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Using multiple regression, neural networks and supprot vector machines to predict lamb carcasses composition

Silva, Filipe; Cortez, Paulo; Cadavez, Vasco
Fonte: EUROSIS-ETI. Escola Superior Agrária de Bragança, CIMO Publicador: EUROSIS-ETI. Escola Superior Agrária de Bragança, CIMO
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
65.99%
The objective of this work was to use a Data Mining (DM) approach to predict, using as predictors the carcass measurements taken at slaughter line, the composition of lamb carcasses. One hundred and twenty ve lambs of Churra Galega Bragan cana breed were slaughtered. During carcasses quartering, a caliper was used to perform subcutaneous fat measurements, over the maximum depth of longissimus muscle (LM), between the 12th and 13th ribs (C12), and between the 1st and 2nd lumbar vertebrae (C1). The Muscle (MP), Bone (BP), Subcutaneous Fat (SFP), Inter-Muscular Fat (IFP), and Kidney Knob and Channel Fat (KKCF) proportions of lamb carcasses were computed. We used the rminer R library and compared three regression techniques: Multiple Regression (MR), Neural Networks (NN) and Support Vector Machines (SVM). The SVM model provided the lowest relative absolute error for the prediction of BP, SFP and KKCF, while MR presented the best predictions for MP and IFP. Also, a sensitivity analysis procedure revealed the C12 measurement as the most relevant predictor for all ve carcass tissues.

THE MONTHLY RAINFALL IN THE RIO DE JANEIRO STATE, BRAZIL: SEASONALITY AND TREND

ARAUJO, Mirian Fernandes Carvalho; GUIMARAES, Ednaldo Carvalho; CARVALHO, Daniel Fonseca de; ARAUJO, Lucio Borges de
Fonte: UNIV FEDERAL UBERLANDIA Publicador: UNIV FEDERAL UBERLANDIA
Tipo: Artigo de Revista Científica
POR
Relevância na Pesquisa
46.05%
The objective of this work was to carry a descriptive analysis in the monthly precipitation of rainfall stations from Rio de Janeiro State, Brazil, using data of position and dispersion and graphical analyses, and to verify the presence of seasonality and trend in these data, with a study about the application of models of time series. The descriptive statistics was to characterize the general behavior of the series in three stations selected which present consistent historical series. The methodology of analysis of variance in randomized blocks and the determination of models of multiple linear regression, considering years and months as predictors variables, disclosed the presence of seasonality, what allowed to infer on the occurrence of repetitive natural phenomena throughout the time and absence of trend in the data. It was applied the methodology of multiple linear regression to removal the seasonality of these time series. The original data had been deducted from the estimates made by the adjusted model and the analysis of variance in randomized blocks for the residues of regression was preceded again. With the results obtained it was possible to conclude that the monthly rainfall present seasonality and they don`t present trend...

O mercado imobiliário residencial da região metropolitana de São Paulo: uma aplicação de modelos de comercialização hedônica de regressão e correlação canônica ; The Real Estate Market of Metropolitan Region of Sao Paulo: an hedonic models application of multiple regression and canonical correlation

Favero, Luiz Paulo Lopes
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 29/09/2005 PT
Relevância na Pesquisa
65.95%
Este trabalho destina-se a realizar um estudo sobre o mercado imobiliário de lançamentos residenciais da Região Metropolitana de São Paulo, tendo como base a utilização de modelos de comercialização hedônica. Para tanto, utiliza-se da Teoria dos Atributos proposta por Lancaster e dos modelos hedônicos e de equilíbrio de sub-mercados propostos por Rosen e Palmquist, a partir dos quais é possível analisar a importância relativa de “pacotes” de atributos, em função dos diferentes perfis sócio-demográficos determinados previamente por meio de análise fatorial elaborada com um grupo de 11 variáveis sócio-demográficas de cada Município da Região Metropolitana e de cada distrito do Município de São Paulo. Por meio de um levantamento realizado com especialistas, com compradores de imóveis residenciais e por meio de anúncios específicos, definiram-se as variáveis hedônicas explicativas e dependentes a serem incluídas nos modelos de regressão múltipla de Box-Cox e de correlação canônica, sob a ótica da demanda e da oferta, para cada perfil sócio-demográfico definido. O método proposto permite a determinação e a avaliação dos “pacotes” representativos de atributos para a composição das condições comerciais dos imóveis residenciais em lançamento na Região Metropolitana de São Paulo...

Inferência estatística para regressão múltipla h-splines; Statistical inference for h-splines multiple regression

Saulo Almeida Morellato
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 14/04/2014 PT
Relevância na Pesquisa
45.99%
Este trabalho aborda dois problemas de inferência relacionados à regressão múltipla não paramétrica: a estimação em modelos aditivos usando um método não paramétrico e o teste de hipóteses para igualdade de curvas ajustadas a partir do modelo. Na etapa de estimação é construída uma generalização dos métodos h-splines, tanto no contexto sequencial adaptativo proposto por Dias (1999), quanto no contexto bayesiano proposto por Dias e Gamerman (2002). Os métodos h-splines fornecem uma escolha automática do número de bases utilizada na estimação do modelo. Estudos de simulação mostram que os resultados obtidos pelos métodos de estimação propostos são superiores aos conseguidos nos pacotes gamlss, mgcv e DPpackage em R. São criados dois testes de hipóteses para testar H0 : f = f0. Um teste de hipóteses que tem sua regra de decisão baseada na distância quadrática integrada entre duas curvas, referente à abordagem sequencial adaptativa, e outro baseado na medida de evidência bayesiana proposta por Pereira e Stern (1999). No teste de hipóteses bayesiano o desempenho da medida de evidência é observado em vários cenários de simulação. A medida proposta apresentou um comportamento que condiz com uma medida de evidência favorável à hipótese H0. No teste baseado na distância entre curvas...

The use of wavelength-dispersive x-ray fluorescence (WDXRF) spectroscopy and multivariate techniques for the assessment of illegal dyes in spices

Adusei, Emmanuel
Fonte: Instituto Superior de Ciências da Saúde Egas Moniz Publicador: Instituto Superior de Ciências da Saúde Egas Moniz
Tipo: Dissertação de Mestrado
Publicado em /07/2014 ENG
Relevância na Pesquisa
46.04%
Dissertação de mestrado Erasmus Mundus para obtenção do grau de mestre em Técnicas Laboratoriais Forenses; Sudan dyes are synthetic azo and diazo compounds that are banned for use in food worldwide including the European Community due to their potential toxicity to humans. The ability of WDXRF spectroscopic technique to predict the levels of adulteration of paprika and sweet pepper suspected to be adulterated with Sudan I-IV, Para Red and Sunset Yellow FCF dyes was investigated in this study. Logistic regression and discriminant analysis classification models were developed to predict the type of adulteration using WDXRF spectral features such as the Compton and Rayleigh scatter intensities and the Compton and Rayleigh ratios. Prediction of the levels of adulteration was assessed by using multiple regression analysis. 83% of the 210 adulterated samples were correctly classified by the logistic regression with 90% sensitivity, 75%specificity with a prediction power of 92% into respective adulteration groups. 86% and 90% correct prediction were obtained for discriminant analysis models with 94% sensitivity and 74% specificity. Three multiple regression models were performed for each data set. Models based on the Compton and Rayleigh ratios...

Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data

Guimarães,Ricardo JPS; Freitas,Corina C; Dutra,Luciano V; Moura,Ana CM; Amaral,Ronaldo S; Drummond,Sandra C; Guerra,Márcio; Scholte,Ronaldo GC; Freitas,Charles R; Carvalho,Omar S
Fonte: Instituto Oswaldo Cruz, Ministério da Saúde Publicador: Instituto Oswaldo Cruz, Ministério da Saúde
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/10/2006 EN
Relevância na Pesquisa
45.99%
The aim of this work is to establish a relationship between schistosomiasis prevalence and social-environmental variables, in the state of Minas Gerais, Brazil, through multiple linear regression. The final regression model was established, after a variables selection phase, with a set of spatial variables which contains the summer minimum temperature, human development index, and vegetation type variables. Based on this model, a schistosomiasis risk map was built for Minas Gerais.

Modelling air temperature for the state of São Paulo, Brazil

Rodríguez-Lado,Luis; Sparovek,Gerd; Vidal-Torrado,Pablo; Dourado-Neto,Durval; Macías-Vázquez,Felipe
Fonte: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz" Publicador: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/10/2007 EN
Relevância na Pesquisa
45.99%
Spatial modelling of air temperature (maximum, mean and minimum) of the State of São Paulo (Brazil) was calculated by multiple regression analysis and ordinary kriging. Climatic data (mean values of five or more years) were obtained from 256 meteorological stations distributed uniformly over the State. The correlation between the climatic dependent variables, with latitude and altitude as independent variables was significant and could explain most of the spatial variability. The coefficients of determination (P < 0.05) varied in the range of 0.924 and 0.953, showing that multiple regression analysis is an accurate method for the modelling of air temperature for the State of São Paulo. Finally, these regression equations were used together with the kriged maps of the residual errors to build 15 digital maps of air temperature using a 0.5 km² Digital Elevation Model in a Geographic Information System.

Heterogeneous genetic (co)variances in simulated closed herds under selection

Lino-Lourenço,Daniela Andressa; Oliveira,Carlos Antonio Lopes de; Martins,Elias Nunes; Leite,Meiby Carneiro de Paula; Maia,Fabiana Martins Costa; Santos,Alexandra Inês dos
Fonte: Editora da Universidade Estadual de Maringá - EDUEM Publicador: Editora da Universidade Estadual de Maringá - EDUEM
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2012 EN
Relevância na Pesquisa
46.04%
Assuming that selection in closed herds can promote reduction in additive genetic variance, multiple regression models were used to estimate this change in additive genetic (co)variance component, over the years when the selection was done. Weights at 550 days (W550) were studied using simulated data of herds submitted to 20 years of selection. (Co)variance components were estimated assuming that the weight at 550 days was a new trait every five years, by multiple-trait analyses involving four traits in the animal model. Three multiple regression equations were fitted-RMI, RMM, RMF-estimating thus the additive genetic (co)variance components for the 20 years of selection and eight years prior to the selection process. The initial years of each generation of selection were used as a covariate in the RMI. In the RMM, intermediate years were used, and the final years were considered in the RMF. The equations showed high coefficients of determination. However, there was no difference in the adjustment between the models. It was observed that the multiple regression models can be used in the estimation of genetic (co)variance components, when heteroscedasticity is assumed over time due to the selection process.

Artificial neural network as an alternative to multiple regression analysis in optimizing formulation parmaeters of cytarabine liposomes

Subramanian, Narayanaswamy; Yajnik, Archit; Murthy, Rayasa S. Ramachandra
Fonte: Springer-Verlag Publicador: Springer-Verlag
Tipo: Artigo de Revista Científica
Publicado em 02/02/2004 EN
Relevância na Pesquisa
46.07%
The objective of the study was to optimize the formulation parameters of cytarabine liposomes by using artificial neural networks (ANN) and multiple regression analysis using 33 factorial design (FD). As model formulations, 27 formulations were prepared. The formulation variables, drug (cytarabine)/lipid (phosphatidyl choline [PC] and cholesterol [Chol]) molar ratio (X1, PC/Chol in percentage ratio of total lipids (X2), and the volume of hydration medium, (X3) were selected as the independent variables; and the percentage drug entrapment (PDE) was selected as the dependent variable. A set of causal factors was used as tutorial data for ANN and fed into a computer. The optimization was performed by minimizing the generalized distance between the predicted values of each response and the optimized one that was obtained individually. In case of 33 factorial design, a second-order full-model polynomial equation and a reduced model were established by subjecting the transformed values of independent variables to multiple regression analysis, and contour plots were drawn using the equation. The optimization methods developed by both ANN and FD were validated by preparing another 5 liposomal formulations. The predetermined PDE and the experimental data were compared with predicted data by pairedt test...

Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease

Zhang, Daoqiang; Shen, Dinggang;
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.11%
Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality...

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 14/03/2012 EN
Relevância na Pesquisa
46.02%
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.

Investigating the Quantitative Structure-Activity Relationships for Antibody Recognition of Two Immunoassays for Polycyclic Aromatic Hydrocarbons by Multiple Regression Methods

Zhang, Yan-Feng; Zhang, Li; Gao, Zhi-Xian; Dai, Shu-Gui
Fonte: Molecular Diversity Preservation International (MDPI) Publicador: Molecular Diversity Preservation International (MDPI)
Tipo: Artigo de Revista Científica
Publicado em 09/07/2012 EN
Relevância na Pesquisa
46.04%
Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants found in the environment. Immunoassays represent useful analytical methods to complement traditional analytical procedures for PAHs. Cross-reactivity (CR) is a very useful character to evaluate the extent of cross-reaction of a cross-reactant in immunoreactions and immunoassays. The quantitative relationships between the molecular properties and the CR of PAHs were established by stepwise multiple linear regression, principal component regression and partial least square regression, using the data of two commercial enzyme-linked immunosorbent assay (ELISA) kits. The objective is to find the most important molecular properties that affect the CR, and predict the CR by multiple regression methods. The results show that the physicochemical, electronic and topological properties of the PAH molecules have an integrated effect on the CR properties for the two ELISAs, among which molar solubility (Sm) and valence molecular connectivity index (3χv) are the most important factors. The obtained regression equations for RisC kit are all statistically significant (p < 0.005) and show satisfactory ability for predicting CR values, while equations for RaPID kit are all not significant (p > 0.05) and not suitable for predicting. It is probably because that the RisC immunoassay employs a monoclonal antibody...

Empirical Likelihood-Based Inference for Multiple Regression and Treatment Comparison

Su, Haiyan ; Liang, Hua
Fonte: Universidade de Rochester Publicador: Universidade de Rochester
Tipo: Tese de Doutorado
ENG
Relevância na Pesquisa
46.09%
Thesis (Ph.D.)--University of Rochester. School of Medicine and Dentistry. Dept. of Biostatistics and Computational Biology, 2009.; Parameter estimation and statistical inference are generally used in the analysis of epidemiological and biomedical data. Traditional parametric methods often im- pose the assumption of normality on the data. When this assumption is violated, methods based on the normal approximation can give biased results. Furthermore, normal approximation-based inference methods require estimation of the asymp- totic variance, which may be di±cult in semi-parametric or non-linear models. Empirical likelihood is a good alternative to make statistical inference for the parameters when the distribution of the data is unspeci¯ed. To derive statisti- cal inference for the parameter of interest, we develop empirical likelihood-based methods along with the Bartlett correction to improve the coverage probability of the parameter. The contributions we make to the existing literature in this dissertation contain two parts. In the ¯rst part, we develop an empirical likelihood-based inference for multiple regression models and show that the empirical likelihood ratio statistic follows a chi-square limiting distribution for several model settings. For high- dimensional parameter vectors...

Comparison of Some Improved Estimators for Linear Regression Model under Different Conditions

Shah, Smit
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Relevância na Pesquisa
46.03%
Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis of risk was made among ridge, Liu and LASSO estimators under orthonormal regression model. I found that LASSO dominates least squares, ridge and Liu estimators over a significant portion of the parameter space for large dimension. Secondly, a simulation study was conducted to compare performance of ridge, Liu and two parameter biased estimator by their mean squared error criterion. I found that two parameter biased estimator performs better than its corresponding ridge regression estimator. Overall, Liu estimator performs better than both ridge and two parameter biased estimator.

Die Analyse der Laufleistung als Beispiel für multifaktorielle Vorhersagerechnungen mit biometrischen Daten; Analysing running performance as an example for the prediction with biometric data determined by multiple factors

Kempter, Gisela Monika
Fonte: Universidade de Tubinga Publicador: Universidade de Tubinga
Tipo: Dissertação
DE_DE
Relevância na Pesquisa
46.04%
Leistungen des Menschen sowie die Pathogenese von Krankheiten werden in der überwiegenden Zahl multifaktoriell bestimmt. Die vielseitigen Einflüsse auf die Laufleistung gelten als größtenteils bekannt. In dem Versuch, modellhaft gängige Lehrmeinungen zu bestärken oder anzufechten, wurden die Akten von 320 Laufsportlern aus dem Freizeit- bis Hochleistungsbereich ausgewertet. Für jede Person lagen die persönlichen Bestzeiten auf einer oder mehrerer der Distanzen 1.500m, 5.000m, 10.000m, Halbmarathon und Marathon vor. Es wurden Messwerte von anthropometrischen Merkmalen, Herz, Lunge und IAS sowie orthopädische Befunde und Trainingsdaten genauer betrachtet. Die Auswertung erfolgte unter Zuhilfenahme der statistischen Methoden Häufigkeitsverteilung, Gruppierung, Korrelation, t-Test und multiple lineare sowie multiple schrittweise Regression. Wie erwartet wurde die IAS als wichtigster einflussnehmender Faktor für alle Distanzen ermittelt. Die weiteren einflussnehmende Faktoren unterschieden sich je nach Distanz. Die Ergebnisse von Alter und Anthropometrie ergaben für fast alle Distanzen einen Beginn des Leistungsabfalls vor Erreichen des 30. Lebensjahres. Für den 1.500-m-Lauf war ein Leistungsabfall ab dem Alter von 25 Jahren zu beobachten. Die Körpergröße zeigte kaum einen Zusammenhang. Der Anteil des Körperfettes war auf allen Distanzen signifikant...

Multiple regression models for lactation curves

Pereira, Marta S. P.; Oliveira, Teresa; Mexia, João Tiago
Fonte: Universidade Aberta de Portugal Publicador: Universidade Aberta de Portugal
Tipo: Artigo de Revista Científica
Publicado em //2007 ENG
Relevância na Pesquisa
55.9%
Several methods have been developed in order to study lactation curves. However, the lactation curves are often not well adjusted since several factors affect milk production. The usual model used to describe a lactation curve is Wood’s Model, which generally uses a logarithmic transformation of an incomplete gamma curve to obtain least squares estimates of three constants: a - a scaling factor associated with average daily yield; b - associated with prepeak curvature; and c associated with post-peak curvature (Wood, 1976). Some disadvantages of Wood’s model are strongly connected with the overestimation of milk production at the beginning of lactation, with underestimation of the lactation peak: the self correlated residuals and highly correlated parameter estimates (Scott et al,1996). Fleischmann’s Method is usually used to estimate total milk production. This method generally overestimates actual yields up to peak lactation as well as yield during the period following the last measurement, but underestimates yields for other periods (Norman et al, 1999). The total milk yield estimate according to this method, considers a constant daily milk production between two records and equal to the mean of these two records, which does not describe the true variation of milk secretion during lactation. The mentioned disadvantages led us to consider the milk curve concept as a graphical representation of milk production described by mathematical models. In our work we considered a new approach using polynomial regression...

Using multiple regression, neural networks and support vector machines to predict lamb carcasses composition

Silva, Filipe; Cortez, Paulo; Cadavez, Vasco
Fonte: EUROSIS Publicador: EUROSIS
Tipo: Conferência ou Objeto de Conferência
Publicado em //2010 ENG
Relevância na Pesquisa
55.94%
The objective of this work was to use a Data Mining (DM) approach to predict, using as predictors the car- cass measurements taken at slaughter line, the compo- sition of lamb carcasses. One hundred and twenty five lambs of Churra Galega Braganc ̧ana breed were slaugh- tered.During carcasses quartering, a caliper was used to perform subcutaneous fat measurements, over the max- imum depth of longissimus muscle (LM), between the 12th and 13th ribs (C12), and between the 1st and 2nd lumbar vertebrae (C1). The Muscle (MP), Bone (BP), Subcutaneous Fat (SFP), Inter-Muscular Fat (IFP), and Kidney Knob and Channel Fat (KKCF) proportions of lamb carcasses were computed. We used the rminer R library and compared three regression techniques: Mul- tiple Regression (MR), Neural Networks (NN) and Sup- port Vector Machines (SVM). The SVM model provided the lowest relative absolute error for the prediction of BP, SFP and KKCF, while MR presented the best pre- dictions for MP and IFP. Also, a sensitivity analysis procedure revealed the C12 measurement as the most relevant predictor for all five carcass tissues.

Adjusting data to body size: A comparison of methods as applied to quantitative trait loci analysis of musculoskeletal phenotypes

Lang, Dean H; Sharkey, Neil A; Lionikas, Arimantas; Mack, Holly; Larsson, Lars; Vogler, George P; Vandenbergh, David J; Blizard, David A; Stout, Joseph T; Stitt, Joseph P; McClearn, Gerald E
Fonte: American Society for Bone and Mineral Research Publicador: American Society for Bone and Mineral Research
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.07%
The aim of this study was to compare three methods of adjusting skeletal data for body size and examine their use in QTL analyses. It was found that dividing skeletal phenotypes by body mass index induced erroneous QTL results. The preferred method of body size adjustment was multiple regression. Introduction: Many skeletal studies have reported strong correlations between phenotypes for muscle, bone, and body size, and these correlations add to the difficulty in identifying genetic influence on skeletal traits that are not mediated through overall body size. Quantitative trait loci (QTL) identified for skeletal phenotypes often map to the same chromosome regions as QTLs for body size. The actions of a QTL identified as influencing BMD could therefore be mediated through the generalized actions of growth on body size or muscle mass. Materials and Methods: Three methods of adjusting skeletal phenotypes to body size were performed on morphologic, structural, and compositional measurements of the femur and tibia in 200-day-old C57BL/6J x DBA/2 (BXD) second generation (F2) mice (n = 400). A common method of removing the size effect has been through the use of ratios. This technique and two alternative techniques using simple and multiple regression were performed on muscle and skeletal data before QTL analyses...

Poder de detecção de "Quantitative Trait Loci", da análise de marcas simples e da regressão linear múltipla; Power of "Quantitative Trait Loci" detection, single market analisys and of the multiple linear regression

Silva, Heyder Diniz; Vencovsky, Roland
Fonte: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz Publicador: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; ; ; ; Formato: application/pdf
Publicado em 01/12/2002 POR
Relevância na Pesquisa
46%
O mapeamento de locos envolvidos no controle gênico de caracteres quantitativos, QTL's, difere dos demais tipos de experimentos conduzidos em genética, por tratar-se, basicamente, de um procedimento de testes múltiplos. Um problema decorrente deste tipo de análise refere-se ao nível de significância conjunto e, consequentemente ao poder da mesma. Em vistas disto avaliou-se, via simulação computacional de dados, o poder de detecção de QTL's da análise de marcas simples, utilizando os critérios da razão de falsas descobertas (FDR) e de Bonferroni para determinação nível de significância conjunto alfa* e da regressão linear múltipla, empregando o procedimento "stepwise" para seleção das marcas. O procedimento baseado em regressão múltipla foi mais poderoso em identificar as marcas associadas a QTL's, do que os procedimentos baseados em testes individuais, utilizando tanto o critério FDR, quanto o de Bonferroni para o controle do nível de significância conjunto. Mesmo nos casos em que esse procedimento apresentou poder ligeiramente inferior aos demais, mostrou a grande vantagem de selecionar apenas as marcas mais fortemente ligadas a QTL's, devendo, portanto, ser preferido para seleção das marcas a serem utilizadas como covariáveis no processo de mapeamento por intervalo múltiplo. Dentre os critérios FDR e de Bonferroni...

Assessment and forecasting of mechanical properties for the 30-year-old Pinus radiata D. Don BY means of the multiple regression system.; ESTIMACIÓN Y PREDICCIÓN DE LAS PROPIEDADES MECÁNICAS En Pinus radiata D. Don DE 30 AÑOS DE EDAD MEDIANTE REGRESIÓN MÚLTIPLE

Rozas, Carlos; Vargas Mc, Gilda; Anzaldo H, José
Fonte: FUPEF DO PARANÁ Publicador: FUPEF DO PARANÁ
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; Artigo Avaliado pelos Pares Formato: application/pdf
Publicado em 04/05/2007 POR
Relevância na Pesquisa
55.91%
This paper presents the results of FONDEF Project D9712006 developed at Bío-Bío University (Chile). The objetive of the study was to develop multiple regression models to estimate mechanical properties of Pinus radiata D. Don wood taking into account physical properpies, wood type (juvenile and adult wood) and different wood source (regions). The models developed allow to obtain good estimates of the mechanical propierties. Parallel hardness, perpendicular hardness, MOR, MOE and maximum stress in parallel compression presented the highest determining coefficients, being 0.82; 0.84; 0.80; 0.70 and 0.70, respectively.; Este estudio presenta los resultados del Proyecto FONDEF D97I2006 desarrollado en la Universidad del Bío-Bío (Chile). El objetivo del estudio fue desarrollar modelos de regresión múltiple para estimar las propiedades mecánicas de la madera de Pinus radiata D. Don, considerando las propiedades físicas de la madera, tipo de madera (juvenil y adulta) y diferentes zonas de crecimiento. Los modelos desarrollados permitieron obtener una buena estimativa de las propiedades mecánicas. La dureza paralela, dureza perpendicular, el MOR, el MOE y el esfuerzo máximo en compresión paralela...