeling of the Galactic and extra-galactic emission, but also of the often stochastic residuals due to imperfect calibration of the data caused by ionospheric and instrumental distortions. To stochastically model these effects, we introduce a new method based on `Gaussian Process Regression' (GPR) which is able to statistically separate the 21-cm signal from most of the foregrounds and other contaminants. Using simulated LOFAR-EoR data that include strong instrumental mode-mixing, we show that this method is capable of recovering the 21-cm signal power spectrum across the entire range k = 0.07 - 0.3 {h cMpc^{-1}}. The GPR method is most optimal, having minimal and controllable impact on the 21-cm signal, when the foregrounds are correlated on frequency scales ≳ 3 MHz and the rms of the signal has σ21cm ≳ 0.1 σnoise. This signal separation improves the 21-cm power-spectrum sensitivity by a factor ≳ 3 compared to foreground avoidance strategies and enables the sensitivity of current and future 21-cm instruments such as the Square Kilometre Array to be fully exploited.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
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...
Methods of Detecting Outliers in A Regression Analysis Model. | Ogu ...
A Boilers data with dependent variable Y (man-Hour) and four independent variables X1 (Boiler Capacity), X2 (Design Pressure), X3 (Boiler Type), X4 (Drum Type) were used. The analysis of the Boilers data reviewed an unexpected group of Outliers. The results from the findings showed that an observation can be outlying ...
Microhabitat analysis using radiotelemetry locations and polytomous logistic regression
Malcolm P. North; Joel H. Reynolds
1996-01-01
Microhabitat analyses often use discriminant function analysis (DFA) to compare vegetation structures or environmental conditions between sites classified by a study animal's presence or absence. These presence/absence studies make questionable assumptions about the habitat value of the comparison sites and the microhabitat data often violate the DFA's...
Singular spectrum analysis, Harmonic regression and El-Nino effect ...
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Keywords: Total ozone; Singular Spectrum Analysis; Spatial interpolation; Multivariate ENSO .... needed for a whole gamut of activities that contribute to the ultimate synthesis ..... −0.0009 3 + 0.0581 2 − 1.0123 + 7.3246, 2 = 0.53…
Yu, Donghai; Du, Ruobing; Xiao, Ji-Chang
2016-07-05
Ninety-six acidic phosphorus-containing molecules with pKa 1.88 to 6.26 were collected and divided into training and test sets by random sampling. Structural parameters were obtained by density functional theory calculation of the molecules. The relationship between the experimental pKa values and structural parameters was obtained by multiple linear regression fitting for the training set, and tested with the test set; the R(2) values were 0.974 and 0.966 for the training and test sets, respectively. This regression equation, which quantitatively describes the influence of structural parameters on pKa , and can be used to predict pKa values of similar structures, is significant for the design of new acidic phosphorus-containing extractants. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Analysis of cost regression and post-accident absence
Wojciech, Drozd
2017-07-01
The article presents issues related with costs of work safety. It proves the thesis that economic aspects cannot be overlooked in effective management of occupational health and safety and that adequate expenditures on safety can bring tangible benefits to the company. Reliable analysis of this problem is essential for the description the problem of safety the work. In the article attempts to carry it out using the procedures of mathematical statistics [1, 2, 3].
A comparison of multiple regression and neural network techniques for mapping in situ pCO2 data
Lefevre, Nathalie; Watson, Andrew J.; Watson, Adam R.
2005-01-01
Using about 138,000 measurements of surface pCO 2 in the Atlantic subpolar gyre (50-70 deg N, 60-10 deg W) during 1995-1997, we compare two methods of interpolation in space and time: a monthly distribution of surface pCO 2 constructed using multiple linear regressions on position and temperature, and a self-organizing neural network approach. Both methods confirm characteristics of the region found in previous work, i.e. the subpolar gyre is a sink for atmospheric CO 2 throughout the year, and exhibits a strong seasonal variability with the highest undersaturations occurring in spring and summer due to biological activity. As an annual average the surface pCO 2 is higher than estimates based on available syntheses of surface pCO 2 . This supports earlier suggestions that the sink of CO 2 in the Atlantic subpolar gyre has decreased over the last decade instead of increasing as previously assumed. The neural network is able to capture a more complex distribution than can be well represented by linear regressions, but both techniques agree relatively well on the average values of pCO 2 and derived fluxes. However, when both techniques are used with a subset of the data, the neural network predicts the remaining data to a much better accuracy than the regressions, with a residual standard deviation ranging from 3 to 11 μatm. The subpolar gyre is a net sink of CO 2 of 0.13 Gt-C/yr using the multiple linear regressions and 0.15 Gt-C/yr using the neural network, on average between 1995 and 1997. Both calculations were made with the NCEP monthly wind speeds converted to 10 m height and averaged between 1995 and 1997, and using the gas exchange coefficient of Wanninkhof
Jaworski Janusz
2016-03-01
Full Text Available Purpose. The aim of the study was to evaluate somatic and functional determinants of sports skill level in badminton players at three consecutive stages of training. Methods. The study examined 96 badminton players aged 11 to 19 years. The scope of the study included somatic characteristics, physical abilities and neurosensory abilities. Thirty nine variables were analysed in each athlete. Coefficients of multiple determination were used to evaluate the effect of structural and functional parameters on sports skill level in badminton players. Results. In the group of younger cadets, quality and effectiveness of playing were mostly determined by the level of physical abilities. In the group of cadets, the most important determinants were physical abilities, followed by somatic characteristics. In this group, coordination abilities were also important. In juniors, the most pronounced was a set of the variables that reflect physical abilities. Conclusions. Models of determination of sports skill level are most noticeable in the group of cadets. In all three groups of badminton players, the dominant effect on the quality of playing is due to a set of the variables that determine physical abilities.
Simple multiple regression model for long range forecasting of Indian summer monsoon rainfall
Sadhuram, Y.; Murthy, T.V.R.
, Wang J, Roy J, Dennis J (1996) The NCEP=NCAR 40-year reanalysis project. Bull Amer Meteorol Soc 77: 437–71 Kaplan A, Cane MA, Kushner Y, Clement AC, Blumenthal MB, Rajagopalan B (1998) Analysis of global sea-surface temperature: 1856–1991. J Geophys Res...
Hsu, Ching-Chi; Lin, Jinn; Chao, Ching-Kong
2011-12-01
Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process. Copyright Â© 2010 Elsevier Ireland Ltd. All rights reserved.
H.H. Mohamad
2013-09-01
This research aims to develop a mathematical model for assessing the expected net profit of any construction company. To achieve the research objective, four steps were performed. First, the main factors affecting firms’ net profit were identified. Second, pertinent data regarding the net profit factors were collected. Third, two different net profit models were developed using the Multiple Regression (MR and the Neural Network (NN techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.
Rachid Darnag
2017-02-01
Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.
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.
Biosensors and multiple mycotoxin analysis
Gaag, B. van der; Spath, S.; Dietrich, H.; Stigter, E.; Boonzaaijer, G.; Osenbruggen, T. van; Koopal, K.
2003-01-01
An immunochemical biosensor assay for the detection of multiple mycotoxins in a sample is described.The inhibition assay is designed to measure four different mycotoxins in a single measurement, following extraction, sample clean-up and incubation with an appropriate cocktail of anti-mycotoxin
A Quality Assessment Tool for Non-Specialist Users of Regression Analysis
Argyrous, George
2015-01-01
This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…
Multiple factor analysis by example using R
Pagès, Jérôme
2014-01-01
Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR).The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The
Exergy Analysis of a Subcritical Reheat Steam Power Plant with Regression Modeling and Optimization
MUHIB ALI RAJPER
2016-07-01
Full Text Available In this paper, exergy analysis of a 210 MW SPP (Steam Power Plant is performed. Firstly, the plant is modeled and validated, followed by a parametric study to show the effects of various operating parameters on the performance parameters. The net power output, energy efficiency, and exergy efficiency are taken as the performance parameters, while the condenser pressure, main steam pressure, bled steam pressures, main steam temperature, and reheat steam temperature isnominated as the operating parameters. Moreover, multiple polynomial regression models are developed to correlate each performance parameter with the operating parameters. The performance is then optimizedby using Direct-searchmethod. According to the results, the net power output, energy efficiency, and exergy efficiency are calculated as 186.5 MW, 31.37 and 30.41%, respectively under normal operating conditions as a base case. The condenser is a major contributor towards the energy loss, followed by the boiler, whereas the highest irreversibilities occur in the boiler and turbine. According to the parametric study, variation in the operating parameters greatly influences the performance parameters. The regression models have appeared to be a good estimator of the performance parameters. The optimum net power output, energy efficiency and exergy efficiency are obtained as 227.6 MW, 37.4 and 36.4, respectively, which have been calculated along with optimal values of selected operating parameters.
A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.
Bersabé, Rosa; Rivas, Teresa
2010-05-01
The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.
MILAD TAZIK
2017-11-01
Full Text Available Identifying cases in which road crashes result in fatality or injury of drivers may help improve their safety. In this study, datasets of crashes happened in TehranQom freeway, Iran, were examined by three models (multiple logistic regression, Bayesian logistic and classification tree to analyse the contribution of several variables to fatal accidents. For multiple logistic regression and Bayesian logistic models, the odds ratio was calculated for each variable. The model which best suited the identification of accident severity was determined based on AIC and DIC criteria. Based on the results of these two models, rollover crashes (OR = 14.58, %95 CI: 6.8-28.6, not using of seat belt (OR = 5.79, %95 CI: 3.1-9.9, exceeding speed limits (OR = 4.02, %95 CI: 1.8-7.9 and being female (OR = 2.91, %95 CI: 1.1-6.1 were the most important factors in fatalities of drivers. In addition, the results of the classification tree model have verified the findings of the other models.
Baba, Toshimi; Gotoh, Yusaku; Yamaguchi, Satoshi; Nakagawa, Satoshi; Abe, Hayato; Masuda, Yutaka; Kawahara, Takayoshi
2017-08-01
This study aimed to evaluate a validation reliability of single-step genomic best linear unbiased prediction (ssGBLUP) with a multiple-lactation random regression test-day model and investigate an effect of adding genotyped cows on the reliability. Two data sets for test-day records from the first three lactations were used: full data from February 1975 to December 2015 (60 850 534 records from 2 853 810 cows) and reduced data cut off in 2011 (53 091 066 records from 2 502 307 cows). We used marker genotypes of 4480 bulls and 608 cows. Genomic enhanced breeding values (GEBV) of 305-day milk yield in all the lactations were estimated for at least 535 young bulls using two marker data sets: bull genotypes only and both bulls and cows genotypes. The realized reliability (R 2 ) from linear regression analysis was used as an indicator of validation reliability. Using only genotyped bulls, R 2 was ranged from 0.41 to 0.46 and it was always higher than parent averages. The very similar R 2 were observed when genotyped cows were added. An application of ssGBLUP to a multiple-lactation random regression model is feasible and adding a limited number of genotyped cows has no significant effect on reliability of GEBV for genotyped bulls. © 2016 Japanese Society of Animal Science.
Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University
2001-01-01
This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.
Nimon, Kim; Lewis, Mitzi; Kane, Richard; Haynes, R Michael
2008-05-01
Multiple regression is a widely used technique for data analysis in social and behavioral research. The complexity of interpreting such results increases when correlated predictor variables are involved. Commonality analysis provides a method of determining the variance accounted for by respective predictor variables and is especially useful in the presence of correlated predictors. However, computing commonality coefficients is laborious. To make commonality analysis accessible to more researchers, a program was developed to automate the calculation of unique and common elements in commonality analysis, using the statistical package R. The program is described, and a heuristic example using data from the Holzinger and Swineford (1939) study, readily available in the MBESS R package, is presented.
Giovanni Leopoldo Rozza
2015-09-01
Full Text Available With world becoming each day a global village, enterprises continuously seek to optimize their internal processes to hold or improve their competitiveness and make better use of natural resources. In this context, decision support tools are an underlying requirement. Such tools are helpful on predicting operational issues, avoiding cost risings, loss of productivity, work-related accident leaves or environmental disasters. This paper has its focus on the prediction of spent liquor caustic concentration of Bayer process for alumina production. Caustic concentration measuring is essential to keep it at expected levels, otherwise quality issues might arise. The organization requests caustic concentration by chemical analysis laboratory once a day, such information is not enough to issue preventive actions to handle process inefficiencies that will be known only after new measurement on the next day. Thereby, this paper proposes using Multiple Linear Regression and Artificial Neural Networks techniques a mathematical model to predict the spent liquor´s caustic concentration. Hence preventive actions will occur in real time. Such models were built using software tool for numerical computation (MATLAB and a statistical analysis software package (SPSS. The models output (predicted caustic concentration were compared with the real lab data. We found evidence suggesting superior results with use of Artificial Neural Networks over Multiple Linear Regression model. The results demonstrate that replacing laboratorial analysis by the forecasting model to support technical staff on decision making could be feasible.
Fisz, Jacek J
2006-12-07
The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi
[A SAS marco program for batch processing of univariate Cox regression analysis for great database].
Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin
2015-02-01
To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.
Kumar, Akansha; Tsvetkov, Pavel V.
2015-01-01
desired power peaking limits, desired effective and infinite neutron multiplication factors, high fast fission factor, high thermal efficiency in the conversion from thermal energy to electrical energy using the Brayton cycle, and high fuel burn-up. It is to be noted that we have kept the total mass of the fuel as constant. In this work, we present a module based (modular) approach to perform the optimization wherein, we have defined the following modules: single fuel pin cell, whole core, thermal–hydraulics, and energy conversion. In each of the modules we have defined a specific set of parameters and optimization objectives. The GA system (GAS), and RS together, play the role of optimizing each of the individual modules, and integrating the modules to determine the final nuclear reactor core. However, implementation of GA could lead to a local minimum or a non-unique set of parameters, those meet the specific optimization objectives. The GA code is built using Java, neutronic analysis using MCNP6, thermal–hydraulics calculations using Java, and regression analysis using R
Robust Multiple Linear Regression.
1982-12-01
difficulty, but it might have more solutions corresponding to local minima. Influence Function of M-Estimates The influence function describes the effect...distributionn n function. In case of M-Estimates the influence function was found to be pro- portional to and given as T(X F)) " C(xpF,T) = .(X.T(F) F(dx...where the inverse of any distribution function F is defined in the usual way as F- (s) = inf{x IF(x) > s) 0<sə Influence Function of L-Estimates In a
Development of a User Interface for a Regression Analysis Software Tool
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.
Ghavami, Raoof; Najafi, Amir; Sajadi, Mohammad; Djannaty, Farhad
2008-09-01
In order to accurately simulate (13)C NMR spectra of hydroxy, polyhydroxy and methoxy substituted flavonoid a quantitative structure-property relationship (QSPR) model, relating atom-based calculated descriptors to (13)C NMR chemical shifts (ppm, TMS=0), is developed. A dataset consisting of 50 flavonoid derivatives was employed for the present analysis. A set of 417 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated and separate multilinear QSPR models were developed between each carbon atom of flavonoid and the calculated descriptors. Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models. Analysis of the results revealed a correlation coefficient and root mean square error (RMSE) of 0.994 and 2.53ppm, respectively, for the prediction set.
Shelley M. ALEXANDER
2009-02-01
Full Text Available We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS-based approaches: logistic regression and Akaike’s Information Criterion (AIC, Multiple Criteria Evaluation (MCE, and Bayesian Analysis (specifically Dempster-Shafer theory. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy, the failure to predict a species where it occurred (omission error and the prediction of presence where there was absence (commission error. Our overall accuracy showed the logistic regression approach was the most accurate (74.51%. The multiple criteria evaluation was intermediate (39.22%, while the Dempster-Shafer (D-S theory model was the poorest (29.90%. However, omission and commission error tell us a different story: logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans[Current Zoology 55(1: 28 – 40, 2009].
Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression
Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels (Belgium); Shabbir, A. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching (Germany); Hornung, G. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium)
2016-11-15
Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.
Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis
Camilleri, Liberato; Cefai, Carmel
2013-01-01
Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…
The analysis of survival data in nephrology: basic concepts and methods of Cox regression
van Dijk, Paul C.; Jager, Kitty J.; Zwinderman, Aeilko H.; Zoccali, Carmine; Dekker, Friedo W.
2008-01-01
How much does the survival of one group differ from the survival of another group? How do differences in age in these two groups affect such a comparison? To obtain a quantity to compare the survival of different patient groups and to account for confounding effects, a multiple regression technique
Spatial regression analysis on 32 years of total column ozone data
Knibbe, J.S.; van der A, J.R.; de Laat, A.T.J.
2014-01-01
Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter
Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...
Quality of life in breast cancer patients--a quantile regression analysis.
Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma
2008-01-01
Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.
Akbari, Somaye; Zebardast, Tannaz; Zarghi, Afshin; Hajimahdi, Zahra
2017-01-01
COX-2 inhibitory activities of some 1,4-dihydropyridine and 5-oxo-1,4,5,6,7,8-hexahydroquinoline derivatives were modeled by quantitative structure-activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R 2 ) of 0.972 and 0.531 for training and test groups, respectively. The quality of the model was evaluated by leave-one-out (LOO) cross validation (LOO correlation coefficient (Q 2 ) of 0.943) and Y-randomization. We also employed a leverage approach for the defining of applicability domain of model. Based on QSAR models results, COX-2 inhibitory activity of selected data set had correlation with BEHm6 (highest eigenvalue n. 6 of Burden matrix/weighted by atomic masses), Mor03u (signal 03/unweighted) and IVDE (Mean information content on the vertex degree equality) descriptors which derived from their structures.
Nose, Takashi; Kobayashi, Takao
In this paper, we propose a technique for estimating the degree or intensity of emotional expressions and speaking styles appearing in speech. The key idea is based on a style control technique for speech synthesis using a multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse of the style control. In the proposed technique, the acoustic features of spectrum, power, fundamental frequency, and duration are simultaneously modeled using the MRHSMM. We derive an algorithm for estimating explanatory variables of the MRHSMM, each of which represents the degree or intensity of emotional expressions and speaking styles appearing in acoustic features of speech, based on a maximum likelihood criterion. We show experimental results to demonstrate the ability of the proposed technique using two types of speech data, simulated emotional speech and spontaneous speech with different speaking styles. It is found that the estimated values have correlation with human perception.
D'Souza, Sonia; Rasmussen, John; Schwirtz, Ansgar
2012-01-01
and valuable ergonomic tool. Objective: To investigate age and gender effects on the torque-producing ability in the knee and elbow in older adults. To create strength scaled equations based on age, gender, upper/lower limb lengths and masses using multiple linear regression. To reduce the number of dependent...... flexors. Results: Males were signifantly stronger than females across all age groups. Elbow peak torque (EPT) was better preserved from 60s to 70s whereas knee peak torque (KPT) reduced significantly (PGender, thigh mass and age best...... predicted KPT (R2=0.60). Gender, forearm mass and age best predicted EPT (R2=0.75). Good crossvalidation was established for both elbow and knee models. Conclusion: This cross-sectional study of muscle strength created and validated strength scaled equations of EPT and KPT using only gender, segment mass...
Wang, Hui; Sui, Weiguo; Xue, Wen; Wu, Junyong; Chen, Jiejing; Dai, Yong
2014-09-01
Immunoglobulin A nephropathy (IgAN) is a complex trait regulated by the interaction among multiple physiologic regulatory systems and probably involving numerous genes, which leads to inconsistent findings in genetic studies. One possibility of failure to replicate some single-locus results is that the underlying genetics of IgAN nephropathy is based on multiple genes with minor effects. To learn the association between 23 single nucleotide polymorphisms (SNPs) in 14 genes predisposing to chronic glomerular diseases and IgAN in Han males, the 23 SNPs genotypes of 21 Han males were detected and analyzed with a BaiO gene chip, and their associations were analyzed with univariate analysis and multiple linear regression analysis. Analysis showed that CTLA4 rs231726 and CR2 rs1048971 revealed a significant association with IgAN. These findings support the multi-gene nature of the etiology of IgAN and propose a potential gene-gene interactive model for future studies.
Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena
2013-01-01
The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.
Resting-state functional magnetic resonance imaging: the impact of regression analysis.
Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi
2015-01-01
To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.
Machine learning of swimming data via wisdom of crowd and regression analysis.
Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing
2017-04-01
Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.
Coelho, Lúcia H G; Gutz, Ivano G R
2006-03-15
A chemometric method for analysis of conductometric titration data was introduced to extend its applicability to lower concentrations and more complex acid-base systems. Auxiliary pH measurements were made during the titration to assist the calculation of the distribution of protonable species on base of known or guessed equilibrium constants. Conductivity values of each ionized or ionizable species possibly present in the sample were introduced in a general equation where the only unknown parameters were the total concentrations of (conjugated) bases and of strong electrolytes not involved in acid-base equilibria. All these concentrations were adjusted by a multiparametric nonlinear regression (NLR) method, based on the Levenberg-Marquardt algorithm. This first conductometric titration method with NLR analysis (CT-NLR) was successfully applied to simulated conductometric titration data and to synthetic samples with multiple components at concentrations as low as those found in rainwater (approximately 10 micromol L(-1)). It was possible to resolve and quantify mixtures containing a strong acid, formic acid, acetic acid, ammonium ion, bicarbonate and inert electrolyte with accuracy of 5% or better.
Zarb, Francis; McEntee, Mark F; Rainford, Louise
2015-06-01
To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.
Hartmann, Frank G.H.; Moers, Frank
1999-01-01
In the contingency literature on the behavioral and organizational effects of budgeting, use of the Moderated Regression Analysis (MRA) technique is prevalent. This technique is used to test contingency hypotheses that predict interaction effects between budgetary and contextual variables. This
Ole E. Barndorff-Nielsen; Neil Shephard
2002-01-01
This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions and covariances change through time. In particular w...
Zhao, Yangbing; Moon, Edmund; Carpenito, Carmine; Paulos, Chrystal M; Liu, Xiaojun; Brennan, Andrea L; Chew, Anne; Carroll, Richard G; Scholler, John; Levine, Bruce L; Albelda, Steven M; June, Carl H
2010-11-15
Redirecting T lymphocyte antigen specificity by gene transfer can provide large numbers of tumor-reactive T lymphocytes for adoptive immunotherapy. However, safety concerns associated with viral vector production have limited clinical application of T cells expressing chimeric antigen receptors (CAR). T lymphocytes can be gene modified by RNA electroporation without integration-associated safety concerns. To establish a safe platform for adoptive immunotherapy, we first optimized the vector backbone for RNA in vitro transcription to achieve high-level transgene expression. CAR expression and function of RNA-electroporated T cells could be detected up to a week after electroporation. Multiple injections of RNA CAR-electroporated T cells mediated regression of large vascularized flank mesothelioma tumors in NOD/scid/γc(-/-) mice. Dramatic tumor reduction also occurred when the preexisting intraperitoneal human-derived tumors, which had been growing in vivo for >50 days, were treated by multiple injections of autologous human T cells electroporated with anti-mesothelin CAR mRNA. This is the first report using matched patient tumor and lymphocytes showing that autologous T cells from cancer patients can be engineered to provide an effective therapy for a disseminated tumor in a robust preclinical model. Multiple injections of RNA-engineered T cells are a novel approach for adoptive cell transfer, providing flexible platform for the treatment of cancer that may complement the use of retroviral and lentiviral engineered T cells. This approach may increase the therapeutic index of T cells engineered to express powerful activation domains without the associated safety concerns of integrating viral vectors. Copyright © 2010 AACR.
Cai, Jun; Shi, Jiamin; Wang, Kuaishe; Wang, Wen; Wang, Qingjuan; Liu, Yingying [Xi' an Univ. of Architecture and Technology, Xi' an (China). School of Metallurgical Engineering; Li, Fuguo [Northwestern Polytechnical Univ., Xi' an (China). School of Materials Science and Engineering
2017-07-15
Constitutive analysis for hot working of Ti-6Al-4V alloy was carried out by using experimental stress-strain data from isothermal hot compression tests. A new kind of constitutive equation called a modified parallel constitutive model was proposed by considering the independent effects of strain, strain rate and temperature. The predicted flow stress data were compared with the experimental data. Statistical analysis was introduced to verify the validity of the developed constitutive equation. Subsequently, the accuracy of the proposed constitutive equations was evaluated by comparing with other constitutive models. The results showed that the developed modified parallel constitutive model based on multiple regression could predict flow stress of Ti-6Al-4V alloy with good correlation and generalization.
Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions
Junzan Zhou
2015-01-01
Full Text Available Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call tree of each business transaction. We define a novel distance based metric computed from call trees for root cause analysis and apply inverted index from methods to business transactions for change impact analysis. Empirical studies show that our approach can effectively and efficiently help developers diagnose root cause of performance problems.
A primer for biomedical scientists on how to execute model II linear regression analysis.
Ludbrook, John
2012-04-01
1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.
Detecting overdispersion in count data: A zero-inflated Poisson regression analysis
Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah
2017-09-01
This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.
Spatial Quantile Regression In Analysis Of Healthy Life Years In The European Union Countries
Trzpiot Grażyna
2016-12-01
Full Text Available The paper investigates the impact of the selected factors on the healthy life years of men and women in the EU countries. The multiple quantile spatial autoregression models are used in order to account for substantial differences in the healthy life years and life quality across the EU members. Quantile regression allows studying dependencies between variables in different quantiles of the response distribution. Moreover, this statistical tool is robust against violations of the classical regression assumption about the distribution of the error term. Parameters of the models were estimated using instrumental variable method (Kim, Muller 2004, whereas the confidence intervals and p-values were bootstrapped.
The impact of healthcare spending on health outcomes: A meta-regression analysis.
Gallet, Craig A; Doucouliagos, Hristos
2017-04-01
While numerous studies assess the impact of healthcare spending on health outcomes, typically reporting multiple estimates of the elasticity of health outcomes (most often measured by a mortality rate or life expectancy) with respect to healthcare spending, the extent to which study attributes influence these elasticity estimates is unclear. Accordingly, we utilize a meta-data set (consisting of 65 studies completed over the 1969-2014 period) to examine these elasticity estimates using meta-regression analysis (MRA). Correcting for a number of issues, including publication selection bias, healthcare spending is found to have the greatest impact on the mortality rate compared to life expectancy. Indeed, conditional on several features of the literature, the spending elasticity for mortality is near -0.13, whereas it is near to +0.04 for life expectancy. MRA results reveal that the spending elasticity for the mortality rate is particularly sensitive to data aggregation, the specification of the health production function, and the nature of healthcare spending. The spending elasticity for life expectancy is particularly sensitive to the age at which life expectancy is measured, as well as the decision to control for the endogeneity of spending in the health production function. With such results in hand, we have a better understanding of how modeling choices influence results reported in this literature. Copyright © 2017 Elsevier Ltd. All rights reserved.
Deng, Yangyang; Parajuli, Prem B.
2011-08-10
Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.
Wanke, Peter [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Instituto de Pesquisa e Pos-Graduacao em Administracao de Empresas (COPPEAD). Centro de Estudos em Logistica
2004-07-01
In this paper, the most relevant multiple regression models for sales forecasting of gas stations, developed over the past ten years, are reviewed. The most significant variables related to gas station sales, the types of the multiple regression models (linear or non-linear), the most common uses in supporting decision making and its limits are presented. The predictive power of each model and its impact on decision-making, such as sensitivity analysis and confidence intervals for independent variables, are also commented. Four models are presented, based on studies conducted in South Africa, Portugal and Brazil. In conclusion, suggestions for future developments are presented based on past developments. (author)
Baxter Lisa K
2008-05-01
Full Text Available Abstract Background There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques. Methods We measured fine particulate matter (PM2.5, nitrogen dioxide (NO2, and elemental carbon (EC outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations. Results PM2.5 was strongly associated with the central site monitor (R2 = 0.68. Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76. EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52. NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction, and with higher concentrations during summer (R2 = 0.56. Conclusion Each pollutant examined displayed somewhat different spatial patterns
Reduced COPD Exacerbation Risk Correlates With Improved FEV1: A Meta-Regression Analysis.
Zider, Alexander D; Wang, Xiaoyan; Buhr, Russell G; Sirichana, Worawan; Barjaktarevic, Igor Z; Cooper, Christopher B
2017-09-01
The mechanism by which various classes of medication reduce COPD exacerbation risk remains unknown. We hypothesized a correlation between reduced exacerbation risk and improvement in airway patency as measured according to FEV 1 . By systematic review, COPD trials were identified that reported therapeutic changes in predose FEV 1 (dFEV 1 ) and occurrence of moderate to severe exacerbations. Using meta-regression analysis, a model was generated with dFEV 1 as the moderator variable and the absolute difference in exacerbation rate (RD), ratio of exacerbation rates (RRs), or hazard ratio (HR) as dependent variables. The analysis of RD and RR included 119,227 patients, and the HR analysis included 73,475 patients. For every 100-mL change in predose FEV 1 , the HR decreased by 21% (95% CI, 17-26; P < .001; R 2 = 0.85) and the absolute exacerbation rate decreased by 0.06 per patient per year (95% CI, 0.02-0.11; P = .009; R 2 = 0.05), which corresponded to an RR of 0.86 (95% CI, 0.81-0.91; P < .001; R 2 = 0.20). The relationship with exacerbation risk remained statistically significant across multiple subgroup analyses. A significant correlation between increased FEV 1 and lower COPD exacerbation risk suggests that airway patency is an important mechanism responsible for this effect. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K
2017-01-01
The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.
Smith, Timothy D.; Steffen, Christopher J., Jr.; Yungster, Shaye; Keller, Dennis J.
1998-01-01
The all rocket mode of operation is shown to be a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. An axisymmetric RBCC engine was used to determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and multiple linear regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inlet diameter ratio. A perfect gas computational fluid dynamics analysis, using both the Spalart-Allmaras and k-omega turbulence models, was performed with the NPARC code to obtain values of vacuum specific impulse. Results from the multiple linear regression analysis showed that for both the full flow and gas generator configurations increasing mixer-ejector area ratio and rocket area ratio increase performance, while increasing mixer-ejector inlet area ratio and mixer-ejector length-to-diameter ratio decrease performance. Increasing injected secondary flow increased performance for the gas generator analysis, but was not statistically significant for the full flow analysis. Chamber pressure was found to be not statistically significant.
Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data
Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.
2014-01-01
In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438
Regression analysis understanding and building business and economic models using Excel
Wilson, J Holton
2012-01-01
The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe
Hulsteijn, Leonie T. van; Corssmit, Eleonora P.M.; Coremans, Ida E.M.; Smit, Johannes W.A.; Jansen, Jeroen C.; Dekkers, Olaf M.
2013-01-01
The primary treatment goal of radiotherapy for paragangliomas of the head and neck region (HNPGLs) is local control of the tumor, i.e. stabilization of tumor volume. Interestingly, regression of tumor volume has also been reported. Up to the present, no meta-analysis has been performed giving an overview of regression rates after radiotherapy in HNPGLs. The main objective was to perform a systematic review and meta-analysis to assess regression of tumor volume in HNPGL-patients after radiotherapy. A second outcome was local tumor control. Design of the study is systematic review and meta-analysis. PubMed, EMBASE, Web of Science, COCHRANE and Academic Search Premier and references of key articles were searched in March 2012 to identify potentially relevant studies. Considering the indolent course of HNPGLs, only studies with ⩾12 months follow-up were eligible. Main outcomes were the pooled proportions of regression and local control after radiotherapy as initial, combined (i.e. directly post-operatively or post-embolization) or salvage treatment (i.e. after initial treatment has failed) for HNPGLs. A meta-analysis was performed with an exact likelihood approach using a logistic regression with a random effect at the study level. Pooled proportions with 95% confidence intervals (CI) were reported. Fifteen studies were included, concerning a total of 283 jugulotympanic HNPGLs in 276 patients. Pooled regression proportions for initial, combined and salvage treatment were respectively 21%, 33% and 52% in radiosurgery studies and 4%, 0% and 64% in external beam radiotherapy studies. Pooled local control proportions for radiotherapy as initial, combined and salvage treatment ranged from 79% to 100%. Radiotherapy for jugulotympanic paragangliomas results in excellent local tumor control and therefore is a valuable treatment for these types of tumors. The effects of radiotherapy on regression of tumor volume remain ambiguous, although the data suggest that regression can
Kinnebrock, Silja; Podolskij, Mark
This paper introduces a new estimator to measure the ex-post covariation between high-frequency financial time series under market microstructure noise. We provide an asymptotic limit theory (including feasible central limit theorems) for standard methods such as regression, correlation analysis...... process can be relaxed and how our method can be applied to non-synchronous observations. We also present an empirical study of how high-frequency correlations, regressions and covariances change through time....
Han, Bing; Jing, Hongyuan; Liu, Jianping; Wu, Zhangzhong [PetroChina Pipeline RandD Center, Langfang, Hebei (China); Hao, Jianbin [School of Petroleum Engineering, Southwest Petroleum University, Chengdu, Sichuan (China)
2010-07-01
Landslides have a serious impact on the integrity of oil and gas pipelines in the tough terrain of Western China. This paper introduces a solving method of axial stress, which uses numerical simulation and regression analysis for the pipelines subjected to landslides. Numerical simulation is performed to analyze the change regularity of pipe stresses for the five vulnerability assessment indexes, which are: the distance between pipeline and landslide tail; the thickness of landslide; the inclination angle of landslide; the pipeline length passing through landslide; and the buried depth of pipeline. A pipeline passing through a certain landslide in southwest China was selected as an example to verify the feasibility and effectiveness of this method. This method has practical applicability, but it would need large numbers of examples to better verify its reliability and should be modified accordingly. Also, it only considers the case where the direction of the pipeline is perpendicular to the primary slip direction of the landslide.
Perinetti, G.
2016-04-01
Full Text Available Introduction: The identification of the onset of the pubertal growth spurt has major clinical implications when dealing with orthodontic treatment in growing subjects. Aim: Through multivariate methods, this study evaluated possible relationships between the gingival crevicular fluid (GCF alkaline phosphatase (ALP activity and pubertal growth spurt and dentition phase. Materials and methods: One hundred healthy growing subjects (62 females, 38 males; mean age, 11.5±2.4 years were enrolled into this doubleblind, prospective, cross-sectional-design study. Phases of skeletal maturation (pre - pubertal, pubertal, post - pubertal was assessed using the cervical vertebral maturation method. Samples of GCF for the ALP activity determination were collected at the mesial and distal sites of the mandibular central incisors. The phases of the dentition were recorded as intermediate mixed, late mixed, or permanent. A multinomial multiple logistic regression model was used to assess relationships of the enzymatic activity to growth phases and dentition phases. Results: The GCF ALP activity was greater in the pubertal growth phase as compared to the pre - pubertal and post - pubertal growth phases. Significant adjusted odds ratios for the GCF ALP activity for the pre - pubertal and post - pubertal subjects, in relation to the pubertal group, were 0.76 and 0.84, respectively. No significant correlations were seen for the dentition phase. Conclusions: The GCF ALP activity is a valid candidate as a non - invasive biomarker for the identification of the pubertal growth spurt irrespective of the dentition phase.
Linard, Joshua I.
2013-01-01
Mitigating the effects of salt and selenium on water quality in the Grand Valley and lower Gunnison River Basin in western Colorado is a major concern for land managers. Previous modeling indicated means to improve the models by including more detailed geospatial data and a more rigorous method for developing the models. After evaluating all possible combinations of geospatial variables, four multiple linear regression models resulted that could estimate irrigation-season salt yield, nonirrigation-season salt yield, irrigation-season selenium yield, and nonirrigation-season selenium yield. The adjusted r-squared and the residual standard error (in units of log-transformed yield) of the models were, respectively, 0.87 and 2.03 for the irrigation-season salt model, 0.90 and 1.25 for the nonirrigation-season salt model, 0.85 and 2.94 for the irrigation-season selenium model, and 0.93 and 1.75 for the nonirrigation-season selenium model. The four models were used to estimate yields and loads from contributing areas corresponding to 12-digit hydrologic unit codes in the lower Gunnison River Basin study area. Each of the 175 contributing areas was ranked according to its estimated mean seasonal yield of salt and selenium.
Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina
2009-01-01
Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R 2 were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R 2 confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.
Ennouri, Karim; Ben Ayed, Rayda; Triki, Mohamed Ali; Ottaviani, Ennio; Mazzarello, Maura; Hertelli, Fathi; Zouari, Nabil
2017-07-01
The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R 2 ) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort.
Yuehjen E. Shao
2013-01-01
Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.
Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming
2017-10-01
Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.
Fereshteh Shiri
2010-08-01
Full Text Available In the present work, support vector machines (SVMs and multiple linear regression (MLR techniques were used for quantitative structure–property relationship (QSPR studies of retention time (tR in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLRand SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD. The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r2 and q2 are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William’s plot. The effects of different descriptors on the retention times are described.
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis.
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-06-01
Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (-1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and body weight
Regression analysis of growth responses to water depth in three wetland plant species
Sorrell, Brian K; Tanner, Chris C; Brix, Hans
2012-01-01
depths from 0 – 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth...
Meng, Yilin; Roux, Benoît
2015-08-11
The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.
Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H
2011-03-20
Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the
Regression Analysis of Top of Descent Location for Idle-thrust Descents
Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg
2013-01-01
In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.
Treating experimental data of inverse kinetic method by unitary linear regression analysis
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)
Regression analysis of informative current status data with the additive hazards model.
Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo
2015-04-01
This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.
Anderson, Carl A; McRae, Allan F; Visscher, Peter M
2006-07-01
Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Analysis of Functional Data with Focus on Multinomial Regression and Multilevel Data
Mousavi, Seyed Nourollah
Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects of application...... and methodological development. Our main Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects...
The use of cognitive ability measures as explanatory variables in regression analysis.
Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J
2012-12-01
Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.
Barndorff-Nielsen, Ole Eiler; Shephard, N.
2004-01-01
This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing...... the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities....
Multiplicative calculus in biomedical image analysis
Florack, L.M.J.; Assen, van H.C.
2011-01-01
We advocate the use of an alternative calculus in biomedical image analysis, known as multiplicative (a.k.a. non-Newtonian) calculus. It provides a natural framework in problems in which positive images or positive definite matrix fields and positivity preserving operators are of interest. Indeed,
Using Correspondence Analysis in Multiple Case Studies
Kienstra, Natascha; van der Heijden, Peter G.M.
2015-01-01
In qualitative research of multiple case studies, Miles and Huberman proposed to summarize the separate cases in a so-called meta-matrix that consists of cases by variables. Yin discusses cross-case synthesis to study this matrix. We propose correspondence analysis (CA) as a useful tool to study
Using correspondence analysis in multiple case studies
Kienstra, N.H.H.; van der Heijden, P.G.M.
2015-01-01
In qualitative research of multiple case studies, Miles and Huberman proposed to summarize the separate cases in a so-called meta-matrix that consists of cases by variables. Yin discusses cross-case synthesis to study this matrix. We propose correspondence analysis (CA) as a useful tool to study
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
Lunøe, Kristoffer; Martínez-Sierra, Justo Giner; Gammelgaard, Bente; Alonso, J Ignacio García
2012-03-01
The analytical methodology for the in vivo study of selenium metabolism using two enriched selenium isotopes has been modified, allowing for the internal correction of spectral interferences and mass bias both for total selenium and speciation analysis. The method is based on the combination of an already described dual-isotope procedure with a new data treatment strategy based on multiple linear regression. A metabolic enriched isotope ((77)Se) is given orally to the test subject and a second isotope ((74)Se) is employed for quantification. In our approach, all possible polyatomic interferences occurring in the measurement of the isotope composition of selenium by collision cell quadrupole ICP-MS are taken into account and their relative contribution calculated by multiple linear regression after minimisation of the residuals. As a result, all spectral interferences and mass bias are corrected internally allowing the fast and independent quantification of natural abundance selenium ((nat)Se) and enriched (77)Se. In this sense, the calculation of the tracer/tracee ratio in each sample is straightforward. The method has been applied to study the time-related tissue incorporation of (77)Se in male Wistar rats while maintaining the (nat)Se steady-state conditions. Additionally, metabolically relevant information such as selenoprotein synthesis and selenium elimination in urine could be studied using the proposed methodology. In this case, serum proteins were separated by affinity chromatography while reverse phase was employed for urine metabolites. In both cases, (74)Se was used as a post-column isotope dilution spike. The application of multiple linear regression to the whole chromatogram allowed us to calculate the contribution of bromine hydride, selenium hydride, argon polyatomics and mass bias on the observed selenium isotope patterns. By minimising the square sum of residuals for the whole chromatogram, internal correction of spectral interferences and mass
Liu, Chenxi; Zhang, Xinping; Wan, Jie
2015-08-01
Inappropriate use and overuse of antibiotics and injections are serious threats to the global population, particularly in developing countries. In recent decades, public reporting of health care performance (PRHCP) has been an instrument to improve the quality of care. However, existing evidence shows a mixed effect of PRHCP. This study evaluated the effect of PRHCP on physicians' prescribing practices in a sample of primary care institutions in China. Segmented regression analysis was used to produce convincing evidence for health policy and reform. The PRHCP intervention was implemented in Qian City that started on 1 October 2013. Performance data on prescription statistics were disclosed to patients and health workers monthly in 10 primary care institutions. A total of 326 655 valid outpatient prescriptions were collected. Monthly effective prescriptions were calculated as analytical units in the research (1st to 31st every month). This study involved multiple assessments of outcomes 13 months before and 11 months after PRHCP intervention (a total of 24 data points). Segmented regression models showed downward trends from baseline on antibiotics (coefficient = -0.64, P = 0.004), combined use of antibiotics (coefficient = -0.41, P < 0.001) and injections (coefficient = -0.5957, P = 0.001) after PRHCP intervention. The average expenditure of patients slightly increased monthly before the intervention (coefficient = 0.8643, P < 0.001); PRHCP intervention also led to a temporary increase in average expenditure of patients (coefficient = 2.20, P = 0.307) but slowed down the ascending trend (coefficient = -0.45, P = 0.033). The prescription rate of antibiotics and injections after intervention (about 50%) remained high. PRHCP showed positive effects on physicians' prescribing behaviour, considering the downward trends on the use of antibiotics and injections and average expenditure through the intervention. However, the effect
Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo
2018-05-10
Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.
DeForest, David K; Brix, Kevin V; Tear, Lucinda M; Adams, William J
2018-01-01
The bioavailability of aluminum (Al) to freshwater aquatic organisms varies as a function of several water chemistry parameters, including pH, dissolved organic carbon (DOC), and water hardness. We evaluated the ability of multiple linear regression (MLR) models to predict chronic Al toxicity to a green alga (Pseudokirchneriella subcapitata), a cladoceran (Ceriodaphnia dubia), and a fish (Pimephales promelas) as a function of varying DOC, pH, and hardness conditions. The MLR models predicted toxicity values that were within a factor of 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [EC10s and EC20s]), 91% of the cases for C. dubia (EC10s and EC20s), and 95% (EC10s) and 91% (EC20s) of the cases for P. promelas. The MLR models were then applied to all species with Al toxicity data to derive species and genus sensitivity distributions that could be adjusted as a function of varying DOC, pH, and hardness conditions (the P. subcapitata model was applied to algae and macrophytes, the C. dubia model was applied to invertebrates, and the P. promelas model was applied to fish). Hazardous concentrations to 5% of the species or genera were then derived in 2 ways: 1) fitting a log-normal distribution to species-mean EC10s for all species (following the European Union methodology), and 2) fitting a triangular distribution to genus-mean EC20s for animals only (following the US Environmental Protection Agency methodology). Overall, MLR-based models provide a viable approach for deriving Al water quality guidelines that vary as a function of DOC, pH, and hardness conditions and are a significant improvement over bioavailability corrections based on single parameters. Environ Toxicol Chem 2018;37:80-90. © 2017 SETAC. © 2017 SETAC.
Chen, Qingxia; Ibrahim, Joseph G
2014-07-01
Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.
Muñoz, Neus; Sánchez-Delgado, Jordi; Baylina, Mireia; Puig, Ignasi; López-Góngora, Sheila; Suarez, David; Calvet, Xavier
2018-06-01
Multiple Helicobacter pylori second-line schedules have been described as potentially useful. It remains unclear, however, which are the best combinations, and which features of second-line treatments are related to better cure rates. The aim of this study was to determine that second-line treatments achieved excellent (>90%) cure rates by performing a systematic review and when possible a meta-analysis. A meta-regression was planned to determine the characteristics of treatments achieving excellent cure rates. A systematic review for studies evaluating second-line Helicobacter pylori treatment was carried out in multiple databases. A formal meta-analysis was performed when an adequate number of comparative studies was found, using RevMan5.3. A meta-regression for evaluating factors predicting cure rates >90% was performed using Stata Statistical Software. The systematic review identified 115 eligible studies, including 203 evaluable treatment arms. The results were extremely heterogeneous, with 61 treatment arms (30%) achieving optimal (>90%) cure rates. The meta-analysis favored quadruple therapies over triple (83.2% vs 76.1%, OR: 0.59:0.38-0.93; P = .02) and 14-day quadruple treatments over 7-day treatments (91.2% vs 81.5%, OR; 95% CI: 0.42:0.24-0.73; P = .002), although the differences were significant only in the per-protocol analysis. The meta-regression did not find any particular characteristics of the studies to be associated with excellent cure rates. Second-line Helicobacter pylori treatments achieving>90% cure rates are extremely heterogeneous. Quadruple therapy and 14-day treatments seem better than triple therapies and 7-day ones. No single characteristic of the treatments was related to excellent cure rates. Future approaches suitable for infectious diseases-thus considering antibiotic resistances-are needed to design rescue treatments that consistently achieve excellent cure rates. © 2018 John Wiley & Sons Ltd.
Fang, Xiande; Xu, Yu
2011-01-01
The empirical model of turbine efficiency is necessary for the control- and/or diagnosis-oriented simulation and useful for the simulation and analysis of dynamic performances of the turbine equipment and systems, such as air cycle refrigeration systems, power plants, turbine engines, and turbochargers. Existing empirical models of turbine efficiency are insufficient because there is no suitable form available for air cycle refrigeration turbines. This work performs a critical review of empirical models (called mean value models in some literature) of turbine efficiency and develops an empirical model in the desired form for air cycle refrigeration, the dominant cooling approach in aircraft environmental control systems. The Taylor series and regression analysis are used to build the model, with the Taylor series being used to expand functions with the polytropic exponent and the regression analysis to finalize the model. The measured data of a turbocharger turbine and two air cycle refrigeration turbines are used for the regression analysis. The proposed model is compact and able to present the turbine efficiency map. Its predictions agree with the measured data very well, with the corrected coefficient of determination R c 2 ≥ 0.96 and the mean absolute percentage deviation = 1.19% for the three turbines. -- Highlights: → Performed a critical review of empirical models of turbine efficiency. → Developed an empirical model in the desired form for air cycle refrigeration, using the Taylor expansion and regression analysis. → Verified the method for developing the empirical model. → Verified the model.
Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A
2014-01-01
Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).
Feldens, Carlos Alberto; Kramer, Paulo Floriani; Ferreira, Simone Helena; Spiguel, Mônica Hermann; Marquezan, Marcela
2010-04-01
This cross-sectional study aimed to investigate the factors associated with dental trauma in preschool children using Poisson regression analysis with robust variance. The study population comprised 888 children aged 3- to 5-year-old attending public nurseries in Canoas, southern Brazil. Questionnaires assessing information related to the independent variables (age, gender, race, mother's educational level and family income) were completed by the parents. Clinical examinations were carried out by five trained examiners in order to assess traumatic dental injuries (TDI) according to Andreasen's classification. One of the five examiners was calibrated to assess orthodontic characteristics (open bite and overjet). Multivariable Poisson regression analysis with robust variance was used to determine the factors associated with dental trauma as well as the strengths of association. Traditional logistic regression was also performed in order to compare the estimates obtained by both methods of statistical analysis. 36.4% (323/888) of the children suffered dental trauma and there was no difference in prevalence rates from 3 to 5 years of age. Poisson regression analysis showed that the probability of the outcome was almost 30% higher for children whose mothers had more than 8 years of education (Prevalence Ratio = 1.28; 95% CI = 1.03-1.60) and 63% higher for children with an overjet greater than 2 mm (Prevalence Ratio = 1.63; 95% CI = 1.31-2.03). Odds ratios clearly overestimated the size of the effect when compared with prevalence ratios. These findings indicate the need for preventive orientation regarding TDI, in order to educate parents and caregivers about supervising infants, particularly those with increased overjet and whose mothers have a higher level of education. Poisson regression with robust variance represents a better alternative than logistic regression to estimate the risk of dental trauma in preschool children.
Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo
2016-11-01
The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.
No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
Maarten van Smeden
2016-11-01
Full Text Available Abstract Background Ten events per variable (EPV is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’. We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.
Quantile regression for the statistical analysis of immunological data with many non-detects.
Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth
2012-07-07
Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.
No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.
van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B
2016-11-24
Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.
Meta-regression analysis of commensal and pathogenic Escherichia coli survival in soil and water.
Franz, Eelco; Schijven, Jack; de Roda Husman, Ana Maria; Blaak, Hetty
2014-06-17
The extent to which pathogenic and commensal E. coli (respectively PEC and CEC) can survive, and which factors predominantly determine the rate of decline, are crucial issues from a public health point of view. The goal of this study was to provide a quantitative summary of the variability in E. coli survival in soil and water over a broad range of individual studies and to identify the most important sources of variability. To that end, a meta-regression analysis on available literature data was conducted. The considerable variation in reported decline rates indicated that the persistence of E. coli is not easily predictable. The meta-analysis demonstrated that for soil and water, the type of experiment (laboratory or field), the matrix subtype (type of water and soil), and temperature were the main factors included in the regression analysis. A higher average decline rate in soil of PEC compared with CEC was observed. The regression models explained at best 57% of the variation in decline rate in soil and 41% of the variation in decline rate in water. This indicates that additional factors, not included in the current meta-regression analysis, are of importance but rarely reported. More complete reporting of experimental conditions may allow future inference on the global effects of these variables on the decline rate of E. coli.
What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis
Thomas, Emily H.; Galambos, Nora
2004-01-01
To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…
A systematic review and meta-regression analysis of mivacurium for tracheal intubation
Vanlinthout, L.E.H.; Mesfin, S.H.; Hens, N.; Vanacker, B.F.; Robertson, E.N.; Booij, L.H.D.J.
2014-01-01
We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent
Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis
Jarrell, Stephen B.; Stanley, T. D.
2004-01-01
The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.
Kovalska, M P; Bürki, E; Schoetzau, A; Orguel, S F; Orguel, S; Grieshaber, M C
2011-04-01
The distinction of real progression from test variability in visual field (VF) series may be based on clinical judgment, on trend analysis based on follow-up of test parameters over time, or on identification of a significant change related to the mean of baseline exams (event analysis). The aim of this study was to compare a new population-based method (Octopus field analysis, OFA) with classic regression analyses and clinical judgment for detecting glaucomatous VF changes. 240 VF series of 240 patients with at least 9 consecutive examinations available were included into this study. They were independently classified by two experienced investigators. The results of such a classification served as a reference for comparison for the following statistical tests: (a) t-test global, (b) r-test global, (c) regression analysis of 10 VF clusters and (d) point-wise linear regression analysis. 32.5 % of the VF series were classified as progressive by the investigators. The sensitivity and specificity were 89.7 % and 92.0 % for r-test, and 73.1 % and 93.8 % for the t-test, respectively. In the point-wise linear regression analysis, the specificity was comparable (89.5 % versus 92 %), but the sensitivity was clearly lower than in the r-test (22.4 % versus 89.7 %) at a significance level of p = 0.01. A regression analysis for the 10 VF clusters showed a markedly higher sensitivity for the r-test (37.7 %) than the t-test (14.1 %) at a similar specificity (88.3 % versus 93.8 %) for a significant trend (p = 0.005). In regard to the cluster distribution, the paracentral clusters and the superior nasal hemifield progressed most frequently. The population-based regression analysis seems to be superior to the trend analysis in detecting VF progression in glaucoma, and may eliminate the drawbacks of the event analysis. Further, it may assist the clinician in the evaluation of VF series and may allow better visualization of the correlation between function and structure owing to VF
Ulbrich, N.; Volden, T.
2018-01-01
Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods.
Dai, Huanping; Micheyl, Christophe
2012-11-01
Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.
Anderson, Carl A.; McRae, Allan F.; Visscher, Peter M.
2006-01-01
Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using...
Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis
Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae
The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.
Geroukis, Asterios; Brorson, Erik
2014-01-01
In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio – using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Analysis of designed experiments by stabilised PLS Regression and jack-knifing
Martens, Harald; Høy, M.; Westad, F.
2001-01-01
Pragmatical, visually oriented methods for assessing and optimising bi-linear regression models are described, and applied to PLS Regression (PLSR) analysis of multi-response data from controlled experiments. The paper outlines some ways to stabilise the PLSR method to extend its range...... the reliability of the linear and bi-linear model parameter estimates. The paper illustrates how the obtained PLSR "significance" probabilities are similar to those from conventional factorial ANOVA, but the PLSR is shown to give important additional overview plots of the main relevant structures in the multi....... An Introduction, Wiley, Chichester, UK, 2001]....