Lillehammer, Marie; Odegård, Jørgen; Meuwissen, Theo H E
2009-03-19
The combination of a sire model and a random regression term describing genotype by environment interactions may lead to biased estimates of genetic variance components because of heterogeneous residual variance. In order to test different models, simulated data with genotype by environment interactions, and dairy cattle data assumed to contain such interactions, were analyzed. Two animal models were compared to four sire models. Models differed in their ability to handle heterogeneous variance from different sources. Including an individual effect with a (co)variance matrix restricted to three times the sire (co)variance matrix permitted the modeling of the additive genetic variance not covered by the sire effect. This made the ability of sire models to handle heterogeneous genetic variance approximately equivalent to that of animal models. When residual variance was heterogeneous, a different approach to account for the heterogeneity of variance was needed, for example when using dairy cattle data in order to prevent overestimation of genetic heterogeneity of variance. Including environmental classes can be used to account for heterogeneous residual variance.
Buffalos milk yield analysis using random regression models
A.S. Schierholt
2010-02-01
Full Text Available Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed, daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genético de Bubalinos (PROMEBUL and from records of EMBRAPA Amazônia Oriental - EAO herd, located in Belém, Pará, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre’s polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre’s polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.
Genetic analysis of somatic cell score in Norwegian cattle using random regression test-day models.
Odegård, J; Jensen, J; Klemetsdal, G; Madsen, P; Heringstad, B
2003-12-01
The dataset used in this analysis contained a total of 341,736 test-day observations of somatic cell scores from 77,110 primiparous daughters of 1965 Norwegian Cattle sires. Initial analyses, using simple random regression models without genetic effects, indicated that use of homogeneous residual variance was appropriate. Further analyses were carried out by use of a repeatability model and 12 random regression sire models. Legendre polynomials of varying order were used to model both permanent environmental and sire effects, as did the Wilmink function, the Lidauer-Mäntysaari function, and the Ali-Schaeffer function. For all these models, heritability estimates were lowest at the beginning (0.05 to 0.07) and higher at the end (0.09 to 0.12) of lactation. Genetic correlations between somatic cell scores early and late in lactation were moderate to high (0.38 to 0.71), whereas genetic correlations for adjacent DIM were near unity. Models were compared based on likelihood ratio tests, Bayesian information criterion, Akaike information criterion, residual variance, and predictive ability. Based on prediction of randomly excluded observations, models with 4 coefficients for permanent environmental effect were preferred over simpler models. More highly parameterized models did not substantially increase predictive ability. Evaluation of the different model selection criteria indicated that a reduced order of fit for sire effects was desireable. Models with zeroth- or first-order of fit for sire effects and higher order of fit for permanent environmental effects probably underestimated sire variance. The chosen model had Legendre polynomials with 3 coefficients for sire, and 4 coefficients for permanent environmental effects. For this model, trajectories of sire variance and heritability were similar assuming either homogeneous or heterogeneous residual variance structure.
Interpreting parameters in the logistic regression model with random effects
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
SDE based regression for random PDEs
Bayer, Christian
2016-01-06
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Canaza-Cayo, Ali William; Lopes, Paulo Sávio; da Silva, Marcos Vinicius Gualberto Barbosa; de Almeida Torres, Robledo; Martins, Marta Fonseca; Arbex, Wagner Antonio; Cobuci, Jaime Araujo
2015-10-01
A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre's polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre's polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from -0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from -0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits.
Neither fixed nor random: weighted least squares meta-regression.
Stanley, T D; Doucouliagos, Hristos
2016-06-20
Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd.
Nonlinear wavelet estimation of regression function with random desigm
张双林; 郑忠国
1999-01-01
The nonlinear wavelet estimator of regression function with random design is constructed. The optimal uniform convergence rate of the estimator in a ball of Besov space Bp,q? is proved under quite genera] assumpations. The adaptive nonlinear wavelet estimator with near-optimal convergence rate in a wide range of smoothness function classes is also constructed. The properties of the nonlinear wavelet estimator given for random design regression and only with bounded third order moment of the error can be compared with those of nonlinear wavelet estimator given in literature for equal-spaced fixed design regression with i.i.d. Gauss error.
Modelling QTL effect on BTA06 using random regression test day models.
Suchocki, T; Szyda, J; Zhang, Q
2013-02-01
In statistical models, a quantitative trait locus (QTL) effect has been incorporated either as a fixed or as a random term, but, up to now, it has been mainly considered as a time-independent variable. However, for traits recorded repeatedly, it is very interesting to investigate the variation of QTL over time. The major goal of this study was to estimate the position and effect of QTL for milk, fat, protein yields and for somatic cell score based on test day records, while testing whether the effects are constant or variable throughout lactation. The analysed data consisted of 23 paternal half-sib families (716 daughters of 23 sires) of Chinese Holstein-Friesian cattle genotyped at 14 microsatellites located in the area of the casein loci on BTA6. A sequence of three models was used: (i) a lactation model, (ii) a random regression model with a QTL constant in time and (iii) a random regression model with a QTL variable in time. The results showed that, for each production trait, at least one significant QTL exists. For milk and protein yields, the QTL effect was variable in time, while for fat yield, each of the three models resulted in a significant QTL effect. When a QTL is incorporated into a model as a constant over time, its effect is averaged over lactation stages and may, thereby, be difficult or even impossible to be detected. Our results showed that, in such a situation, only a longitudinal model is able to identify loci significantly influencing trait variation.
Generalized and synthetic regression estimators for randomized branch sampling
David L. R. Affleck; Timothy G. Gregoire
2015-01-01
In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (...
Simultaneous confidence bands for Cox regression from semiparametric random censorship.
Mondal, Shoubhik; Subramanian, Sundarraman
2016-01-01
Cox regression is combined with semiparametric random censorship models to construct simultaneous confidence bands (SCBs) for subject-specific survival curves. Simulation results are presented to compare the performance of the proposed SCBs with the SCBs that are based only on standard Cox. The new SCBs provide correct empirical coverage and are more informative. The proposed SCBs are illustrated with two real examples. An extension to handle missing censoring indicators is also outlined.
Approximation by randomly weighting method in censored regression model
无
2009-01-01
Censored regression ("Tobit") models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the per-formance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.
Approximation by randomly weighting method in censored regression model
WANG ZhanFeng; WU YaoHua; ZHAO LinCheng
2009-01-01
Censored regression ("Tobit") models have been in common use,and their linear hypothesis testings have been widely studied.However,the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters.In this paper,we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic.It is shown that,under both the null and local alternative hypotheses,conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic.Therefore,the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters.At the same time,we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model.Simulation studies illustrate that the performance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.
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.
Genetic evaluation of European quails by random regression models
Flaviana Miranda Gonçalves
2012-09-01
Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.
Multivariate parametric random effect regression models for fecundability studies.
Ecochard, R; Clayton, D G
2000-12-01
Delay until conception is generally described by a mixture of geometric distributions. Weinberg and Gladen (1986, Biometrics 42, 547-560) proposed a regression generalization of the beta-geometric mixture model where covariates effects were expressed in terms of contrasts of marginal hazards. Scheike and Jensen (1997, Biometrics 53, 318-329) developed a frailty model for discrete event times data based on discrete-time analogues of Hougaard's results (1984, Biometrika 71, 75-83). This paper is on a generalization to a three-parameter family distribution and an extension to multivariate cases. The model allows the introduction of explanatory variables, including time-dependent variables at the subject-specific level, together with a choice from a flexible family of random effect distributions. This makes it possible, in the context of medically assisted conception, to include data sources with multiple pregnancies (or attempts at pregnancy) per couple.
Gaussian conditional random fields for regression in remote sensing
Radosavljevic, Vladan
In recent years many remote sensing instruments of various properties have been employed in an attempt to better characterize important geophysical phenomena. Satellite instruments provide an exceptional opportunity for global long-term observations of the land, the biosphere, the atmosphere, and the oceans. The collected data are used for estimation and better understanding of geophysical parameters such as land cover type, atmospheric properties, or ocean temperature. Achieving accurate estimations of such parameters is an important requirement for development of models able to predict global climate changes. One of the most challenging climate research problems is estimation of global composition, load, and variability of aerosols, small airborne particles that reflect and absorb incoming solar radiation. The existing algorithm for aerosol prediction from satellite observations is deterministic and manually tuned by domain scientist. In contrast to domain-driven method, we show that aerosol prediction is achievable by completely data-driven approaches. These statistical methods consist of learning of nonlinear regression models to predict aerosol load using the satellite observations as inputs. Measurements from unevenly distributed ground-based sites over the world are used as proxy to ground-truth outputs. Although statistical methods achieve better accuracy than deterministic method this setup is appropriate when data are independently and identically distributed (IID). The IID assumption is often violated in remote sensing where data exhibit temporal, spatial, or spatio-temporal dependencies. In such cases, the traditional supervised learning approaches could result in a model with degraded accuracy. Conditional random fields (CRF) are widely used for predicting output variables that have some internal structure. Most of the CRF research has been done on structured classification where the outputs are discrete. We propose a CRF model for continuous outputs
Random forest regression for magnetic resonance image synthesis.
Jog, Amod; Carass, Aaron; Roy, Snehashis; Pham, Dzung L; Prince, Jerry L
2017-01-01
By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.
Robust linear registration of CT images using random regression forests
Konukoglu, Ender; Criminisi, Antonio; Pathak, Sayan; Robertson, Duncan; White, Steve; Haynor, David; Siddiqui, Khan
2011-03-01
Global linear registration is a necessary first step for many different tasks in medical image analysis. Comparing longitudinal studies1, cross-modality fusion2, and many other applications depend heavily on the success of the automatic registration. The robustness and efficiency of this step is crucial as it affects all subsequent operations. Most common techniques cast the linear registration problem as the minimization of a global energy function based on the image intensities. Although these algorithms have proved useful, their robustness in fully automated scenarios is still an open question. In fact, the optimization step often gets caught in local minima yielding unsatisfactory results. Recent algorithms constrain the space of registration parameters by exploiting implicit or explicit organ segmentations, thus increasing robustness4,5. In this work we propose a novel robust algorithm for automatic global linear image registration. Our method uses random regression forests to estimate posterior probability distributions for the locations of anatomical structures - represented as axis aligned bounding boxes6. These posterior distributions are later integrated in a global linear registration algorithm. The biggest advantage of our algorithm is that it does not require pre-defined segmentations or regions. Yet it yields robust registration results. We compare the robustness of our algorithm with that of the state of the art Elastix toolbox7. Validation is performed via 1464 pair-wise registrations in a database of very diverse 3D CT images. We show that our method decreases the "failure" rate of the global linear registration from 12.5% (Elastix) to only 1.9%.
Genetic parameters for various random regression models to describe the weight data of pigs
Huisman, A.E.; Veerkamp, R.F.; Arendonk, van J.A.M.
2002-01-01
Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random
Genetic parameters for different random regression models to describe weight data of pigs
Huisman, A.E.; Veerkamp, R.F.; Arendonk, van J.A.M.
2001-01-01
Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random
The inclusion of herd-year-season by sire interaction in the ...
Three separate models were used in the DFREML analysis of the data. In the first ... by sire interaction was included, also as an additional random factor. Both these ..... Mixed model least squares and maximum likelihood computer program.
Random Decrement and Regression Analysis of Traffic Responses of Bridges
Asmussen, J. C.; Ibrahim, S. R.; Brincker, Rune
The topic of this paper is the estimation of modal parameters from ambient data by applying the Random Decrement technique. The data from the Queensborough Bridge over the Fraser River in Vancouver, Canada have been applied. The loads producing the dynamic response are ambient, e.g. wind, traffic...... and small ground motion. The Random Decrement technique is used to estimate the correlation function or the free decays from the ambient data. From these functions, the modal parameters are extracted using the Ibrahim Time Domain method. The possible influence of the traffic mass load on the bridge...... of the analysis using the Random Decrement technique are compared with results from an analysis based on fast Fourier transformations....
Random Decrement and Regression Analysis of Traffic Responses of Bridges
Asmussen, J. C.; Ibrahim, S. R.; Brincker, Rune
1996-01-01
The topic of this paper is the estimation of modal parameters from ambient data by applying the Random Decrement technique. The data fro the Queensborough Bridge over the Fraser River in Vancouver, Canada have been applied. The loads producing the dynamic response are ambient, e. g. wind, traffic...... and small ground motion. The random Decrement technique is used to estimate the correlation function or the free decays from the ambient data. From these functions, the modal parameters are extracted using the Ibrahim Time domain method. The possible influence of the traffic mass load on the bridge...... of the analysis using the Random decrement technique are compared with results from an analysis based on fast Fourier transformations....
Strathe, Anders B; Mark, Thomas; Nielsen, Bjarne; Do, Duy Ngoc; KADARMIDEEN, Haja N.; Jensen, Just
2014-01-01
Random regression models were used to estimate covariance functions between cumulated feed intake (CFI) and body weight (BW) in 8424 Danish Duroc pigs. Random regressions on second order Legendre polynomials of age were used to describe genetic and permanent environmental curves in BW and CFI. Based on covariance functions, residual feed intake (RFI) was defined and derived as the conditional genetic variance in feed intake given mid-test breeding value for BW and rate of gain. The heritabili...
Pegorer, Marcelo F; Vasconcelos, José L M; Trinca, Luzia A; Hansen, Peter J; Barros, Ciro M
2007-03-01
Heat stress has negative effects on pregnancy rates of lactating dairy cattle. There are genetic differences in tolerance to heat stress; Bos taurus indicus (B. t. indicus) cattle and embryos are more thermotolerant than Bos taurus taurus (B. t. taurus). In the present study, the effects of sire and sire breed on conception and embryonic/fetal loss rates of lactating Holstein cows during the Brazilian summer were determined. In Experiment 1, cows (n=302) were AI after estrus detection or at a fixed-time with semen from one Gyr (B. t. indicus) or one Holstein sire (B. t. taurus). Pregnancy was diagnosed 80 days after AI. In Experiment 2, cows (n=811) were AI with semen from three Gyr and two Holstein sires. Pregnancy was diagnosed at 30-40 and at 60-80 days after AI. Cows diagnosed pregnant at the first examination but non-pregnant at the second were considered as having lost their embryo or fetus. Data were analyzed by logistic regression. The model considered the effect of sire within breed, sire breed, days postpartum, period of lactation, and AI type (AI after estrus versus fixed-time). There was no effect of the AI type, days postpartum or milk production on conception or embryonic loss rates. The use of Gyr bulls increased pregnancy rate when compared to Holstein bulls [9.1% (60/657) versus 5.0% (23/456), respectively, P=0.008; data from Experiments 1 and 2 combined]. Additionally, in Experiment 2, cows inseminated using semen from sire #4 (Gyr) had lower embryonic loss (10%) when compared with other B. t. indicus (35.3% and 40%) or B. t. taurus sires (18.2% and 38.5%, P=0.03). In conclusion, the use of B. t. indicus sires may result in higher conception rates in lactating Holstein cows during summer heat stress. Moreover, sire can affect embryonic loss and selection of bulls according to this criterion may result in higher parturition rates in lactating Holstein cows.
Random regression models in the evaluation of the growth curve of Simbrasil beef cattle
Mota, M.; Marques, F.A.; Lopes, P.S.; Hidalgo, A.M.
2013-01-01
Random regression models were used to estimate the types and orders of random effects of (co)variance functions in the description of the growth trajectory of the Simbrasil cattle breed. Records for 7049 animals totaling 18,677 individual weighings were submitted to 15 models from the third to the
Random regression models in the evaluation of the growth curve of Simbrasil beef cattle
Mota, M.; Marques, F.A.; Lopes, P.S.; Hidalgo, A.M.
2013-01-01
Random regression models were used to estimate the types and orders of random effects of (co)variance functions in the description of the growth trajectory of the Simbrasil cattle breed. Records for 7049 animals totaling 18,677 individual weighings were submitted to 15 models from the third to the f
Comparing spatial regression to random forests for large environmental data sets
Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputatio...
Genetic analysis of tolerance to infections using random regressions: a simulation study
Kause, A.
2011-01-01
Tolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits,
Asymptotic properties for the semiparametric regression model with randomly censored data
王启华; 郑忠国
1997-01-01
Suppose that the patients’ survival times,Y,are random variables following the semiparametric regression model Y=Xβ+g(T)+ε,where (X,T) is a radom vector taking values in R×[0,1],β is an unknown parameter,g(·) is an unknown smooth regression function and εis the random error with zero mean and variance σ2.It is assumed that (X,T) is independent of ε.The estimators βn and gm(·) ofβ and g(·) are defined,respectively,when the observations are randomly censored on the right and the censoring distribution is unknown.Moreover,it isshown that βm is asymptotically normal and gm(·) is weak consistence with rate Op(n-1/3).
The limiting behavior of the estimated parameters in a misspecified random field regression model
Dahl, Christian Møller; Qin, Yu
convenient new uniform convergence results that we propose. This theory may have applications beyond those presented here. Our results indicate that classical statistical inference techniques, in general, works very well for random field regression models in finite samples and that these models succesfully......This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection...... of nonlinear functions and it has the added advantage that there is no "curse of dimensionality."Contrary to existing literature on the asymptotic properties of the estimated parameters in random field models our results do not require that the explanatory variables are sampled on a grid. However...
Petersen, Jørgen Holm
2016-01-01
. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one-dimensional integrals that may be solved numerically. Good properties of the resulting estimator......This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...
The limiting behavior of the estimated parameters in a misspecified random field regression model
Dahl, Christian Møller; Qin, Yu
, as a consequence the random field model specification introduces non-stationarity and non-ergodicity in the misspecified model and it becomes non-trivial, relative to the existing literature, to establish the limiting behavior of the estimated parameters. The asymptotic results are obtained by applying some...... convenient new uniform convergence results that we propose. This theory may have applications beyond those presented here. Our results indicate that classical statistical inference techniques, in general, works very well for random field regression models in finite samples and that these models succesfully...
Strathe, Anders B; Mark, Thomas; Nielsen, Bjarne
. Based on covariance functions, residual feed intake (RFI) was defined and derived as the conditional genetic variance in feed intake given mid-test breeding value for BW and rate of gain. The heritability of RFI over the entire period was 0.36, but more interestingly, the genetic variance of RFI was 6......Random regression models were used to estimate covariance functions between cumulated feed intake (CFI) and body weight (BW) in 8424 Danish Duroc pigs. Random regressions on second order Legendre polynomials of age were used to describe genetic and permanent environmental curves in BW and CFI......% of the genetic variance in feed intake, revealing that a minor component of feed intake was genetically independent of maintenance and growth. In conclusion, the approach derived herein led to a consistent definition of RFI, where genomic breeding values were easily obtained...
Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B
2016-09-01
Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that
Silva, F G; Torres, R A; Brito, L F; Euclydes, R F; Melo, A L P; Souza, N O; Ribeiro, J I; Rodrigues, M T
2013-12-11
The objective of this study was to identify the best random regression model using Legendre orthogonal polynomials to evaluate Alpine goats genetically and to estimate the parameters for test day milk yield. On the test day, we analyzed 20,710 records of milk yield of 667 goats from the Goat Sector of the Universidade Federal de Viçosa. The evaluated models had combinations of distinct fitting orders for polynomials (2-5), random genetic (1-7), and permanent environmental (1-7) fixed curves and a number of classes for residual variance (2, 4, 5, and 6). WOMBAT software was used for all genetic analyses. A random regression model using the best Legendre orthogonal polynomial for genetic evaluation of milk yield on the test day of Alpine goats considered a fixed curve of order 4, curve of genetic additive effects of order 2, curve of permanent environmental effects of order 7, and a minimum of 5 classes of residual variance because it was the most economical model among those that were equivalent to the complete model by the likelihood ratio test. Phenotypic variance and heritability were higher at the end of the lactation period, indicating that the length of lactation has more genetic components in relation to the production peak and persistence. It is very important that the evaluation utilizes the best combination of fixed, genetic additive and permanent environmental regressions, and number of classes of heterogeneous residual variance for genetic evaluation using random regression models, thereby enhancing the precision and accuracy of the estimates of parameters and prediction of genetic values.
Multilevel covariance regression with correlated random effects in the mean and variance structure.
Quintero, Adrian; Lesaffre, Emmanuel
2017-09-01
Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Riley, D G; Coleman, S W; Chase, C C; Olson, T A; Hammond, A C
2007-01-01
The objective of this research was to assess the genetic control of BW, hip height, and the ratio of BW to hip height (n = 5,055) in Brahman cattle through 170 d on feed using covariance function-random regression models. A progeny test of Brahman sires (n = 27) generated records of Brahman steers and heifers (n = 724) over 7 yr. Each year after weaning, calves were assigned to feedlot pens, where they were fed a high-concentrate grain diet. Body weights and hip heights were recorded every 28 d until cattle reached a targeted fatness level. All calves had records through 170 d on feed; subsequent records were excluded. Models included contemporary group (sex-pen-year combinations, n = 63) and age at the beginning of the feeding period as a covariate. The residual error structure was modeled as a random effect, with 2 levels corresponding to two 85-d periods on feed. Information criterion values indicated that linear, random regression coefficients on Legendre polynomials of days on feed were most appropriate to model additive genetic effects for all 3 traits. Cubic (hip height and BW:hip height ratio) or quartic (BW) polynomials best modeled permanent environmental effects. Estimates of heritability across the 170-d feeding period ranged from 0.31 to 0.53 for BW, from 0.37 to 0.53 for hip height, and from 0.23 to 0.6 for BW:hip height ratio. Estimates of the permanent environmental proportion of phenotypic variance ranged from 0.44 to 0.58 for BW, 0.07 to 0.26 for hip height, and 0.30 to 0.48 for BW:hip height ratio. Within-trait estimates of genetic correlation on pairs of days on feed (at 28-d intervals) indicated lower associations of BW:hip height ratio EBV early and late in the feeding period but large positive associations for BW or hip height EBV throughout. Estimates of genetic correlations among the 3 traits indicated almost no association of BW:hip height ratio and hip height EBV. The ratio of BW to hip height in cattle has previously been used as an
Pretto, D; Vallas, M; Pärna, E; Tänavots, A; Kiiman, H; Kaart, T
2014-12-01
Genetic parameters of milk rennet coagulation time (RCT) and curd firmness (a30) among the first 3 lactations in Holstein cows were estimated. The data set included 39,960 test-day records from 5,216 Estonian Holstein cows (the progeny of 306 sires), which were recorded from April 2005 to May 2010 in 98 herds across the country. A multiple-lactation random regression animal model was used. Individual milk samples from each cow were collected during routine milk recording. These samples were analyzed for milk composition and coagulation traits with intervals of 2 to 3 mo in each lactation (7 to 305 DIM) and from first to third lactation. Mean heritabilities were 0.36, 0.32, and 0.28 for log-transformed RCT [ln(RCT)] and 0.47, 0.40, and 0.62 for a30 for parities 1, 2, and 3, respectively. Mean repeatabilities for ln(RCT) were 0.53, 0.55, and 0.56, but 0.59, 0.61, and 0.68 for a30 for parities 1, 2 and 3, respectively. Mean genetic correlations between ln(RCT) and a30 were -0.19, -0.14, and 0.02 for parities 1, 2, and 3, respectively. Mean genetic correlations were 0.91, 0.79, and 0.99 for ln(RCT), and 0.95, 0.94, and 0.94 for a30 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Due to these high genetic correlations, we concluded that for a proper genetic evaluation of milk coagulation properties it is sufficient to record RCT and a30 only in the first lactation. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Bowden, Jack; Davey Smith, George; Burgess, Stephen
2015-04-01
The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger's test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation. © The Author 2015; Published by Oxford University Press on behalf of the International Epidemiological Association.
Ryu, Duchwan
2010-09-28
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.
Random regression models using different functions to model milk flow in dairy cows.
Laureano, M M M; Bignardi, A B; El Faro, L; Cardoso, V L; Tonhati, H; Albuquerque, L G
2014-09-12
We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.
Huang, Lei; Jin, Yan; Gao, Yaozong; Thung, Kim-Han; Shen, Dinggang
2016-10-01
Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the current models such as linearity assumption and missing data exclusion. Here, we present a nonlinear supervised sparse regression-based random forest (RF) framework to predict a variety of longitudinal AD clinical scores. Furthermore, we propose a soft-split technique to assign probabilistic paths to a test sample in RF for more accurate predictions. In order to benefit from the longitudinal scores in the study, unlike the previous studies that often removed the subjects with missing scores, we first estimate those missing scores with our proposed soft-split sparse regression-based RF and then utilize those estimated longitudinal scores at all the previous time points to predict the scores at the next time point. The experiment results demonstrate that our proposed method is superior to the traditional RF and outperforms other state-of-art regression models. Our method can also be extended to be a general regression framework to predict other disease scores.
Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
Li'ai Wang; Xudong Zhou; Xinkai Zhu; Zhaodi Dong; Wenshan Guo
2016-01-01
Wheat biomass can be estimated using appropriate spectral vegetation indices. However, the accuracy of estimation should be further improved for on-farm crop management. Previous studies focused on developing vegetation indices, however limited research exists on modeling algorithms. The emerging Random Forest (RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling. The objectives of this study were to (1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass, (2) test the performance of the RF regression model, and (3) compare the performance of the RF algorithm with support vector regression (SVR) and artificial neural network (ANN) machine-learning algorithms for wheat biomass estimation. Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing, booting, and anthesis stages of growth. Fifteen vegetation indices were calculated based on these images. In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition. The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage, and its robustness is as good as SVR but better than ANN. The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.
Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
Li’ai Wang; Xudong Zhou; Xinkai Zhu; Zhaodi Dong; Wenshan Guo
2016-01-01
Wheat biomass can be estimated using appropriate spectral vegetation indices.However,the accuracy of estimation should be further improved for on-farm crop management.Previous studies focused on developing vegetation indices,however limited research exists on modeling algorithms.The emerging Random Forest(RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling.The objectives of this study were to(1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass,(2) test the performance of the RF regression model,and(3) compare the performance of the RF algorithm with support vector regression(SVR) and artificial neural network(ANN) machine-learning algorithms for wheat biomass estimation.Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing,booting,and anthesis stages of growth.Fifteen vegetation indices were calculated based on these images.In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition.The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage,and its robustness is as good as SVR but better than ANN.The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.
Batista, E O S; Vieira, L M; Sá Filho, M F; Carvalho, P D; Rivera, H; Cabrera, V; Wiltbank, M C; Baruselli, P S; Souza, A H
2016-03-01
declined as number of breedings per service sire increased. Future randomized studies need to explore whether changes in P/AI ranking to EAI versus TAI are due to specific semen characteristics.
Random regression models for daily feed intake in Danish Duroc pigs
Strathe, Anders Bjerring; Mark, Thomas; Jensen, Just
The objective of this study was to develop random regression models and estimate covariance functions for daily feed intake (DFI) in Danish Duroc pigs. A total of 476201 DFI records were available on 6542 Duroc boars between 70 to 160 days of age. The data originated from the National test station...... and were collected using ACEMO electronic feeders in the period of 2008 to 2011. The pedigree was traced back to 1995 and included 17222 animals. The phenotypic feed intake curve was decomposed into a fixed curve, being specific to the barn-year-season effect and curves associated with the random pen....... Eigenvalues of the genetic covariance function showed that 33% of genetic variability was explained by the individual genetic curve of the pigs. This proportion was covered by linear (27%) and quadratic (6%) coefficients. Genetic eigenfunctions revealed that altering the shape of the feed intake curve...
Steyerberg Ewout W
2011-05-01
Full Text Available Abstract Background Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models. Methods We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI enrolled in eight Randomized Controlled Trials (RCTs and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4, Stata (GLLAMM, SAS (GLIMMIX and NLMIXED, MLwiN ([R]IGLS and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC, R package MCMCglmm and SAS experimental procedure MCMC. Three data sets (the full data set and two sub-datasets were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted. Results The packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal models for the main study and when based on a relatively large number of level-1 (patient level data compared to the number of level-2 (hospital level data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in
Casas, E; Cundiff, L V
2003-04-01
Postweaning growth, feed efficiency, and carcass traits were analyzed on 1,422 animals obtained by mating F1 cows to F1 (Belgian Blue x British breeds) or Charolais sires. Cows were obtained from mating Hereford, Angus, and MARC IIIHereford, 1/4 Angus, 1/4 Pinzgauer, and 1/4 Red Poll) dams to Hereford or Angus (British breeds), Tuli, Boran, Brahman, or Belgian Blue sires. Breed groups were fed in replicated pens and slaughtered serially in each of 2 yr. Postweaning average daily gain; live weight; hot carcass weight; fat depth; longissimus area; estimated kidney, pelvic, and heart fat (percentage); percentage Choice; marbling score; USDA yield grade; retail product yield (percentage); retail product weight; fat yield (percentage); fat weight; bone yield (percentage); and bone weight were analyzed in this population. Quadratic regressions of pen mean weight on days fed and of cumulative ME consumption on days fed were used to estimate gain, ME consumption and efficiency (Mcal of ME/kg of gain) over time (0 to 200 d on feed), and weight (300 to 550 kg) intervals. Maternal grandsire breed was significant (P carcass weight, longissimus area, and bone weight. Sex class was a significant (P < 0.001) source of variation for all traits except for percentage Choice, marbling score, retail product yield, and fat yield. Interactions between maternal grandsire and sire breed were nonexistent. Sire and grandsire breed effects can be optimized by selection and use of appropriate crossbreeding systems.
Ferrell, C L; Jenkins, T G
1998-02-01
Objectives of the study were to determine the influence of Angus (A), Boran (BO), Brahman (BR), Hereford (H), or Tuli (T) sires on body composition, composition of gain, and energy utilization of crossbred steers during the finishing period. Beginning at 300 kg, 96 steers were adjusted to a high-corn diet and individual feeding. Steers were assigned, by sire breed, to be killed as an initial slaughter group or fed either a limited amount or ad libitum for 140 d then killed. Organ weights, carcass traits, and body composition were evaluated. The statistical model included sire breed (S), treatment (Trt), and the S x Trt interaction. Ad libitum feed intake was least for BO- and T-, intermediate for BR- and H-, and greatest for A-sired steers. Rates of weight, fat, and energy gains were similar for A-, H-, and BR-sired steers but less (P .12). Rates of water, fat, and protein gain increased linearly with increased rate of BW gain, but relationships differed (P < .05) among sire breeds. Linear regression analyses indicated energy requirements for maintenance and efficiency of energy use for energy gain differed (P < .05) among sire breeds. Evaluation by nonlinear regression indicated that heat production increased exponentially and energy gain increased asymptotically as feed intake increased above maintenance.
H. Tonhati
2010-02-01
Full Text Available The objectives of this study were to estimate (covariance functions for additive genetic and permanent environmental effects, as well as the genetic parameters for milk yield over multiple parities, using random regressions models (RRM. Records of 4,757 complete lactations of Murrah breed buffaloes from 12 herds were analyzed. Ages at calving were between 2 and 11 years. The model included the additive genetic and permanent environmental random effects and the fixed effects of contemporary groups (herd, year and calving season and milking frequency (1 or 2. A cubic regression on Legendre orthogonal polynomials of ages was used to model the mean trend. The additive genetic and permanent environmental effects were modeled by Legendre orthogonal polynomials. Residual variances were considered homogenous or heterogeneous, modeled through variance functions or step functions with 5, 7 or 10 classes. Results from Akaike’s and Schwarz’s Bayesian information criterion indicated that a RRM considering a third order polynomial for the additive genetic and permanent environmental effects and a step function with 5 classes for residual variances fitted best. Heritability estimates obtained by this model varied from 0.10 to 0.28. Genetic correlations were high between consecutive ages, but decreased when intervals between ages increased
Random and bias errors in simple regression-based calculations of sea-level acceleration
Howd, P.; Doran, K. J.; Sallenger, A. H.
2012-12-01
We examine the random and bias errors associated with three simple regression-based methods used to calculate the acceleration of sea-level elevation (SL). These methods are: (1) using ordinary least-squares regression (OLSR) to fit a single second-order (in time) equation to an entire elevation time series; (2) using a sliding regression window with OLRS 2nd order fits to provide time and window length dependent estimates; and (3) using a sliding regression window with OLSR 1st order fits to provide time and window length dependent estimates of sea level rate differences (SLRD). A Monte Carlo analysis using synthetic elevation time series with 9 different noise formulations (red, AR(1), and white noise at 3 variance levels) is used to examine the error structure associated with the three analysis methods. We show that, as expected, the single-fit method (1), while providing statistically unbiased estimates of the mean acceleration over an interval, by statistical design does not provide estimates of time-varying acceleration. This technique cannot be expected to detect recent changes in SL acceleration, such as those predicted by some climate models. The two sliding window techniques show similar qualitative results for the test time series, but differ dramatically in their statistical significance. Estimates of acceleration based on the 2nd order fits (2) are numerically smaller than the rate differences (3), and in the presence of near-equal residual noise, are more difficult to detect with statistical significance. We show, using the SLRD estimates from tide gauge data, how statistically significant changes in sea level accelerations can be detected at different temporal and spatial scales.
A review of R-packages for random-intercept probit regression in small clusters
Haeike Josephy
2016-10-01
Full Text Available Generalized Linear Mixed Models (GLMMs are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based inference. As these seldom yield satisfactory results when analyzing binary outcomes from small clusters, estimation within the Structural Equation Modeling (SEM framework is proposed as an alternative. We compare the performance of R-packages for random-intercept probit regression relying on: the Laplace approximation, adaptive Gaussian quadrature (AGQ, penalized quasi-likelihood, an MCMC-implementation, and integrated nested Laplace approximation within the GLMM-framework, and a robust diagonally weighted least squares estimation within the SEM-framework. In terms of bias for the fixed and random effect estimators, SEM usually performs best for cluster size two, while AGQ prevails in terms of precision (mainly because of SEM's robust standard errors. As the cluster size increases, however, AGQ becomes the best choice for both bias and precision.
APPLICATION OF RANDOM REGRESSION MODELS FOR GROWTH TRAITS OF NELLORE CATTLE IN BRAZIL
Wéverton José Lima fONSECA
2016-11-01
Full Text Available Thepurpose of thisreview isto show the increase in number of researches on covariance components and genetic evaluation using random regression models (RRM for growth traits of Nellore cattle. Random regression models (RRM, also known as infinite-dimension models have been used to estimate variance components and genetic parameters for weight of beef cattle. In addition, those models are a standard alternative for genetic analyses of longitudinal data, however, the availibility of computational resources for performing genetic evaluations widely is an obstacle. Traits related to animal growth are adopted as selection criteria in beef cattle breeding programs, because the remuneration of cattle breeders is made based on the weight of carcasses. In recent years, RRM have been adopted as standard procedure in relation to the analysis of longitudinal data in animal breeding. Objetivou-se com esta revisão de literatura compilar informações sob a avaliação genética utilizando modelos de regressão aleatória (MRA para características de crescimento em bovinos da raça Nelore. Os modelos de regressão aleatória (MRA, denominados modelos de dimensão infinita, estão sendo utilizados para estimar os componentes de variância e parâmetros genéticos de pesos de bovinos de corte. Os MRA têm se tornado uma alternativa padrão para análises genéticas de dados longitudinais, onde um dos entraves destes modelos está relacionado à disponibilidade de memória e tempo computacional para a realização de avaliações genéticas em larga escala. Características relacionadas ao crescimento animal são adotadas em programas de melhoramento genético de bovinos de corte como critérios de seleção, pelo fato da remuneração, dos produtores, ser feita com base no peso das carcaças. Nos últimos anos, os MRA têm sido adotados como procedimento padrão para análise de dados longitudinais em melhoramento genético animal.
Futao Guo
2016-10-01
Full Text Available Frequent and intense anthropogenic fires present meaningful challenges to forest management in the boreal forest of China. Understanding the underlying drivers of human-caused fire occurrence is crucial for making effective and scientifically-based forest fire management plans. In this study, we applied logistic regression (LR and Random Forests (RF to identify important biophysical and anthropogenic factors that help to explain the likelihood of anthropogenic fires in the Chinese boreal forest. Results showed that the anthropogenic fires were more likely to occur at areas close to railways and were significantly influenced by forest types. In addition, distance to settlement and distance to road were identified as important predictors for anthropogenic fire occurrence. The model comparison indicated that RF had greater ability than LR to predict forest fires caused by human activity in the Chinese boreal forest. High fire risk zones in the study area were identified based on RF, where we recommend increasing allocation of fire management resources.
M. Farshad
2013-09-01
Full Text Available This paper presents a novel method based on machine learning strategies for fault locating in high voltage direct current (HVDC transmission lines. In the proposed fault-location method, only post-fault voltage signals measured at one terminal are used for feature extraction. In this paper, due to high dimension of input feature vectors, two different estimators including the generalized regression neural network (GRNN and the random forest (RF algorithm are examined to find the relation between the features and the fault location. The results of evaluation using training and test patterns obtained by simulating various fault types in a long overhead transmission line with different fault locations, fault resistance and pre-fault current values have indicated the efficiency and the acceptable accuracy of the proposed approach.
Genetic parameters for tunisian holsteins using a test-day random regression model.
Hammami, H; Rekik, B; Soyeurt, H; Ben Gara, A; Gengler, N
2008-05-01
Genetic parameters of milk, fat, and protein yields were estimated in the first 3 lactations for registered Tunisian Holsteins. Data included 140,187; 97,404; and 62,221 test-day production records collected on 22,538; 15,257; and 9,722 first-, second-, and third-parity cows, respectively. Records were of cows calving from 1992 to 2004 in 96 herds. (Co)variance components were estimated by Bayesian methods and a 3-trait-3-lactation random regression model. Gibbs sampling was used to obtain posterior distributions. The model included herd x test date, age x season of calving x stage of lactation [classes of 25 days in milk (DIM)], production sector x stage of lactation (classes of 5 DIM) as fixed effects, and random regression coefficients for additive genetic, permanent environmental, and herd-year of calving effects, which were defined as modified constant, linear, and quadratic Legendre coefficients. Heritability estimates for 305-d milk, fat and protein yields were moderate (0.12 to 0.18) and in the same range of parameters estimated in management systems with low to medium production levels. Heritabilities of test-day milk and protein yields for selected DIM were higher in the middle than at the beginning or the end of lactation. Inversely, heritabilities of fat yield were high at the peripheries of lactation. Genetic correlations among 305-d yield traits ranged from 0.50 to 0.86. The largest genetic correlation was observed between the first and second lactation, potentially due to the limited expression of genetic potential of superior cows in later lactations. Results suggested a lack of adaptation under the local management and climatic conditions. Results should be useful to implement a BLUP evaluation for the Tunisian cow population; however, results also indicated that further research focused on data quality might be needed.
Cláudio Vieira de Araújo
2006-06-01
Full Text Available Registros de produção de leite de 68.523 controles leiteiros de 8.536 vacas da raça Holandesa, filhas de 537 reprodutores, distribuídas em 266 rebanhos, com parições nos anos de 1996 a 2001, foram utilizados na comparação de modelos de regressão aleatória, para estimação de componentes de variância. Os modelos de regressão aleatória diferiram entre si pelo grau do polinômio de Legendre utilizado para descrever a trajetória da curva de lactação dos animais. Os modelos incluíram os efeitos rebanho-mês-ano do controle, composição genética dos animais, freqüência de ordenhas diárias, regressão polinomial em cada classe de idade-estação de parto para descrever a parte fixa da lactação e regressão polinomial aleatória relacionadas aos efeitos genético direto e de ambiente permanente. As estimativas de herdabilidade obtidas oscilaram de 0,122 a 0,291. Verificou-se que o modelo de regressão aleatória que utilizou a maior ordem para os polinômios de Legendre descreveu melhor a variação genética da produção de leite, de acordo com o critério de Akaike.Data comprising 68,523 test day milk yield of 8,536 cows of the Holstein breed, daughters of 537 sires, distributed in 266 herds, calving from 1996 to 2001, were used to compare random regression models, for estimating variance. Test day records (TD were analyzed by different random regression models regarding the function used to describe the trajectory of the lactation curve of the animals. Legendre orthogonal polynomials function of second, third and fourth order were used. The random regression models included the effects of herd-month-year of the control, genetic group of the animals; the frequency of the daily milk; regression coefficients for each class of age-season (in order to describe the fixed part of the lactation curve and random regression coefficients related to the direct genetic and the permanent environmental effects. The heritability estimates
Yu, Dong-Jun; Li, Yang; Hu, Jun; Yang, Xibei; Yang, Jing-Yu; Shen, Hong-Bin
2015-01-01
Disulfide connectivity is an important protein structural characteristic. Accurately predicting disulfide connectivity solely from protein sequence helps to improve the intrinsic understanding of protein structure and function, especially in the post-genome era where large volume of sequenced proteins without being functional annotated is quickly accumulated. In this study, a new feature extracted from the predicted protein 3D structural information is proposed and integrated with traditional features to form discriminative features. Based on the extracted features, a random forest regression model is performed to predict protein disulfide connectivity. We compare the proposed method with popular existing predictors by performing both cross-validation and independent validation tests on benchmark datasets. The experimental results demonstrate the superiority of the proposed method over existing predictors. We believe the superiority of the proposed method benefits from both the good discriminative capability of the newly developed features and the powerful modelling capability of the random forest. The web server implementation, called TargetDisulfide, and the benchmark datasets are freely available at: http://csbio.njust.edu.cn/bioinf/TargetDisulfide for academic use.
BOX-COX transformation and random regression models for fecal egg count data
Marcos Vinicius Silva
2012-01-01
Full Text Available Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants fecal egg count (FEC is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used to achieve normality before analysis. However, the transformed data are often not normally distributed, especially when data are extremely skewed. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6,375 FEC measures were obtained for 410 animals between 1992 and 2003 from the Beltsville Agricultural Research Center Angus herd. Original data were transformed using an extension of the Box-Cox transformation to approach normality and to estimate (covariance components. We also proposed using random regression models (RRM for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM and restricted maximum likelihood. Within the different orders of Legendre polynomials used, those with more parameters (order 4 adjusted FEC data best. Results indicated that the transformation of FEC data utilizing the Box-Cox transformation family was effective in reducing the skewness and kurtosis, and dramatically increased estimates of heritability, and measurements of FEC obtained in the period between 12 and 26 weeks in a 26-week experimental challenge period are genetically correlated.
Multi-fidelity Gaussian process regression for prediction of random fields
Parussini, L. [Department of Engineering and Architecture, University of Trieste (Italy); Venturi, D., E-mail: venturi@ucsc.edu [Department of Applied Mathematics and Statistics, University of California Santa Cruz (United States); Perdikaris, P. [Department of Mechanical Engineering, Massachusetts Institute of Technology (United States); Karniadakis, G.E. [Division of Applied Mathematics, Brown University (United States)
2017-05-01
We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgers equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.
Evaluation of random forest regression for prediction of breeding value from genomewide SNPs
Rupam Kumar Sarkar; A. R. Rao; Prabina Kumar Meher; T. Nepolean; T. Mohapatra
2015-06-01
Genomic prediction is meant for estimating the breeding value using molecular marker data which has turned out to be a powerful tool for efficient utilization of germplasm resources and rapid improvement of cultivars. Model-based techniques have been widely used for prediction of breeding values of genotypes from genomewide association studies. However, application of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the optimum values of tuning parameters of RF have been identified and applied to predict the breeding value of genotypes based on genomewide single-nucleotide polymorphisms (SNPs), where the number of SNPs ($P$ variables) is much higher than the number of genotypes ($n$ observations) ($P >> n$). Further, a comparison was made with the model-based genomic prediction methods, namely, least absolute shrinkage and selection operator (LASSO), ridge regression (RR) and elastic net (EN) under $P >> n$. It was found that the correlations between the predicted and observed trait response were 0.591, 0.539, 0.431 and 0.587 for RF, LASSO, RR and EN, respectively, which implies superiority of the RF over the model-based techniques in genomic prediction. Hence, we suggest that the RF methodology can be used as an alternative to the model-based techniques for the prediction of breeding value at genome level with higher accuracy.
Shirali, Mahmoud; Nielsen, Vivi Hunnicke; Møller, Steen Henrik
Heritability of residual feed intake (RFI) increased from low to high over the growing period in male and female mink. The lowest heritability for RFI (male: 0.04 ± 0.01 standard deviation (SD); female: 0.05 ± 0.01 SD) was in early and the highest heritability (male: 0.33 ± 0.02; female: 0.34 ± 0...... at the end compared to the early growing period suggesting that heterogeneous residual variance should be considered for analyzing feed efficiency data in mink. This study suggests random regression methods are suitable for analyzing feed efficiency and that genetic selection for RFI in mink is promising........02 SD) was achieved at the late growth stages. The genetic correlation between different growth stages for RFI showed a high association (0.91 to 0.98) between early and late growing periods. However, phenotypic correlations were lower from 0.29 to 0.50. The residual variances were substantially higher...
Doe productivity indices and sire effects of a heterogeneous rabbit ...
IJAAAR
Doe productivity indices are important in evaluating rabbit population since it influences the efficiency and profitability of rabbit ... Key words: Heterogeneous rabbit population, Doe productivity, Sire families). ..... Paris, France, 11-14. Cited in:.
Manafiazar, G; McFadden, T; Goonewardene, L; Okine, E; Basarab, J; Li, P; Wang, Z
2013-01-01
Residual Feed Intake (RFI) is a measure of energy efficiency. Developing an appropriate model to predict expected energy intake while accounting for multifunctional energy requirements of metabolic body weight (MBW), empty body weight (EBW), milk production energy requirements (MPER), and their nonlinear lactation profiles, is the key to successful prediction of RFI in dairy cattle. Individual daily actual energy intake and monthly body weight of 281 first-lactation dairy cows from 1 to 305 d in milk were recorded at the Dairy Research and Technology Centre of the University of Alberta (Edmonton, AB, Canada); individual monthly milk yield and compositions were obtained from the Dairy Herd Improvement Program. Combinations of different orders (1-5) of fixed (F) and random (R) factors were fitted using Legendre polynomial regression to model the nonlinear lactation profiles of MBW, EBW, and MPER over 301 d. The F5R3, F5R3, and F5R2 (subscripts indicate the order fitted) models were selected, based on the combination of the log-likelihood ratio test and the Bayesian information criterion, as the best prediction equations for MBW, EBW, and MPER, respectively. The selected models were used to predict daily individual values for these traits. To consider the body reserve changes, the differences of predicted EBW between 2 consecutive days were considered as the EBW change between these days. The smoothed total 301-d actual energy intake was then linearly regressed on the total 301-d predicted traits of MBW, EBW change, and MPER to obtain the first-lactation RFI (coefficient of determination=0.68). The mean of predicted daily average lactation RFI was 0 and ranged from -6.58 to 8.64 Mcal of NE(L)/d. Fifty-one percent of the animals had an RFI value below the mean (efficient) and 49% of them had an RFI value above the mean (inefficient). These results indicate that the first-lactation RFI can be predicted from its component traits with a reasonable coefficient of
Carolino, Nuno; Gama, Luis T
2008-01-01
Records from up to 19 054 registered cows and 10 297 calves in 155 herds of the Alentejana cattle breed were used to study the effects of individual (Fi) and maternal (Fm) inbreeding on reproductive, growth and carcass traits, as well as assessing the importance of non-linear associations between inbreeding and performance, and evaluating the differences among sire-families in the effect of Fi and Fm on calf weight at 7 months of age (W7M). Overall, regression coefficients of performance traits on inbreeding were small, indicating a minor but still detrimental effect of both Fi and Fm on most traits. The traits with the highest percentage impact of Fi were total number of calvings through life and calf weight at 3 months of age (W3M), followed by longevity and number of calves produced up to 7 years, while the highest effect of Fm was on W3M. Inbreeding depression on feed efficiency and carcass traits was extremely small and not significant. No evidence was found of a non-linear association between inbreeding and performance for the traits analyzed. Large differences were detected among sire-families in inbreeding depression on W7M, for both Fi and Fm, encouraging the possibility of incorporating sire effects on inbreeding depression into selection decisions.
Kiessling Arndt H
2011-10-01
Full Text Available Abstract Background We assessed the hemodynamic performance of various prostheses and the clinical outcomes after aortic valve replacement, in different age groups. Methods One-hundred-and-twenty patients with isolated aortic valve stenosis were included in this prospective randomized randomised trial and allocated in three age-groups to receive either pulmonary autograft (PA, n = 20 or mechanical prosthesis (MP, Edwards Mira n = 20 in group 1 (age 75. Clinical outcomes and hemodynamic performance were evaluated at discharge, six months and one year. Results In group 1, patients with PA had significantly lower mean gradients than the MP (2.6 vs. 10.9 mmHg, p = 0.0005 with comparable left ventricular mass regression (LVMR. Morbidity included 1 stroke in the PA population and 1 gastrointestinal bleeding in the MP subgroup. In group 2, mean gradients did not differ significantly between both populations (7.0 vs. 8.9 mmHg, p = 0.81. The rate of LVMR and EF were comparable at 12 months; each group with one mortality. Morbidity included 1 stroke and 1 gastrointestinal bleeding in the stentless and 3 bleeding complications in the MP group. In group 3, mean gradients did not differ significantly (7.8 vs 6.5 mmHg, p = 0.06. Postoperative EF and LVMR were comparable. There were 3 deaths in the stented group and no mortality in the stentless group. Morbidity included 1 endocarditis and 1 stroke in the stentless compared to 1 endocarditis, 1 stroke and one pulmonary embolism in the stented group. Conclusions Clinical outcomes justify valve replacement with either valve substitute in the respective age groups. The PA hemodynamically outperformed the MPs. Stentless valves however, did not demonstrate significantly superior hemodynamics or outcomes in comparison to stented bioprosthesis or MPs.
Bertipaglia, T S; Carreño, L O D; Aspilcueta-Borquis, R R; Boligon, A A; Farah, M M; Gomes, F J; Machado, C H C; Rey, F S B; da Fonseca, R
2015-08-01
Random regression models (RRM) and multitrait models (MTM) were used to estimate genetic parameters for growth traits in Brazilian Brahman cattle and to compare the estimated breeding values obtained by these 2 methodologies. For RRM, 78,641 weight records taken between 60 and 550 d of age from 16,204 cattle were analyzed, and for MTM, the analysis consisted of 17,385 weight records taken at the same ages from 12,925 cattle. All models included the fixed effects of contemporary group and the additive genetic, maternal genetic, and animal permanent environmental effects and the quadratic effect of age at calving (AAC) as covariate. For RRM, the AAC was nested in the animal's age class. The best RRM considered cubic polynomials and the residual variance heterogeneity (5 levels). For MTM, the weights were adjusted for standard ages. For RRM, additive heritability estimates ranged from 0.42 to 0.75, and for MTM, the estimates ranged from 0.44 to 0.72 for both models at 60, 120, 205, 365, and 550 d of age. The maximum maternal heritability estimate (0.08) was at 140 d for RRM, but for MTM, it was highest at weaning (0.09). The magnitude of the genetic correlations was generally from moderate to high. The RRM adequately modeled changes in variance or covariance with age, and provided there was sufficient number of samples, increased accuracy in the estimation of the genetic parameters can be expected. Correlation of bull classifications were different in both methods and at all the ages evaluated, especially at high selection intensities, which could affect the response to selection.
Emanuela Tullo
2014-12-01
Full Text Available In tropical environments, lactation curves with lower peaks and higher persistency (PS might be desirable from both an economical and a physiological point of view. The objective of this study was to obtain genetic parameters for test day (TD yields, and PS for the tropical breed Carora and to compare these with results from a standard 305-d-milk yield animal model. Four random regression models (RRM were used on a dataset composed of 95,606 TD records collected in Venezuela and tested to find the best fitting the data. Estimated daily heritabilities for milk yields ranged from 0.21 to 0.30, with the lowest values around the peak of lactation. Lactation repeatabilities ranged from 0.50 to 0.56. Correlations between the breeding values obtained with the RRM and the lactation model currently used in Venezuela [single trait Animal Model (stAM] are quite high and positive (Pearson correlation=0.71 and Spearman correlation=0.72. Correlations between PS and 305-d-milk yield estimated breeding values (EBV ranged from -0.18 (PS as the deviation of daily productions in the interval 50-279 days in milk from a point at the end of lactation to 0.52 (PS as EBV difference between the second and the first stage of lactation. The use of PS indexes accounting for milk yield may allow the selection of individuals able to express their potential genetic values in tropical environment, without incurring in excessive heat stress losses.
Doron, J; Martinent, G
2016-06-23
Understanding more about the stress process is important for the performance of athletes during stressful situations. Grounded in Lazarus's (1991, 1999, 2000) CMRT of emotion, this study tracked longitudinally the relationships between cognitive appraisal, coping, emotions, and performance in nine elite fencers across 14 international matches (representing 619 momentary assessments) using a naturalistic, video-assisted methodology. A series of hierarchical linear modeling analyses were conducted to: (a) explore the relationships between cognitive appraisals (challenge and threat), coping strategies (task- and disengagement oriented coping), emotions (positive and negative) and objective performance; (b) ascertain whether the relationship between appraisal and emotion was mediated by coping; and (c) examine whether the relationship between appraisal and objective performance was mediated by emotion and coping. The results of the random coefficient regression models showed: (a) positive relationships between challenge appraisal, task-oriented coping, positive emotions, and performance, as well as between threat appraisal, disengagement-oriented coping and negative emotions; (b) that disengagement-oriented coping partially mediated the relationship between threat and negative emotions, whereas task-oriented coping partially mediated the relationship between challenge and positive emotions; and (c) that disengagement-oriented coping mediated the relationship between threat and performance, whereas task-oriented coping and positive emotions partially mediated the relationship between challenge and performance. As a whole, this study furthered knowledge during sport performance situations of Lazarus's (1999) claim that these psychological constructs exist within a conceptual unit. Specifically, our findings indicated that the ways these constructs are inter-related influence objective performance within competitive settings.
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.
Strobl, Carolin; Malley, James; Tutz, Gerhard
2009-01-01
Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and…
van der Meer, D.; Hoekstra, P. J.; van Donkelaar, Marjolein M. J.; Bralten, Janita; Oosterlaan, J; Heslenfeld, Dirk J.; Faraone, S. V.; Franke, B.; Buitelaar, J. K.; Hartman, C. A.
2017-01-01
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression
Meer, D. van der; Hoekstra, P.J.; Donkelaar, M.M.J. van; Bralten, J.B.; Oosterlaan, J.; Heslenfeld, D.; Faraone, S.V; Franke, B.; Buitelaar, J.K.; Hartman, C.A.
2017-01-01
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression
Al-Samarai, F R; Abdulrahman, Y K; Mohammed, F A; Al-Zaidi, F H; Al-Anbari, N N
2015-01-01
A total of 956 lactation records of Holstein cows kept at Kaa Albon station, Imuran Governorate, Yemen during the period from 1991 to 2003 were used to investigate the effect of some genetic and non-genetic factors (Sire, parity, season of calving, year of calving and age at first calving as covariate) on the Total Milk Yield (TMY), Lactation Length (LL), and Dry Period (DP). Components of variance for the random effects (mixed model) were estimated by Restricted Maximum Likelihood (REML) methodology. Sires were evaluated for the TMY by three methods, Best Linear Unbiased Prediction (BLUP) using Harvey program, Transmitting Ability (TA) according to the Least Square Means of sire progeny (TALSM) and according to Means (TAM). Results showed that TMY and DP were affected significantly (P < 0.01) by all factors except season of calving and age at first calving, while LL was affected significantly (P< 0.01) only by year of calving and parity. The averages of the TMY, LL, and DP were 3919.66 kg, 298.28 days, and 114.13 days respectively. The corresponding estimates of heritability (h(2)) were 0.35, 0.06, and 0.14 respectively. The highest and lowest BLUP values of sires for the TMY were - 542.44 kg and 402.14 kg, while the corresponding estimates for TALSM and TAM were - 470.38, 380.88 kg and - 370.12, 388.50 kg respectively. The Spearman rank correlation coefficients among BLUP, TALSM and TAM ranged from 0.81 to 0.67. These results provide evidence that the selection of sires will improve the TMY in this herd because of the wide differences in genetic poetical among sires, and a moderate estimation of heritability.
Andonov, S; Ødegård, J; Svendsen, M; Ådnøy, T; Vegara, M; Klemetsdal, G
2013-03-01
One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.
Shabani, Farzin; Kumar, Lalit; Solhjouy-fard, Samaneh
2016-05-01
The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm (Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations
Shabani, Farzin; Kumar, Lalit; Solhjouy-fard, Samaneh
2017-08-01
The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm ( Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations
Principal component approach in variance component estimation for international sire evaluation
Jakobsen Jette
2011-05-01
Full Text Available Abstract Background The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model. Methods This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC and the so-called bottom-up REML approach (bottom-up PC, in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (covariance matrix. Results Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (covariance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in
Naghibi, Seyed Amir; Pourghasemi, Hamid Reza; Dixon, Barnali
2016-01-01
Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.
Meric de Bellefon, G.; van Duysen, J. C.; Sridharan, K.
2017-08-01
The stacking fault energy (SFE) plays an important role in deformation behavior and radiation damage of FCC metals and alloys such as austenitic stainless steels. In the present communication, existing expressions to calculate SFE in those steels from chemical composition are reviewed and an improved multivariate linear regression with random intercepts is used to analyze a new database of 144 SFE measurements collected from 30 literature references. It is shown that the use of random intercepts can account for experimental biases in these literature references. A new expression to predict SFE from austenitic stainless steel compositions is proposed.
Shirali, Mahmoud; Nielsen, Vivi Hunnicke; Møller, Steen Henrik
2014-01-01
The aim of this study was to determine genetic background of longitudinal residual feed intake (RFI) and body weight (BW) growth in farmed mink using random regression methods considering heterogeneous residual variances. Eight BW measures for each mink was recorded every three weeks from 63 to 210...... days of age for 2139 male mink and the same number of females. Cumulative feed intake was calculated six times with three weeks interval based on daily feed consumption between weighing’s from 105 to 210 days of age. Heritability estimates for RFI increased by age from 0.18 (0.03, standard deviation...... be obtained by only considering RFI estimate and BW at pelting, however, lower genetic correlations than unity indicate that extra genetic gain can be obtained by including estimates of these traits at the growing period. This study suggests random regression methods are suitable for analysing feed efficiency...
Larsen, Klaus; Merlo, Juan
2005-01-01
The logistic regression model is frequently used in epidemiologic studies, yielding odds ratio or relative risk interpretations. Inspired by the theory of linear normal models, the logistic regression model has been extended to allow for correlated responses by introducing random effects. However......, the model does not inherit the interpretational features of the normal model. In this paper, the authors argue that the existing measures are unsatisfactory (and some of them are even improper) when quantifying results from multilevel logistic regression analyses. The authors suggest a measure...... of heterogeneity, the median odds ratio, that quantifies cluster heterogeneity and facilitates a direct comparison between covariate effects and the magnitude of heterogeneity in terms of well-known odds ratios. Quantifying cluster-level covariates in a meaningful way is a challenge in multilevel logistic...
LaloŅ Denis
2004-05-01
Full Text Available Abstract Some analytical and simulated criteria were used to determine whether a priori genetic differences among groups, which are not accounted for by the relationship matrix, ought to be fitted in models for genetic evaluation, depending on the data structure and the accuracy of the evaluation. These criteria were the mean square error of some extreme contrasts between animals, the true genetic superiority of animals selected across groups, i.e. the selection response, and the magnitude of selection bias (difference between true and predicted selection responses. The different statistical models studied considered either fixed or random genetic groups (based on six different years of birth versus ignoring the genetic group effects in a sire model. Including fixed genetic groups led to an overestimation of selection response under BLUP selection across groups despite the unbiasedness of the estimation, i.e. despite the correct estimation of differences between genetic groups. This overestimation was extremely important in numerical applications which considered two kinds of within-station progeny test designs for French purebred beef cattle AI sire evaluation across years: the reference sire design and the repeater sire design. When assuming a priori genetic differences due to the existence of a genetic trend of around 20% of genetic standard deviation for a trait with h2 = 0.4, in a repeater sire design, the overestimation of the genetic superiority of bulls selected across groups varied from about 10% for an across-year selection rate p = 1/6 and an accurate selection index (100 progeny records per sire to 75% for p = 1/2 and a less accurate selection index (20 progeny records per sire. This overestimation decreased when the genetic trend, the heritability of the trait, the accuracy of the evaluation or the connectedness of the design increased. Whatever the data design, a model of genetic evaluation without groups was preferred to a model
Künzi Niklaus
2002-01-01
Full Text Available Abstract A random regression model for daily feed intake and a conventional multiple trait animal model for the four traits average daily gain on test (ADG, feed conversion ratio (FCR, carcass lean content and meat quality index were combined to analyse data from 1 449 castrated male Large White pigs performance tested in two French central testing stations in 1997. Group housed pigs fed ad libitum with electronic feed dispensers were tested from 35 to 100 kg live body weight. A quadratic polynomial in days on test was used as a regression function for weekly means of daily feed intake and to escribe its residual variance. The same fixed (batch and random (additive genetic, pen and individual permanent environmental effects were used for regression coefficients of feed intake and single measured traits. Variance components were estimated by means of a Bayesian analysis using Gibbs sampling. Four Gibbs chains were run for 550 000 rounds each, from which 50 000 rounds were discarded from the burn-in period. Estimates of posterior means of covariance matrices were calculated from the remaining two million samples. Low heritabilities of linear and quadratic regression coefficients and their unfavourable genetic correlations with other performance traits reveal that altering the shape of the feed intake curve by direct or indirect selection is difficult.
Sørensen, By Ole H
2016-10-01
Organizational-level occupational health interventions have great potential to improve employees' health and well-being. However, they often compare unfavourably to individual-level interventions. This calls for improving methods for designing, implementing and evaluating organizational interventions. This paper presents and discusses the regression discontinuity design because, like the randomized control trial, it is a strong summative experimental design, but it typically fits organizational-level interventions better. The paper explores advantages and disadvantages of a regression discontinuity design with an embedded randomized control trial. It provides an example from an intervention study focusing on reducing sickness absence in 196 preschools. The paper demonstrates that such a design fits the organizational context, because it allows management to focus on organizations or workgroups with the most salient problems. In addition, organizations may accept an embedded randomized design because the organizations or groups with most salient needs receive obligatory treatment as part of the regression discontinuity design. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Polyandry in dragon lizards: inbred paternal genotypes sire fewer offspring.
Frère, Celine H; Chandrasoma, Dani; Whiting, Martin J
2015-04-01
Multiple mating in female animals is something of a paradox because it can either be risky (e.g., higher probability of disease transmission, social costs) or provide substantial fitness benefits (e.g., genetic bet hedging whereby the likelihood of reproductive failure is lowered). The genetic relatedness of parental units, particularly in lizards, has rarely been studied in the wild. Here, we examined levels of multiple paternity in Australia's largest agamid lizard, the eastern water dragon (Intellagama lesueurii), and determined whether male reproductive success is best explained by its heterozygosity coefficient or the extent to which it is related to the mother. Female polyandry was the norm: 2/22 clutches (9.2%) were sired by three or more fathers, 17/22 (77.2%) were sired by two fathers, and only 3/22 (13.6%) clutches were sired by one father. Moreover, we reconstructed the paternal genotypes for 18 known mother-offspring clutches and found no evidence that females were favoring less related males or that less related males had higher fitness. However, males with greater heterozygosity sired more offspring. While the postcopulatory mechanisms underlying this pattern are not understood, female water dragons likely represent another example of reproduction through cryptic means (sperm selection/sperm competition) in a lizard, and through which they may ameliorate the effects of male-driven precopulatory sexual selection.
Concealed target detection using augmented reality with SIRE radar
Saponaro, Philip; Kambhamettu, Chandra; Ranney, Kenneth; Sullivan, Anders
2013-05-01
The Synchronous Impulse Reconstruction (SIRE) forward-looking radar, developed by the U.S. Army Research Laboratory (ARL), can detect concealed targets using ultra-wideband synthetic aperture technology. The SIRE radar has been mounted on a Ford Expedition and combined with other sensors, including a pan/tilt/zoom camera, to test its capabilities of concealed target detection in a realistic environment. Augmented Reality (AR) can be used to combine the SIRE radar image with the live camera stream into one view, which provides the user with information that is quicker to assess and easier to understand than each separated. In this paper we present an AR system which utilizes a global positioning system (GPS) and inertial measurement unit (IMU) to overlay a SIRE radar image onto a live video stream. We describe a method for transforming 3D world points in the UTM coordinate system onto the video stream by calibrating for the intrinsic parameters of the camera. This calibration is performed offline to save computation time and achieve real time performance. Since the intrinsic parameters are affected by the zoom of the camera, we calibrate at eleven different zooms and interpolate. We show the results of a real time transformation of the SAR imagery onto the video stream. Finally, we quantify both the 2D error and 3D residue associated with our transformation and show that the amount of error is reasonable for our application.
Huang, Lei
2015-09-30
To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.
Maria Gabriela Campolina Diniz Peixoto
2014-05-01
Full Text Available The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524 of test-day milk yield (TDMY from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects, whereas the contemporary group, calving age (linear and quadratic effects and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.
Maas, Iris L; Nolte, Sandra; Walter, Otto B; Berger, Thomas; Hautzinger, Martin; Hohagen, Fritz; Lutz, Wolfgang; Meyer, Björn; Schröder, Johanna; Späth, Christina; Klein, Jan Philipp; Moritz, Steffen; Rose, Matthias
2017-02-01
To compare treatment effect estimates obtained from a regression discontinuity (RD) design with results from an actual randomized controlled trial (RCT). Data from an RCT (EVIDENT), which studied the effect of an Internet intervention on depressive symptoms measured with the Patient Health Questionnaire (PHQ-9), were used to perform an RD analysis, in which treatment allocation was determined by a cutoff value at baseline (PHQ-9 = 10). A linear regression model was fitted to the data, selecting participants above the cutoff who had received the intervention (n = 317) and control participants below the cutoff (n = 187). Outcome was PHQ-9 sum score 12 weeks after baseline. Robustness of the effect estimate was studied; the estimate was compared with the RCT treatment effect. The final regression model showed a regression coefficient of -2.29 [95% confidence interval (CI): -3.72 to -.85] compared with a treatment effect found in the RCT of -1.57 (95% CI: -2.07 to -1.07). Although the estimates obtained from two designs are not equal, their confidence intervals overlap, suggesting that an RD design can be a valid alternative for RCTs. This finding is particularly important for situations where an RCT may not be feasible or ethical as is often the case in clinical research settings. Copyright © 2016 Elsevier Inc. All rights reserved.
Sources of (co)variation in alternative siring routes available to male great tits (Parus major)
Araya-Ajoy, Yimen G.; Kuhn, Sylvia; Mathot, Kimberley J.; Mouchet, Alexia; Mutzel, Ariane; Nicolaus, Marion; Wijmenga, Jan J.; Kempenaers, Bart; Dingemanse, Niels J.
2016-01-01
Males of socially monogamous species can increase their siring success via within-pair and extra-pair fertilizations. In this study, we focused on the different sources of (co)variation between these siring routes, and asked how each contributes to total siring success. We quantified the
Bull fertility evaluations for Angus service sires bred to Holstein cows
Sire conception rate (SCR), a phenotypic evaluation of service-sire fertility implemented in August 2008, is based on data from the most recent 4 years, conventional-semen breedings up to 7 services, and cow parities 1 through 5. Many US dairy cows are now being bred to Angus sires because beef pric...
Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele
2015-11-01
The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225 mm of cumulative rainfall in 7 h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire
Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren; Wang, Shijun; Lu, Le; Zhang, Weidong; Liu, Jianfei; Wei, Zhuoshi; Summers, Ronald M
2015-01-01
Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, prandom forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse.
Liu Yingan; Wei Bocheng
2008-01-01
Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity (Kantz and Schreiber (1997)). Tsai (1986) introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR(1) errors. Liu (2003) introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL(p, 0, 1) errors. Therefore, the important problems in regres- sion model are detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL(p, q, 1) random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedas-ticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai (1986), Wei, et al (1995), and Liu, et al (2003).
Chong Wei
2015-01-01
Full Text Available Logistic regression models have been widely used in previous studies to analyze public transport utilization. These studies have shown travel time to be an indispensable variable for such analysis and usually consider it to be a deterministic variable. This formulation does not allow us to capture travelers’ perception error regarding travel time, and recent studies have indicated that this error can have a significant effect on modal choice behavior. In this study, we propose a logistic regression model with a hierarchical random error term. The proposed model adds a new random error term for the travel time variable. This term structure enables us to investigate travelers’ perception error regarding travel time from a given choice behavior dataset. We also propose an extended model that allows constraining the sign of this error in the model. We develop two Gibbs samplers to estimate the basic hierarchical model and the extended model. The performance of the proposed models is examined using a well-known dataset.
Edificio de oficinas. Gualca – Sire, Turín
Casalegno, G.
1958-09-01
Full Text Available Aunque puede considerarse como una unidad arquitectónica, puesto que constituye una sola manzana, en realidad está dividido en dos partes casi iguales: una, orientada al mediodía, propiedad de la sociedad "Gualca", proyectada por Casalegno y dedicada a departamentos de alquiler, y otra, propiedad de la sociedad "Sire", con proyecto de Ceresa y Levi-Montalcini.
C.K.P. Dorneles
2009-04-01
Full Text Available Foram utilizados 21.702 registros de produção de leite no dia do controle de 2.429 vacas primíparas da raça Holandesa, filhas de 233 touros, coletados em 33 rebanhos do Estado do Rio Grande do Sul, para estimar parâmetros genéticos para produção de leite no dia do controle. O modelo de regressão aleatória ajustado aos controles leiteiros entre o sexto e o 305º dia de lactação incluiu o efeito de rebanho-ano-mês do controle, idade da vaca no parto e os parâmetros do polinômio de Legendre de ordem quatro, para modelar a curva média da produção de leite da população e parâmetros do mesmo polinômio, para modelar os efeitos aleatórios genético-aditivo e de ambiente permanente. As variâncias genéticas e de ambiente permanente para produção de leite no dia do controle variaram, respectivamente, de 2,38 a 3,14 e de 7,55 a 10,35. As estimativas de herdabilidade aumentaram gradativamente do início (0,14 para o final do período de lactação (0,20, indicando ser uma característica de moderada herdabilidade. As correlações genéticas entre as produções de leite de diferentes estágios leiteiros variaram de 0,33 a 0,99 e foram maiores entre os controles adjacentes. As correlações de ambiente permanente seguiram a mesma tendência das correlações genéticas. O modelo de regressão aleatória com polinômio de Legendre de ordem quatro pode ser considerado como uma boa ferramenta para estimação de parâmetros genéticos para a produção de leite ao longo da lactação.A total of 21,702 records of milk production from 2,429 first-lactation Holstein cows, sired by 233 bulls, collected in 33 herds in the State of Rio Grande do Sul from 1991 to 2003, were used to estimate genetic parameters for that characteristic. The random regression model adjusted to test day from the 6th and the 305th lactation day included the effect of herd-year-month of the test day, the age of the cow at parturition, and the order fourth Legendre
Shirali, M; Nielsen, V H; Møller, S H; Jensen, J
2015-10-01
The aim of this study was to determine the genetic background of longitudinal residual feed intake (RFI) and BW gain in farmed mink using random regression methods considering heterogeneous residual variances. The individual BW was measured every 3 weeks from 63 to 210 days of age for 2139 male+female pairs of juvenile mink during the growing-furring period. Cumulative feed intake was calculated six times with 3-week intervals based on daily feed consumption between weighing's from 105 to 210 days of age. Genetic parameters for RFI and BW gain in males and females were obtained using univariate random regression with Legendre polynomials containing an animal genetic effect and permanent environmental effect of litter along with heterogeneous residual variances. Heritability estimates for RFI increased with age from 0.18 (0.03, posterior standard deviation (PSD)) at 105 days of age to 0.49 (0.03, PSD) and 0.46 (0.03, PSD) at 210 days of age in male and female mink, respectively. The heritability estimates for BW gain increased with age and had moderate to high range for males (0.33 (0.02, PSD) to 0.84 (0.02, PSD)) and females (0.35 (0.03, PSD) to 0.85 (0.02, PSD)). RFI estimates during the growing period (105 to 126 days of age) showed high positive genetic correlations with the pelting RFI (210 days of age) in male (0.86 to 0.97) and female (0.92 to 0.98). However, phenotypic correlations were lower from 0.47 to 0.76 in males and 0.61 to 0.75 in females. Furthermore, BW records in the growing period (63 to 126 days of age) had moderate (male: 0.39, female: 0.53) to high (male: 0.87, female: 0.94) genetic correlations with pelting BW (210 days of age). The result of current study showed that RFI and BW in mink are highly heritable, especially at the late furring period, suggesting potential for large genetic gains for these traits. The genetic correlations suggested that substantial genetic gain can be obtained by only considering the RFI estimate and BW at pelting
Oliveira, H R; Silva, F F; Siqueira, O H G B D; Souza, N O; Junqueira, V S; Resende, M D V; Borquis, R R A; Rodrigues, M T
2016-05-01
We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from -0.58 to 0.03, -0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats.
Mirjam J Knol
Full Text Available BACKGROUND: In randomized controlled trials (RCTs, the odds ratio (OR can substantially overestimate the risk ratio (RR if the incidence of the outcome is over 10%. This study determined the frequency of use of ORs, the frequency of overestimation of the OR as compared with its accompanying RR in published RCTs, and we assessed how often regression models that calculate RRs were used. METHODS: We included 288 RCTs published in 2008 in five major general medical journals (Annals of Internal Medicine, British Medical Journal, Journal of the American Medical Association, Lancet, New England Journal of Medicine. If an OR was reported, we calculated the corresponding RR, and we calculated the percentage of overestimation by using the formula . RESULTS: Of 193 RCTs with a dichotomous primary outcome, 24 (12.4% presented a crude and/or adjusted OR for the primary outcome. In five RCTs (2.6%, the OR differed more than 100% from its accompanying RR on the log scale. Forty-one of all included RCTs (n = 288; 14.2% presented ORs for other outcomes, or for subgroup analyses. Nineteen of these RCTs (6.6% had at least one OR that deviated more than 100% from its accompanying RR on the log scale. Of 53 RCTs that adjusted for baseline variables, 15 used logistic regression. Alternative methods to estimate RRs were only used in four RCTs. CONCLUSION: ORs and logistic regression are often used in RCTs and in many articles the OR did not approximate the RR. Although the authors did not explicitly misinterpret these ORs as RRs, misinterpretation by readers can seriously affect treatment decisions and policy making.
Cristian Kelen Pinto Dorneles
2009-08-01
Full Text Available Foram utilizados 21.702 registros de produção de leite no dia do controle de 2.429 vacas primíparas da raça Holandesa, filhas de 233 touros, coletados em 33 rebanhos do Estado do Rio Grande do Sul, entre 1992 e 2003, para estimar parâmetros genéticos, para três medidas de persistência (PS1, PS2 e PS3 e para a produção de leite até 305 dias (P305 de lactação. Os modelos de regressão aleatória ajustados aos controles leiteiros entre o sexto e o 300o dia de lactação incluíram o efeito de rebanho-ano-mês do controle, a idade da vaca ao parto e os parâmetros do polinômio de Legendre de ordem quatro, para modelar a curva média da produção de leite da população e os parâmetros do mesmo polinômio, para modelar os efeitos aleatórios genético-aditivo direto e de ambiente permanente. As estimativas de herdabilidade obtidas foram 0,05, 0,08 e 0,19, respectivamente, para PS1, PS2 e PS3 e 0,25, para P305 sugerindo a possibilidade de ganho genético por meio da seleção para PS3 e para P305. As correlações genéticas entre as três medidas de persistência e P305, variaram de -0,05 a 0,07, indicando serem persistência e produção, características determinadas por grupos de genes diferentes. Assim, consequentemente, a seleção para P305, geralmente praticada, não promove progresso genético para a persistência.There were used 21,702 test day milk yields from 2,429 first parity Holstein breed cows, daughters of 2,031 dams and 233 sires, distributed over 33 herds in the state of Rio Grande do Sul, from 1992 to 2003. Genetic parameters for three measures of lactation persistency (PS1, PS2 e PS3 and for milk production to 305 days (P305 were evaluated. A random regression model adjusted by fourth order Legendre polynomial was used. The random regression model adjusted to test day between the sixth and the 305th lactation day included the herd-year-season of the test day, the age of the cow at the parturition effects and the
Sethi, Ankur; Bajaj, Anurag; Khosla, Sandeep; Arora, Rohit R
2016-01-01
During last 2 decades, multiple studies have evaluated omega-3 polyunsaturated fatty acids (ω-3 PUFA) supplementation for cardiovascular prevention. The benefit found in previous studies was not demonstrated in more contemporary trials. We aimed to investigate effect of study characteristics, particularly concomitant statin therapy on results of randomized controlled trials. We systematically searched electronic databases for randomized controlled trials evaluating ω-3 PUFA supplementation and reporting clinical outcomes. A meta-analysis was performed using a random effect model, followed by a meta-regression of dose, docosahexaenoic acid/eicosapentaenoic acid (DHA/EPA) ratio, and duration of treatment and use of lipid-lowering/statin therapy in control group. Twenty-three studies with 77,776 patients (38,910 PUFA; 38,866 controls) were included. PUFA had no effect on total mortality [risk ratio (RR) = 0.96; 95% confidence interval (CI), 0.92-1.01] and myocardial infarction (RR = 0.87; 95% CI, 0.73-1.02), but marginally reduced cardiovascular mortality (RR = 0.93; 95% CI, 0.87-0.98). Lower control group statin use (b = 0.222, P = 0.027) and higher DHA/EPA (b = -0.105, P = 0.033) ratio was associated with higher reduction in total mortality. Duration and dose had no effect. None of the variables except duration had significant effect on reduction in cardiovascular mortality by PUFA supplementation. There was evidence of publication bias. Statin use may mitigate, and higher DHA/EPA ratio is associated with the beneficial effect of PUFA supplementation.
SIRE: a MIMO radar for landmine/IED detection
Ojowu, Ode; Wu, Yue; Li, Jian; Nguyen, Lam
2013-05-01
Multiple-input multiple-output (MIMO) radar systems have been shown to have significant performance improvements over their single-input multiple-output (SIMO) counterparts. For transmit and receive elements that are collocated, the waveform diversity afforded by this radar is exploited for performance improvements. These improvements include but are not limited to improved target detection, improved parameter identifiability and better resolvability. In this paper, we present the Synchronous Impulse Reconstruction Radar (SIRE) Ultra-wideband (UWB) radar designed by the Army Research Lab (ARL) for landmine and improvised explosive device (IED) detection as a 2 by 16 MIMO radar (with collocated antennas). Its improvement over its SIMO counterpart in terms of beampattern/cross range resolution are discussed and demonstrated using simulated data herein. The limitations of this radar for Radio Frequency Interference (RFI) suppression are also discussed in this paper. A relaxation method (RELAX) combined with averaging of multiple realizations of the measured data is presented for RFI suppression; results show no noticeable target signature distortion after suppression. In this paper, the back-projection (delay and sum) data independent method is used for generating SAR images. A side-lobe minimization technique called recursive side-lobe minimization (RSM) is also discussed for reducing side-lobes in this data independent approach. We introduce a data-dependent sparsity based spectral estimation technique called Sparse Learning via Iterative Minimization (SLIM) as well as a data-dependent CLEAN approach for generating SAR images for the SIRE radar. These data-adaptive techniques show improvement in side-lobe reduction and resolution for simulated data for the SIRE radar.
Franke, D E
1997-10-01
Comparisons were made among F1 steers sired by Brahman and alternative subtropically adapted breeds of bulls for feedlot and carcass traits when steers were produced from Angus- and Hereford-type dams. Brahman-derivative breeds included Brangus, Beefmaster, and Santa Gertrudis. Brangus- and Beefmaster-sired steers weighed less at slaughter, whereas carcasses of Brangus- and Santa Gertrudis-sired steers had more marbling than those of Brahman-sired steers. Brahman-sired steer carcasses had greater longissimus muscle area than carcasses of Santa Gertrudis-sired steers. Other Zebu breeds compared to Brahman were Boran, Gir, Indu-Brazil, Nellore, Red Brahman, and Sahiwal. Steers by Brahman sires had higher slaughter weights than steers by Boran, Gir, Nellore, or Sahiwal sires. Hot carcass weights of Brahman-sired steers were also higher than those of Boran- and Sahiwal-sired steers. Steer carcasses by Brahman sires had greater longissimus muscle area than those of steers by Sahiwal sires. Non-Zebu breeds included Tuli and Senepol. Steers by Tuli sires grew slower, had lower slaughter weights, and their carcasses weighed less than those of Brahman-sired steers. Brahman-sired steer carcasses had greater longissimus muscle area but less marbling than carcasses of Tuli-sired steers. These data suggest that steers by Brahman sires have an advantage for slaughter weight over steers by Brangus, Beefmaster, Boran, Gir, Nellore, Sahiwal, and Tuli sires, but their carcasses are at a disadvantage for marbling score compared with those by Brangus, Boran, Nellore, and Tuli sires.
Florian Naudet
Full Text Available BACKGROUND: To compare response to antidepressants between randomized controlled trials (RCTs and observational trials. METHODS AND FINDINGS: Published and unpublished studies (from 1989 to 2009 were searched for by 2 reviewers on Medline, the Cochrane library, Embase, clinicaltrials.gov, Current Controlled Trial, bibliographies and by mailing key organisations and researchers. RCTs and observational studies on fluoxetine or venlafaxine in first-line treatment for major depressive disorder reported in English, French or Spanish language were included in the main analysis. Studies including patients from a wider spectrum of depressive disorders (anxious depression, minor depressive episode, dysthymia were added in a second analysis. The main outcome was the pre-/post-treatment difference on depression scales standardised to 100 (17-item or 21-item Hamilton Rating Scale for Depression or Montgomery and Åsberg Rating Scale in each study arm. A meta-regression was conducted to adjust the comparison between observational studies and RCTs on treatment type, study characteristics and average patient characteristics. 12 observational studies and 109 RCTs involving 6757 and 11035 patients in 12 and 149 arms were included in the main analysis. Meta-regression showed that the standardised treatment response in RCTs is greater by a magnitude of 4.59 (2.61 to 6.56. Study characteristics were related to standardised treatment response, positively (study duration, number of follow-up assessments, outpatients versus inpatients, per protocol analysis versus intention to treat analysis or negatively (blinded design, placebo design. At patient level, response increased with baseline severity and decreased with age. Results of the second analysis were consistent with this. CONCLUSIONS: Response to antidepressants is greater in RCTs than in observational studies. Observational studies should be considered as a necessary complement to RCTs.
The effect of ignoring individual heterogeneity in Weibull log-normal sire frailty models
Damgaard, Lars Holm; Korsgaard, Inge Riis; Simonsen, J;
2006-01-01
The objective of this study was, by means of simulation, to quantify the effect of ignoring individual heterogeneity in Weibull sire frailty models on parameter estimates and to address the consequences for genetic inferences. Three simulation studies were evaluated, which included 3 levels...... the software Survival Kit for the incomplete sire model. For the incomplete sire model, the Monte Carlo and Survival Kit parameter estimates were similar. This study established that when unobserved individual heterogeneity was ignored, the parameter estimates that included sire effects were biased toward zero...
Silva, F G; Torres, R A; Silva, L P; Ventura, H T; Silva, F F; Carneiro, A P S; Nascimento, M; Rodrigues, M T
2014-12-19
Random regression models have been used in evaluating test-day milk yield, providing accurate estimates of genetic values in animals. However, herd evaluation with only information from the first lactation may not be the best option from an economic perspective. Other factors should be taken into account, particularly other lactations. Our objective in this study was to analyze the genetic divergence between the first four lactations of Alpine goats. The RENPED software was used to perform descriptive statistics, check for errors in pedigree, recode the data, and for Pearson's and Spearman's correlations. The WOMBAT software was used to estimate the variance components and predict the breeding values. The CALC software was adopted to calculate the percentage of coincidence between the ranking of the animals and the animals kept in common at each lactation evaluation. The results show that selection using only the first lactation in small herds with a low degree of technology can be employed as a palliative measure, in view of the difficulty in evaluating all lactations. However, the selection of breeding goats and the production of catalogues should not be based only on the first lactation, because the results demonstrate inversions in the classification of the best breeders when other lactations are analyzed.
The effect of ignoring individual heterogeneity in Weibull log-normal sire frailty models.
Damgaard, L H; Korsgaard, I R; Simonsen, J; Dalsgaard, O; Andersen, A H
2006-06-01
The objective of this study was, by means of simulation, to quantify the effect of ignoring individual heterogeneity in Weibull sire frailty models on parameter estimates and to address the consequences for genetic inferences. Three simulation studies were evaluated, which included 3 levels of individual heterogeneity combined with 4 levels of censoring (0, 25, 50, or 75%). Data were simulated according to balanced half-sib designs using Weibull log-normal animal frailty models with a normally distributed residual effect on the log-frailty scale. The 12 data sets were analyzed with 2 models: the sire model, equivalent to the animal model used to generate the data (complete sire model), and a corresponding model in which individual heterogeneity in log-frailty was neglected (incomplete sire model). Parameter estimates were obtained from a Bayesian analysis using Gibbs sampling, and also from the software Survival Kit for the incomplete sire model. For the incomplete sire model, the Monte Carlo and Survival Kit parameter estimates were similar. This study established that when unobserved individual heterogeneity was ignored, the parameter estimates that included sire effects were biased toward zero by an amount that depended in magnitude on the level of censoring and the size of the ignored individual heterogeneity. Despite the biased parameter estimates, the ranking of sires, measured by the rank correlations between true and estimated sire effects, was unaffected. In comparison, parameter estimates obtained using complete sire models were consistent with the true values used to simulate the data. Thus, in this study, several issues of concern were demonstrated for the incomplete sire model.
Cho, C I; Alam, M; Choi, T J; Choy, Y H; Choi, J G; Lee, S S; Cho, K H
2016-05-01
The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first
Sources of (co)variation in alternative siring routes available to male great tits (Parus major).
Araya-Ajoy, Yimen G; Kuhn, Sylvia; Mathot, Kimberley J; Mouchet, Alexia; Mutzel, Ariane; Nicolaus, Marion; Wijmenga, Jan J; Kempenaers, Bart; Dingemanse, Niels J
2016-10-01
Males of socially monogamous species can increase their siring success via within-pair and extra-pair fertilizations. In this study, we focused on the different sources of (co)variation between these siring routes, and asked how each contributes to total siring success. We quantified the fertilization routes to siring success, as well as behaviors that have been hypothesized to affect siring success, over a five-year period for a wild population of great tits Parus major. We considered siring success and its fertilization routes as "interactive phenotypes" arising from phenotypic contributions of both members of the social pair. We show that siring success is strongly affected by the fecundity of the social (female) partner. We also demonstrate that a strong positive correlation between extra-pair fertilization success and paternity loss likely constrains the evolution of these two routes. Moreover, we show that more explorative and aggressive males had less extra-pair fertilizations, whereas more explorative females laid larger clutches. This study thus demonstrates that (co)variation in siring routes is caused by multiple factors not necessarily related to characteristics of males. We thereby highlight the importance of acknowledging the multilevel structure of male fertilization routes when studying the evolution of male mating strategies.
Meister, Ramona; Jansen, Alessa; Härter, Martin; Nestoriuc, Yvonne; Kriston, Levente
2017-06-01
We aimed to investigate placebo and nocebo reactions in randomized controlled trials (RCT) of pharmacological treatments for persistent depressive disorder (PDD). We conducted a systematic electronic search and included RCTs investigating antidepressants for the treatment of PDD. Outcomes were the number of patients experiencing response and remission in placebo arms (=placebo reaction). Additional outcomes were the incidence of patients experiencing adverse events and related discontinuations in placebo arms (=nocebo reaction). A priori defined effect modifiers were analyzed using a series of meta-regression analyses. Twenty-three trials were included in the analyses. We found a pooled placebo response rate of 31% and a placebo remission rate of 22%. The pooled adverse event rate and related discontinuations were 57% and 4%, respectively. All placebo arm outcomes were positively associated with the corresponding medication arm outcomes. Placebo response rate was associated with a greater proportion of patients with early onset depression, a smaller chance to receive placebo and a larger sample size. The adverse event rate in placebo arms was associated with a greater proportion of patients with early onset depression, a smaller proportion of females and a more recent publication. Pooled placebo and nocebo reaction rates in PDD were comparable to those in episodic depression. The identified effect modifiers should be considered to assess unbiased effects in RCTs, to influence placebo and nocebo reactions in practice. Limitations result from the methodology applied, the fact that we conducted only univariate analyses, and the number and quality of included trials. Copyright © 2017 Elsevier B.V. All rights reserved.
Igor de Oliveira Biassus
2010-12-01
Full Text Available Total numbers of 56,508, 35,091 and 8,326 records of milk, fat, and protein test-day yields, respectively, were used to estimate genetic parameters for six persistency measures on milk, fat and protein productions of Holstein cows reared in Minas Gerais state. Covariance components for additive genetic effects and permanent environmental effects were estimated by REML in random regression models using Legendre polynomials from the third to the sixth order. Overall, models with the highest orders of Legendre polynomials showed the best quality of adjustments of these productive records. Heritability estimates obtained by the models for persistence in milk, fat, and protein yields ranged from 0.04 to 0.32, from 0.00 to 0.23, and from 0.00 to 0.27, respectively. Values of genetic correlation estimates between persistence and total 305-day milk, fat, and protein yields ranged from -0.38 to 0.54, from -0.39 to 0.97, and from -0.78 to 0.67, respectively. Persistence measurement proposed by Jakobsen (PS2 is preferential for using in further genetic evaluations for persistence in milk, fat and protein yields of Holstein cows in Minas Gerais state.Os totais de 56.508, 35.091 e 8.326 registros, respectivamente, de produção de leite, de gordura e de proteína no dia do controle foram usados para estimar parâmetros genéticos para seis medidas de persistência na produção de leite, de gordura e de proteína de vacas da raça Holandesa criadas em rebanhos do Estado de Minas Gerais. Os componentes de covariância para os efeitos genético aditivo e de ambiente permanente foram estimados via REML por modelos de regressão aleatória com polinômios de Legendre de ordens 3 a 6. Em geral, os modelos com as mais altas ordens dos polinômios de Legendre apresentaram a melhor qualidade no ajuste desses registros produtivos. As estimativas de herdabilidade obtidas pelos modelos para as persistências nas produções de leite, de gordura e de proteína variaram
Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele
2015-04-01
first phase of the work addressed to identify the spatial relationships between the landslides location and the 13 related factors by using the Frequency Ratio bivariate statistical method. The analysis was then carried out by adopting a multivariate statistical approach, according to the Logistic Regression technique and Random Forests technique that gave best results in terms of AUC. The models were performed and evaluated with different sample sizes and also taking into account the temporal variation of input variables such as burned areas by wildfire. The most significant outcome of this work are: the relevant influence of the sample size on the model results and the strong importance of some environmental factors (e.g. land use and wildfires) for the identification of the depletion zones of extremely rapid shallow landslides.
Thrift, F A
1997-10-01
Comparisons involving Brahman and Brahman-derivative (Brangus, Santa Gertrudis, Beef-master, Simbrah, Braford) sires indicate the following: 1) cows mated to Brangus and Santa Gertrudis bulls had a shorter gestation length than cows mated to Brahman bulls, 2) calves sired by Brangus and Beefmaster bulls were lighter at birth and weaning than calves sired by Brahman bulls, and 3) birth and weaning weights were similar for calves sired by Santa Gertrudis and Brahman bulls and for calves sired by Simbrah and Brahman bulls. Comparisons involving Brahman and other Zebu (Sahiwal, Nellore, Gir, Indu-Brazil, Boran, Romana Red) sires indicate that gestation length was slightly longer for cows mated to Sahiwal and Nellore bulls and that, relative to the Brahman, birth and weaning weights were similar to or lighter for calves sired by bulls of the other Zebu breeds. The only exception to this pattern was birth weight of Indu-Brazil-sired calves, which were heavier than calves sired by Brahman bulls. Comparisons involving Brahman and non-Zebu subtropically adapted (Tuli, Senepol) sires indicate that cows mated to Tuli bulls had a slightly shorter gestation length than cows mated to Brahman bulls and that birth and weaning weights of calves sired by Tuli and Senepol bulls were lighter than those of calves sired by Brahman bulls.
Jaime Araújo Cobuci
2011-03-01
Full Text Available Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM. Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year or random lactation curves (additive genetic and permanent enviroment. Akaike information criterion (AIC and Bayesian information criterion (BIC indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.Os registros de produção de leite no dia do controle das três primeiras lactações de 25,5 mil vacas da raça Holandesa foram utilizados para estimar parâmetros genéticos para produção de leite usando duas alternativas de definição da regressão fixa dos modelos de regressão aleatória (MRA. Os polinômios de Legendre de ordens 4 e 5 foram usados para modelar as regressões das curvas fixas (definidas com base nas médias das produções de leite no dia do controle da população ou de múltiplas sub-populações formadas pelo
2014-09-18
CDF Cumulative Distribution Function CEMA Correlation Electro-Magnetic Attack DPA Differential Power Analysis DRA Dimensionality Reduction Assessment... CEMA ) SCA attacks are examined. A novel method to find time samples with high information leakage of sensitive data using the adjusted coefficient of...correlation R2a in a linear regression attack is introduced [92]. Three linear regression attacks from current literature [34, 50, 115] and CEMA [19] are
Mota, R R; Lopes, P S; Marques, L F A; Silva, L P; Conceição Pessoa, M; Almeida Torres, R; Resende, M D V
2013-11-22
Weight records of Simmental beef cattle were used in a genetic evaluation of growth with and without embryo transfer (ET). A random regression model in which ET individuals were excluded (RRM1) contained 29,510 records from 10,659 animals, while another model that did not exclude these animals (RRM2) contained 62,895 records from 23,160 animals. The fixed and random regressions were represented by continuous functions, and a model with an order of three for the fixed curve and random effects was used to consider the homogeneity of residual variance. In general, the (co)variance components were similar in both models, except the maternal permanent environment and residual components. The direct heritability in RRM1 and RRM2 showed the same behavior with oscillations along the growth curve and were slightly higher in RRM1. Generally, the estimated correlations were the same and smaller as the ages distanced themselves. The inclusion of animals from ET in genetic evaluations can be done using random regression models; the inclusion of these animals would provide potential accuracy gains and greater genetic gains per unit time because of the reduction in the generation interval from the use of this reproductive technique.
Ibrahim Fayad
2014-11-01
Full Text Available Estimating forest canopy height from large-footprint satellite LiDAR waveforms is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. In this study, canopy height in French Guiana was estimated using multiple linear regression models and the Random Forest technique (RF. This analysis was either based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System spaceborne LiDAR data and terrain information derived from the SRTM (Shuttle Radar Topography Mission DEM (Digital Elevation Model or on Principal Component Analysis (PCA of GLAS waveforms. Results show that the best statistical model for estimating forest height based on waveform metrics and digital elevation data is a linear regression of waveform extent, trailing edge extent, and terrain index (RMSE of 3.7 m. For the PCA based models, better canopy height estimation results were observed using a regression model that incorporated both the first 13 principal components (PCs and the waveform extent (RMSE = 3.8 m. Random Forest regressions revealed that the best configuration for canopy height estimation used all the following metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE = 3.4 m. Waveform extent was the variable that best explained canopy height, with an importance factor almost three times higher than those for the other three metrics (leading edge, trailing edge, and terrain index. Furthermore, the Random Forest regression incorporating the first 13 PCs and the waveform extent had a slightly-improved canopy height estimation in comparison to the linear model, with an RMSE of 3.6 m. In conclusion, multiple linear regressions and RF regressions provided canopy height estimations with similar precision using either LiDAR metrics or PCs. However, a regression model (linear regression or RF based on the PCA of waveform samples with waveform
Macfarlane, J M; Lewis, R M; Emmans, G C; Young, M J; Simm, G
2009-01-01
The utility of x-ray computed tomography (CT) scanning in predicting carcass tissue distribution and fat partitioning in vivo in terminal sire sheep was examined using data from 160 lambs representing combinations of 3 breeds (Charollais, Suffolk, and Texel), 3 genetic lines, and both sexes. One-fifth of the lambs were slaughtered at each of 14, 18, and 22 wk of age, and the remaining two-fifths at 26 wk of age. The left side of each carcass was dissected into 8 joints with each joint dissected into fat (intermuscular and subcutaneous), lean, and bone. Chemical fat content of the LM was measured. Tissue distribution was described by proportions of total carcass tissue and lean weight contained within the leg, loin, and shoulder regions of the carcass and within the higher-priced joints. Fat partitioning variables included proportion of total carcass fat contained in the subcutaneous depot and intramuscular fat content of the LM. Before slaughter, all lambs were CT scanned at 7 anatomical positions (ischium, midshaft of femur, hip, second and fifth lumbar vertebrae, sixth and eighth thoracic vertebrae). Areas of fat, lean, and bone (mm(2)) and average fat and lean density (Hounsfield units) were measured from each cross-sectional scan. Areas of intermuscular and subcutaneous fat were measured on 2 scans (ischium and eighth thoracic vertebra). Intramuscular fat content was predicted with moderate accuracy (R(2) = 56.6) using information from only 2 CT scans. Four measures of carcass tissue distribution were predicted with moderate to high accuracy: the proportion of total carcass (R(2) = 54.7) and lean (R(2) = 46.2) weight contained in the higher-priced joints and the proportion of total carcass (R(2) = 77.7) and lean (R(2) = 55.0) weight in the leg region. Including BW in the predictions did not improve their accuracy (P > 0.05). Although breed-line-sex combination significantly affected fit of the regression for some tissue distribution variables, the values
Francesca M. Sarti
2015-07-01
Full Text Available The Appenninica breed is an Italian meat sheep; the rams are approved according to a phenotypic index that is based on an average daily gain at performance test. The 8546 live weights of 1930 Appenninica male lambs tested in the performance station of the ASSONAPA (National Sheep Breeders Association, Italy from 1986 to 2010 showed a great variability in age at weighing and in number of records by year. The goal of the study is to verify the feasibility of the estimation of a genetic index for weight in the Appenninica sheep by a mixed model, and to explore the use of random regression to avoid the corrections for weighing at different ages. The heritability and repeatability (mean±SE of the average live weight were 0.27±0.04 and 0.54±0.08 respectively; the heritabilities of weights recorded at different weighing days ranged from 0.27 to 0.58, while the heritabilities of weights at different ages showed a narrower variability (0.29÷0.41. The estimates of live weight heritability by random regressions ranged between 0.34 at 123 d of age and 0.52 at 411 d. The results proved that the random regression model is the most adequate to analyse the data of Appenninica breed.
Guo, Z; Lund, M S; Madsen, P; Korsgaard, I; Jensen, J
2002-06-01
The objectives of this study were to test for heterogeneity of genetic and environmental variance among completed and extended records from different lactations or different days in milk (DIM) and to build a model that accounts for this heterogeneity. A total of 147,457 305-d milk yield records from Danish Jersey cows calving between 1984 and early 1999 from two regions of Denmark were used in this study. Results showed that DIM and parity influenced parameters estimated from an animal model with repeated records. Therefore, the data were analyzed using random-regression models that allow the covariance between measurements to change gradually with DIM and parity. Random regressions were fitted for additive genetic effects and permanent environmental effects using second- or third-order normalized Legendre polynomials for DIM and parity. Variances of random-regression coefficients associated with all orders of the polynomials were significant. Based on these parameter estimates, a covariance function (CF) was defined. The CF showed that the heritability decreases over parities, but within each parity heritability increases with DIM, whereas variance of permanent environmental effects increases over parities and decreases with DIM. Generally, genetic correlations were higher between records with similar DIM and parity. The results indicate that there are problems with the extension procedure used to predict 305-d milk yields. Using the covariance functions estimated in this study, breeding values could be predicted that take into account the covariance structure between records from different parities and different DIM.
Evaluation of Dormer sires for litter size and lamb mortality using a ...
Keywords: Categorical traits, Dormer sheep, sire evaluation, threshold model. * To whom .... for Newton-Raphson iterations was l0 and 12 for litter size and mortality, respectively. ... The mathematical theory of quantitative gene- tics. Clarendon ...
Aderbal Cavalcante-Neto
2011-12-01
Full Text Available Objetivou-se comparar modelos de regressão aleatória com diferentes estruturas de variância residual, a fim de se buscar a melhor modelagem para a característica tamanho da leitegada ao nascer (TLN. Utilizaram-se 1.701 registros de TLN, que foram analisados por meio de modelo animal, unicaracterística, de regressão aleatória. As regressões fixa e aleatórias foram representadas por funções contínuas sobre a ordem de parto, ajustadas por polinômios ortogonais de Legendre de ordem 3. Para averiguar a melhor modelagem para a variância residual, considerou-se a heterogeneidade de variância por meio de 1 a 7 classes de variância residual. O modelo geral de análise incluiu grupo de contemporâneo como efeito fixo; os coeficientes de regressão fixa para modelar a trajetória média da população; os coeficientes de regressão aleatória do efeito genético aditivo-direto, do comum-de-leitegada e do de ambiente permanente de animal; e o efeito aleatório residual. O teste da razão de verossimilhança, o critério de informação de Akaike e o critério de informação bayesiano de Schwarz apontaram o modelo que considerou homogeneidade de variância como o que proporcionou melhor ajuste aos dados utilizados. As herdabilidades obtidas foram próximas a zero (0,002 a 0,006. O efeito de ambiente permanente foi crescente da 1ª (0,06 à 5ª (0,28 ordem, mas decrescente desse ponto até a 7ª ordem (0,18. O comum-de-leitegada apresentou valores baixos (0,01 a 0,02. A utilização de homogeneidade de variância residual foi mais adequada para modelar as variâncias associadas à característica tamanho da leitegada ao nascer nesse conjunto de dado.The objective of this work was to compare random regression models with different residual variance structures, so as to obtain the best modeling for the trait litter size at birth (LSB in swine. One thousand, seven hundred and one records of LSB were analyzed. LSB was analyzed by means of a
Radunz, A E; Loerch, S C; Lowe, G D; Fluharty, F L; Zerby, H N
2009-09-01
Wagyu-sired (n = 20) and Angus-sired (n = 19) steers and heifers were used to compare the effects of sire breed on feedlot performance, carcass characteristics, and meat tenderness. Calves were weaned at 138 +/- 5 d of age and individually fed a finishing diet consisting of 65% whole corn, 20% protein/vitamin/mineral supplement, and 15% corn silage on a DM basis. Heifers and steers were slaughtered at 535 and 560 kg of BW, respectively. Carcasses were ribbed between the 12th and 13th (USDA grading system) and the 6th and 7th ribs (Japanese grading system) to measure fat thickness, LM area (LMA), and intramuscular fat (IMF). Two steaks were removed from the 12th rib location and aged for 72 h and 14 d to determine Warner-Bratzler shear force and cooking loss. Sire breed x sex interactions were not significant (P > 0.05). Angus-sired calves had greater (P Angus. Sire breed did not affect (P > 0.20) HCW, 12th-rib fat, or USDA yield grade. Carcasses of Wagyu had greater (P = 0.0001) marbling scores at the 12th rib than those of Angus (770.9 vs. 597.3 +/- 41.01, respectively). Carcasses of Wagyu also had greater (P Angus, resulting in a greater proportion of carcasses grading Prime (65.0 vs. 21.1%; P = 0.006). Carcasses from Wagyu tended (P = 0.08) to have greater LMA at the 12th rib, whereas Angus carcasses had greater (P Angus and Wagyu had similar (P > 0.50) tenderness at aging times of 72 h and 14 d. Cooking loss was greater (P Angus than Wagyu steaks at 72 h and 14 d. Using Wagyu sires vs. Angus sires on British-based commercial cows combined with early weaning management strategies has the potential to produce a product with greater marbling, but is unlikely to significantly enhance tenderness.
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.
Gómez, M D; Menendez-Buxadera, A; Valera, M; Molina, A
2010-10-01
A total of 71 522 records (from 3154 horses) with the times per kilometre (TPK), recorded in Spanish Trotter horses (individual races) from racing performances held from 1991 to 2007, were available for this study. The TPK values for the different age groups (young and adult horses) and different distances (1600-2700 m) were considered as different traits, and a bi character random regression model (RRM) was applied to estimate the (co)variance components throughout the trajectory of age groups and distances. The following effects were considered as fixed: the combination of hippodrome-date of race (404 levels); sex of the animals (3 levels); type of start (2 levels) and a fixed regression of Legendre polynomials (order 2). Those considered as random effects were the random regression Legendre polynomial (order 1) for animals (9201 animals in the pedigree); the individual environment permanent (3154 animals with data) and the driver (n = 957 levels). The residual variance was considered as heterogeneous with two classes (ages). The heritability estimated by distance ranged from 0.12 to 0.34, with a different trajectory for the two age groups. Within each age group, the genetic correlations between adjacent distances were high (>0.90), but decreased when the differences between them were over 400 metres for both age groups. The genetic correlations for the same distance across the age groups ranged from 0.47 to 0.78. Accordingly, the analysed trait (TPK) can be considered as positive genetic correlated but as different traits along the trajectory of distance and age. Therefore, some re-ranking should be expected in the breeding value of the horses at different characteristics of the racing. The use of RRM is recommended because it allows us to estimate the breeding value along the whole trajectory of race competition.
Sarmento, J L R; Torres, R A; Sousa, W H; Lôbo, R N B; Albuquerque, L G; Lopes, P S; Santos, N P S; Bignard, A B
2016-06-20
Polynomial functions of different orders were used to model random effects associated with weight of Santa Ines sheep from birth to 196 days. Fixed effects included in the models were contemporary groups, age of ewe at lambing, and fourth-order Legendre polynomials for age to represent the average growth curve. In the random part, functions of different orders were included to model variances associated with direct additive and maternal genetic effects and with permanent environmental effects of the animal and mother. Residual variance was fitted by a sixth-order ordinary polynomial for age. The higher the order of the functions, the better the model fit the data. According to the Akaike information criterion and likelihood ratio test, a continuous function of order, five, five, seven, and three for direct additive genetic, maternal genetic, animal permanent environmental, and maternal permanent environmental effects (k = 5573), respectively, was sufficient to model changes in (co)variances with age. However, a more parsimonious model of order three, three, five, and three (k = 3353) was suggested based on Schwarz's Bayesian information criterion for the same effects. Since it was a more flexible model, model k = 5573 provided inconsistent genetic parameter estimates when compared to the biologically expected result. Predicted breeding values obtained with models k = 3353 and k = 5573 differed, especially at young ages. Model k = 3353 adequately fit changes in variances and covariances with time, and may be used to describe changes in variances with age in the Santa Ines sheep studied.
Bruno Bastos Teixeira
2012-09-01
Full Text Available Objetivou-se comparar diferentes modelos de regressão aleatória por meio de funções polinomiais de Legendre de diferentes ordens, para avaliar o que melhor se ajusta ao estudo genético da curva de crescimento de codornas de corte. Foram avaliados dados de 2136 matrizes de codorna de corte, dos quais 1026 pertenciam ao grupo genético UFV1 e 1110 ao grupo UFV2. As codornas foram pesadas nos 1°, 7°, 14°, 21°, 28°, 35°, 42°, 77°, 112° e 147° dias de idade e seus pesos utilizados para a análise. Foram testadas duas possíveis modelagens de variância residual heterogênea, sendo agrupadas em 3 e 5 classes de idade. Após, foi realizado o estudo do modelo de regressão aleatória que melhor aplica-se à curva de crescimento das codornas. A comparação entre os modelos foi feita pelo Critério de Informação de Akaike (AIC, Critério de Informação Bayesiano de Schwarz (BIC, Logaritmo da função de verossimilhança (Log e L e teste da razão de verossimilhança (LRT, ao nível de 1%. O modelo que considerou a heterogeneidade de variância residual CL3 mostrou-se adequado à linhagem UFV1, e o modelo CL5 à linhagem UFV2. Uma função polinomial de Legendre com ordem 5, para efeito genético aditivo direto e 5 para efeito permanente de animal, para a linhagem UFV1 e, com ordem 3, para efeito genético aditivo direto e 5 para efeito permanente de animal para a linhagem UFV2, deve ser utilizada na avaliação genética da curva de crescimento das codornas de corte.The objective was to compare different random regression models using Legendre polynomial functions of different orders, to evaluate what best fits the genetic study of the growth curve of meat quails. It was evaluated data from 2136 cut dies quail, of which 1026 belonged to genetic group UFV1 and 1110 the group UFV2. Quail were weighed at 10, 70, 140, 210, 280, 350, 420, 770, 1120 and 1470 days of age, and weights used for the analysis. It was tested two possible modeling
Singh, Ajay; Singh, Avtar; Singh, Manvendra; Prakash, Ved; Ambhore, G. S.; Sahoo, S. K.; Dash, Soumya
2016-01-01
A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM) considering different order of Legendre polynomial for the additive genetic effect (4th order) and the permanent environmental effect (5th order). Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11) to 0.99 (TD-4 and TD-5). The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields. PMID:26954137
Bytyqi, H; Ødegård, J; Mehmeti, H; Vegara, M; Klemetsdal, G
2007-08-01
A total of 25,160 milk test-day records from 2,516 cows in first lactation of 3 dairy cattle breeds [Simmental (n = 1,900), Brown Swiss (n = 444), and Tyrol Grey (n = 172)] in Kosovo were analyzed using nested repeatability and random regression test-day models with varying (co)variance structures. The different models were compared based on likelihood-based criteria. The best model was a second-order random regression model, with heterogeneous cow variance per breed and heterogeneous residual variance per lactation month and breed, which was used for further analysis. The highest milk production was found in Brown Swiss, followed by Simmental and Tyrol Grey. Substantial breed differences were found for the trajectories of cow and residual variances by month of lactation, with the highest variances found for Brown Swiss, followed by Simmental and Tyrol Grey. High cow and residual variances indicated a high degree of environmental sensitivity on the macro- and microenvironmental levels, respectively. Thus, these results indicate increased environmental sensitivity for breeds with higher genetic potential for milk production. These results support the conclusion that dairy cattle production under the current environmental conditions of Kosovo should be based on a breed with moderate production that is robust to the diet offered (e.g., Tyrol Grey).
Thepparat, Mongkol; Boonkum, Wuttigrai; Duangjinda, Monchai; Tumwasorn, Sornthep; Nakavisut, Sansak; Thongchumroon, Thumrong
2015-07-01
The objectives of this study were to compare covariance functions (CF) and estimate the heritability of milk yield from test-day records among exotic (Saanen, Anglo-Nubian, Toggenburg and Alpine) and crossbred goats (Thai native and exotic breed), using a random regression model. A total of 1472 records of test-day milk yield were used, collected from 112 does between 2003 and 2006. CF of the study were Wilmink function, second- and third-order Legendre polynomials, and linear splines 4 knots located at 5, 25, 90 and 155 days in milk (SP25-90) and 5, 35, 95 and 155 of days in milk (SP35-95). Variance components were estimated by restricted maximum likelihood method (REML). Goodness of fit, Akaike information criterion (AIC), percentage of squared bias (PSB), mean square error (MSE), and empirical correlation (RHO) between the observed and predicted values were used to compare models. The results showed that CF had an impact on (co)variance estimation in random regression models (RRM). The RRM with splines 4 knots located at 5, 25, 90 and 155 of days in milk had the lowest AIC, PSB and MSE, and the highest RHO. The heritability estimated throughout lactation obtained with this model ranged from 0.13 to 0.23. © 2014 Japanese Society of Animal Science.
The Effect of Sperm Morphology and Sire Fertility on Calving Rate of Finnish Ayrshire AI Bulls.
Attia, S; Katila, T; Andersson, M
2016-02-01
Good-quality semen is a prerequisite for successful and profitable artificial insemination (AI) of modern dairy cattle. Fertility of the bulls is evaluated with andrological examinations and semen analyses, such as morphology. However, little attention has been paid to the inheritance of bull fertility. In this study, we correlated sperm morphology, birth year and station of 695 AI bulls with calving rate (CR). Sperm morphology was clearly associated with CR underlining the usefulness of morphological examination in the assessment of fertility. The correlation between the proportion of normal spermatozoa and CR was significant (p bulls with the CR of their 27 sires to study the inheritance of fertility. Sire's CR did not correlate with the CR of the sons (p = 0.218). This result indicates that at least when sires of acceptable CR are used to produce sons for use in AI the inheritance of CR is not significantly correlated.
Zwald, N R; Weigel, K A; Chang, Y M; Welper, R D; Clay, J S
2004-12-01
The objective of this study was to determine the feasibility of genetic selection for health traits in dairy cattle using data recorded in on-farm herd management software programs. Data regarding displaced abomasum (DA), ketosis (KET), mastitis (MAST), lameness (LAME), cystic ovaries (CYST), and metritis (MET) were collected between January 1, 2001 and December 31, 2003 in herds using Dairy Comp 305, DHI-Plus, or PCDART herd management software programs. All herds in this study were either participants in the Alta Genetics (Watertown, WI) Advantage progeny testing program or customers of the Dairy Records Management Systems (Raleigh, NC) processing center. Minimum lactation incidence rates were applied to ensure adequate reporting of these disorders within individual herds. After editing, DA, KET, MAST, LAME, CYST, and MET data from 75,252 (313), 52,898 (250), 105,029 (429), 50,611 (212), 65,080 (340), and 97,318 (418) cows (herds) remained for analysis. Average lactation incidence rates were 0.03, 0.10, 0.20, 0.10, 0.08, and 0.21 for DA, KET, MAST, LAME, CYST, and MET (including retained placenta), respectively. Data for each disorder were analyzed separately using a threshold sire model that included a fixed parity effect and random sire and herd-year-season of calving effects; both first lactation and all lactation analyses were carried out. Heritability estimates from first lactation (all lactation) analyses were 0.18 (0.15) for DA, 0.11 (0.06) for KET, 0.10 (0.09) for MAST, 0.07 (0.06) for LAME, 0.08 (0.05) for CYST, and 0.08 (0.07) for MET. Corresponding heritability estimates for the pooled incidence rate of all diseases between calving and 50 d postpartum were 0.12 and 0.10 for the first and all lactation analyses, respectively. Mean differences in PTA for probability of disease between the 10 best and 10 worst sires were 0.034 for DA, 0.069 for KET, 0.130 for MAST, 0.054 for LAME, 0.039 for CYST, and 0.120 for MET. Based on the results of this study, it
Barnard, Andrew R.
A new measurement technique, Supersonic Intensity in Reverberant Environments (SIRE), has been developed analytically, and validated numerically and experimentally. The SIRE technique permits the measurement of narrowband radiated sound power and directivity in an environment with unknown field conditions. This type of measurement has previously been limited to environments with exact field conditions, such as the free field. Due to long acoustic wavelengths, underwater anechoic tanks are not cost-effective for low frequency measurements, nor are at-sea measurements time- or cost-effective. Unlike SIRE, techniques like nearfield acoustic holography (NAH) rely on knowledge of exact field conditions, which are usually unknown in a realistic measurement environment. SIRE is a cost effective, repeatable laboratory technique for narrowband evaluation of complex structural acoustic sources submerged in water. The technique leverages underwater acoustic intensity vector sensors in the near field of a source and allows the outgoing acoustic waves to be separated from unwanted incoming acoustic waves. Supersonic wavenumber filtering rejects the evanescent potions of the acoustic pressure and particle velocity from the separated, outward-propagating sound pressure and particle velocity. The SIRE technique was applied to a monopole source, dipole source, and point-driven, thin-walled cylinder with massive end caps. All sources were placed in an underwater reverberant tank and measured using custom underwater vector sensors specifically designed and built to reduce electromagnetic interference (EMI). The results are compared with theory, the ANSI S12.51 standard one-third-octave reverberation room method, and free field NAH. SIRE is shown to accurately measure radiated sound power to within the limits of ANSI S12.51. SIRE is also shown to accurately measure the directivity indices of simple sources to within +/-3 dB. Finally, a coupled finite element/boundary element (FE
Vahedpoor, Zahra; Jamilian, Mehri; Bahmani, Fereshteh; Aghadavod, Esmat; Karamali, Maryam; Kashanian, Maryam; Asemi, Zatollah
2017-02-01
We are not aware of any study examining the effects of long term vitamin D administration on regression and metabolic status of patients with cervical intraepithelial neoplasia grade 1 (CIN1). This study was performed to evaluate the effects of long-term vitamin D administration on regression and metabolic status of patients with CIN1. This randomized, double-blind, placebo-controlled trial was performed among 58 women diagnosed with CIN1. CIN1 diagnosis was performed based on specific diagnostic procedures of biopsy, pathological diagnosis, and colposcopy. Patients were randomly allocated into two groups to take 50,000 IU vitamin D3 supplements (n = 29) or placebo (n = 29) every 2 weeks for 6 months. Fasting blood samples were taken at the beginning of the study and end-of-trial to measure related markers. After 6 months of vitamin D administration, greater percentage of women in the vitamin D group had regressed CIN1 (84.6 vs. 53.8%, P = 0.01) than those in the placebo group. Long-term vitamin D supplementation increased serum-25(OH) vitamin D levels in the intervention group compared to the placebo group (+12.3 ± 11.4 vs. -0.1 ± 3.7 ng/mL, P vitamin D intake led to significant decreases in serum insulin levels (-5.3 ± 7.3 vs. +2.4 ± 5.9 μIU/mL, P vitamin D supplements compared with the placebo group. In conclusion, vitamin D3 administration for 6 months among women with CIN1 resulted in its regression and had beneficial effects on markers of insulin metabolism, plasma NO, TAC, GSH and MDA levels. Clinical trial registration number www.irct.ir : IRCT201412065623N30.
Calsbeek, Ryan; Sinervo, Barry
2004-03-01
Sexual selection theory predicts that paternal quality should drive female investment in progeny. We tested whether polyandrous female side-blotched lizards, Uta stansburiana, would adjust within-clutch progeny investment according to sire phenotypes. In two different years, polyandrous females selectively used sperm from larger sires to produce sons and used sperm from smaller sires to produce daughters. This cryptic sperm choice had significant effects on progeny survival to maturity that were consistent with sexually antagonistic effects associated with sire body size. Large sires produced sons with high viability and small sires produced daughters with high viability. These results are consistent with our previous findings that alleles for male body size have different fitness effects in male and female progeny. Breeding experiments in the laboratory indicate that results from the wild are more likely due to female choice than biased sperm production by males. Our results demonstrate highly refined gender-specific female choice for sperm and indicate that sire body size may signal the quality of sons or daughters that a sire will produce.
Sire conception rate (SCR), a service-sire fertility evaluation implemented in August 2008, is based on up to 7 conventional-semen breedings for parities 1 through 5 (Ccow). The same procedure was used to derive SCR for other types of breedings: sexed semen for cows (Scow) and conventional semen and...
Jackson, Dan; White, Ian R; Riley, Richard D
2013-03-01
Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Gould, Rebecca L; Coulson, Mark C; Howard, Robert J
2012-02-01
To review the magnitude and duration of and factors associated with effects of cognitive behavioral therapy (CBT) for anxiety disorders in older people. Electronic literature databases and the Cochrane Trials Registry were searched for articles. A systematic critical review, random-effects meta-analysis, and meta-regression of randomized controlled trials were conducted. Community outpatient clinics. People with diagnoses of anxiety disorders. Outcome measures of anxiety and depression. Twelve studies were included. CBT was significantly more effective than treatment as usual or being on a waiting list at reducing anxiety symptoms at 0-month follow-up, with the effect size being moderate, but when CBT was compared with an active control condition, the between-group difference in favor of CBT was not statistically significant, and the effect size was small. At 6- but not 3- or 12-month follow-up, CBT was significantly more effective at reducing anxiety symptoms than an active control condition, although the effect size was again small. Meta-regression analyses revealed only one factor (type of control group) to be significantly associated with the magnitude of effect sizes. The review confirms the effectiveness of CBT for anxiety disorders in older people but is suggestive of lower efficacy in older than working-age people. The small effect sizes in favor of CBT over an active control condition illustrate the need to investigate other treatment approaches that may be used to substitute or augment CBT to increase the effectiveness of treatment of anxiety disorders in older people. © 2012, Copyright the Authors Journal compilation © 2012, The American Geriatrics Society.
Mangla, Rohit; Kumar, Shashi; Nandy, Subrata
2016-05-01
SAR and LiDAR remote sensing have already shown the potential of active sensors for forest parameter retrieval. SAR sensor in its fully polarimetric mode has an advantage to retrieve scattering property of different component of forest structure and LiDAR has the capability to measure structural information with very high accuracy. This study was focused on retrieval of forest aboveground biomass (AGB) using Terrestrial Laser Scanner (TLS) based point clouds and scattering property of forest vegetation obtained from decomposition modelling of RISAT-1 fully polarimetric SAR data. TLS data was acquired for 14 plots of Timli forest range, Uttarakhand, India. The forest area is dominated by Sal trees and random sampling with plot size of 0.1 ha (31.62m*31.62m) was adopted for TLS and field data collection. RISAT-1 data was processed to retrieve SAR data based variables and TLS point clouds based 3D imaging was done to retrieve LiDAR based variables. Surface scattering, double-bounce scattering, volume scattering, helix and wire scattering were the SAR based variables retrieved from polarimetric decomposition. Tree heights and stem diameters were used as LiDAR based variables retrieved from single tree vertical height and least square circle fit methods respectively. All the variables obtained for forest plots were used as an input in a machine learning based Random Forest Regression Model, which was developed in this study for forest AGB estimation. Modelled output for forest AGB showed reliable accuracy (RMSE = 27.68 t/ha) and a good coefficient of determination (0.63) was obtained through the linear regression between modelled AGB and field-estimated AGB. The sensitivity analysis showed that the model was more sensitive for the major contributed variables (stem diameter and volume scattering) and these variables were measured from two different remote sensing techniques. This study strongly recommends the integration of SAR and LiDAR data for forest AGB estimation.
Hofer Andreas
2001-11-01
Full Text Available Abstract Daily feed intake data of 1 279 French Landrace (FL, 1 039 boars and 240 castrates and 2 417 Large White (LW, 2 032 boars and 385 castrates growing pigs were recorded with electronic feed dispensers in three French central testing stations from 1992–1994. Male (35 to 95 kg live body weight or castrated (100 kg live body weight group housed, ad libitum fed pigs were performance tested. A quadratic polynomial in days on test with fixed regressions for sex and batch, random regressions for additive genetic, pen, litter and individual permanent environmental effects was used, with two different models for the residual variance: constant in model 1 and modelled with a quadratic polynomial depending on the day on test dm as follows in model 2: . Variance components were estimated from weekly means of daily feed intake by means of a Bayesian analysis using Gibbs sampling. Posterior means of (covariances were calculated using 800 000 samples from four chains (200 000 each. Heritability estimates of regression coefficients were 0.30 (FL model 1, 0.21 (FL model 2, 0.14 (LW1 and 0.14 (LW2 for the intercept, 0.04 (FL1, 0.04 (FL2, 0.11 (LW1 and 0.06 (LW2 for the linear, 0.03 (FL1, 0.04 (FL2 0.11 (LW1 and 0.06 (LW2 for the quadratic term. Heritability estimates for weekly means of daily feed intake were the lowest in week 4 (FL1: 0.11, FL2: 0.11 and week 1 (LW1: 0.09, LW2: 0.10, and the highest in week 11 (FL1: 0.25, FL2: 0.24 and week 8 (LW1: 0.19, LW2: 0.18, respectively. Genetic eigenfunctions revealed that altering the shape of the feed intake curve by selection is difficult.
Ota, Kazutaka; Awata, Satoshi; Morita, Masaya; Yokoyama, Ryota; Kohda, Masanori
2014-01-01
To examine how territorial males counter reproductive parasites, we examined the paternity of broods guarded by territorial males using 5 microsatellite loci and factors that determine siring success in a wild population of the Lake Tanganyika cichlid Lamprologus lemairii. Females enter rock holes (nests) and spawn inside, and territorial males release milt over the nest openings. Sneakers attempt to dart into the nests, but territorial males often interrupt the attempt. The body size of territorial males (territorial defense ability) and the size of nest opening (the ability to prevent sneakers from nest intrusions) are predicted to be factors that affect paternity at the premating stage, whereas milt quality traits are factors that affect paternity at the postmating stage. Parentage analyses of 477 offspring revealed that most clutches have few or no cuckolders, and territorial males sired >80% of eggs in 7 of the 10 analyzed clutches. Larger territorial males that spawned in nests with narrower openings had greater siring success. In contrast, none of the milt traits affected the siring success. These suggest that territorial male L. lemairii adopt premating strategies whereby they effectively prevent reproductive parasitism.
The mating behaviour and reproduction performance in a multi-sire mating system for pigs
Kongsted, Anne Grete; Hermansen, John Erik
2008-01-01
An important aim of organic animal production is to allow natural animal behaviour. Regarding reproduction techniques, artificial insemination is permitted but natural mating is preferred. The outdoor multi-sire system, where the sows are placed in large paddocks with a group of boars, is one...
Giovanni Bittante
2010-01-01
Full Text Available The purpose of this study was to estimate the effects of herd origin of bull, AI stud and sire identification number (ID on official estimated breeding values (EBV for production traits of Holstein Friesian proven bulls. The data included 1,005 Italian Holstein-Friesian bulls, sons of 76 sires, born in 100 herds and progeny tested by 10 AI studs. Bulls were required to have date of first proof between September 1992 and September 1997, to be born in a herd with at least one other bull and to have sire and dam with official EBV when bull was selected for progeny testing. Records of sires with only one son were also discarded. The dependent variable analyzed was the official genetic evaluation for a “quantity and quality of milk” index (ILQ. The linear model to predict breeding values of bulls included the fixed class effects of herd origin of bull, AI testing organization, birth year of bull, and estimated breeding values of sire and dam, both as linear covariates. The R2of the model was 45% and a significant effect was found for genetic merit of sire (P for herd origin of bull (P nificant. The range of herd origin effect was 872 kg of ILQ. However, in this study, the causes of this result were not clear; it may be due to numerous factors, one of which may be preferential treatment on dams of bulls. Analyses of resid- uals on breeding value of proven bulls for ILQ showed a non significant effect of sire ID, after adjusting for parent aver- age, herd origin effect and birth year effect. Although the presence of bias in genetic evaluation of dairy bulls is not evi- dent, further research is recommended firstly to understand the reasons of the significant herd origin effect, secondly to monitor and guarantee the greatest accuracy and reliability of genetic evaluation procedures.
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
Sun, C; Madsen, P; Nielsen, U S
2009-01-01
Comparisons between a sire model, a sire-dam model, and an animal model were carried out to evaluate the ability of the models to predict breeding values of fertility traits, based on data including 471,742 records from the first lactation of Danish Holstein cows, covering insemination years from...... (EBV) from the animal model and the sire-dam model were close to 1 for all the traits, and those between the animal model and the sire model ranged from 0.95 to 0.97. Model ability to predict sire breeding value was assessed using 4 criteria: 1) the correlation between sire EBV from 2 data subsets...... (DATAA and DATAB); 2) the correlation between sire EBV from training data (DATAA or DATAB) and yield deviation from test data (DATAB or DATAA) in a cross-validation procedure; 3) the correlation between the EBV of proven bulls, obtained from the whole data set (DATAT) and from a reduced set of data...
The effect of breed of sire and age at feeding on muscle tenderness in the beef chuck.
Christensen, K L; Johnson, D D; West, R L; Marshall, T T; Hargrove, D D
1991-09-01
Steers (n = 59) produced from the mating of Braford, Simbrah, Senepol, and Simmental bulls to Brahman- and Romana Red-sired cows and Brahman bulls to Angus cows were used in this study. Effects of sire breed and age at feeding on muscle tenderness in the major muscles of the chuck when steers were fed to 1.0 cm 12th rib fat were determined. There were no muscle tenderness effects due to sire breed group, with the exception of the serratus ventralis muscle, which was more tender in Brahman- and Braford-sired steers than in Simmental-sired steers. Additionally, the supraspinatus muscle from the yearlings was lower in shear value than that from the calves. The Brahman-sired steers had serratus ventralis muscles with higher percentages (P less than .05) of intramuscular fat than those of Braford-, Simbrah-, and Simmental-sired steers. Fat deposited within the muscle or between muscles in the chuck was not related to muscle tenderness as measured by Warner-Bratzler shear values. Also, percentages of intramuscular fat of the triceps brachii, serratus ventralis, or supraspinatus muscles were not influenced (P greater than .05) by age at feeding.
Meyskens, F L; Surwit, E; Moon, T E; Childers, J M; Davis, J R; Dorr, R T; Johnson, C S; Alberts, D S
1994-04-06
Retinoids enhance differentiation of most epithelial tissues. Epidemiologic studies have shown an inverse relationship between dietary intake or serum levels of vitamin A and the development of cervical dysplasia and/or cervical cancer. Pilot and phase I investigations demonstrated the feasibility of the local delivery of all-trans-retinoic acid (RA) to the cervix using a collagen sponge insert and cervical cap. A phase II trial produced a clinical complete response rate of 50%. This randomized phase III trial was designed to determine whether topically applied RA reversed moderate cervical intraepithelial neoplasia (CIN) II or severe CIN. Analyses were based on 301 women with CIN (moderate dysplasia, 151 women; severe dysplasia, 150 women), evaluated by serial colposcopy, Papanicolaou cytology, and cervical biopsy. Cervical caps with sponges containing either 1.0 mL of 0.372% beta-trans-RA or a placebo were inserted daily for 4 days when women entered the trial, and for 2 days at months 3 and 6. Patients receiving treatment and those receiving placebo were similar with respect to age, ethnicity, birth-control methods, histologic features of the endocervical biopsy specimen and koilocytotic atypia, and percentage of involvement of the cervix at study. Treatment effects were compared using Fisher's exact test and logistic regression methods. Side effects were recorded, and differences were compared using Fisher's exact test. RA increased the complete histologic regression rate of CIN II from 27% in the placebo group to 43% in the retinoic acid treatment group (P = .041). No treatment difference between the two arms was evident in the severe dysplasia group. More vaginal and vulvar side effects were seen in the patients receiving RA, but these effects were mild and reversible. A short course of locally applied RA can reverse CIN II, but not more advanced dysplasia, with acceptable local side effects. A derivative of vitamin A can reverse or suppress an epithelial
Mousel, M R; Notter, D R; Leeds, T D; Zerby, H N; Moeller, S J; Lewis, G S
2013-05-01
Postfabrication carcass component weights of 517 crossbred wether lambs were analyzed to evaluate 4 terminal-sire breeds. Wethers were produced over 3 yr from single-sire matings of 22 Columbia, 22 USMARC-Composite (Composite), 21 Suffolk, and 17 Texel rams to adult Rambouillet ewes. Lambs were reared to weaning in an extensive western rangeland production system and finished in a feedlot on a high-energy finishing diet. When wethers reached a mean BW of 54.4, 61.2, or 68.0 kg, they were transported to The Ohio State University abattoir for harvest. After refrigeration for approximately 24 h, chilled carcass weight (CCW) was measured, carcasses were fabricated according to Style A of Institutional Meat Purchase Specifications, and postfabrication weights were recorded. At comparable numbers of days on feed, Suffolk-sired lambs had heavier (P wholesale cut weights) and high-value trimming loss were greatest (P 0.06) flank weight. Data adjusted to comparable CCW reduced the number of significant sire-breed effects and changed sire-breed rankings of carcass component weights, for which sire breeds differed. After adjusting, Suffolk-sired lambs had lighter (P < 0.05) loins than Columbia- and Composite-sired lambs, Composite-sired lambs had heavier (P < 0.05) high-value cuts than Suffolk-sired lambs, and Suffolk- and Columbia-sired lambs had heavier (P < 0.05) necks than Texel-sired lambs. At predicted backfat thickness of 6.6 mm, Composite-sired lambs had a greater (P < 0.05) percentage of high-value cuts than Suffolk-sired lambs before but not after trimming. Producers can use these results to select terminal-sire breeds that will complement their production system and improve lamb value.
Casas, E; Thallman, R M; Cundiff, L V
2011-04-01
The objective of this study was to characterize breeds representing diverse biological types for birth and weaning traits in crossbred cattle. Gestation length, calving difficulty, percentage of unassisted calving, percentage of perinatal survival, percentage of survival from birth to weaning, birth weight, BW at 200 d, and ADG were measured in 2,500 calves born and 2,395 calves weaned. Calves were obtained by mating Hereford, Angus, and MARC III (one-fourth Hereford, one-fourth Angus, one-fourth Pinzgauer, and one-fourth Red Poll) mature cows to Hereford or Angus (British breed), Brahman, Tuli, Boran, and Belgian Blue sires. Calves were born during the spring seasons of 1992, 1993, and 1994. Sire breed was significant for all traits (P Brahman sires (1.24), whereas the offspring of Tuli sires had the least amount of calving difficulty (1.00). Offspring from all sire breeds had similar perinatal survival and survival from birth to weaning (average of 97.2 and 96.2%, respectively), with the exception of offspring from Brahman sires, which had less (92.8 and 90.4%, respectively). Progeny of Brahman sires were heaviest at birth (45.7 kg), followed by offspring from British breed, Boran, and Belgian Blue sires (average of 42.4 kg). The lightest offspring at birth were from Tuli sires (38.6 kg). Progeny derived from Brahman sires were the heaviest at 200 d (246 kg), and they grew faster (1.00 kg/d) than offspring from any other group. The progeny of British breeds and the Belgian Blue breed had an intermediate BW at 200 d (238 kg) and an intermediate ADG (average of 0.98 kg/d). The progeny of Boran and Tuli sires were the lightest at 200 d (227 kg) and had the least ADG (0.93 kg/d). Male calves had a longer gestation length, had a greater incidence of calving difficulty, had greater mortality to weaning, were heavier, and grew faster than female calves. Sire breed effects can be optimized by selection and use of appropriate crossbreeding systems.
Brokamp, Cole; Jandarov, Roman; Rao, M. B.; LeMasters, Grace; Ryan, Patrick
2017-02-01
Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.
Using "random forest" for classification and regression%随机森林模型在分类与回归分析中的应用
李欣海
2013-01-01
"Random forest" is an algorithm developed by Breiman and Cutler in 2001.It runs by constructing multiple decision trees while training and outputing the class that is the mode of the classes output by individual trees.It has improved performance over single decision trees,and it is much more efficient than traditional machine learning techniques,e.g.artificial neural networks,especially when the dataset is large.Random forest can handle up to thousands of explanatory variables.It can be used to rank the importance of variables when the R package "random.forest" is implemented.It is suitable for demonstrating the nonlinear effect of variables,and it can model complex interactions among variables.Random forest is robust for outliers.In this paper,three examples are used to introduce how to use random forest for a discrimination problem (the dependent variable has multiple categories) for presence-absence data (the deperdent variable has two categories),and for regression(the dependent variable is a continuous variable).%随机森林(random forest)模型是由Breiman和Cutler在2001年提出的一种基于分类树的算法.它通过对大量分类树的汇总提高了模型的预测精度,是取代神经网络等传统机器学习方法的新的模型.随机森林的运算速度很快,在处理大数据时表现优异.随机森林不需要顾虑一般回归分析面临的多元共线性的问题,不用做变量选择.现有的随机森林软件包给出了所有变量的重要性.另外,随机森林便于计算变量的非线性作用,而且可以体现变量间的交互作用(interaction).它对离群值也不敏感.本文通过3个案例,分别介绍了随机森林在昆虫种类的判别分析、有无数据的分析(取代逻辑斯蒂回归)和回归分析上的应用.案例的数据格式和R语言代码可为研究随机森林在分类与回归分析中的应用提供参考.
The influence of season and sire on the results of superovulation and embryo transfer
Zdeňka Hegedűšová
2006-01-01
Full Text Available The aim of the study was evaluate the influence of season and sires on profit and quality of embryos after superovulated treatment. Next we evaluated the conception rate after transfer of fresh and frozen embryos.In 1991–2004 there were used the beef cattle. Into the basic statistic evaluation it was involved 487 realised embryo recoveries and 2008 realised transfers in 1991–2004. Data for database were obtaining from ETprotocols – ET team Research Institute for Cattle Breeding, Ltd., Rapotin, prof. Říha. The data processing was carried out by means of the common variation-statistical methods.The best results were achieved in summer (suitable 3.68 ± 3.65; the ratio of the suitable and total: 59.3% and in autumn (suitable 3.54 ± 3.80; the ratio: 54.48% and the good results, little different from the summer and autumn results, were achieved in spring.The average number of the recovered ova of the chosen breeds sires were variable (from 6.60 ± 6.17 in Blonde d´Aquitaine to 17.16 ± 6.66 in Charolais. The most of the suitable embryos was recovered in the donors inseminated by the Hereford breed sires (7.15 ± 6.42. It was evaluated the above-average conception in the Simmental breed (63.43 %.
Cowan, C M; Dentine, M R; Ax, R L; Schuler, L A
1990-05-01
Digestion of genomic DNA with the restriction endonuclease Avail disclosed a probable insertion deletion of approximately 200 base pairs (bp) near the prolactin gene. Two alleles were apparent as three distinct hybridization patterns. These alleles were statistically associated with quantitative trait loci among sons of one elite Holstein sire family. The favorable genotype was correlated with the presence of a 1.15-kb hybridization band inherited from the sire when genomic DNA was probed with a full-length cDNA for prolactin. Pedigree estimates of genetic merit among genotypes were similar, differing by only 19.3 kg for milk in ancestor merit. Comparisons of genetic estimates for quantitative yield traits in offspring of this heterozygous sire showed significant (Pcheese yield dollars, and protein dollars. The estimated differences between homozygous genotypes for USDA Transmitting Abilities of PDM, PD$, Cheese Yield $ and Protein $ were 282.93 kg, $74.35, $48.58 and $53.67, respectively. However, the estimated breeding values from progeny ranged over 900 kg in transmitting ability for milk. Frequency of the favorable marker allele was estimated to be 0.231 in the elite cow population used as dams of sons. These results demonstrate the potential of molecular biological techniques to discriminate between individuals within a family and to predict breeding values for selection schemes.
Intramuscular fat in the longissimus muscle is reduced in lambs from sires selected for leanness.
Pannier, L; Pethick, D W; Geesink, G H; Ball, A J; Jacob, R H; Gardner, G E
2014-02-01
Selection for lean growth through Australian Sheep Breeding Values (ASBVs) for post weaning weight (PWWT), eye muscle depth (PEMD) and c-site fat depth (PFAT) raises concerns regarding declining intramuscular fat (IMF) levels. Reducing PFAT decreased IMF by 0.84% for Terminal sired lambs. PEMD decreased IMF by 0.18% across all sire types. Female lambs had higher IMF levels and this was unexplained by total carcass fatness. The negative phenotypic association between measures of muscling (shortloin muscle weight, eye muscle area) and IMF, and positive association between fatness and IMF, was consistent with other literature. Hot carcass weight increased IMF by 2.08% between 12 and 40 kg, reflective of development of IMF as lambs approach maturity. Selection objectives with low PFAT sires will reduce IMF, however the lower impact of PEMD and absence of a PWWT effect, will enable continued selection for lean growth without influencing IMF. Alternatively, the negative impact of PFAT could be off-set by inclusion of an IMF ASBV. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
Herring, A D; Sanders, J O; Knutson, R E; Lunt, D K
1996-05-01
Birth (n = 308), weaning (n = 291), feedlot and carcass (n = 142), and yearling heifer traits (n = 139) were evaluated in F1 calves sired by Brahman (BR), Boran (BO), and Tuli (TU) bulls and born to multiparous Hereford and Angus cows. Calves sired by BR were heaviest (P Brahman crosses had larger (P Brahman F1 heifers had larger (P carcass quality traits, but not for carcass yield traits, among these three breeds.
Ha, Duck-Min; Jung, Dae-Yun; Park, Man Jong; Park, Byung-Chul; Lee, C Young
2014-01-01
The present study was performed to investigate the effects of two groups of sires with 'medium' and 'high' weight gain potentials (M-sires and H-sires, respectively) on growth of their progenies on varying planes of nutrition during the growing-finishing period. The ADG of the M-sires' progeny was greater (P plane of nutrition (H plane) followed by the medium (M) and low (L) planes (0.65, 0.61, and 0.51 kg, respectively; P planes vs. L plane (0.63, 0.62, and 0.54 kg, respectively). The ADG of pigs on the M or H plane during the grower phase and switched to the H plane thereafter (M-to-H or H-to-H planes) was greater than that of pigs on the L-to-L planes (0.99 vs. 0.78 kg) during the early finisher phase in the M-sires' progeny (P planes did not differ from that of pigs on the M-to-M or H-to-M planes (0.94 vs. 0.96 kg). Results suggest that the H-to-H or H-to-M planes and M-to-M or M-to-L planes are optimal for maximal growth of the M- and H-sires' progenies, respectively.
Santana, Mário L; Bignardi, Annaiza Braga; Pereira, Rodrigo Junqueira; Menéndez-Buxadera, Alberto; El Faro, Lenira
2016-02-01
The present study had the following objectives: to compare random regression models (RRM) considering the time-dependent (days in milk, DIM) and/or temperature × humidity-dependent (THI) covariate for genetic evaluation; to identify the effect of genotype by environment interaction (G×E) due to heat stress on milk yield; and to quantify the loss of milk yield due to heat stress across lactation of cows under tropical conditions. A total of 937,771 test-day records from 3603 first lactations of Brazilian Holstein cows obtained between 2007 and 2013 were analyzed. An important reduction in milk yield due to heat stress was observed for THI values above 66 (-0.23 kg/day/THI). Three phases of milk yield loss were identified during lactation, the most damaging one at the end of lactation (-0.27 kg/day/THI). Using the most complex RRM, the additive genetic variance could be altered simultaneously as a function of both DIM and THI values. This model could be recommended for the genetic evaluation taking into account the effect of G×E. The response to selection in the comfort zone (THI ≤ 66) is expected to be higher than that obtained in the heat stress zone (THI > 66) of the animals. The genetic correlations between milk yield in the comfort and heat stress zones were less than unity at opposite extremes of the environmental gradient. Thus, the best animals for milk yield in the comfort zone are not necessarily the best in the zone of heat stress and, therefore, G×E due to heat stress should not be neglected in the genetic evaluation.
Berhan, Yifru; Berhan, Asres
2014-07-01
During perimenopause, vasomotor symptoms are known to have a detrimental effect on women's functional ability and quality of life. For symptomatic women not eligible for hormonal therapy, desvenlafaxine is an option, but its safety margin and tolerability are not yet determined. A computer-based literature search was done in the databases of MEDLINE, Cochrane library, and HINARI (Health InterNetwork Access to Research Initiative). Meta-analysis was conducted by including double-blind randomized controlled studies on the effectiveness and safety of desvenlafaxine in the treatment of hot flashes. The effectiveness, safety and tolerability of desvenlafaxine were determined by standardized mean differences (SMDs) and Mantel-Haenszel odds ratio. Subgroup analysis based on doses of desvenlafaxine and linear meta-regression analyses were performed for several covariates. Heterogeneity testing, the risk of bias assessment and sensitivity analyses were done. Desvenlafaxine was associated with a statistically significant reduction in the number and severity of daily moderate to severe hot flashes. The number of nighttime awakenings because of hot flashes was also significantly decreased. However, the rate of desvenlafaxine treatment discontinuation because of adverse events was a significantly higher than placebo treated women and the risk ratios of adverse events like asthenia, hypertension, anorexia, constipation, diarrhea, dry mouth, nausea, dizziness, insomnia, somnolence and mydriasis were very high. Desvenlafaxine is effective in the treatment of hot flashes but it is strongly associated with several adverse events and treatment discontinuation. Further clinical trials focusing only on desvenlafaxine related adverse events are highly warranted before it is approved for public use.
INFLUENCE OF AGE, GENDER AND SIRE LINE ON YOUNG CATTLE BEHAVIOUR TRAITS
Jan Broucek
2013-03-01
Full Text Available The aim of this study was to test effects of age, gender, and sire line on dairy cattle behaviour. We have analyzed results of ethological tests for 40 Holstein breed animals (23 males and 17 females, offsprings of three sires. Maintenance behaviour were observed at the age of 90, 130 and 170 days. Behaviour in the maze was conducted at the age of 119 days, an open-field test was applied at the age of 124, 168, and 355 days. The social behaviour was determined by feeding on 155th day of the age. The times and the number of periods in all activities of maintenance behaviour were changing significantly (P<0.001 according to the age. The total time of lying, lying with ruminating, ruminating, feeding was increasing from the age of 90 days to the age of 170 days, on the other hand the time of standing was decreasing. The times of total lying, lying with ruminating, total ruminating, feeding were increased, and time of standing was decreased from the age of 90 days to the age of 170 days. Calves spent more time lying on the left side than on the right side. The number of ruminating periods was increasing according to the age. Eating periods were decreasing from the age of 90 to 170 days. The most of lying periods were recorded at the age of 130 days. The differences between sex were found in total time of lying, lying on the right side (P<0.05, and the males rest longer and had more periods of lying than females. We have found differences in times of feeding (P<0.001, total lying, standing (P<0.01, and lying on the left side (P<0.05 according to sire by comparing behaviour of the calves. Sire genotypes were significantly manifested in period number of total lying (P<0.001, lying on the right side, feeding (P<0.01, and standing (P<0.05. Males stood in the first part of maze longer than females (P<0.001, also length of total standing was longer by bulls (P<0.01. Heifers took shorter time to leave the maze than bulls (P<0.05. Sire lineages significantly
Posavac, Steven S.; Posavac, Emil J.
2017-01-01
The authors describe the Pennies for Milk exercise, a participative classroom experience in which students generate a regression to the mean effect within the context of simulated household milk purchases. Regression to the mean is a ubiquitous threat for marketing researchers and managers but is often hard for students to understand. The Pennies…
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Nick, Todd G; Campbell, Kathleen M
2007-01-01
The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.
Akther, Shirin; Huang, Zhiqi; Liang, Mingkun; Zhong, Jing; Fakhrul, Azam A K M; Yuhi, Teruko; Lopatina, Olga; Salmina, Alla B; Yokoyama, Shigeru; Higashida, Chiharu; Tsuji, Takahiro; Matsuo, Mie; Higashida, Haruhiro
2015-01-01
Parental behaviors involve complex social recognition and memory processes and interactive behavior with children that can greatly facilitate healthy human family life. Fathers play a substantial role in child care in a small but significant number of mammals, including humans. However, the brain mechanism that controls male parental behavior is much less understood than that controlling female parental behavior. Fathers of non-monogamous laboratory ICR mice are an interesting model for examining the factors that influence paternal responsiveness because sires can exhibit maternal-like parental care (retrieval of pups) when separated from their pups along with their pairmates because of olfactory and auditory signals from the dams. Here we tested whether paternal behavior is related to femininity by the aromatization of testosterone. For this purpose, we measured the immunoreactivity of aromatase [cytochrome P450 family 19 (CYP19)], which synthesizes estrogen from androgen, in nine brain regions of the sire. We observed higher levels of aromatase expression in these areas of the sire brain when they engaged in communicative interactions with dams in separate cages. Interestingly, the number of nuclei with aromatase immunoreactivity in sires left together with maternal mates in the home cage after pup-removing was significantly larger than that in sires housed with a whole family. The capacity of sires to retrieve pups was increased following a period of 5 days spent with the pups as a whole family after parturition, whereas the acquisition of this ability was suppressed in sires treated daily with an aromatase inhibitor. The results demonstrate that the dam significantly stimulates aromatase in the male brain and that the presence of the pups has an inhibitory effect on this increase. These results also suggest that brain aromatization regulates the initiation, development, and maintenance of paternal behavior in the ICR male mice.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Genetic parameters of calving ease using sire-maternal grandsire model in Korean Holsteins
Mahboob Alam
2017-09-01
Full Text Available Objective Calving ease (CE is a complex reproductive trait of economic importance in dairy cattle. This study was aimed to investigate the genetic merits of CE for Holsteins in Korea. Methods A total of 297,614 field records of CE, from 2000 to 2015, from first parity Holstein heifers were recorded initially. After necessary data pruning such as age at first calving (18 to 42 mo, gestation length, and presence of sire information, final datasets for CE consisted of 147,526 and 132,080 records for service sire calving ease (SCE and daughter calving ease (DCE evaluations, respectively. The CE categories were ordered and scores ranged from CE1 to CE5 (CE1, easy; CE2, slight assistance; CE3, moderate assistance; CE4, difficult calving; CE5, extreme difficulty calving. A linear transformation of CE score was obtained on each category using Snell procedure, and a scaling factor was applied to attain the spread between 0 (CE5 and 100% (CE1. A sire-maternal grandsire model analysis was performed using ASREML 3.0 software package. Results The estimated direct heritability (h2 from SCE and DCE evaluations were 0.11±0.01 and 0.08±0.01, respectively. Maternal h2 estimates were 0.05±0.02 and 0.04±0.01 from SCE and DCE approaches, respectively. Estimates of genetic correlations between direct and maternal genetic components were −0.68±0.09 (SCE and −0.71±0.09 (DCE. The average direct genetic effect increased over time, whereas average maternal effect was low and consistent. The estimated direct predicted transmitting ability (PTA was desirable and increasing over time, but the maternal PTA was undesirable and decreasing. Conclusion The evidence on sufficient genetic variances in this study could reflect a possible selection improvement over time regarding ease of calving. It is expected that the estimated genetic parameters could be a valuable resource to formulate sire selection and breeding plans which would be directed towards the reduction of
Perry, D; Nicholls, P J; Thompson, J M
1998-01-01
Fatty acid composition and the melting point of subcutaneous fat was determined in 18 Hereford, 25 Brahman x Hereford, 22 Simmental x Hereford, and 15 Friesian x Hereford steers that were grown out on pasture at two sites and slaughtered when the mean weight of the Herefords at each site was ca. 450 kg. Multivariate and univariate analyses tested the relations of fatty acid composition, degree of saturation, and melting point with sire breed, environment, age, and carcass characteristics. Hereford and Brahman steers were fatter than the Simmental and Friesian steers. Fat from Brahman-sired steers had a melting point 2.5 degrees C lower than fat from the Bos taurus-sired steers at the same age and had a higher proportion of unsaturated fatty acids, independent of variation in carcass weight and fatness. Melting point and degree of saturation decreased as age increased. Step-down discriminant analyses identified a set of three acids (14:0, 16:0, and 17:1) that differed among sire breeds, independent of differences in melting point: the acids 14:0 and 16:0 discriminated between Brahman and Bos taurus steers and 17:1 between Hereford and Simmental and Friesian steers. Increase in fatness was associated with an increase in 17:1, but, at the same fatness, no acids discriminated among the Bos taurus-sired steers. The use of Bos indicus cattle or their crossbreeds in situations in which hard-setting fat is likely may mitigate the problem.
Linear vs. piecewise Weibull model for genetic evaluation of sires for longevity in Simmental cattle
Nikola Raguž
2014-09-01
Full Text Available This study was focused on genetic evaluation of longevity in Croatian Simmental cattle using linear and survival models. The main objective was to create a genetic model that is most appropriate to describe the longevity data. Survival analysis, using piecewise Weibull proportional hazards model, used all information on the length of productive life including censored as well as uncensored observations. Linear models considered culled animals only. The relative milk production within herd had a highest impact on cows’ longevity. In comparison of estimated genetic parameters among methods, survival analysis yielded higher heritability value (0.075 than linear sire (0.037 and linear animal model (0.056. When linear models were used, genetic trend of Simmental bulls for longevity was slightly increasing over the years, unlike a decreasing trend in case of survival analysis methodology. Average reliability of bulls’ breeding values was higher in case of survival analysis. The rank correlations between survival analysis and linear models bulls’ breeding values for longevity were ranged between 0.44 and 0.46 implying huge differences in ranking of sires.
Philipp, Marianne; Jakobsen, Ruth Bruus; Nachman, Gøsta Støger
2009-01-01
, hermaphrodite, and male individuals. The sex expression of males and hermaphrodites can vary over years for the same individual, while females are always females. Previous studies have shown that outcrossed seeds from females become seedlings with higher survival and growth rates than those from outcrossed...... seeds from hermaphrodites.Questions: (1) Do pollen grains from males exhibit some advantage over pollen from (1) Do pollen grains from males exhibit some advantage over pollen from hermaphrodites? In particular, do they sire more seeds than hermaphrodites? (2) Is the reproductive system of S. acaulis...... stable or is it evolving towards one with fewer morphs (i.e. dioecy or gynodioecy)?Hypothesis: Pollen from male plants is better at siring seeds on females than pollen from Pollen from male plants is better at siring seeds on females than pollen from hermaphrodites.Study system: A subdioecious population...
Hyeon-Suk Park
2017-09-01
Full Text Available Objective The objective of the present study was to compare growth performance of Berkshire purebred pigs (BB, Hereford (HB and/or Tamworth (TB sired Berkshire crossbred pigs reared in a hoop structure in two experiments. Methods In the first experiment, BB was compared to TB while HB and TB were compared in the second. Body weights (BW were recorded at 3 days of age and every 28 days from birth until 140 days of age. There was no significant difference between the BW of BB and TB, but HB was heavier than TB by 84 days of age. Least square means of average daily gain (ADG were evaluated using one-way analysis of variance. Results The mean parity (±standard deviation of the sows was 3.42±2.14 and a total of 45 farrowing occurred from year 2012 to 2014. The mean number of total born, number born alive, number of mummies, and number weaned were 9.23±2.52, 7.87±2.53, 0.04±0.21, and 5.94±2.74, respectively. Parity did not have a significant effect on the growth performance of the pigs. For BB and TB, there was only one time frame in which there was a significant difference in the ADG: between 28 and 56 days of age. For HB and TB, the overall ADG of HB was significantly greater than the total ADG of TB. Conclusion The breed of the sire did not affect the growth performance of the progeny between Berkshire purebreds and Tamworth×Berkshire crossbreds. The breed of the sire did have an effect between Hereford and Tamworth sired Berkshire crossbreds (p<0.05. The Hereford sired pigs were found to have increased growth performance compared to Tamworth sired.
Cláudio Vieira de Araújo
2006-06-01
Full Text Available Registros de produção de leite de 68.523 controles leiteiros de 8.536 vacas da raça Holandesa, com parições nos anos de 1996 a 2001, foram utilizados na comparação entre modelos de regressão aleatória para estimação de componentes de variância. Os registros de controle leiteiro foram analisados como características múltiplas, considerando cada controle uma característica distinta. Os mesmos registros de controle leiteiro foram analisados como dados longitudinais, por meio de modelos de regressão aleatória, que diferiram entre si pela função utilizada para descrever a trajetória da curva de lactação dos animais. As funções utilizadas foram a exponencial de Wilmink, a função de Ali e Schaeffer e os polinômios de Legendre de segundo e quarto graus. A comparação entre modelos foi realizada com base nos seguintes critérios: estimativas de componentes de variância, obtidas no modelo multicaractístico e por regressão aleatória; valores da variância residual; e valores do logaritmo da função de verossimilhança. As estimativas de herdabilidade obtidas por meio dos modelos de características múltiplas variaram de 0,110 a 0,244. Para os modelos de regressão aleatória, esses valores oscilaram de 0,127 a 0,301, observando-se as maiores estimativas nos modelos com maior número de parâmetros. Verificou-se que os modelos de regressão aleatória que utilizaram os polinômios de Legendre descreveram melhor a variação genética da produção de leite.Data comprising 68,523 test day milk yield of 8,536 cows of the Holstein breed, calving from 1996 to 2001, were used to compare random regression models, for estimating variance components. Test day records (TD were analyzed as multiple traits, considering each TD as a different trait. The test day records were analyzed as longitudinal traits by different random regression models regarding the function used to describe the trajectory of the lactation curve of the animals
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Freetly, H C; Kuehn, L A; Cundiff, L V
2011-08-01
The objective of this study was to evaluate the growth curves of females to determine if mature size and relative rates of maturation among breeds differed. Body weight and hip height data were fitted to the nonlinear function BW = f(age) = A - Be(k×age), where A is an estimate of mature BW and k determines the rate that BW or height moves from B to A. Cows represented progeny from 28 Hereford, 38 Angus, 25 Belgian Blue, 34 Brahman, 8 Boran, and 9 Tuli sires. Bulls from these breeds were mated by AI to Angus, Hereford, and MARC III composite (1/4 Angus, 1/4 Hereford, 1/4 Red Poll, and 1/4 Pinzgauer) cows to produce calves in 1992, 1993, and 1994. These matings resulted in 516 mature cows whose growth curves were subsequently evaluated. Hereford-sired cows tended to have heavier mature BW, as estimated by parameter A, than Angus- (P=0.09) and Brahman-sired cows (P=0.06), and were heavier than the other breeds (P Brahman-sired cows (P=0.94). Brahman-sired cows had a heavier mature BW than Boran- (P Brahman (P Brahman-sired cows took longer to mature than Boran- (P=0.03) or Belgian Blue-sired cows (P=0.003). Belgian Blue-sired cows were faster maturing than Tuli-sired cows (P=0.02). Brahman-sired cows had reached a greater proportion of their mature BW at puberty than had Hereford- (P < 0.001), Tuli- (P=0.003), and Belgian Blue-sired cows (P=0.001). Boran-sired cows tended to have reached a greater proportion of their mature BW at puberty than had Angus-sired cows (P=0.09), and had reached a greater proportion of their mature BW at puberty than had Hereford- (P < 0.001), Tuli- (P < 0.001), and Belgian Blue-sired cows (P < 0.001). Within species of cattle, the relative range in proportion of mature BW at puberty (Bos taurus 0.56 through 0.58, and Bos indicus 0.60) was highly conserved, suggesting that proportion of mature BW is a more robust predictor of age at puberty across breeds than is absolute weight or age. © 2011 American Society of Animal Science. All rights
Petersen, Mette Bisgaard; Tolver, Anders; Husted, Louise
2016-01-01
admitted with acute colitis (random...... forest analysis. Ponies and Icelandic horses made up 59% of the population, whilst the remaining 41% were horses. Blood lactate concentration at admission was the only individual parameter significantly associated with probability of survival to discharge (P
Rachel Santos Bueno
2005-08-01
study the effects of sire x herd and sire x herd-year interactions on genetic values of Brown Swiss breed sires. The (covariance components were estimated by the restricted maximum likelihood method, by using three models with multitrait. In these models, the genetic group, season of calving and herd-year class were considered as fixed effects, while the animal effects, the permanent environment, the interaction of either sire x herd or sire x herd-year were considered as random ones, when the interaction was considered in the model, and the error as well. The likelihood ratio test was used to verify the effectiveness in including the interaction effects into models. The estimates of components of the genetic addictive and residual (covariances did not change when the models were adjusted for the interaction effects. Therefore, the heritability coefficients approximated to each others. The heritability estimate were of 0.40 for both characteristics, and the genetic correlation among the characteristics of 0.94, except when the model considered the effect of the interaction sire x herd. The heritability of fat yield was of 0.39, and the genetic correlation among the characteristics of 0.95. The proportion of the total variance explained by the sire x herd and the sire x herd -year interactions was low, but almost null for milk yield, and about 1% for fat yield. The natural logarithm of likelihood function increased, when the interaction effects were included in the models. Pearson and Spearman correlations among the genetic values obtained by these models were superior than 0.99 for both milk and fat yields, and above 0.897 among the studied characteristics.
Late foetal life nutrient restriction and sire genotype affect postnatal performance of lambs
Tygesen, Malin Plumhoff; Tauson, Anne-Helen; Blache, D.
2008-01-01
This experiment investigates the effects of maternal nutrient restriction in late gestation on the offsprings' postnatal metabolism and performance. Forty purebred Shropshire twin lambs born to ewes fed either a high-nutrition diet (H) (according to standard) or a low-nutrition (L) diet (50% during...... the last 6 weeks of gestation) were studied from birth until 145 days of age. In each feeding group, two different sires were represented, ‘growth' (G) and ‘meat' (M), having different breeding indices for the lean : fat ratio. Post partum all ewes were fed the same diet. Lambs born to L-ewes had...... significantly lower birth weights and pre-weaning growth rates. This was especially pronounced in L-lambs born to the M-ram, which also had markedly lower pre-weaning glucose concentrations than the other three groups of lambs. L-lambs converted milk to live weight with an increased efficiency in week 3 of life...
Growth, puberty, and carcass characteristics of Brahman-, Senepol-, and Tuli-sired F1 Angus bulls.
Chase, C C; Chenoweth, P J; Larsen, R E; Hammond, A C; Olson, T A; West, R L; Johnson, D D
2001-08-01
Postweaning growth, sexual development, libido, and carcass data were collected from two consecutive calf crops using 31 Brahman x Angus (B x A), 41 Senepol x Angus (S x A), and 38 Tuli x Angus (T x A) F1 bulls. Following weaning (by mid-September) and preconditioning, at the start of the study (late September) bulls were fed concentrate (three times each week at a rate equivalent to 4.5 kg/d) on bahiagrass pasture for approximately 250 d. At the start of the study and at 28-d intervals, BW, hip height, and scrotal circumference (SC) were measured. Concurrently at 28-d intervals, when the SC of a bull was > or = 23 cm, semen collection was attempted using electroejaculation. Ejaculates were evaluated for presence of first spermatozoa (FS), 50 x 10(6) sperm with at least 10% motility (PU), and 500 x 10(6) sperm with at least 50% motility (PP). After all bulls reached PP they were subjected to two libido tests. Carcass data were collected on all bulls (n = 110) and Warner-Bratzler shear (WBS) force values were assessed on a subset (n = 80). For both years, B x A bulls were heavier (P 0.10) gain in BW or hip height during the study. Scrotal circumference of T x A bulls was larger (P 0.10) of breed type by the end of the study. At PU and PP, B x A bulls were older (P carcass traits; B x A bulls had the heaviest (P carcass weight, greatest (P 0.10) USDA quality grade. In conclusion, tropically adapted F1 bulls produced from Senepol (Bos taurus) and Tuli (Sanga) sires bred to Angus cows in Florida had lighter BW, shorter hip heights, and smaller carcasses than those from Brahman sires but reached puberty earlier and had higher libido scores and lower WBS force values.
Ki-Chang Hong
2010-01-01
Full Text Available This study evaluated how various porcine sires affected muscle fibre characteristics, with respect to production traits. Sires from Berkshire, Duroc, Meishan, and Yorkshire pigs were mated to Meishan dams (BM, DM, MM, and YM offspring, respectively. A total of 96 pigs were evaluated for muscle fibre characteristics and production traits. The progeny from Duroc and Yorkshire sires had the greatest number of total fibres (P<0.05 and exhibited less backfat thickness (P<0.001 and larger loin muscle areas (P<0.05 than BM pigs. The DM and BM crossbreds showed higher marbling (P<0.01, and colour scores (P<0.05, as well as lower shear force scores (P<0.001. The MM pigs had greater proportional area of type IIb muscle fibres (P<0.05, and also displayed higher drip loss (P<0.01, higher lightness (P<0.001, and a greater incidence of PSE pork (pale, soft, and exudative; 25% than DM, BM, and YM. These results showed that a greater number of total muscle fibres without increasing the cross sectional area of fibres improved lean meat production, and that a lower proportion of type IIb fibres was associated with better meat quality. For these reasons, the Duroc sire × Meishan dam crossbreed emerged as the most appropriate mating type examined herein to simultaneously enhance both lean meat production and meat quality.
Service-sire conception rate (SCR), a phenotypic fertility evaluation based on conventional (nonsexed) inseminations from parities 1 through 5, was implemented by USDA in August 2008. Using insemination data from 2005 through 2009, the SCR procedure was applied separately for nulliparous heifer inse...
Philipp, Marianne; Jakobsen, Ruth Bruus; Nachman, Gøsta Støger
2009-01-01
was performed in which females were hand A pollen-competition experiment was performed in which females were hand pollinated with a mixture of pollen from males and hermaphrodites, all with known isozyme alleles, which allowed determination of who sired each seed. We recorded plant size, flower morphology...
García de la Torre, Nuria; Durán, Alejandra; Del Valle, Laura; Fuentes, Manuel; Barca, Idoya; Martín, Patricia; Montañez, Carmen; Perez-Ferre, Natalia; Abad, Rosario; Sanz, Fuencisla; Galindo, Mercedes; Rubio, Miguel A; Calle-Pascual, Alfonso L
2013-08-01
The aims are to define the regression rate in newly diagnosed type 2 diabetes after lifestyle intervention and pharmacological therapy based on a SMBG (self-monitoring of blood glucose) strategy in routine practice as compared to standard HbA1c-based treatment and to assess whether a supervised exercise program has additional effects. St Carlos study is a 3-year, prospective, randomized, clinic-based, interventional study with three parallel groups. Hundred and ninety-five patients were randomized to the SMBG intervention group [I group; n = 130; Ia: SMBG (n = 65) and Ib: SMBG + supervised exercise (n = 65)] and to the HbA1c control group (C group) (n = 65). The primary outcome was to estimate the regression rate of type 2 diabetes (HbA1c 4 kg was 3.6 (1.8-7); p < 0.001. This study shows that the use of SMBG in an educational program effectively increases the regression rate in newly diagnosed type 2 diabetic patients after 3 years of follow-up. These data suggest that SMBG-based programs should be extended to primary care settings where diabetic patients are usually attended.
Duda, David P.; Minnis, Patrick
2009-01-01
Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.
Severino Cavalcante de Sousa Júnior
2010-05-01
Full Text Available Foram utilizados 35.732 registros de peso do nascimento aos 660 dias de idade de 8.458 animais da raça Tabapuã para estimar funções de covariância utilizando modelos de regressão aleatória sobre polinômios de Legendre. Os modelos incluíram: como aleatórios, os efeitos genético aditivo direto, materno, de ambiente permanente de animal e materno; como fixos, os efeitos de grupo de contemporâneo; como covariáveis, a idade do animal à pesagem e a idade da vaca ao parto (linear e quadrática; e sobre a idade à pesagem, polinômio ortogonal de Legendre (regressão cúbica foi considerado para modelar a curva média da população. O resíduo foi modelado considerando sete classes de variância e os modelos foram comparados pelos critérios de informação Bayesiano de Schwarz e Akaike. O melhor modelo apresentou ordens 4, 3, 6, 3 para os efeitos genético aditivo direto e materno, de ambiente permanente de animal e materno, respectivamente. As estimativas de covariância e herdabilidades, obtidas utilizando modelo bicaracter, e de regressão aleatória foram semelhantes. As estimativas de herdabilidade para o efeito genético aditivo direto, obtidas com o modelo de regressão aleatória, aumentaram do nascimento (0,15 aos 660 dias de idade (0,45. Maiores estimativas de herdabilidade materna foram obtidas para pesos medidos logo após o nascimento. As correlações genéticas variaram de moderadas a altas e diminuíram com o aumento da distância entre as pesagens. A seleção para maiores pesos em qualquer idade promove maior ganho de peso do nascimento aos 660 dias de idade.In order to estimate covariance functions by using random regression models on Legendre polynomials, 35,732 weight records from birth to 660 days of age of 8,458 animals of Tabapuã cattle were used. The models included: as random effects, direct additive genetic effect, maternal effect, and animal and maternal permanent environmental effets; contemporary groups
Puri, Basant K; Martins, Julian G
2014-05-01
Concerns about growth retardation and unknown effects on long-term brain development with stimulants have prompted interest in polyunsaturated fatty acid supplementation (PUFA) as an alternative treatment. However, randomized controlled trials (RCTs) and meta-analyses of PUFA supplementation in ADHD have shown marginal benefit, and uncertainty exists as to which, if any, PUFA might be effective in alleviating symptoms of ADHD. We conducted an updated meta-analysis of RCTs in ADHD together with multivariable meta-regression analyses using data on PUFA content obtained from independent fatty acid methyl ester analyses of each study PUFA regimen. The PubMed, Embase and PsycINFO databases were searched with no start date and up to 28th July 2013. Study inclusion criteria were: randomized design, placebo controlled, PUFA preparation as active intervention, reporting change scores on ADHD rating-scale measures. Rating-scale measures of inattention and hyperactive-impulsive symptoms were extracted, study authors were contacted to obtain missing data, studies not reporting negative findings had these data imputed, and study quality was assessed using the Jadad system plus other indicators. Random-effects models were used for pooled effects and for meta-regression analyses. Standardized mean differences (SMD) in inattention, hyperactive-impulsive and combined symptoms were assessed as rated by parents, teachers or all raters. The influence of study characteristics and PUFA regimen content was explored in multivariable meta-regression analyses. The overall pooled estimate from 18 studies showed that combined ADHD symptoms rated by all raters decreased with PUFA supplementation; SMD -0.192 (95% CI: -0.297, -0.086; Phyperactive-impulsive symptoms. Certain fatty acids present in placebo preparations may potentially have been psychoactive. This meta-analysis provides modest evidence of PUFA effectiveness in ADHD, especially GLA and EPA for inattention symptoms; however, evidence
赵文芝; 田铮; 夏志明
2009-01-01
A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed.The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise.Moreover,the consistence of the test is proved while the rate of convergence is given.The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data.
Sire effect on early and late embryonic death in French Holstein cattle.
Ledoux, D; Ponsart, C; Grimard, B; Gatien, J; Deloche, M C; Fritz, S; Lefebvre, R; Humblot, P
2015-05-01
We investigated the effect of maternal sire on early pregnancy failure (between D0, day of insemination and D90) in their progeny during the first and second lactations (n=3508) in the Holstein breed. The estimated breeding value (EBV) for cow fertility of 12 bulls (reliability⩾0.95) was used to create the following three groups: low, medium and high EBV (EBV from -0.7 to 1 expressed as genetic standard deviation relative to the mean of the breed). In their daughters (93 to 516 per bull), progesterone measurement was carried out on the day of artificial insemination (AI; D0) to check whether the cows were in the follicular phase and on D18 to 25 to assess non-fertilisation-early embryonic mortality (NF-EEM). Late embryonic mortality (LEM) and early foetal death (FD) were determined by ultrasonography on D45 and D90 and by the return to oestrus after the first AI. Frequencies of NF-EEM, LEM, FD and pregnancy were 33.3%, 11.7%, 1.4% and 48.5% and incidences were 35.1, 19.0, 2.7 and 51.1, respectively. Sire EBV was significantly related to the incidences of pregnancy failure between D0 and D90, fertilisation failure-early embryonic mortality (FF-EEM) and LEM but not to the incidence of FD between D45 and D90 of pregnancy. The relative risk (RR) of FF-EEM was significantly higher (RR=1.2; P<0.05) for the progeny group of low EBV bulls when compared with high EBV bulls. The same effect was observed when comparing LEM of the progeny groups from the low EBV bulls to those from moderate and high EBV bulls (RR, respectively, of 1.3 and 1.4; P<005). The incidence of FF-EEM was significantly higher when cows were inseminated before 80 days postpartum compared with later, and for the extreme values of the difference between milk fat and protein content measured during the first 3 months of lactation. FF-EEM was also significantly related to the year of observation. The incidence of LEM was higher for the highest producing cows and was influenced by interaction between milk
Miller, R H; Norman, H D; Wright, J R; Cole, J B
2009-05-01
A retrospective study of the impact of the estimated breeding values of sires and maternal grandsires for somatic cell score (SCS) on productive life (PL) of Holsteins and Jerseys was conducted. Data included records from 2,626,425 Holstein and 142,725 Jersey cows. The sires and maternal grandsires of cows were required to have been available through artificial insemination and to have predicted transmitting ability (PTA) SCS evaluations based on 35 or more daughters. A weighted function (WPTA) of sire and maternal grandsire PTA for SCS was used: (sire PTA + 0.5 maternal grandsire PTA)/1.5. The 3 dependent variables were PL, frequency of cows culled for mastitis, and first-lactation SCS. The model included effects of herd, birth year, and WPTA (WPTA was categorized into groups: or =3.30). For analysis of first-lactation SCS, calving year and calving month were substituted for birth year. Differences among WPTA groups were highly significant: as WPTA increased, PL decreased, whereas percentage culled for mastitis and first-lactation SCS increased. The range in PL from lowest to highest WPTA was 5.07 mo for Holsteins and 4.73 mo for Jerseys. Corresponding differences for percentage culled for mastitis were 7.0 and 5.6% and for SCS were 0.95 and 1.04 (for Holsteins and Jerseys, respectively). Although phenotypic studies suggest that cows with extremely low SCS were less resistant to mastitis, our results showed consistent improvements in PL, percentage culled for mastitis, and SCS of daughters when bulls were chosen for low PTA SCS.
Morales, J I; Serrano, M P; Cámara, L; Berrocoso, J D; López, J P; Mateos, G G
2013-08-01
In total, 240 pigs were used to compare growth performance and carcass quality traits of immunocastrated males (ICM), surgically castrated males (SCM), and intact females (IF) of crossbreds from Large White × Landrace females and Duroc (DU) or Pietrain (PI) sires destined to the dry-cured industry. Between the 2 Improvac injections (87 and 137 d of age), ICM and IF had less ADG than SCM (P SCM (2.33, 2.55, and 2.77 kg/d; respectively; P SCM and IF (P SCM (0.346, 0.323, and 0.300 g/g; respectively; P SCM had greater ADG than IF (P SCM or IF. Carcasses from IF were leaner than carcasses from SCM with carcasses from ICM being intermediate (P SCM. Intramuscular fat content was lower for IF than for SCM with that of ICM being intermediate (3.5 vs. 3.9 and 3.7%; P SCM and IF. Intramuscular fat content in LM was less for IF than for SCM with ICM intermediate. Crossbreds from Duroc sires grew faster and had more intramuscular fat but less ham yield than crossbreds from Pietrain sires. Therefore, ICM should be preferred to SCM and Duroc crossbreds should be preferred to Pietrain crossbreds to produce carcasses destined to the production of primal cuts for the dry-cured industry.
Santana Isabel
2011-08-01
Full Text Available Abstract Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI, but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.
Casas, E; Thallman, R M; Cundiff, L V
2012-09-01
The objective of this study was to characterize breeds representing diverse biological types for birth and weaning traits in crossbred cattle (Bos taurus). Gestation length, calving difficulty, percentage of unassisted calving, percentage of perinatal survival, percentage of survival from birth to weaning, birth weight, weaning weight, BW at 205 d, and ADG was measured in 1,370 calves born and 1,285 calves weaned. Calves were obtained by mating Hereford, Angus, and MARC III (1/4 Hereford, 1/4 Angus, 1/4 Pinzgauer, and 1/4 Red Poll) mature cows to Hereford or Angus (British breeds), Norwegian Red, Swedish Red and White, Wagyu, and Friesian sires. Calves were born during the spring of 1997 and 1998. Sire breed was significant for gestation length, birth weight, BW at 205 d, and ADG (P gestation length (282 d), whereas offspring from Wagyu sires had the longest gestation length (286 d). Progeny from British breeds were the heaviest at birth (40.5 kg) and at 205 d (237 kg), and grew faster (0.97 kg/d) than offspring from other breeds. Offspring from Wagyu sires were the lightest at birth (36.3 kg) and at 205 d (214 kg), and had the slowest growth (0.91 kg/d). Dam breed was significant for gestation length (P gestation length (284 d), whereas offspring from Angus cows had the shortest (282 d). Offspring from MARC III cows were the heaviest at birth (39.4 kg) when compared with offspring from Hereford (38.2 kg) and Angus (38.6 kg) cows. Progeny from Angus cows were the heaviest at 205 d (235 kg) and grew faster (0.96 kg/d), whereas offspring from Hereford cows were the lightest at 205 d (219 kg) and were the slowest in growth (0.88 kg/d). Sex was significant for gestation length (P = 0.026), birth weight, BW at 205 d, and ADG (P gestation length (284 d) when compared with female calves (283 d). Males were heavier than females at birth and at 205 d, and grew faster. Sire breed effects can be optimized by selection and use of appropriate crossbreeding systems.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Marks, Timothy R; Seaton, Philip T; Pritchard, Hugh W
2014-09-01
Pollinator-limited seed-set in some terrestrial orchids is compensated for by the presence of long-lived flowers. This study tests the hypothesis that pollen from these insect-pollinated orchids should be desiccation tolerant and relatively long lived using four closely related UK terrestrial species; Anacamptis morio, Dactylorhiza fuchsii, D. maculata and Orchis mascula. Pollen from the four species was harvested from inflorescences and germinated in vitro, both immediately and also after drying to simulate interflower transit. Their tolerance to desiccation and short-term survival was additionally assessed after 3 d equilibration at a range of relative humidities (RHs), and related to constructed sorption isotherms (RH vs. moisture content, MC). Ageing of D. fuchsii pollen was further tested over 2 months against temperature and RH, and the resultant survival curves were subjected to probit analysis, and the distribution of pollen death in time (σ) was determined. The viability and siring ability, following artificial pollinations, were determined in D. fuchsii pollen following storage for 6 years at -20 °C. The pollen from all four species exhibited systematic increases in germinability and desiccation tolerance as anthesis approached, and pollen from open flowers generally retained high germinability. Short-term storage revealed sensitivity to low RH, whilst optimum survival occurred at comparable RHs in all species. Similarly, estimated pollen life spans (σ) at differing temperatures were longest under the dry conditions. Despite a reduction in germination and seeds per capsule, long-term storage of D. fuchsii pollen did not impact on subsequent seed germination in vitro. Substantial pollen desiccation tolerance and life span of the four entomophilous orchids reflects a resilient survival strategy in response to unpredictable pollinator visitation, and presents an alternative approach to germplasm conservation. © The Author 2014. Published by Oxford
Keady, Sarah M; Kenny, David A; Ohlendieck, Kay; Doyle, Sean; Keane, M G; Waters, Sinéad M
2013-02-01
Bovine skeletal muscle is a tissue of significant value to the beef industry and global economy. Proteomic analyses offer the opportunity to detect molecular mechanisms regulating muscle growth and intramuscular fat accumulation. The current study aimed to investigate differences in protein abundance in skeletal muscle tissue of cattle from two breeds of contrasting maturity (early vs. late maturing), adiposity, and muscle growth potential, namely, Belgian Blue (BB) × Holstein Friesian and Aberdeen Angus (AA) × Holstein Friesian. Twenty AA (n = 10) and BB (n = 10) sired steers, the progeny of sires of either high or low genetic merit, expressed as expected progeny difference for carcass weight (EPDcwt), and bred through AI, were evaluated as 4 genetic groups, BB-High, BB-Low, AA-High, and AA-Low (n = 5 per treatment). Chemical composition analysis of M. longissimus lumborum showed greater protein and moisture and decreased lipid concentrations for BB-sired compared with AA-sired steers. To investigate the effects of both sire breed and EPDcwt on M. longissimus lumborum, proteomic analysis was performed using 2-dimensional difference gel electrophoresis followed by mass spectrometry. Proteins were identified from their peptide sequences, using the National Center for Biotechnology Information (NCBI) and Swiss-prot databases. Metabolic enzymes involved in glycolysis (glycogen phosphorylase, phosphoglycerate mutase) and the citric acid cycle (aconitase 2, oxoglutarate dehydrogenase) were increased in AA- vs. BB-sired steers. Expression of proteins involved in cell structure, such as myosin light chain isoforms and troponins I and T, were also altered due to sire breed. Furthermore, heat shock protein β-1 and peroxiredoxin 6, involved in cell defense, had increased abundance in muscle of AA-sired relative to BB-sired steers. Protein abundance of glucose-6-phosphate isomerase, enolase-3, and pyruvate kinase was greater in AA-sired animals of High compared with Low
Lenira El Faro
2003-10-01
Full Text Available Foram utilizados quatorze modelos de regressão aleatória, para ajustar 86.598 dados de produção de leite no dia do controle de 2.155 primeiras lactações de vacas Caracu, truncadas aos 305 dias. Os modelos incluíram os efeitos fixos de grupo contemporâneo e a covariável idade da vaca ao parto. Uma regressão ortogonal de ordem cúbica foi usada para modelar a trajetória média da população. Os efeitos genéticos aditivos e de ambiente permanente foram modelados por meio de regressões aleatórias, usando polinômios ortogonais de Legendre, de ordens cúbicas. Diferentes estruturas de variâncias residuais foram testadas e consideradas por meio de classes contendo 1, 10, 15 e 43 variâncias residuais e de funções de variâncias (FV usando polinômios ordinários e ortogonais, cujas ordens variaram de quadrática até sêxtupla. Os modelos foram comparados usando o teste da razão de verossimilhança, o Critério de Informação de Akaike e o Critério de Informação Bayesiano de Schwar. Os testes indicaram que, quanto maior a ordem da função de variâncias, melhor o ajuste. Dos polinômios ordinários, a função de sexta ordem foi superior. Os modelos com classes de variâncias residuais foram aparentemente superiores àqueles com funções de variância. O modelo com homogeneidade de variâncias foi inadequado. O modelo com 15 classes heterogêneas foi o que melhor ajustou às variâncias residuais, entretanto, os parâmetros genéticos estimados foram muito próximos para os modelos com 10, 15 ou 43 classes de variâncias ou com FV de sexta ordem.Fourteen random regression models were used to adjust 86,595 test-day milk records of 2,155 first lactation of native Caracu cows. The models include fixed effects of contemporary group and age of cow as covariable. A cubic regression on Legendre orthogonal polynomial of days in milk was used to model the mean trend and the additive genetic and permanent environmental regressions
Regression analysis by example
Chatterjee, Samprit; Hadi, Ali S
2012-01-01
.... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...
José Lindenberg Rocha Sarmento
2010-08-01
Full Text Available Utilizaram-se 17.767 registros de peso de 4.210 cordeiros da raça Santa Inês com o objetivo de comparar modelos de regressão aleatória com diferentes estruturas para modelar a variância residual em estudos genéticos da curva de crescimento. Os efeitos fixos incluídos na análise foram: grupo contemporâneo e idade da ovelha no parto. As regressões fixas e aleatórias foram ajustadas por meio de polinômios de Legendre de ordens 4 e 3, respectivamente. A variância residual foi ajustada por meio de classes heterogêneas e por funções de variância empregando polinômios ordinários e de Legendre de ordens 2 a 8. O modelo considerando homogeneidade de variâncias residuais mostrou-se inadequado. De acordo com os critérios utilizados, a variância residual contendo sete classes heterogêneas proporcionou melhor ajuste, embora um mais parcimonioso, com cinco classes, pudesse ser utilizado sem perdas na qualidade de ajuste da variância nos dados. O ajuste de funções de variância com qualquer ordem foi melhor que o obtido por meio de classes. O polinômio ordinário de ordem 6 proporcionou melhor ajuste entre as estruturas testadas. A modelagem do resíduo interferiu nas estimativas de variâncias e parâmetros genéticos. Além da alteração da classificação dos reprodutores, a magnitude dos valores genéticos preditos apresenta variações significativas, de acordo com o ajuste da variância residual empregado.It was used 17,767 weight records of 4,210 Santa Inês breed lambs aiming to compare random regression models with different structures to model the residual variance in genetic studies of the growth curve. The fixed effects included in the analysis were contemporary group and age of the ewe at lambing. Fixed and random regressions were fitted through Legendry polynomials of orders 4 and 3, respectively. The residual variance was fitted by heterogeneous classes and by functions of variances employing ordinary polynomials and
L.S. Freitas
2010-04-01
Full Text Available Estimaram-se a herdabilidade e as correlações genéticas e de ambiente permanente entre seis medidas de persistência da lactação de vacas da raça Guzerá, utilizando modelo de regressão aleatória. Foram considerados 8276 registros de produção de leite no dia do controle, na primeira lactação, de 1021 vacas. A regressão aleatória foi calculada pela função logarítmica de Ali e Schaeffer e pelo polinômio de Legendre, obtendo-se os coeficientes para os efeitos fixos, genético aditivo e de ambiente permanente. A função que mais se adequou aos dados foi a de Ali e Schaeffer, mas apresentou problemas de convergência. Os resultados evidenciaram que a persistência é uma característica com herdabilidade de valor moderado e de baixa correlação com o valor genético para produção de leite aos 305 dias, indicando a possibilidade de se obter resposta à seleção para mudança na curva de lactação sem afetar negativamente a produção total de leite na lactação. A medida de persistência que calcula a diferença de produção de leite entre as fases intermediária e inicial da lactação apresentou alta correlação com a produção aos 305 dias.The heritability and the genetic and permanent environment correlations were estimated among six different measures of persistency in the lactation of Guzerat cow, using the Random Regression Model. A total of 8,403 records from 1,034 first lactation cows were evaluated. The Random Regression Model was calculated by the logarithmic function of Ali and Schaeffer and Legendre polynomials to get coefficients for fixed, additive genetic and permanent environment effects. Ali and Schaeffer was the function that better fit to the data, but it had convergence problems. The results showed that persistence is a trait with moderate heritability, and low correlation with genetic value for 305-d milk production which allows to select animals in order to alter the format of the curve of production
Luciele Cristina Pelicioni
2009-01-01
permanent environmental effects, using random regression models. The random effects included were modeled by regression on Legendre Polynomials with orders ranging from linear to quartic. The models were compared through the likelihood ratio test, Akaike's information criterion and the Schwarz's Bayesian information criterion. The model with 18 heterogeneous classes was the one that best fitted the residual variances, according to the statistical tests; however, the model with variance function of 5th order also showed to be appropriate. The direct heritability estimates were higher than those found in literature, ranging from 0.04 to 0.53, showing similar trends when compared to those estimated using univariate models. Selection on body weight in any age should improve the body weight in all ages in the studied interval.
Maroco, João; Silva, Dina; Rodrigues, Ana; Guerreiro, Manuela; Santana, Isabel; de Mendonça, Alexandre
2011-08-17
Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Press' Q test showed that all classifiers performed better than chance alone (p Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a
Riley, D G; Chase, C C; Pringle, T D; West, R L; Johnson, D D; Olson, T A; Hammond, A C; Coleman, S W
2003-10-01
The objectives of this study were to assess the influence of sire on mu- and m-calpain activities, to evaluate the relationships of activities of these enzymes to other traits related to beef palatability, and to assess the influence of sire on the rate of tenderization (as measured by myofibril fragmentation index [MFI]) in Brahman longissimus muscle. Brahman calves (n = 87), sired by nine bulls, were born, weaned, fed, and slaughtered in central Florida. Traits evaluated were mu- and m-calpain activities and MFI after 1, 7, 14, and 21 d of aging. Other traits were analyzed to determine their associations with mu- and m-calpain activity and MFI, including calpastatin activity, percentage of raw and cooked lipids, Warner-Bratzler shear force (WBSF) values after 7, 14, and 21 d of aging, and sensory panel rating of tenderness, juiciness, and connective tissue amount after 14 d of aging. Data were analyzed using a model with sire, sex, year, and slaughter group (calves of the same sex slaughtered on the same date) as fixed effects, and adjusted to a constant adjusted 12th-rib fat thickness. Sire affected mu-calpain activity (P carcass sorting program represents an alternative consideration for tenderization improvement programs.
Asres Berhan
Full Text Available BACKGROUND: The development of tipranavir and darunavir, second generation non-peptidic HIV protease inhibitors, with marked improved resistance profiles, has opened a new perspective on the treatment of antiretroviral therapy (ART experienced HIV patients with poor viral load control. The aim of this study was to determine the virologic response in ART experienced patients to tipranavir-ritonavir and darunavir-ritonavir based regimens. METHODS AND FINDINGS: A computer based literature search was conducted in the databases of HINARI (Health InterNetwork Access to Research Initiative, Medline and Cochrane library. Meta-analysis was performed by including randomized controlled studies that were conducted in ART experienced patients with plasma viral load above 1,000 copies HIV RNA/ml. The odds ratios and 95% confidence intervals (CI for viral loads of <50 copies and <400 copies HIV RNA/ml at the end of the intervention were determined by the random effects model. Meta-regression, sensitivity analysis and funnel plots were done. The number of HIV-1 patients who were on either a tipranavir-ritonavir or darunavir-ritonavir based regimen and achieved viral load less than 50 copies HIV RNA/ml was significantly higher (overall OR = 3.4; 95% CI, 2.61-4.52 than the number of HIV-1 patients who were on investigator selected boosted comparator HIV-1 protease inhibitors (CPIs-ritonavir. Similarly, the number of patients with viral load less than 400 copies HIV RNA/ml was significantly higher in either the tipranavir-ritonavir or darunavir-ritonavir based regimen treated group (overall OR = 3.0; 95% CI, 2.15-4.11. Meta-regression showed that the viral load reduction was independent of baseline viral load, baseline CD4 count and duration of tipranavir-ritonavir or darunavir-ritonavir based regimen. CONCLUSIONS: Tipranavir and darunavir based regimens were more effective in patients who were ART experienced and had poor viral load control. Further
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...
张仁寿; 罗林开; 吕伟航; 任晓怡
2012-01-01
This paper studies the nonlinear relation between the balance of trade and the affection factors. The nonlinear regression model for the balance of trade is constructed by random forest (RF) method. Moreover, the ranking of importance for affection factors is given out. The empirical results in this paper reveal the balance of trade in China is affected mainly by M2, GDP, CPI, Exchange reserves, M1 and Gross Industrial output, which is helpful for the government decision-making.%研究了部分经济指标与贸易差额之间的非线性关系，该非线性模型采用随机森林回归模型，同时，得出了各个指标的影响因子．实验结果表明：我国的贸易差额主要受到M2，GDP，CPI，外汇储备，M1和工业总产值的影响，研究可供政府决策参考．
José Ernandes Rufino de Sousa
2008-12-01
Full Text Available O objetivo deste trabalho foi comparar modelos de regressão aleatória com diferentes estruturas de variâncias residuais, na estimação dos componentes de covariância e parâmetros genéticos de características de crescimento de caprinos. A função de regressão por meio de polinômios ortogonais de Legendre de ordem quatro foi usada para modelar a trajetória média de crescimento animal, e de ordem três, para modelar os efeitos genéticos aditivos direto e materno e de ambiente permanente. Diferentes estruturas de variâncias residuais foram consideradas, por meio de classes de uma a sete variâncias residuais, e funções de variâncias com uso dos polinômios ordinários e de Legendre, com ordens que variaram de linear até a do quarto grau. Os modelos foram comparados pelo teste de razão de verossimilhança, o critério de informação de Akaike e o critério bayesiano de Schwarz. Os modelos com funções de variâncias residuais foram superiores aos de classes de variância. O polinômio ordinário de terceira ordem apresentou melhores resultados. Os parâmetros genéticos são influenciados pelo ajuste da variância residual, no entanto, os estimados pelos modelos que consideraram classes de variância, polinômio ordinário e polinômio de Legendre, de terceira ordem, são semelhantes.The objective of this work was to compare random regression models with different structures of residual variances, in the estimate of covariance components and genetic parameters for growth traits in goats. Regression functions using Legendre orthogonal polynomials of the fourth order were used for modeling animal growth trajectory, and polynomial of third order for modeling direct and maternal additive genetic effects, and permanent environmental effect. Different residual variance structures were considered using alternatively step functions (from one to seven different classes of residual variances or variance functions using ordinary and
McLaren, A; Lambe, N R; Morgan-Davies, C; Mrode, R; Brotherstone, S; Conington, J; Morgan-Davies, J; Bunger, L
2014-06-01
The objective of this study was to define different terminal sire flock environments, based on a range of environmental factors, and then investigate the presence of genotype by environment interactions (G×E) between the environments identified. Data from 79 different terminal sire flocks (40 Texel, 21 Charollais and 18 Suffolk), were analysed using principal coordinate and non-hierarchical cluster analyses, the results of which identified three distinct environmental cluster groups. The type of grazing, climatic conditions and the use of vitamins and mineral supplements were found to be the most important factors in the clustering of flocks. The presence of G×E was then investigated using data from the Charollais flocks only. Performance data were collected for 12 181 lambs, between 1990 and 2010, sired by 515 different sires. Fifty six of the sires had offspring in at least two of the three different cluster groups and pedigree information was available for a total of 161 431 animals. Traits studied were the 21-week old weight (21WT), ultrasound muscle depth (UMD) and log transformed backfat depth (LogUFD). Heritabilities estimated for each cluster, for each trait, ranged from 0.32 to 0.45. Genetic correlations estimated between Cluster 1 and Cluster 2 were all found to be significantly lower than unity, indicating the presence of G×E. They were 0.31 (±0.17), 0.68 (±0.14) and 0.18 (±0.21) for 21WT, UMD and LogUFD, respectively. Evidence of sires re-ranking across clusters was also observed. Providing a suitable strategy can be identified, there is potential for the optimisation of future breeding programmes, by taking into account the G×E observed. This would enable farmers to identify and select animals with an increased knowledge as to how they will perform in their specific farm environment thus reducing any unexpected differences in performance.
E.S. Sakaguti
2002-08-01
Full Text Available Para estabelecer a melhor forma de considerar os efeitos fixos dos modelos de regressão aleatória, avaliou-se a utilização de funções polinomiais na descrição de curvas de crescimento e no efeito da idade da vaca sobre pesos corporais de 41.415 bovinos jovens da raça Tabapuã, criados em regime de pasto. A idade da vaca ao parto e o sexo do animal influenciaram os pesos nos primeiros dois anos de vida, e o efeito da idade da mãe sobre o desenvolvimento dos animais mostrou-se dependente da idade dos filhos. Altos coeficientes de determinação (R²>0,98 foram alcançados utilizando-se o efeito da idade da vaca no dia da pesagem do animal (i.e., a idade da vaca ao parto mais a idade do animal no dia de sua pesagem em polinômios de, no mínimo, segundo grau, e curvas de crescimentos médios, diferenciadas para machos e fêmeas, descritas por meio de polinômios de, no mínimo, terceiro grau.This study was undertaken to establish the best way of considering the fixed effects in random regression models for genetic evaluations. The use of polynomial functions to describe growth curves and age-of-dam effects was evaluated by analyzing body weight of 41,415 young Tabapuã beef cattle, born from 1975 to 1997 and raised under pasture conditions. Age-of-dam and sex effects showed significant influence on body weights of animals younger than 2-year-old. Age-of-dam effect on weights of offspring showed to be dependent on age of animals. High goodness of fit (R²>0.98 were reached using age of dam at weighing day (i.e., sum of age of dam at birth plus age of animal at weighing day with at least second degree polynomials and growth curves fitted to each sex separately with at least third degree polynomials.
P. Tholon
2011-04-01
Full Text Available The objective of this work was to determine genetic parameters for body weight of tinamou in captivity. It was used random regression models in analyses of data by considering the direct additive genetic (DA and permanent environmental effects of the animal (PE as random effects. Residual variances were modeled by using a fifth-order variance function. The mean population growth curve was fitted by sixth-order Legendre orthogonal polynomials. Direct additive genetic effects and animal environmental permanent effect were modeled by using Legendre polynomials of order two to nine. The best results were obtained by models with orders of fit of 6 for direct additive genetic effect and of order 3 for permanent effect by Akaike information criterion and of order 3 for both additive genetic effect and permanent effect by Schwarz Bayesian information criterion and likelihood ratio test. Heritability estimates ranged from 0.02 to 0.57. The first eigenvalue explained 94% and 90% of the variation from additive direct and permant environmental effects, respectively. Selection of tinamou for body weight is more effective after 112 days of age.Com este trabalho objetivou-se determinar parâmetros genéticos para peso corporal de perdizes em cativeiro. Foram utilizados modelos de regressão aleatória na análise dos dados considerando os efeitos genéticos aditivos diretos (AD e de ambiente permanente de animal (AP como aleatórios. As variâncias residuais foram modeladas utilizando-se funções de variância de ordem 5. A curva média da população foi ajustada por polinômios ortogonais de Legendre de ordem 6. Os efeitos genéticos aditivos diretos e de ambiente permanente de animal foram modelados utilizando-se polinômios de Legendre de segunda a nona ordem. Os melhores resultados foram obtidos pelos modelos de ordem 6 de ajuste para os efeitos genéticos aditivos diretos e de ordem 3 para os de ambiente permanente pelo Critério de Informação de
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
Claudio Napolis Costa
2005-10-01
número de estimativas negativas entre as PLC do início e fim da lactação do que a FAS. Exceto para a FAS, observou-se redução das estimativas de correlação genética próximas à unidade entre as PLC adjacentes para valores negativos entre as PLC no início e no fim da lactação. Entre os polinômios de Legendre, o de quinta ordem apresentou um melhor o ajuste das PLC. Os resultados indicam o potencial de uso de regressão aleatória, com os modelos LP5 e a FAS apresentando-se como os mais adequados para a modelagem das variâncias genética e de efeito permanente das PLC da raça Gir.Data comprising 8,183 test day records of 1,273 first lactations of Gyr cows from herds supervised by ABCZ were used to estimate variance components and genetic parameters for milk yield using repeatability and random regression animal models by REML. Genetic modelling of logarithmic (FAS, exponential (FW curves was compared to orthogonal Legendre polynomials (LP of order 3 to 5. Residual variance was assumed to be constant in all (ME=1 or some periods of lactation (ME=4. Lactation milk yield in 305-d was also adjusted by an animal model. Genetic variance, heritability and repeatability for test day milk yields estimated by a repeatability animal model were 1.74 kg2, 0.27, and 0.76, respectively. Genetic variance and heritability estimates for lactation milk yield were respectively 121,094.6 and 0.22. Heritability estimates from FAS and FW, respectively, decreased from 0,59 and 0.74 at the beginning of lactation to 0.20 at the end of the period. Except for a fifth-order LP with ME=1, heritability estimates decreased from around 0,70 at early lactation to 0,30 at the end of lactation. Residual variance estimates were slightly smaller for logarithimic than for exponential curves both for homogeneous and heterogeneous variance assumptions. Estimates of residual variance in all stages of lactation decreased as the order of LP increased and depended on the assumption about ME
Osmar Jesus Macedo
2009-08-01
Full Text Available Covariance functions and random regression models have been considered as an alternative for data adjustment, in sequence, stemming from the same animal along time and which presents a structured pattern of covariance. Aiming to evaluate the performance of random regression models based on the Legendre, modified Jacobi and trigonometric functions, data concerning the weights of Nellore breed animals were used from birth to the 800th day of life, in models that assumed direct additive and animal permanent environmental effects coefficients. The Schwarz Bayesian information criterion (BIC led to the selection of the models Legendre of order six (ML6, Jacobi of order five (MJ5 and trigonometric of order six (MT6, the ML6 model presenting the lowest BIC. At the extremity of the interval, the MJ5 model presented lower variance of component estimates than those obtained through the ML6 model, however the estimates were in accordance to the medium part of the interval; while the estimates from the MT6 model were oscillating and different from those obtained through the other models. At the extremity of the interval, the heritability coefficient estimates (2 obtained through the MJ5 model were lower than those obtained through the ML6 model, however, in the medium part of the interval, they were in accordance, remaining between 0.2 and 0.3. The values obtained through the MT6 model were different from those obtained through the other models, remaining between 0.35 and 0.40 on the first 285th days and then dropping to 0.01 on the 800th days of life. The means of the estimated growth curves started to distance from the data mean tendency from the 470th days on, and in this interval, the MT6 model was the most suitable.O uso de funções de covariância e regressão aleatória tem sido considerado como uma alternativa para o ajuste de dados longitudinais com estrutura de covariância padrão, pois são mensurados no mesmo animal ao longo do tempo. Com
A 3-yr study was conducted to comprehensively evaluate Columbia, Suffolk, USMARC-Composite (Composite), and Texel breeds as terminal sires in an extensive rangeland production system. The objective was to estimate breed-of-ram effects on ewe fertility, prolificacy, and dystocia, and sire breed effe...
Unitary Response Regression Models
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Flexible survival regression modelling
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
Ritsner, Michael S; Strous, Rael D
2010-01-01
While neurosteroids exert multiple effects in the central nervous system, their associations with neurocognitive deficits in schizophrenia are not yet fully understood. The purpose of this study was to identify the contribution of circulating levels of dehydroepiandrosterone (DHEA), its sulfate (DHEAS), androstenedione, and cortisol to neurocognitive deficits through DHEA administration in schizophrenia. Data regarding cognitive function, symptom severity, daily doses, side effects of antipsychotic agents and blood levels of DHEA, DHEAS, androstenedione and cortisol were collected among 55 schizophrenia patients in a double-blind, randomized, placebo-controlled, crossover trial with DHEA at three intervals: upon study entry, after 6weeks of DHEA administration (200mg/d), and after 6weeks of a placebo period. Multiple regression analysis was applied for predicting sustained attention, memory, and executive function scores across three examinations controlling for clinical, treatment and background covariates. Findings indicated that circulating DHEAS and androstenedione levels are shown as positive predictors of cognitive functioning, while DHEA level as negative predictor. Overall, blood neurosteroid levels and their molar ratios accounted for 16.5% of the total variance in sustained attention, 8-13% in visual memory tasks, and about 12% in executive functions. In addition, effects of symptoms, illness duration, daily doses of antipsychotic agents, side effects, education, and age of onset accounted for variability in cognitive functioning in schizophrenia. The present study suggests that alterations in circulating levels of neurosteroids and their molar ratios may reflect pathophysiological processes, which, at least partially, underlie cognitive dysfunction in schizophrenia. Copyright 2009 Elsevier Ltd. All rights reserved.
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
Kristin Marie Lassen
2014-06-01
Full Text Available Premise of the study: Microsatellite primers were developed for an indigenous fruit tree, Parkia biglobosa, as a tool to study reproductive biology and population structure. Here we use the primers to determine the number of fathers per pod. Methods and Results: Microsatellite loci were enriched in a genomic sample and isolated using pyrosequencing. Eleven primer pairs were characterized in two populations of P. biglobosa in Burkina Faso (each with 40 trees. The number of alleles per locus ranged from eight to 15, and one locus had null alleles. We genotyped seeds from 24 open-pollinated pods. The genotypic profiles of seeds per pod suggest that all seeds are outcrossed and that only one pollen donor sires all ovules in a single fruit. Conclusions: Ten microsatellite markers were highly polymorphic. All seeds per pod of P. biglobosa were full siblings. The markers will be useful for reproductive and population genetic studies.
Lassen, Kristin Marie; Kjær, Erik Dahl; Ouédraogo, Moussa; Nielsen, Lene Rostgaard
2014-01-01
• Premise of the study: Microsatellite primers were developed for an indigenous fruit tree, Parkia biglobosa, as a tool to study reproductive biology and population structure. Here we use the primers to determine the number of fathers per pod. • Methods and Results: Microsatellite loci were enriched in a genomic sample and isolated using pyrosequencing. Eleven primer pairs were characterized in two populations of P. biglobosa in Burkina Faso (each with 40 trees). The number of alleles per locus ranged from eight to 15, and one locus had null alleles. We genotyped seeds from 24 open-pollinated pods. The genotypic profiles of seeds per pod suggest that all seeds are outcrossed and that only one pollen donor sires all ovules in a single fruit. • Conclusions: Ten microsatellite markers were highly polymorphic. All seeds per pod of P. biglobosa were full siblings. The markers will be useful for reproductive and population genetic studies. PMID:25202634
A role of the sires and dams in the hermaphrodite phenomenon linked with polled Damascus goat breed
M. Roukbi
2013-12-01
Full Text Available The selection for polled character as preferential in Damascus breed leads to spread homozygous individuals for the polled gene and polled intersexes and consequently further economic losses in this breed. It’s very important to study the genetic origin, the role of sirs and dams in the development of intersexuality linked with hornlessness, and evaluate some other effects in the excess of the intersexes in caprine herd. To perform this work data of 52 intersexes issues from mating 19 polled bucks with 12 horned and 37 polled goats in Humeimeh research station, belonging to General commission for agricultural scientific research, were collected and analyzed by mean of Chi-Square (SAS, 1998. The results showed the statistical effect of sires (P≤0.007 and the unstististical effect (P≥0.05 of dames on the development of polled intersexes in Damascus goat breed. The number of kids intersexes were repeated 10, 5, 4, 3, 2 and 1 for 1, 2, 2 and 1, five and eight sire number respectively. Whereas the number of kids intersexes were repeated only 2 and 1 for 3 and 46 goat number respectively. The sex of the kids, kidding type and horned goat character have all highly significant effect (P≤0.001 and this because intersex cases issues of single births and twin birth: twin to male, twin to female, and triple births: twin to male and female, and twin to tow males respectively were repeated 17, 18, 14, 2 and 1 respectively. Also, single births, twin births and triple births were repeated 17, 32 and 3 respectively. Cases of intersexuality issues from horned and polled goats were repeated 14 and 38 respectively. It was concluded the important role of hornlessness genetic and multiple births in the development of polled intersexes in Damascus goat breed.
2007-01-01
Vestlusringis on maavalitsuse haridus- ja kultuuriosakonna peaspetsialist Anita Punamäe, Parksepa lasteaia juhataja Anu Sarap, Päkapiku lasteaia juhataja asetäitja õppekasvatustöö alal Ester Peterson, Sõlekese lasteaia õpetaja Sire Kala, lapsevanem ja lasteaia Punamütsike hoolekogu liige Consuelo Laanemäe-Räim
Luis Gabriel González Herrera
2008-09-01
of Gyr cows calving between 1990 and 2005 were used to estimate genetic parameters of monthly test-day milk yield (TDMY. Records were analyzed by random regression models (MRA that included the additive genetic and permanent environmental random effects and the contemporary group, age of cow at calving (linear and quadratic components and the average trend of the population as fixed effects. Random trajectories were fitted by Wilmink's (WIL and Ali & Schaeffer's (AS parametric functions. Residual variances were fitted by step functions with 1, 4, 6 or 10 classes. The contemporary group was defined by herd-year-season of test-day and included at least three animals. Models were compared by Akaike's and Schwarz's Bayesian (BIC information criterion. The AS function used for modeling the additive genetic and permanent environmental effects with heterogeneous residual variances adjusted with a step function with four classes was the best fitted model. Heritability estimates ranged from 0.21 to 0.33 for the AS function and from 0.17 to 0.30 for WIL function and were larger in the first half of the lactation period. Genetic correlations between TDMY were high and positive for adjacent test-days and decreased as days between records increased. Predicted breeding values for total 305-day milk yield (MRA305 and specific periods of lactation (obtained by the mean of all breeding values in the periods using the AS function were compared with that predicted by a standard model using accumulated 305-day milk yield (PTA305 by rank correlation. The magnitude of correlations suggested differences may be observed in ranking animals by using the different criteria which were compared in this study.
Gilberto Romeiro de Oliveira Menezes
2010-08-01
Full Text Available Utilizaram-se 10.238 registros semanais de produção de leite no dia do controle, provenientes de 388 primeiras lactações de cabras da raça Saanen, na avaliação de seis medidas da persistência da lactação, a fim de verificar qual a mais adequada para o uso em avaliações genéticas para a característica. As seis medidas avaliadas são adaptações de medidas utilizadas em bovinos de leite, obtidas por substituir, nas fórmulas, os valores de referência de bovinos pelos de caprinos. Os valores usados nos cálculos foram obtidos de modelos de regressão aleatória. As estimativas de herdabilidade para as medidas de persistência variaram entre 0,03 e 0,09. As correlações genéticas entre medidas de persistência e produção de leite até 268 dias variaram entre -0,64 e 0,67. Por apresentar a menor correlação genética com produção aos 268 dias (0,14, a medida de persistência PS4, obtida pelo somatório dos valores do 41º ao 240º dia de lactação como desvios da produção aos 40 dias de lactação, é a mais indicada em avaliações genéticas para persistência da lactação em cabras da raça Saanen. Assim, a seleção de cabras de melhor persistência da lactação não altera a produção aos 268 dias. Em razão da baixa herdabilidade dessa medida (0,03, pequenas respostas à seleção são esperadas neste rebanho.It was used 10,238 weekly milk production records on the control day from the first 388 lactations of Saanen goats on the evalution of six lactation persistency measures in order to find out which was the best fitted for using in genetic evaluations on this trait. These six evaluated measures are adaptations from those used on dairy cattle, obtained by replacing, in the formula, bovine reference values by the goat ones. The values used in the calculations were obtained from random regression models. Heritability estimates for persistency measures ranged from 0.03 to 0.09. Genetic correlations between
Maria Eugênia Zerlotti Mercadante
2002-07-01
Full Text Available Os parâmetros genéticos para dias ao parto foram estimados usando um modelo de regressão aleatória, com polinômios ortogonais da idade na monta (em anos como covariável. Os registros de dias ao parto (4.118 foram provenientes de 926 vacas de três rebanhos Nelore experimentais, sendo os rebanhos seleção e tradicional selecionados para maior peso ao sobreano, e o rebanho controle selecionado para a média do peso ao sobreano. As variâncias genética aditiva e permanente de ambiente foram descritas por uma função polinomial de ordem 4, com nove medidas de erro, resultando em variâncias fenotípica e genética aditiva altas nas idades mais avançadas, principalmente após a 6ª monta. As herdabilidades estimadas aumentaram de 0,08 a 0,28 da 1ª à 6ª monta. As correlações genéticas foram médias entre o primeiro desempenho e os demais (0,32 a 0,66, altas entre os desempenhos adjacentes (0,98 a 0,99, e um pouco menores entre os não adjacentes (0,63 a 0,98. A seleção para peso não alterou o valor genético médio das vacas dos rebanhos selecionados, entretanto, os valores genéticos médios das vacas do rebanho controle mostraram tendência de queda no decorrer dos anos.Genetic parameters for days to calving were estimated using a random regression model, with orthogonal polynomials of age at breeding season (in years as covariable. The records of days to calving (4,118 came from 929 cows from three experimental Nelore herds, been the selection and traditional herds selected for higher yearling weight and the control herd selected for the mean of yearling weight. Genetic and permanent environmental variances were described by a fourth order polynomial function, with 9 measures of error. The phenotypic and additive genetic variances were high in late records, especially after the 6th breeding season. Heritabilities estimates increased from 0.08 to 0.28, from first up to 6th breeding season. Genetic correlations were moderate
Gilberto Romeiro de Oliveira Menezes
2011-07-01
Full Text Available Foram utilizados 10.238 registros semanais de produção de leite no dia do controle leiteiro provenientes de 388 primeiras lactações de cabras da raça Saanen visando comparar diferentes modelos de regressão aleatória (MRA. Primeiramente, foram comparados cinco modelos, cujos termos exponenciais da função de Wilmink assumiram os seguintes valores -0,0350; -0,0500; -0,0565; -0,0680 e -0,1000 (W0350, W0500, W0565, W0680 e W1000, respectivamente, considerando-se homogeneidade de variância residual ao longo da lactação. No modelo W0500, o valor -0,0500 foi mantido, enquanto nos modelos W0350, W0565, W0680 e W1000 foram usados os valores -0,0350; -0,0565; -0,0680; e -0,1000, respectivamente, em substituição ao valor -0,0500, proposto no modelo original utilizado para bovinos de leite. Escolhido o melhor modelo, segundo o ln L, foram avaliadas, pelos critérios AIC, BIC e ln L, a homogeneidade e heterogeneidade da variância residual: homogeneidade, duas, três, quatro, cinco e seis classes ao longo da lactação. De acordo com o critério usado, o modelo W0350 apresenta o melhor ajuste dentre os avaliados. Com relação à variância residual, a utilização de seis classes ao longo da lactação é indicada pelos critérios AIC, BIC, ln L e teste de razão de verossimilhança. As estimativas de herdabilidade ao longo da lactação, para o melhor modelo, variam de 0,07 (2ª semana de lactação a 0,25 (20ª semana de lactação.It was used 10,238 weekly test day records from 388 first lactations of Saanen goats with the objective of comparing random regression models (RRM. Firstly, it was compared five models, whose exponential terms of Wilmink function assumed the following values: -0.0350; -0.0500; -0.0565; -0.0680 and -0.1000 (W0350, W0500, W0565, W0680 and W1000, respectively by considering homogeneity of residual variance over the lactation period. The value -0.0500 was kept in the model W0500 whereas models W0350, W0565, W0680 and W
Michel Pompeu Barros de Oliveira Sá
2012-12-01
Full Text Available BACKGROUND: Most recent published meta-analysis of randomized controlled trials (RCTs showed that off-pump coronary artery bypass graft surgery (CABG reduces incidence of stroke by 30% compared with on-pump CABG, but showed no difference in other outcomes. New RCTs were published, indicating need of new meta-analysis to investigate pooled results adding these further studies. METHODS: MEDLINE, EMBASE, CENTRAL/CCTR, SciELO, LILACS, Google Scholar and reference lists of relevant articles were searched for RCTs that compared outcomes (30-day mortality for all-cause, myocardial infarction or stroke between off-pump versus on-pump CABG until May 2012. The principal summary measures were relative risk (RR with 95% Confidence Interval (CI and P values (considered statistically significant when INTRODUÇÃO: A meta-análise mais recente de estudos randomizados controlados (ERC mostrou que cirurgia de revascularização (CRM sem circulação extracorpórea (CEC reduz a incidência de acidente vascular cerebral em 30% em comparação com CRM com CEC, mas não mostrou diferença em outros resultados. Novos ERCs foram publicados, indicando necessidade de nova meta-análise para investigar resultados agrupados adicionando esses estudos. MÉTODOS: MEDLINE, EMBASE, CENTRAL / CCTR, SciELO, LILACS, Google Scholar e listas de referências de artigos relevantes foram pesquisados para ERCs que compararam os resultados de 30 dias (mortalidade por todas as causas, infarto do miocárdio ou acidente vascular cerebral - AVC entre CRM com CEC versus sem CEC até maio de 2012. As medidas sumárias principais foram o risco relativo (RR com intervalo de confiança de 95% (IC e os valores de P (considerado estatisticamente significativo quando <0,05. Os RR foram combinados entre os estudos usando modelo de efeito randômico de DerSimonian-Laird. Meta-análise e meta-regressão foram concluídas usando o software versão Meta-Análise Abrangente 2 (Biostat Inc., Englewood
Autistic epileptiform regression.
Canitano, Roberto; Zappella, Michele
2006-01-01
Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.
Scaled Sparse Linear Regression
Sun, Tingni
2011-01-01
Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...
Dimension Reduction and Discretization in Stochastic Problems by Regression Method
Ditlevsen, Ove Dalager
1996-01-01
The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation, ......, Slepian models, Stochastic finite elements.......The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation...
Rolling Regressions with Stata
Kit Baum
2004-01-01
This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...
Guijun YANG; Lu LIN; Runchu ZHANG
2007-01-01
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Riley, D G; Burke, J M; Chase, C C; Coleman, S W
2016-01-01
The use of Brahman in cow-calf production offers some adaptation to the harsh characteristics of endophyte-infected tall fescue. Criollo breeds, such as the Romosinuano, may have similar adaptation. The objectives were to estimate genetic effects in Romosinuano, Angus, and crossbred cows for their weights, weights of their calves, and ratios (calf weight:cow weight and cow weight change:calf weight gain) across lactation and to assess the influence of forage on traits and estimates. Cows ( = 91) were bred to Charolais bulls after their second parity. Calves ( = 214) were born from 2006 to 2009. Cows and calves were weighed in early (April and June), mid- (July), and late lactation (August and October). Animal was a random effect in analyses of calf data; sire was random in analyses of cow records and ratios. Fixed effects investigated included calf age, calf sex, cow age-year combinations, sire breed of cow, dam breed of cow, and interactions. Subsequent analyses evaluated the effect of forage grazed: endophyte-free or endophyte-infected tall fescue. Estimates of maternal heterosis for calf weight ranged from 9.3 ± 4.3 to 15.4 ± 5.7 kg from mid-lactation through weaning ( Angus cows and lower ( Angus cows had the lowest ( < 0.05) ratios (negative) of cow weight change:calf weight gain, indicating an energy-deficit condition. Cows grazing endophyte-free tall fescue had more negative ( < 0.05) values for this trait but not in early lactation ( < 0.05). Estimates of heterosis ranged from 12.8 ± 9.5 to 28.6 ± 9.4 kg for cow weight, 7.9 ± 3.0 to 15.8 ± 5.0 kg for cow weight change, and 0.07 ± 0.03 to 0.27 ± 0.1 for cow weight change:calf weight gain. Direct Romosinuano effects ranged from 14.8 ± 4.2 to 49.8 ± 7.7 kg for cow weight change and 0.2 ± 0.04 to 0.51 ± 0.14 for cow weight change:calf weight gain. The adaptive ability of Romosinuano in temperate fescue regions may be favorable with respect to relative cow and calf weight but may be a consequence of
Transductive Ordinal Regression
Seah, Chun-Wei; Ong, Yew-Soon
2011-01-01
Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...
Joanir Pereira Eler
2000-12-01
Full Text Available A interação touro x rebanho foi avaliada em uma população com 30.789 registros de animais da raça Nelore nascidos entre 1984 e 1994 em doze fazendas localizadas em três Estados do Sudeste e Centro-Oeste brasileiro, com um total de 48.495 animais no pedigree. As características consideradas foram os pesos ao nascer (PESNAS e à desmama (PESDES e o ganho de peso da desmama ao sobreano (GP345. O efeito da interação touro x rebanho foi considerado aleatório em modelos animais uni e bicaraterística, usando MTDFREML. Esse efeito foi importante para PESNAS (6% da variância fenotípica e influenciou os componentes de variância e covariância e, conseqüentemente, os parâmetros genéticos. O efeito foi menor (cerca de 1% da variância fenotípica para PESDES, mas alterou as estimativas dos componentes de variância e covariância. Para GP345, o efeito foi pequeno, embora significativo pelos verossimilhança. As correlações genéticas entre efeitos direto e materno são próximas de zero, ou até mesmo positivas, se a interação touro x rebanho for incluída no modelo, e sempre negativas se ela for omitida.Sire x herd interactions were studied in 30,789 records of birth (BW and weaning weight (WW and weight gain from weaning to 18 months of age (G345 of Nellore cattle born from 1984 to 1994 in twelve farms located in three states of central and southeastern Brazil, with a total of 48.495 animals in pedigree. Sire x herd interaction was considered as a random effect in single trait and two traits animal models using MTDFREML. This effect was important for BW (6% of the phenotypic variance and it both affected variance and covariance components and, consequently, genetic parameters. The effect was smaller for WW (around 1% of the phenotypic variance, but influenced the estimates of (co variance components. For G345, Sire x Herd effect was small. Likelihood tests showed that this effect was significant for all traits. This study showed that
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Coleman, Lucy W; Hickson, Rebecca E; Schreurs, Nicola M; Martin, Natalia P; Kenyon, Paul R; Lopez-Villalobos, Nicolas; Morris, Stephen T
2016-11-01
Steers from Angus, Angus×Holstein Friesian, Angus×Holstein Friesian-Jersey and Angus×Jersey cows and a Hereford sire were measured for their carcass and meat quality characteristics. Steers from the Angus×Holstein Friesian cows had a greater final body weight and carcass weight (P<0.05). Steers from Angus×Jersey cows had the lowest carcass weight and dressing-out percentage (P<0.05). There was a greater fat depth over the rump at 12 and 18months of age for the steers from Angus cows (P<0.05) but, not at 24months of age. The steers had similar meat quality characteristics across the breed groups. Steers from Angus×Holstein Friesian and Angus×Jersey cows had a higher ratio of n6 to n3 fatty acids. Using beef-cross-dairy cows to produce steers for meat production does not impact on meat quality. Using Jersey in the breed cross reduced the carcass tissues in the live weight and the potential meat yield.
Neary, Joseph M; Gould, Daniel H; Garry, Franklyn B; Knight, Anthony P; Dargatz, David A; Holt, Timothy N
2013-03-01
Producer reports from ranches over 2,438 meters in southwest Colorado suggest that the mortality of preweaned beef calves may be substantially higher than the national average despite the selection of low pulmonary pressure herd sires for over 20 years. Diagnostic investigations of this death loss problem have been limited due to the extensive mountainous terrain over which these calves are grazed with their dams. The objective of the current study was to determine the causes of calf mortality on 5 high-altitude ranches in Colorado that have been selectively breeding sires with low pulmonary pressure (branding (6 weeks of age) in the spring to weaning in the fall (7 months of age). Clinical signs were recorded, and blood samples were taken from sick calves. Postmortem examinations were performed, and select tissue samples were submitted for aerobic culture and/or histopathology. On the principal study ranch, 9.6% (59/612) of the calves that were branded in the spring either died or were presumed dead by weaning in the fall. In total, 28 necropsies were performed: 14 calves (50%) had lesions consistent with pulmonary hypertension and right-sided heart failure, and 14 calves (50%) died from bronchopneumonia. Remodeling of the pulmonary arterial system, indicative of pulmonary hypertension, was evident in the former and to varying degrees in the latter. There is a need to better characterize the additional risk factors that complicate pulmonary arterial pressure testing of herd sires as a strategy to control pulmonary hypertension.
Paschal, J C; Sanders, J O; Kerr, J L; Lunt, D K; Herring, A D
1995-02-01
Postweaning, feedlot, and carcass data from crossbred calves sired by five Bos indicus breeds and one Bos taurus breed were evaluated. Data included records from F1 calves out of multiparous Hereford cows sired by Angus, Gray Brahman, Gir, Indu-Brazil, Nellore, and Red Brahman bulls. The Zebu crosses grew faster postweaning and were heavier and taller as yearlings than the Angus crosses (P Brahman crosses were faster gaining and were heavier at a year of age than the Gir, Indu-Brazil, and Nellore. The Nellore crosses were significantly taller than the Gray Brahman- and Gir-sired crosses; the Indu-Brazil and Red Brahman were intermediate. Angus crosses were lightest on and off feed but were not significantly different from Gir, and Red and Gray Brahman were heaviest (P Brahman for final weight. The Angus cross was more desirable (P Brahman, Indu-Brazil, and Angus crosses; Red Brahman crosses were intermediate Angus crosses had the lightest carcasses but not significantly lighter than the Indu-Brazil, Gir, or Nellore. Red Brahman-cross carcasses were heaviest and Gray Brahman-cross carcasses were intermediate.(ABSTRACT TRUNCATED AT 250 WORDS)
Whitley, N; Morrow, W E M; See, M T; Oh, S-H
2012-10-01
The objective of this study was to compare body weight, ADG, and feed:gain ratio of antibiotic-free pigs from Yorkshire dams and sired by Yorkshire (YY), Berkshire (BY), Large Black (LBY) or Tamworth (TY) boars. All the crossbred pigs in each of three trials were raised as one group from weaning to finishing in the same deep-bedded hoop, providing a comfortable environment for the animals which allowed rooting and other natural behaviors. Birth, weaning and litter weights were measured and recorded. From approximately 50 kg to market weight (125 kg), feed intake and body weights were recorded manually (body weight) or using a FIRE (Feed Intake Recording Equipment, Osborne Industries Inc. Osborne, Kansas) system with eight individual feeding stations. Feed intake data for 106 finishing pigs between 140 and 210 d of age and the resulting weights and feed conversion ratios were analyzed by breed type. Least square means for body weights (birth, weaning and to 240 d) were estimated with Proc Mixed in SAS 9.2 for fixed effects such as crossbreed and days of age within the sire breed. The differences within fixed effects were compared using least significant differences with DIFF option. Individual birth weights and weaning weights were influenced by sire breed (pgrowth performance in the outdoor environment was satisfactory.
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
Constrained Sparse Galerkin Regression
Loiseau, Jean-Christophe
2016-01-01
In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.
Practical Session: Logistic Regression
Clausel, M.; Grégoire, G.
2014-12-01
An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
N. Whitley
2012-10-01
Full Text Available The objective of this study was to compare body weight, ADG, and feed:gain ratio of antibiotic-free pigs from Yorkshire dams and sired by Yorkshire (YY, Berkshire (BY, Large Black (LBY or Tamworth (TY boars. All the crossbred pigs in each of three trials were raised as one group from weaning to finishing in the same deep-bedded hoop, providing a comfortable environment for the animals which allowed rooting and other natural behaviors. Birth, weaning and litter weights were measured and recorded. From approximately 50 kg to market weight (125 kg, feed intake and body weights were recorded manually (body weight or using a FIRE (Feed Intake Recording Equipment, Osborne Industries Inc. Osborne, Kansas system with eight individual feeding stations. Feed intake data for 106 finishing pigs between 140 and 210 d of age and the resulting weights and feed conversion ratios were analyzed by breed type. Least square means for body weights (birth, weaning and to 240 d were estimated with Proc Mixed in SAS 9.2 for fixed effects such as crossbreed and days of age within the sire breed. The differences within fixed effects were compared using least significant differences with DIFF option. Individual birth weights and weaning weights were influenced by sire breed (p<0.05. For birth weight, BY pigs were the lightest, TY and YY pigs were the heaviest but similar to each other and LBY pigs were intermediate. For weaning weights, BY and LBY pigs were heavier than TY and YY pigs. However, litter birth and weaning weights were not influenced by sire breed, and average daily gain was also not significantly different among breed types. Tamworth sired pigs had lower overall body weight gain, and feed conversion was lower in TY and YY groups than BY and LBY groups (p<0.05, however, number of observations was somewhat limited for feed conversion and for Tamworth pigs. Overall, no convincing differences among breed types were noted for this study, but growth performance in
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
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.
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Software Regression Verification
2013-12-11
of recursive procedures. Acta Informatica , 45(6):403 – 439, 2008. [GS11] Benny Godlin and Ofer Strichman. Regression verifica- tion. Technical Report...functions. Therefore, we need to rede - fine m-term. – Mutual termination. If either function f or function f ′ (or both) is non- deterministic, then their
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.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Hansen, Henrik; Tarp, Finn
2001-01-01
. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via...... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes....
Robust Nonstationary Regression
1993-01-01
This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed which allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities and they belong to the same ...
TWO REGRESSION CREDIBILITY MODELS
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
Vânia Cardoso
2003-08-01
é e no pós-desmama; o segundo discriminou entre animais melhores em desenvolvimento e nas avaliações visuais à desmama e o contrário ao sobreano, e animais com características opostas a estas; o terceiro componente ressaltou diferenças em precocidade reprodutiva. Para os touros e grupos de reprodutores, mesmo após intensa seleção, a maior parte da variação ainda existente esteve associada a diferenças em precocidade.Cluster analysis principles were used to allot bulls to be used as Multiple Service sires (MS. Expected Progeny Differences (EPDs on pre- and postweaning traits were used to calculate distances between bulls. EPDs were standardized and weighed to form a final selection index. The criteria to form lots was based on minimizing the sum of all standardized distances for all possible pairs of bulls. The program was tested on a set of 158 top bulls from a Nelore herd. A set of 4,740 breeding cows was used to evaluate three breeding strategies with the goal of producing genetically superior individuals: (1 random mating; (2 single sire under designed matings by a specific program (PAD; and (3 lots of multiple sires under designed matings using PAD. Principal components was used to obtain the genetic biotypes existing in these populations. Extreme values in EPDs amongst MS lots averages and overall were very similar, indicating the program's ability to preserve total variance. Variances of EPDs from calf crops obtained under the use of PAD were increased three fold. Using designed matings by PAD allowed an increase of 70% in the number of animals which would be recognized with official and fiscal benefits in comparison with random mating. First principal components for cows, indicated that most genetic variability is accounted for preweaning traits and visual scores at postweaning; the second indicated animals can be contrasted as good weaners and poor performer at yearling and vice-versa; the third component showed diferences in sexual precocity. For
随机右删失非参数回归模型的影响分析%Influence Analysis of Non-parametric Regression Model with Random Right Censorship
王淑玲; 冯予; 刘刚
2012-01-01
In this paper, the primary model is transformed to non-parametric regression model; Then, local influence is discussed and concise influence matrix is obtained; At last, example is given to illustrate our results.%将随机删失非参数固定设计回归模型转化为非参数回归模型进行研究；然后对此模型作了局部影响分析,得到计算影响矩阵及最大影响曲率方向的简洁公式；最后通过实例分析,验证了分析方法的有效性.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Karim Hardani*
2012-05-01
Full Text Available A 10-month-old baby presented with developmental delay. He had flaccid paralysis on physical examination.An MRI of the spine revealed malformation of the ninth and tenth thoracic vertebral bodies with complete agenesis of the rest of the spine down that level. The thoracic spinal cord ends at the level of the fifth thoracic vertebra with agenesis of the posterior arches of the eighth, ninth and tenth thoracic vertebral bodies. The roots of the cauda equina appear tightened down and backward and ended into a subdermal fibrous fatty tissue at the level of the ninth and tenth thoracic vertebral bodies (closed meningocele. These findings are consistent with caudal regression syndrome.
Waters Sinead M
2010-07-01
Full Text Available Abstract Background Leptin modulates appetite, energy expenditure and the reproductive axis by signalling via its receptor the status of body energy stores to the brain. The present study aimed to quantify the associations between 10 novel and known single nucleotide polymorphisms in genes coding for leptin and leptin receptor with performance traits in 848 Holstein-Friesian sires, estimated from performance of up to 43,117 daughter-parity records per sire. Results All single nucleotide polymorphisms were segregating in this sample population and none deviated (P > 0.05 from Hardy-Weinberg equilibrium. Complete linkage disequilibrium existed between the novel polymorphism LEP-1609, and the previously identified polymorphisms LEP-1457 and LEP-580. LEP-2470 associated (P Conclusions Several leptin polymorphisms (LEP-2470, LEP-1238, LEP-963, Y7F and R25C associated with the energetically expensive process of lactogenesis. Only SNP Y7F associated with energy storage. Associations were also observed between leptin polymorphisms and calving difficulty, gestation length and calf perinatal mortality. The lack of an association between the leptin variants investigated with calving interval in this large data set would question the potential importance of these leptin variants, or indeed leptin, in selection for improved fertility in the Holstein-Friesian dairy cow.
Uncertainty quantification in DIC with Kriging regression
Wang, Dezhi; DiazDelaO, F. A.; Wang, Weizhuo; Lin, Xiaoshan; Patterson, Eann A.; Mottershead, John E.
2016-03-01
A Kriging regression model is developed as a post-processing technique for the treatment of measurement uncertainty in classical subset-based Digital Image Correlation (DIC). Regression is achieved by regularising the sample-point correlation matrix using a local, subset-based, assessment of the measurement error with assumed statistical normality and based on the Sum of Squared Differences (SSD) criterion. This leads to a Kriging-regression model in the form of a Gaussian process representing uncertainty on the Kriging estimate of the measured displacement field. The method is demonstrated using numerical and experimental examples. Kriging estimates of displacement fields are shown to be in excellent agreement with 'true' values for the numerical cases and in the experimental example uncertainty quantification is carried out using the Gaussian random process that forms part of the Kriging model. The root mean square error (RMSE) on the estimated displacements is produced and standard deviations on local strain estimates are determined.
Functional Regression for Quasar Spectra
Ciollaro, Mattia; Freeman, Peter; Genovese, Christopher; Lei, Jing; O'Connell, Ross; Wasserman, Larry
2014-01-01
The Lyman-alpha forest is a portion of the observed light spectrum of distant galactic nuclei which allows us to probe remote regions of the Universe that are otherwise inaccessible. The observed Lyman-alpha forest of a quasar light spectrum can be modeled as a noisy realization of a smooth curve that is affected by a `damping effect' which occurs whenever the light emitted by the quasar travels through regions of the Universe with higher matter concentration. To decode the information conveyed by the Lyman-alpha forest about the matter distribution, we must be able to separate the smooth `continuum' from the noise and the contribution of the damping effect in the quasar light spectra. To predict the continuum in the Lyman-alpha forest, we use a nonparametric functional regression model in which both the response and the predictor variable (the smooth part of the damping-free portion of the spectrum) are function-valued random variables. We demonstrate that the proposed method accurately predicts the unobserv...
A logistic regression estimating function for spatial Gibbs point processes
Baddeley, Adrian; Coeurjolly, Jean-François; Rubak, Ege
We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related...
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Combining Alphas via Bounded Regression
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
曹文哲; 应俊; 陈广飞; 周丹
2016-01-01
目的：应用随机森林算法和Logistic回归算法，分析2型糖尿病并发视网膜病变的关联因素并构建风险预测模型。方法采用2011~2013年中国人民解放军总医院2型糖尿病住院患者的电子病历信息，主要利用其中的糖尿病诊断数据、糖尿病糖化数据以及糖尿病生化检查数据，应用Logistic回归和随机森林算法，根据ROC曲线下面积比较两种模型的预测效果。结果在随机森林模型的39个变量重要性评分中，糖化血红蛋白、空腹血糖、尿素、肌酐、尿酸、年龄、冠心病和慢性肾病得分较高且具有临床意义，Logistic回归模型最终纳入性别、血糖控制情况（糖化血红蛋白浓度）、慢性肾病、冠心病、心梗和癌症6个因素，ROC曲线下面积提示随机森林模型预测效果优于Logistic回归模型。结论本次研究随机森林算法分析结果给出了各个因素指标的重要性评分，为2型糖尿病并发视网膜病变的早期诊断以及优化诊断流程提供了一定的依据。%Objective To analyze the relevant factors of type 2 diabetes mellitus complicated with retinopathy and to construct the risk prediction model based on machine learning, the random forest algorithm, and the Logistic regression algorithm based on the epidemiological design.Methods To analyze the data from the electronic medical record of patients with type 2 diabetes mellitus complicated with retinopathy in the General Hospital of PLA during 2011-2013. The main focus was on the diagnostic data of diabetes mellitus, the glycosylated data, and biochemical examination data. The prediction effect of the two models were compared with the Logistic regression algorithm and random forest algorithm according the area under the ROC curve.Results Among the 39 variables in the the random forest models, blood glucose control (HbAlc), fasting glucose, urea, creatinine, uric acid, age, coronary heart disease (CHD), and
Lassen, Kristin Marie; Kjær, Erik Dahl; Ouédraogo, Moussa
2014-01-01
Premise of the study: Microsatellite primers were developed for an indigenous fruit tree, Parkia biglobosa, as a tool to study reproductive biology and population structure. Here we use the primers to determine the number of fathers per pod. Methods and Results: Microsatellite loci were enriched...... in a genomic sample and isolated using pyrosequencing. Eleven primer pairs were characterized in two populations of P. biglobosa in Burkina Faso (each with 40 trees). The number of alleles per locus ranged from eight to 15, and one locus had null alleles. We genotyped seeds from 24 open-pollinated pods....... The genotypic profiles of seeds per pod suggest that all seeds are outcrossed and that only one pollen donor sires all ovules in a single fruit. Conclusions: Ten microsatellite markers were highly polymorphic. All seeds per pod of P. biglobosa were full siblings. The markers will be useful for reproductive...
Nguyen, Lam; Wong, David; Ressler, Marc; Koenig, Francois; Stanton, Brian; Smith, Gregory; Sichina, Jeffrey; Kappra, Karl
2007-04-01
The U.S. Army Research Laboratory (ARL), as part of a mission and customer funded exploratory program, has developed a new low-frequency, ultra-wideband (UWB) synthetic aperture radar (SAR) for forward imaging to support the Army's vision of an autonomous navigation system for robotic ground vehicles. These unmanned vehicles, equipped with an array of imaging sensors, will be tasked to help detect man-made obstacles such as concealed targets, enemy minefields, and booby traps, as well as other natural obstacles such as ditches, and bodies of water. The ability of UWB radar technology to help detect concealed objects has been documented in the past and could provide an important obstacle avoidance capability for autonomous navigation systems, which would improve the speed and maneuverability of these vehicles and consequently increase the survivability of the U. S. forces on the battlefield. One of the primary features of the radar is the ability to collect and process data at combat pace in an affordable, compact, and lightweight package. To achieve this, the radar is based on the synchronous impulse reconstruction (SIRE) technique where several relatively slow and inexpensive analog-to-digital (A/D) converters are used to sample the wide bandwidth of the radar signals. We conducted an experiment this winter at Aberdeen Proving Ground (APG) to support the phenomenological studies of the backscatter from positive and negative obstacles for autonomous robotic vehicle navigation, as well as the detection of concealed targets of interest to the Army. In this paper, we briefly describe the UWB SIRE radar and the test setup in the experiment. We will also describe the signal processing and the forward imaging techniques used in the experiment. Finally, we will present imagery of man-made obstacles such as barriers, concertina wires, and mines.
Time-adaptive quantile regression
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Polynomial Regression on Riemannian Manifolds
Hinkle, Jacob; Fletcher, P Thomas; Joshi, Sarang
2012-01-01
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
Nonparametric regression with martingale increment errors
Delattre, Sylvain
2010-01-01
We consider the problem of adaptive estimation of the regression function in a framework where we replace ergodicity assumptions (such as independence or mixing) by another structural assumption on the model. Namely, we propose adaptive upper bounds for kernel estimators with data-driven bandwidth (Lepski's selection rule) in a regression model where the noise is an increment of martingale. It includes, as very particular cases, the usual i.i.d. regression and auto-regressive models. The cornerstone tool for this study is a new result for self-normalized martingales, called ``stability'', which is of independent interest. In a first part, we only use the martingale increment structure of the noise. We give an adaptive upper bound using a random rate, that involves the occupation time near the estimation point. Thanks to this approach, the theoretical study of the statistical procedure is disconnected from usual ergodicity properties like mixing. Then, in a second part, we make a link with the usual minimax th...
Covariance Functions and Random Regression Models in the ...
ARC-IRENE
modelled to account for heterogeneity of variance by AY. ... Results suggest that selection for CW could be effective and that RRM could be .... permanent environmental effects; and εij is the temporary environmental effect or measurement error. .... (1999), however, obtained correlations that were variable as low as 0.23 ...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record
Regression Testing Cost Reduction Suite
Mohamed Alaa El-Din
2014-08-01
Full Text Available The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
Rank regression: an alternative regression approach for data with outliers.
Chen, Tian; Tang, Wan; Lu, Ying; Tu, Xin
2014-10-01
Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But semi-parametric models are quite sensitive to outlying observations, so the generated estimates are unreliable when study data includes outliers. In this situation, some researchers trim the extreme values prior to conducting the analysis, but the ad-hoc rules used for data trimming are based on subjective criteria so different methods of adjustment can yield different results. Rank regression provides a more objective approach to dealing with non-normal data that includes outliers. This paper uses simulated and real data to illustrate this useful regression approach for dealing with outliers and compares it to the results generated using classical regression models and semi-parametric regression models.
Mendonça, L G D; Litherland, N B; Lucy, M C; Keisler, D H; Ballou, M A; Hansen, L B; Chebel, R C
2013-06-01
Objectives were to compare parameters related to innate immune responses and somatotropic axis of Holstein (HO) and Montbéliarde (MO)-sired crossbred cows during the transition from late gestation to early lactation. Cows (40 HO and 47 MO-sired crossbred) were enrolled in the study 45d before expected calving date (study d 0=calving). Polymorphonuclear leukocytes (PMNL) isolated from blood samples collected weekly from study d -7 to 21 and on study d 42 were used for determination of percentage of PMNL positive for phagocytosis (PA+) and oxidative burst (OB+), intensity of PA and OB, percentage of PMNL expressing CD18 (CD18+) and L-selectin (LS+), and intensity of CD18 and LS expression. Blood was sampled weekly from study d -7 to 14 and on study d 28, 42, and 56 for determination of insulin, growth hormone (GH), leptin, and insulin-like growth factor (IGF)-1 concentrations. Blood sampled weekly from study d -14 to 21 and on study d 42 was used to determine cortisol concentration. Liver biopsies were performed on study d -14, 7, 14, and 28 for determination of mRNA expression for insulin receptor B (IRB), total GH receptor (GHRtot), GHR variant 1A (GHR1A), and IGF-1. Data were analyzed by ANOVA for repeated measures or by ANOVA using the GLM procedure of SAS (SAS Institute Inc., Cary, NC). Intensity of CD18 expression was greater in PMNL from crossbred cows compared with PMNL from HO cows [1,482.1 ± 82.3 vs. 1,286.6 ± 69.8 geometric mean fluorescence intensity (GMFI)]. Furthermore, among HO cows, the percentage of PA+ PMNL on study d -7 (64.4 ± 5.2%) tended to be greater than on study d 0 (57.1 ± 5.1%), but no differences in percentage of PA+ PMNL between study d -7 and 0 were observed in crossbred cows. Similarly, intensity of PA in PA+ PMNL from HO cows decreased from study d -7 to 0 (4,750.6 ± 1,217.0 vs. 1,964.7 ± 1,227.9 GMFI), but no changes in intensity of PA in PA+ PMNL from crossbred cows were observed. On study d 0, intensity of PA tended to be
ORDINAL REGRESSION FOR INFORMATION RETRIEVAL
无
2008-01-01
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Wrong Signs in Regression Coefficients
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
From Rasch scores to regression
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
A Matlab program for stepwise regression
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
XRA image segmentation using regression
Jin, Jesse S.
1996-04-01
Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.
Meaney, Christopher; Moineddin, Rahim
2014-01-24
In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the
Progression and regression of the atherosclerotic plaque.
de Feyter, P J; Vos, J; Deckers, J W
1995-08-01
In animals in which atherosclerosis was induced experimentally (by a high cholesterol diet) regression of the atherosclerotic lesion was demonstrated after serum cholesterol was reduced by cholesterol- lowering drugs or a low-fat diet. Whether regression of advanced coronary arterly lesions also takes place in humans after a similar intervention remains conjectural. However, several randomized studies, primarily employing lipid-lowering intervention or comprehensive changes in lifestyle, have demonstrated, using serial angiograms, that it is possible to achieve less progression, arrest or even (small) regression of atherosclerotic lesions. The lipid-lowering trials (NHBLI, CLAS, POSCH, FATS, SCOR and STARS) studied 1240 symptomatic patients, mostly men, with moderately elevated cholesterol levels and moderately severe angiographic-proven coronary artery disease. A variety of lipid-lowering drugs, in addition to a diet, were used over an intervention period ranging from 2 to 3 years. In all but one study (NHBLI), the progression of coronary atherosclerosis was less in the treated group, but regression was induced in only a few patients. The overall relative risk of progression of coronary atherosclerosis was 0 x 62 and 2 x 13, respectively. The induced angiographic differences were small and did not produce any significant haemodynamic benefit. The most important result was tht the disease process could be stabilized in the majority of patients. Three comprehensive lifestyle change trials (the Lifestyle Heart study, STARS and the Heidelberg Study) studied 183 patients, who were subjected to stress management, and/or intensive exercise, in addition to a low fat diet, over a period ranging from 1 to 3 years. All three trials demonstrated less progression, and more regression with overall relative risks of 0 x 40 and 2 x 35 respectively, in the intervention groups. Angiographic trials demonstrated that retardation or arrest of coronary atherosclerosis was possible
Diametral creep prediction of pressure tube using statistical regression methods
Kim, D. [Korea Advanced Inst. of Science and Technology, Daejeon (Korea, Republic of); Lee, J.Y. [Korea Electric Power Research Inst., Daejeon (Korea, Republic of); Na, M.G. [Chosun Univ., Gwangju (Korea, Republic of); Jang, C. [Korea Advanced Inst. of Science and Technology, Daejeon (Korea, Republic of)
2010-07-01
Diametral creep prediction of pressure tube in CANDU reactor is an important factor for ROPT calculation. In this study, pressure tube diametral creep prediction models were developed using statistical regression method such as linear mixed model for longitudinal data analysis. Inspection and operating condition data of Wolsong unit 1 and 2 reactors were used. Serial correlation model and random coefficient model were developed for pressure tube diameter prediction. Random coefficient model provided more accurate results than serial correlation model. (author)
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal c
Flakemore, Aaron Ross; Malau-Aduli, Bunmi Sherifat; Nichols, Peter David; Malau-Aduli, Aduli Enoch Othniel
2017-01-01
Omega-3 long-chain (≥C20) polyunsaturated fatty acids (ω3 LC-PUFA) confer important attributes to health-conscious meat consumers due to the significant role they play in brain development, prevention of coronary heart disease, obesity and hypertension. In this study, the ω3 LC-PUFA content of raw and cooked Longissimus thoracis et lumborum (LTL) muscle from genetically divergent Australian prime lambs supplemented with dietary degummed crude canola oil (DCCO) was evaluated. Samples of LTL muscle were sourced from 24 first cross ewe and wether lambs sired by Dorset, White Suffolk and Merino rams joined to Merino dams that were assigned to supplemental regimes of degummed crude canola oil (DCCO): a control diet at 0 mL/kg DM of DCCO (DCCOC); 25 mL/kg DM of DCCO (DCCOM) and 50 mL/kg DCCO (DCCOH). Lambs were individually housed and offered 1 kg/day/head for 42 days before being slaughtered. Samples for cooked analysis were prepared to a core temperature of 70 °C using conductive dry-heat. Within raw meats: DCCOH supplemented lambs had significantly (P culinary preparation method can be used as effective management tools to deliver nutritionally improved ω3 LC-PUFA lamb to meat consumers.
Perkins, S D; Key, C N; Garrett, C F; Foradori, C D; Bratcher, C L; Kriese-Anderson, L A; Brandebourg, T D
2014-02-01
Mechanisms underlying variation in residual feed intake (RFI), a heritable feed efficiency measure, are poorly understood while the relationship between RFI and meat quality is uncertain. To address these issues, 2 divergent cohorts consisting of High (HRFI) and Low (LRFI) RFI individuals were created by assessing RFI in 48 Angus-sired steers during a 70 d feeding trial to identify steers with divergent RFI. The association of RFI with indices of meat quality and expression of genes within hypothalamic and adipose tissue was then determined in LRFI and HRFI steers. While on test, feed intake was recorded daily with BW and hip heights recorded at 14 d intervals. Ultrasound measurements of rib eye area (REA) and backfat (BF) were recorded initially and before harvest. Carcass and growth data were analyzed using a mixed model with RFI level (LRFI, HRFI) as the independent variable. The least-square means (lsmeans) for RFI were -1.25 and 1.51 for the LRFI and HRFI cohorts (P intake was higher for the HRFI individuals versus the LRFI steers (P feed efficiency in steers while the gonadotropin axis may also influence feed efficiency.
Interpretation of Standardized Regression Coefficients in Multiple Regression.
Thayer, Jerome D.
The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for…
Inferential Models for Linear Regression
Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
[Is regression of atherosclerosis possible?].
Thomas, D; Richard, J L; Emmerich, J; Bruckert, E; Delahaye, F
1992-10-01
Experimental studies have shown the regression of atherosclerosis in animals given a cholesterol-rich diet and then given a normal diet or hypolipidemic therapy. Despite favourable results of clinical trials of primary prevention modifying the lipid profile, the concept of atherosclerosis regression in man remains very controversial. The methodological approach is difficult: this is based on angiographic data and requires strict standardisation of angiographic views and reliable quantitative techniques of analysis which are available with image processing. Several methodologically acceptable clinical coronary studies have shown not only stabilisation but also regression of atherosclerotic lesions with reductions of about 25% in total cholesterol levels and of about 40% in LDL cholesterol levels. These reductions were obtained either by drugs as in CLAS (Cholesterol Lowering Atherosclerosis Study), FATS (Familial Atherosclerosis Treatment Study) and SCOR (Specialized Center of Research Intervention Trial), by profound modifications in dietary habits as in the Lifestyle Heart Trial, or by surgery (ileo-caecal bypass) as in POSCH (Program On the Surgical Control of the Hyperlipidemias). On the other hand, trials with non-lipid lowering drugs such as the calcium antagonists (INTACT, MHIS) have not shown significant regression of existing atherosclerotic lesions but only a decrease on the number of new lesions. The clinical benefits of these regression studies are difficult to demonstrate given the limited period of observation, relatively small population numbers and the fact that in some cases the subjects were asymptomatic. The decrease in the number of cardiovascular events therefore seems relatively modest and concerns essentially subjects who were symptomatic initially. The clinical repercussion of studies of prevention involving a single lipid factor is probably partially due to the reduction in progression and anatomical regression of the atherosclerotic plaque
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Logistic regression for circular data
Al-Daffaie, Kadhem; Khan, Shahjahan
2017-05-01
This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
Quasi-least squares regression
Shults, Justine
2014-01-01
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitu
Maintaining genetic stability in a control flock of South African ...
differential ismaintained. A more random .... The calculation of the selection differential is of course only ..... by determining the regression equations of progeny group means on ... first lambs sired by own-ram replacements were born in 1971).
Regression of lumbar disk herniation
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Heteroscedasticity checks for regression models
无
2001-01-01
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Growth Regression and Economic Theory
Elbers, Chris; Gunning, Jan Willem
2002-01-01
In this note we show that the standard, loglinear growth regression specificationis consistent with one and only one model in the class of stochastic Ramsey models. Thismodel is highly restrictive: it requires a Cobb-Douglas technology and a 100% depreciationrate and it implies that risk does not af
Correlation Weights in Multiple Regression
Waller, Niels G.; Jones, Jeff A.
2010-01-01
A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…
Ridge Regression for Interactive Models.
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are favorable to…
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
Regression Discontinuity Designs with Multiple Rating-Score Variables
Reardon, Sean F.; Robinson, Joseph P.
2012-01-01
In the absence of a randomized control trial, regression discontinuity (RD) designs can produce plausible estimates of the treatment effect on an outcome for individuals near a cutoff score. In the standard RD design, individuals with rating scores higher than some exogenously determined cutoff score are assigned to one treatment condition; those…
Systematic evaluation of land use regression models for NO₂
Wang, M.|info:eu-repo/dai/nl/345480279; Beelen, R.M.J.|info:eu-repo/dai/nl/30483100X; Eeftens, M.R.|info:eu-repo/dai/nl/315028300; Meliefste, C.; Hoek, G.|info:eu-repo/dai/nl/069553475; Brunekreef, B.|info:eu-repo/dai/nl/067548180
2012-01-01
Land use regression (LUR) models have become popular to explain the spatial variation of air pollution concentrations. Independent evaluation is important. We developed LUR models for nitrogen dioxide (NO(2)) using measurements conducted at 144 sampling sites in The Netherlands. Sites were randomly
On the null distribution of Bayes factors in linear regression
We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
Karabatsos, George; Walker, Stephen G.
2013-01-01
The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
Multivariate Regression with Monotone Missing Observation of the Dependent Variables
Raats, V.M.; van der Genugten, B.B.; Moors, J.J.A.
2002-01-01
Multivariate regression is discussed, where the observations of the dependent variables are (monotone) missing completely at random; the explanatory variables are assumed to be completely observed.We discuss OLS-, GLS- and a certain form of E(stimated) GLS-estimation.It turns out that
Coverage Accuracy of Confidence Intervals in Nonparametric Regression
Song-xi Chen; Yong-song Qin
2003-01-01
Point-wise confidence intervals for a nonparametric regression function with random design points are considered. The confidence intervals are those based on the traditional normal approximation and the empirical likelihood. Their coverage accuracy is assessed by developing the Edgeworth expansions for the coverage probabilities. It is shown that the empirical likelihood confidence intervals are Bartlett correctable.
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Regression Verification Using Impact Summaries
Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana
2013-01-01
Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program
Mario Luiz Martinez
2000-06-01
Full Text Available Informações relativas a aspectos físicos, como volume (VOL, turbilhonamento (TURB, motilidade (MOT, vigor (VIG e concentração (CONC, e aspectos morfológicos, como defeitos maiores (DMA e menores (DME, de 807 coletas de sêmen (CS de 105 touros da raça Gir selecionados para leite, e suas respectivas medidas de circunferência escrotal (CE, foram utilizadas para estudar os fatores que afetam essas características e suas correlações. Modelos uni e bivariados, que incluíram os efeitos da central de inseminação, do ano e da época da CS ou da medida da CE , da idade à CS ou da medida CE e touro, foram utilizados para avaliar os efeitos destas fontes de variação e as correlações entre as características. Os efeitos da central de inseminação e de touro foram significativos para todas as características. A idade afetou apenas CE, TURB e NEMOV (= VOL x CONC x MOT. As repetibilidades, estimadas pelo modelo univariado, variaram de 0,23, para CONC, a 0,70, para TURB. As correlações fenotípicas estimadas pelos modelos bivariados foram, em geral, no sentido favorável. As correlações de Pearson entre a CE e as demais características variaram de -0,19 a 0,35. As correlações obtidas, em geral no sentido favorável, sugerem que a CE pode ser utilizada como característica de eliminação prévia dos touros que são candidatos à coleta de sêmen.Data related with semen characteristics such as volume (VOL, gross motility (TURB, motility (MOT, vigor (VIG, concentration (CONC, major defects (DMA and minor defects (DME from 807 semen output (CS of 105 sires of the Gyr breed selected for milk production, and their measurements of scrotal circumference (CE were used to study factors that affect these traits and the correlations among them. Uni and bivariate animal models that included the fixed effects of AI company, year and season of CS or CE measurement, age at CS or CE measurement, and random animal (bull effect were used to evaluate
An Application on Multinomial Logistic Regression Model
Abdalla M El-Habil
2012-03-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE This study aims to identify an application of Multinomial Logistic Regression model which is one of the important methods for categorical data analysis. This model deals with one nominal/ordinal response variable that has more than two categories, whether nominal or ordinal variable. This model has been applied in data analysis in many areas, for example health, social, behavioral, and educational.To identify the model by practical way, we used real data on physical violence against children, from a survey of Youth 2003 which was conducted by Palestinian Central Bureau of Statistics (PCBS. Segment of the population of children in the age group (10-14 years for residents in Gaza governorate, size of 66,935 had been selected, and the response variable consisted of four categories. Eighteen of explanatory variables were used for building the primary multinomial logistic regression model. Model had been tested through a set of statistical tests to ensure its appropriateness for the data. Also the model had been tested by selecting randomly of two observations of the data used to predict the position of each observation in any classified group it can be, by knowing the values of the explanatory variables used. We concluded by using the multinomial logistic regression model that we can able to define accurately the relationship between the group of explanatory variables and the response variable, identify the effect of each of the variables, and we can predict the classification of any individual case.
Polynomial Regressions and Nonsense Inference
Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Producing The New Regressive Left
Crone, Christine
to be a committed artist, and how that translates into supporting al-Assad’s rule in Syria; the Ramadan programme Harrir Aqlak’s attempt to relaunch an intellectual renaissance and to promote religious pluralism; and finally, al-Mayadeen’s cooperation with the pan-Latin American TV station TeleSur and its ambitions...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...... coalition (Iran, Hizbollah, Syria), capitalises on a series of factors that bring them together in spite of their otherwise diverse worldviews and agendas. The New Regressive Left is united by resistance against the growing influence of Saudi Arabia in the religious, cultural, political, economic...
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Heteroscedasticity checks for regression models
ZHU; Lixing
2001-01-01
［1］Carroll, R. J., Ruppert, D., Transformation and Weighting in Regression, New York: Chapman and Hall, 1988.［2］Cook, R. D., Weisberg, S., Diagnostics for heteroscedasticity in regression, Biometrika, 1988, 70: 1—10.［3］Davidian, M., Carroll, R. J., Variance function estimation, J. Amer. Statist. Assoc., 1987, 82: 1079—1091.［4］Bickel, P., Using residuals robustly I: Tests for heteroscedasticity, Ann. Statist., 1978, 6: 266—291.［5］Carroll, R. J., Ruppert, D., On robust tests for heteroscedasticity, Ann. Statist., 1981, 9: 205—209.［6］Eubank, R. L., Thomas, W., Detecting heteroscedasticity in nonparametric regression, J. Roy. Statist. Soc., Ser. B, 1993, 55: 145—155.［7］Diblasi, A., Bowman, A., Testing for constant variance in a linear model, Statist. and Probab. Letters, 1997, 33: 95—103.［8］Dette, H., Munk, A., Testing heteoscedasticity in nonparametric regression, J. R. Statist. Soc. B, 1998, 60: 693—708.［9］Müller, H. G., Zhao, P. L., On a semi-parametric variance function model and a test for heteroscedasticity, Ann. Statist., 1995, 23: 946—967.［10］Stute, W., Manteiga, G., Quindimil, M. P., Bootstrap approximations in model checks for regression, J. Amer. Statist. Asso., 1998, 93: 141—149.［11］Stute, W., Thies, G., Zhu, L. X., Model checks for regression: An innovation approach, Ann. Statist., 1998, 26: 1916—1939.［12］Shorack, G. R., Wellner, J. A., Empirical Processes with Applications to Statistics, New York: Wiley, 1986.［13］Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 1979, 7: 1—26.［14］Wu, C. F. J., Jackknife, bootstrap and other re-sampling methods in regression analysis, Ann. Statist., 1986, 14: 1261—1295.［15］H rdle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993, 21: 1926—1947.［16］Liu, R. Y., Bootstrap procedures under some non-i.i.d. models, Ann. Statist., 1988, 16: 1696—1708.［17
Clustered regression with unknown clusters
Barman, Kishor
2011-01-01
We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...
Robust nonlinear regression in applications
Lim, Changwon; Sen, Pranab K.; Peddada, Shyamal D.
2013-01-01
Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose gr...
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Gaertner, S J; Rouquette, F M; Long, C R; Turner, J W
1992-08-01
Braham-Hereford F1 dams have been used to evaluate the influence of grazing pressure on forage attributes and animal performance at the Texas A&M University Agricultural Research Center at Overton. Data for this study were compiled from 1,909 records of Simmental-sired calves born to Braham-Hereford F1 cows from 1975 to 1990. Birth weight and weaning weight were analyzed independently to estimate the influence of year, season of birth, dam age, weaning age, and sex of calf. The effect of stocking rate as represented by levels of forage availability on weaning weights and subsequent birth weights was measured. Within the fall and winter calving seasons, lactating dams grazing at a high stocking rate produced calves with the lowest subsequent birth weights. Lactating dams assigned to creep-fed treatments had calves with the heaviest subsequent birth weights. Although dams that were less than 3.5 yr of age had calves with the lightest birth weights, there was no apparent decline in birth weight of calves from dams 12 to 17 yr old. Year, sex of calf, age of dam, stocking rate, season of birth, age at weaning, and birth weight were significant factors affecting weaning weight (P less than .01). Fall-born calves grazing cool-season annual pastures were heavier at weaning (267.6 kg) than either winter- (252.0 kg) or spring-born calves (240.9 kg). A stocking rate x season-of-birth interaction was observed for birth weight and weaning weight (P less than .05). Differences in weaning weight from low- vs high-stocked pastures were greater for fall-born calves (61.6 kg) than for winter-born calves (48.7).(ABSTRACT TRUNCATED AT 250 WORDS)
Large unbalanced credit scoring using Lasso-logistic regression ensemble.
Hong Wang
Full Text Available Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.
Large unbalanced credit scoring using Lasso-logistic regression ensemble.
Wang, Hong; Xu, Qingsong; Zhou, Lifeng
2015-01-01
Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.
Genetics Home Reference: caudal regression syndrome
... Twitter Home Health Conditions caudal regression syndrome caudal regression syndrome Enable Javascript to view the expand/collapse ... Download PDF Open All Close All Description Caudal regression syndrome is a disorder that impairs the development ...
Edgington, Eugene
2007-01-01
Statistical Tests That Do Not Require Random Sampling Randomization Tests Numerical Examples Randomization Tests and Nonrandom Samples The Prevalence of Nonrandom Samples in Experiments The Irrelevance of Random Samples for the Typical Experiment Generalizing from Nonrandom Samples Intelligibility Respect for the Validity of Randomization Tests Versatility Practicality Precursors of Randomization Tests Other Applications of Permutation Tests Questions and Exercises Notes References Randomized Experiments Unique Benefits of Experiments Experimentation without Mani
On Weighted Support Vector Regression
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Forecasting Using Random Subspace Methods
T. Boot (Tom); D. Nibbering (Didier)
2016-01-01
textabstractRandom subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two appr
Random Intercept and Random Slope 2-Level Multilevel Models
Rehan Ahmad Khan
2012-11-01
Full Text Available Random intercept model and random intercept & random slope model carrying two-levels of hierarchy in the population are presented and compared with the traditional regression approach. The impact of students’ satisfaction on their grade point average (GPA was explored with and without controlling teachers influence. The variation at level-1 can be controlled by introducing the higher levels of hierarchy in the model. The fanny movement of the fitted lines proves variation of student grades around teachers.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
A. Dalle Zotte
2016-06-01
Full Text Available The aim of the study was to evaluate some meat physical quality and muscle fibre properties of rabbit meat when considering 2 sire breeds (SB: Vienna Blue [VB]; Burgundy Fawn [BF]; both coloured and slow-growing breeds, several parity orders (P: 1, 2, ≥3, gender (G, and 2 slaughter seasons (SS: spring, summer in an organic production system. The effect of storage time (ST at frozen state (2 mo at –20°C of Longissimus lumborum (LL meat was also evaluated. Animals were slaughtered when they reached 2.8 kg of live weight. Then, pH and L*a*b* colour values of Biceps femoris (BF and LL muscles, water loss and Warner-Bratzler shear force of LL and hind leg (HL meat, and the fibre typing and enzymatic activity of LL muscle were analysed. LL meat from females showed higher b* values than males (0.04 vs. –1.25; P<0.05. Significant (P<0.05 SB×P, SB×G and P×G interactions were observed for the b* value of LL: VB and BF crossbreds presented a higher b* value when born as P≥3 and P2 respectively, VB females showed higher b* value than VB males, and P2 and P≥3 produced males with a significantly lower b* value. HL thawing losses were significantly (P<0.05 higher in rabbits slaughtered in summer than in those slaughtered in spring, whereas the opposite result was obtained for LL meat (P<0.01. Cooking loss of LL meat was significantly lower in P2 group than P≥3 group (P<0.05. The lactate dehydrogenase activity in LL muscle was higher in VB than in BF crossbreds (930 vs. 830 IU; P<0.05, albeit not supported by differences in fibre type distribution. The ST significantly (P<0.01 reduced pH, a* and b* colour values, and increased lightness of LL meat. It was concluded that the crossbreeds derived from VB and BF genotypes and farmed organically did not show remarkable sexual dimorphism, considering their elder slaughter age than rabbits reared under intensive conditions. Physical quality of meat was mainly affected by slaughter season, indicating
McGee, M; Welch, C M; Ramirez, J A; Carstens, G E; Price, W J; Hall, J B; Hill, R A
2014-11-01
Feeding behavior has the potential to enhance prediction of feed intake and to improve understanding of the relationships between behavior, DMI, ADG, and residual feed intake (RFI) in beef cattle. Two cohorts, born in 2009 and 2010, the progeny of Red Angus bulls (n = 58 heifers and n = 53 steers), were evaluated during the growing phase, and the latter group of steers was also evaluated during the finishing phase. All behavior analyses were based on 7 feeding behavior traits (bunk visit frequency, bunk visit duration [BVDUR], feed bout frequency, feed bout duration, meal frequency, meal duration, and average meal intake) and their relationships with ADG, DMI, and RFI. During the growing phase, feeding duration traits were most indicative of DMI with positive correlations between BVDUR and DMI for cohort 1 steers, growing phase (n = 28, r = 0.52, P = 0.00); cohort 2 steers, growing phase (n = 25, r = 0.44, P = 0.01); and cohort 2 heifers, growing phase (n = 29, r = 0.28 P = 0.05). There were similar trends toward correlation of BVDUR and RFI for both steer groups and cohort 1 heifers, growing phase (C1HG; n = 29; r = 0.27, P = 0.06; r = 0.30, P = 0.07; and r = 0.26, P = 0.08, respectively). Feed bout frequency was correlated with ADG in C1HG and in cohort 2 steers, finishing phase (r = -0.31, P = 0.04, and r = 0.43, P = 0.01, respectively). Feed bout duration was correlated with ADG in heifer groups (r = 0.29 and r = 0.28, P = 0.05 for both groups) and DMI for all growing phase animals (r = 0.29 to 0.55, P ≤ 0.05 for all groups). Evaluation of growing vs. finishing phase steer groups suggests that all behaviors, RFI, and DMI, but not ADG, are correlated through the growing and finishing phases (P ≤ 0.01 for all variables excluding ADG), implying that feeding behaviors determined during the growing phase are strong predictors of DMI in either life stage. Sire maintenance energy EPD effects (measured as high or low groups) on progeny feeding behaviors revealed a
Prediction, Regression and Critical Realism
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... of prediction necessary and possible in spatial planning of urban development. Finally, the political implications of positions within theory of science rejecting the possibility of predictions about social phenomena are addressed....... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...
Nonparametric Regression with Common Shocks
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Practical Session: Multiple Linear Regression
Clausel, M.; Grégoire, G.
2014-12-01
Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).
Lumbar herniated disc: spontaneous regression
Yüksel, Kasım Zafer
2017-01-01
Background Low back pain is a frequent condition that results in substantial disability and causes admission of patients to neurosurgery clinics. To evaluate and present the therapeutic outcomes in lumbar disc hernia (LDH) patients treated by means of a conservative approach, consisting of bed rest and medical therapy. Methods This retrospective cohort was carried out in the neurosurgery departments of hospitals in Kahramanmaraş city and 23 patients diagnosed with LDH at the levels of L3−L4, L4−L5 or L5−S1 were enrolled. Results The average age was 38.4 ± 8.0 and the chief complaint was low back pain and sciatica radiating to one or both lower extremities. Conservative treatment was administered. Neurological examination findings, durations of treatment and intervals until symptomatic recovery were recorded. Laségue tests and neurosensory examination revealed that mild neurological deficits existed in 16 of our patients. Previously, 5 patients had received physiotherapy and 7 patients had been on medical treatment. The number of patients with LDH at the level of L3−L4, L4−L5, and L5−S1 were 1, 13, and 9, respectively. All patients reported that they had benefit from medical treatment and bed rest, and radiologic improvement was observed simultaneously on MRI scans. The average duration until symptomatic recovery and/or regression of LDH symptoms was 13.6 ± 5.4 months (range: 5−22). Conclusions It should be kept in mind that lumbar disc hernias could regress with medical treatment and rest without surgery, and there should be an awareness that these patients could recover radiologically. This condition must be taken into account during decision making for surgical intervention in LDH patients devoid of indications for emergent surgery. PMID:28119770
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....
Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.
Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg
2009-11-01
G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
Varying-coefficient functional linear regression
Wu, Yichao; Müller, Hans-Georg; 10.3150/09-BEJ231
2011-01-01
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite numb...
Knowledge and Awareness: Linear Regression
Monika Raghuvanshi
2016-12-01
Full Text Available Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task. Economically driven state of development in younger generations is an impediment to the correct manner of development. As youths are at the learning phase, they can be molded to follow a correct lifestyle. Awareness and knowledge are important components of any formal or informal environmental education. The purpose of this study is to evaluate the relationship of these components among students of secondary/ senior secondary schools who have undergone a formal study of environment in their curricula. A suitable instrument is developed in order to measure the elements of Awareness and Knowledge among the participants of the study. Data was collected from various secondary and senior secondary school students in the age group 14 to 20 years using cluster sampling technique from the city of Bikaner, India. Linear regression analysis was performed using IBM SPSS 23 statistical tool. There exists a weak relation between knowledge and awareness about environmental issues, caused due to routine practices mishandling; hence one component can be complemented by other for improvement in both. Knowledge and awareness are crucial factors and can provide huge opportunities in any field. Resource utilization for economic solutions may pave the way for eco-friendly products and practices. If green practices are inculcated at the learning phase, they may become normal routine. This will also help in repletion of the environment.
Streamflow forecasting using functional regression
Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
2016-07-01
Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
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.
A note on the maximum likelihood estimator in the gamma regression model
Jerzy P. Rydlewski
2009-01-01
Full Text Available This paper considers a nonlinear regression model, in which the dependent variable has the gamma distribution. A model is considered in which the shape parameter of the random variable is the sum of continuous and algebraically independent functions. The paper proves that there is exactly one maximum likelihood estimator for the gamma regression model.
First Look at Photometric Reduction via Mixed-Model Regression (Poster abstract)
Dose, E.
2016-12-01
(Abstract only) Mixed-model regression is proposed as a new approach to photometric reduction, especially for variable-star photometry in several filters. Mixed-model regression adds to normal multivariate regression certain "random effects": categorical-variable terms that model and extract specific systematic errors such as image-to-image zero-point fluctuations (cirrus effect) or even errors in comp-star catalog magnitudes.
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
Spontaneous Regression of an Incidental Spinal Meningioma
Yilmaz, Ali; Kizilay, Zahir; Sair, Ahmet; Avcil, Mucahit; Ozkul, Ayca
2015-01-01
AIM: The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. CASE REPORT...
Common pitfalls in statistical analysis: Logistic regression.
Ranganathan, Priya; Pramesh, C S; Aggarwal, Rakesh
2017-01-01
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
GUO TieXin; CHEN XinXiang
2009-01-01
The purpose of this paper is to provide a random duality theory for the further development of the theory of random conjugate spaces for random normed modules.First,the complicated stratification structure of a module over the algebra L(μ,K) frequently makes our investigations into random duality theory considerably different from the corresponding ones into classical duality theory,thus in this paper we have to first begin in overcoming several substantial obstacles to the study of stratification structure on random locally convex modules.Then,we give the representation theorem of weakly continuous canonical module homomorphisms,the theorem of existence of random Mackey structure,and the random bipolar theorem with respect to a regular random duality pair together with some important random compatible invariants.
无
2009-01-01
The purpose of this paper is to provide a random duality theory for the further development of the theory of random conjugate spaces for random normed modules. First, the complicated stratification structure of a module over the algebra L(μ, K) frequently makes our investigations into random duality theory considerably difierent from the corresponding ones into classical duality theory, thus in this paper we have to first begin in overcoming several substantial obstacles to the study of stratification structure on random locally convex modules. Then, we give the representation theorem of weakly continuous canonical module homomorphisms, the theorem of existence of random Mackey structure, and the random bipolar theorem with respect to a regular random duality pair together with some important random compatible invariants.
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Unbalanced Regressions and the Predictive Equation
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Kindahl H
2004-03-01
Full Text Available The high incidence of stillbirth in Swedish Holstein heifers has increased continuously during the last 15 years to an average of 11% today. The pathological reasons behind the increased incidence of stillbirth are unknown. The present experiment was undertaken to investigate possible causes of stillbirth and to study possible physiological markers for predicting stillbirth. Twenty Swedish Holstein dairy heifers sired by bulls with breeding values for a high risk of stillbirth (n = 12 (experimental group and a low risk of stillbirth (n = 8 (control group, group B were selected based on information in the Swedish AI-data base. The experimental group consisted of 2 subgroups of heifers (groups A1 and A2 inseminated with 2 different bulls with 3.5% and 9% higher stillbirth rates than the average, and the control group consisted of heifers pregnant with 5 different bulls with 0%–6% lower stillbirth rates than the average. The bull used for group A1 had also calving difficulties due to large calves as compared to the bull in group A2 showing no calving difficulties. The heifers were supervised from 6–7 months of pregnancy up to birth, and the pregnancies and parturitions were compared between groups regarding hormonal levels, haematology, placental characteristics and calf viability. In group A1, 1 stillborn, 1 weak and 4 normal calves were recorded. In group A2, 2 stillborn and 4 normal calves were registered. All animals in the control group gave birth to a normal living calf without any assistance. The weak calf showed deviating profiles of body temperature, saturated oxygen and heart rates, compared with the normal living calves. No differences of the placentome thickness, measured in vivo by ultrasonography were seen between the groups. The number of leukocytes and differential cell counts in groups A1 and A2 followed the profiles found in the control group. In group A1, a slight decrease of oestrone sulphate (E1SO4 levels was found in the
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Synthesizing Regression Results: A Factored Likelihood Method
Wu, Meng-Jia; Becker, Betsy Jane
2013-01-01
Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported…
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…
Regression with Sparse Approximations of Data
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected by...
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Analyzing genotype-by-environment interaction using curvilinear regression
Dulce Gamito Santinhos Pereira
2012-12-01
Full Text Available In the context of multi-environment trials, where a series of experiments is conducted across different environmental conditions, the analysis of the structure of genotype-by-environment interaction is an important topic. This paper presents a generalization of the joint regression analysis for the cases where the response (e.g. yield is not linear across environments and can be written as a second (or higher order polynomial or another non-linear function. After identifying the common form regression function for all genotypes, we propose a selection procedure based on the adaptation of two tests: (i a test for parallelism of regression curves; and (ii a test of coincidence for those regressions. When the hypothesis of parallelism is rejected, subgroups of genotypes where the responses are parallel (or coincident should be identified. The use of the Scheffé multiple comparison method for regression coefficients in second-order polynomials allows to group the genotypes in two types of groups: one with upward-facing concavity (i.e. potential yield growth, and the other with downward-facing concavity (i.e. the yield approaches saturation. Theoretical results for genotype comparison and genotype selection are illustrated with an example of yield from a non-orthogonal series of experiments with winter rye (Secalecereale L.. We have deleted 10 % of that data at random to show that our meteorology is fully applicable to incomplete data sets, often observed in multi-environment trials.
Regression with Sparse Approximations of Data
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Assumptions of Multiple Regression: Correcting Two Misconceptions
Matt N. Williams
2013-09-01
Full Text Available In 2002, an article entitled - Four assumptions of multiple regression that researchers should always test- by.Osborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times.(as of August 2013, and it is one of the first results displayed in a Google search for - regression.assumptions- . While Osborne and Waters' efforts in raising awareness of the need to check assumptions.when using regression are laudable, we note that the original article contained at least two fairly important.misconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires the.assumption of normally distributed variables; and secondly, that measurement errors necessarily cause.underestimation of simple regression coefficients. In this article, we clarify that multiple regression models.estimated using ordinary least squares require the assumption of normally distributed errors in order for.trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or.predictor variables. Secondly, we point out that regression coefficients in simple regression models will be.biased (toward zero estimates of the relationships between variables of interest when measurement error is.uncorrelated across those variables, but that when correlated measurement error is present, regression.coefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary of.the assumptions of multiple regression when using ordinary least squares.
Functional linear regression via canonical analysis
He, Guozhong; Wang, Jane-Ling; Yang, Wenjing; 10.3150/09-BEJ228
2011-01-01
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.
Regression in children with autism spectrum disorders.
Malhi, Prahbhjot; Singhi, Pratibha
2012-10-01
To understand the characteristics of autistic regression and to compare the clinical and developmental profile of children with autism spectrum disorders (ASD) in whom parents report developmental regression with age matched ASD children in whom no regression is reported. Participants were 35 (Mean age = 3.57 y, SD = 1.09) children with ASD in whom parents reported developmental regression before age 3 y and a group of age and IQ matched 35 ASD children in whom parents did not report regression. All children were recruited from the outpatient Child Psychology Clinic of the Department of Pediatrics of a tertiary care teaching hospital in North India. Multi-disciplinary evaluations including neurological, diagnostic, cognitive, and behavioral assessments were done. Parents were asked in detail about the age at onset of regression, type of regression, milestones lost, and event, if any, related to the regression. In addition, the Childhood Autism Rating Scale (CARS) was administered to assess symptom severity. The mean age at regression was 22.43 mo (SD = 6.57) and large majority (66.7%) of the parents reported regression between 12 and 24 mo. Most (75%) of the parents of the regression-autistic group reported regression in the language domain, particularly in the expressive language sector, usually between 18 and 24 mo of age. Regression of language was not an isolated phenomenon and regression in other domains was also reported including social skills (75%), cognition (31.25%). In majority of the cases (75%) the regression reported was slow and subtle. There were no significant differences in the motor, social, self help, and communication functioning between the two groups as measured by the DP II.There were also no significant differences between the two groups on the total CARS score and total number of DSM IV symptoms endorsed. However, the regressed children had significantly (t = 2.36, P = .021) more social deficits as per the DSM IV as
Using Regression Mixture Analysis in Educational Research
Cody S. Ding
2006-11-01
Full Text Available Conventional regression analysis is typically used in educational research. Usually such an analysis implicitly assumes that a common set of regression parameter estimates captures the population characteristics represented in the sample. In some situations, however, this implicit assumption may not be realistic, and the sample may contain several subpopulations such as high math achievers and low math achievers. In these cases, conventional regression models may provide biased estimates since the parameter estimates are constrained to be the same across subpopulations. This paper advocates the applications of regression mixture models, also known as latent class regression analysis, in educational research. Regression mixture analysis is more flexible than conventional regression analysis in that latent classes in the data can be identified and regression parameter estimates can vary within each latent class. An illustration of regression mixture analysis is provided based on a dataset of authentic data. The strengths and limitations of the regression mixture models are discussed in the context of educational research.
João Francisco Pereira Bastos
1999-01-01
Full Text Available Para estimar a magnitude do efeito do pai do feto (PDF sobre as características produtivas e reprodutivas em fêmeas da raça Pitangueiras, foram analisadas 2287 lactações de 618 vacas, filhas de 82 reprodutores e acasaladas com 68 touros. As análises estatísticas foram executadas usando-se o programa LSMLMW (Mixed Model Least Squares and Maximum Likelihood Computer Program. O PDF não influenciou a produção de leite na lactação subseqüente, porém seu efeito foi significativo para produção e porcentagem de gordura, período de serviço, período de gestação e intervalo de partos. As estimativas dos efeitos do PDF, expressas como porcentagem da variância total foram: 0,81; 1,67; e 6,43%, respectivamente, para produção de leite e produção e porcentagem de gordura. Esta contribuição para as características reprodutivas foi estimada em 7,76% para o período de serviço; 1,23% para o período de gestação; e 7,57% para o intervalo de partos. As implicações genéticas do efeito do PDF sobre as características produtivas foram de pequena importância econômica, contudo, no desempenho reprodutivo, este efeito foi significativo e mensurável.To estimate the magnitude of sire of fetus (SOF effects on productive and reproductive traits in females of Pitangueiras breed, 2287 lactations of 618 cows, daughters of 82 bulls, mated with 68 sires were analyzed. The statistical analyses were made using LSMLMW program (Mixed Model Least Squares and Maximum Likelihood Computer Program. Sire of fetus (SOF did not affect the milk production in the subsequent lactation, however its effect was significant for the percent fat and production, days open, gestation length and calving intervals. The estimates of SOF effects, expressed as a percentage of total variance, were .81, 1.67 and 6.43% for milk and percent fat and yield, respectively. This contribution on the reproductive traits was estimated in 7.76% for days open, 1.23% for gestation
Beta blockers & left ventricular hypertrophy regression.
George, Thomas; Ajit, Mullasari S; Abraham, Georgi
2010-01-01
Left ventricular hypertrophy (LVH) particularly in hypertensive patients is a strong predictor of adverse cardiovascular events. Identifying LVH not only helps in the prognostication but also in the choice of therapeutic drugs. The prevalence of LVH is age linked and has a direct correlation to the severity of hypertension. Adequate control of blood pressure, most importantly central aortic pressure and blocking the effects of cardiomyocyte stimulatory growth factors like Angiotensin II helps in regression of LVH. Among the various antihypertensives ACE-inhibitors and angiotensin receptor blockers are more potent than other drugs in regressing LVH. Beta blockers especially the newer cardio selective ones do still have a role in regressing LVH albeit a minor one. A meta-analysis of various studies on LVH regression shows many lacunae. There have been no consistent criteria for defining LVH and documenting LVH regression. This article reviews current evidence on the role of Beta Blockers in LVH regression.
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...
High-dimensional regression with unknown variance
Giraud, Christophe; Verzelen, Nicolas
2011-01-01
We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasize is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso esti- mator and some references are collected for some more general models, including multivariate regression and nonparametric regression.
Regression calibration with heteroscedastic error variance.
Spiegelman, Donna; Logan, Roger; Grove, Douglas
2011-01-01
The problem of covariate measurement error with heteroscedastic measurement error variance is considered. Standard regression calibration assumes that the measurement error has a homoscedastic measurement error variance. An estimator is proposed to correct regression coefficients for covariate measurement error with heteroscedastic variance. Point and interval estimates are derived. Validation data containing the gold standard must be available. This estimator is a closed-form correction of the uncorrected primary regression coefficients, which may be of logistic or Cox proportional hazards model form, and is closely related to the version of regression calibration developed by Rosner et al. (1990). The primary regression model can include multiple covariates measured without error. The use of these estimators is illustrated in two data sets, one taken from occupational epidemiology (the ACE study) and one taken from nutritional epidemiology (the Nurses' Health Study). In both cases, although there was evidence of moderate heteroscedasticity, there was little difference in estimation or inference using this new procedure compared to standard regression calibration. It is shown theoretically that unless the relative risk is large or measurement error severe, standard regression calibration approximations will typically be adequate, even with moderate heteroscedasticity in the measurement error model variance. In a detailed simulation study, standard regression calibration performed either as well as or better than the new estimator. When the disease is rare and the errors normally distributed, or when measurement error is moderate, standard regression calibration remains the method of choice.
Enhanced piecewise regression based on deterministic annealing
ZHANG JiangShe; YANG YuQian; CHEN XiaoWen; ZHOU ChengHu
2008-01-01
Regression is one of the important problems in statistical learning theory. This paper proves the global convergence of the piecewise regression algorithm based on deterministic annealing and continuity of global minimum of free energy w.r.t temperature, and derives a new simplified formula to compute the initial critical temperature. A new enhanced piecewise regression algorithm by using "migration of prototypes" is proposed to eliminate "empty cell" in the annealing process. Numerical experiments on several benchmark datasets show that the new algo-rithm can remove redundancy and improve generalization of the piecewise regres-sion model.
Geodesic least squares regression on information manifolds
Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, Ghent, Belgium and Laboratory for Plasma Physics, Royal Military Academy, Brussels (Belgium)
2014-12-05
We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply this to scaling laws in magnetic confinement fusion.
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
Logistic Regression for Evolving Data Streams Classification
YIN Zhi-wu; HUANG Shang-teng; XUE Gui-rong
2007-01-01
Logistic regression is a fast classifier and can achieve higher accuracy on small training data. Moreover,it can work on both discrete and continuous attributes with nonlinear patterns. Based on these properties of logistic regression, this paper proposed an algorithm, called evolutionary logistical regression classifier (ELRClass), to solve the classification of evolving data streams. This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier, to keep this classifier if its performance is deteriorated by the reason of bursting noise, or to construct a new classifier if a major concept drift is detected. The intensive experimental results demonstrate the effectiveness of this algorithm.
New ridge parameters for ridge regression
A.V. Dorugade
2014-04-01
Full Text Available Hoerl and Kennard (1970a introduced the ridge regression estimator as an alternative to the ordinary least squares (OLS estimator in the presence of multicollinearity. In ridge regression, ridge parameter plays an important role in parameter estimation. In this article, a new method for estimating ridge parameters in both situations of ordinary ridge regression (ORR and generalized ridge regression (GRR is proposed. The simulation study evaluates the performance of the proposed estimator based on the mean squared error (MSE criterion and indicates that under certain conditions the proposed estimators perform well compared to OLS and other well-known estimators reviewed in this article.
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Robust regression for large-scale neuroimaging studies.
Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand
2015-05-01
Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies.
REGRESSION ANALYSIS OF PRODUCTIVITY USING MIXED EFFECT MODEL
Siana Halim
2007-01-01
Full Text Available Production plants of a company are located in several areas that spread across Middle and East Java. As the production process employs mostly manpower, we suspected that each location has different characteristics affecting the productivity. Thus, the production data may have a spatial and hierarchical structure. For fitting a linear regression using the ordinary techniques, we are required to make some assumptions about the nature of the residuals i.e. independent, identically and normally distributed. However, these assumptions were rarely fulfilled especially for data that have a spatial and hierarchical structure. We worked out the problem using mixed effect model. This paper discusses the model construction of productivity and several characteristics in the production line by taking location as a random effect. The simple model with high utility that satisfies the necessary regression assumptions was built using a free statistic software R version 2.6.1.
Inverse probability weighted Cox regression for doubly truncated data.
Mandel, Micha; de Uña-Álvarez, Jacobo; Simon, David K; Betensky, Rebecca A
2017-09-08
Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. © 2017, The International Biometric Society.
Principal regression analysis and the index leverage effect
Reigneron, Pierre-Alain; Allez, Romain; Bouchaud, Jean-Philippe
2011-09-01
We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call ‘Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode away from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.
A Randomized Experiment Comparing Random and Cutoff-Based Assignment
Shadish, William R.; Galindo, Rodolfo; Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.
2011-01-01
In this article, we review past studies comparing randomized experiments to regression discontinuity designs, mostly finding similar results, but with significant exceptions. The latter might be due to potential confounds of study characteristics with assignment method or with failure to estimate the same parameter over methods. In this study, we…
Incremental Net Effects in Multiple Regression
Lipovetsky, Stan; Conklin, Michael
2005-01-01
A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…
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.
Dealing with Outliers: Robust, Resistant Regression
Glasser, Leslie
2007-01-01
Least-squares linear regression is the best of statistics and it is the worst of statistics. The reasons for this paradoxical claim, arising from possible inapplicability of the method and the excessive influence of "outliers", are discussed and substitute regression methods based on median selection, which is both robust and resistant, are…
Competing Risks Quantile Regression at Work
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2017-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We use...
Implementing Variable Selection Techniques in Regression.
Thayer, Jerome D.
Variable selection techniques in stepwise regression analysis are discussed. In stepwise regression, variables are added or deleted from a model in sequence to produce a final "good" or "best" predictive model. Stepwise computer programs are discussed and four different variable selection strategies are described. These…
Regression Model With Elliptically Contoured Errors
Arashi, M; Tabatabaey, S M M
2012-01-01
For the regression model where the errors follow the elliptically contoured distribution (ECD), we consider the least squares (LS), restricted LS (RLS), preliminary test (PT), Stein-type shrinkage (S) and positive-rule shrinkage (PRS) estimators for the regression parameters. We compare the quadratic risks of the estimators to determine the relative dominance properties of the five estimators.
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.
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Atherosclerotic plaque regression: fact or fiction?
Shanmugam, Nesan; Román-Rego, Ana; Ong, Peter; Kaski, Juan Carlos
2010-08-01
Coronary artery disease is the major cause of death in the western world. The formation and rapid progression of atheromatous plaques can lead to serious cardiovascular events in patients with atherosclerosis. The better understanding, in recent years, of the mechanisms leading to atheromatous plaque growth and disruption and the availability of powerful HMG CoA-reductase inhibitors (statins) has permitted the consideration of plaque regression as a realistic therapeutic goal. This article reviews the existing evidence underpinning current therapeutic strategies aimed at achieving atherosclerotic plaque regression. In this review we also discuss imaging modalities for the assessment of plaque regression, predictors of regression and whether plaque regression is associated with a survival benefit.
Pathological assessment of liver fibrosis regression
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Consistent estimators in random censorship semiparametric models
王启华
1996-01-01
For the fixed design regression modelwhen Y, are randomly censored on the right, the estimators of unknown parameter and regression function g from censored observations are defined in the two cases .where the censored distribution is known and unknown, respectively. Moreover, the sufficient conditions under which these estimators are strongly consistent and pth (p>2) mean consistent are also established.
Stephanov, M A; Wettig, T
2005-01-01
We review elementary properties of random matrices and discuss widely used mathematical methods for both hermitian and nonhermitian random matrix ensembles. Applications to a wide range of physics problems are summarized. This paper originally appeared as an article in the Wiley Encyclopedia of Electrical and Electronics Engineering.
Quantile regression applied to spectral distance decay
Rocchini, D.; Cade, B.S.
2008-01-01
Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.
Hypotheses testing for fuzzy robust regression parameters
Kula, Kamile Sanli [Ahi Evran University, Department of Mathematics, 40200 Kirsehir (Turkey)], E-mail: sanli2004@hotmail.com; Apaydin, Aysen [Ankara University, Department of Statistics, 06100 Ankara (Turkey)], E-mail: apaydin@science.ankara.edu.tr
2009-11-30
The classical least squares (LS) method is widely used in regression analysis because computing its estimate is easy and traditional. However, LS estimators are very sensitive to outliers and to other deviations from basic assumptions of normal theory [Huynh H. A comparison of four approaches to robust regression. Psychol Bull 1982;92:505-12; Stephenson D. 2000. Available from: (http://folk.uib.no/ngbnk/kurs/notes/node38.html); Xu R, Li C. Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets and Systems 2001;119:215-23.]. If there exists outliers in the data set, robust methods are preferred to estimate parameters values. We proposed a fuzzy robust regression method by using fuzzy numbers when x is crisp and Y is a triangular fuzzy number and in case of outliers in the data set, a weight matrix was defined by the membership function of the residuals. In the fuzzy robust regression, fuzzy sets and fuzzy regression analysis was used in ranking of residuals and in estimation of regression parameters, respectively [Sanli K, Apaydin A. Fuzzy robust regression analysis based on the ranking of fuzzy sets. Inernat. J. Uncertainty Fuzziness and Knowledge-Based Syst 2008;16:663-81.]. In this study, standard deviation estimations are obtained for the parameters by the defined weight matrix. Moreover, we propose another point of view in hypotheses testing for parameters.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Relative risk regression analysis of epidemiologic data.
Prentice, R L
1985-11-01
Relative risk regression methods are described. These methods provide a unified approach to a range of data analysis problems in environmental risk assessment and in the study of disease risk factors more generally. Relative risk regression methods are most readily viewed as an outgrowth of Cox's regression and life model. They can also be viewed as a regression generalization of more classical epidemiologic procedures, such as that due to Mantel and Haenszel. In the context of an epidemiologic cohort study, relative risk regression methods extend conventional survival data methods and binary response (e.g., logistic) regression models by taking explicit account of the time to disease occurrence while allowing arbitrary baseline disease rates, general censorship, and time-varying risk factors. This latter feature is particularly relevant to many environmental risk assessment problems wherein one wishes to relate disease rates at a particular point in time to aspects of a preceding risk factor history. Relative risk regression methods also adapt readily to time-matched case-control studies and to certain less standard designs. The uses of relative risk regression methods are illustrated and the state of development of these procedures is discussed. It is argued that asymptotic partial likelihood estimation techniques are now well developed in the important special case in which the disease rates of interest have interpretations as counting process intensity functions. Estimation of relative risks processes corresponding to disease rates falling outside this class has, however, received limited attention. The general area of relative risk regression model criticism has, as yet, not been thoroughly studied, though a number of statistical groups are studying such features as tests of fit, residuals, diagnostics and graphical procedures. Most such studies have been restricted to exponential form relative risks as have simulation studies of relative risk estimation
Variable and subset selection in PLS regression
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Regressive language in severe head injury.
Thomsen, I V; Skinhoj, E
1976-09-01
In a follow-up study of 50 patients with severe head injuries three patients had echolalia. One patient with initially global aphasia had echolalia for some weeks when he started talking. Another patient with severe diffuse brain damage, dementia, and emotional regression had echolalia. The dysfunction was considered a detour performance. In the third patient echolalia and palilalia were details in a total pattern of regression lasting for months. The patient, who had extensive frontal atrophy secondary to a very severe head trauma, presented an extreme state of regression returning to a foetal-body pattern and behaving like a baby.
Regression of altitude-produced cardiac hypertrophy.
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
Regression of altitude-produced cardiac hypertrophy.
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
In precision agriculture regression has been used widely to quality the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attemp...
Multiple Instance Regression with Structured Data
Wagstaff, Kiri L.; Lane, Terran; Roper, Alex
2008-01-01
This slide presentation reviews the use of multiple instance regression with structured data from multiple and related data sets. It applies the concept to a practical problem, that of estimating crop yield using remote sensed country wide weekly observations.
Prediction of Dynamical Systems by Symbolic Regression
Quade, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-01-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting a...
Some Simple Computational Formulas for Multiple Regression
Aiken, Lewis R., Jr.
1974-01-01
Short-cut formulas are presented for direct computation of the beta weights, the standard errors of the beta weights, and the multiple correlation coefficient for multiple regression problems involving three independent variables and one dependent variable. (Author)
Spontaneous Regression of an Incidental Spinal Meningioma
Ali Yilmaz
2015-12-01
Full Text Available AIM: The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. CASE REPORT: We report a 17 year old man with a cervical meningioma which was incidentally detected. In his cervical MRI an extradural, cranio-caudal contrast enchanced lesion at C2-C3 levels of the cervical spinal cord was detected. Despite the slight compression towards the spinal cord, he had no symptoms and refused any kind of surgical approach. The meningioma was followed by control MRI and it spontaneously regressed within six months. There were no signs of hemorrhage or calcification. CONCLUSION: Although it is a rare condition, the clinicians should consider that meningiomas especially incidentally diagnosed may be regressed spontaneously.
Spontaneous Regression of an Incidental Spinal Meningioma.
Yilmaz, Ali; Kizilay, Zahir; Sair, Ahmet; Avcil, Mucahit; Ozkul, Ayca
2016-03-15
The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. We report a 17 year old man with a cervical meningioma which was incidentally detected. In his cervical MRI an extradural, cranio-caudal contrast enchanced lesion at C2-C3 levels of the cervical spinal cord was detected. Despite the slight compression towards the spinal cord, he had no symptoms and refused any kind of surgical approach. The meningioma was followed by control MRI and it spontaneously regressed within six months. There were no signs of hemorrhage or calcification. Although it is a rare condition, the clinicians should consider that meningiomas especially incidentally diagnosed may be regressed spontaneously.
Vectors, a tool in statistical regression theory
Corsten, L.C.A.
1958-01-01
Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding e
Patterns of Regression in Rett Syndrome
J Gordon Millichap
2002-10-01
Full Text Available Patterns and features of regression in a case series of 53 girls and women with Rett syndrome were studied at the Institute of Child Health and Great Ormond Street Children’s Hospital, London, UK.
A new bivariate negative binomial regression model
Faroughi, Pouya; Ismail, Noriszura
2014-12-01
This paper introduces a new form of bivariate negative binomial (BNB-1) regression which can be fitted to bivariate and correlated count data with covariates. The BNB regression discussed in this study can be fitted to bivariate and overdispersed count data with positive, zero or negative correlations. The joint p.m.f. of the BNB1 distribution is derived from the product of two negative binomial marginals with a multiplicative factor parameter. Several testing methods were used to check overdispersion and goodness-of-fit of the model. Application of BNB-1 regression is illustrated on Malaysian motor insurance dataset. The results indicated that BNB-1 regression has better fit than bivariate Poisson and BNB-2 models with regards to Akaike information criterion.
Heteroscedastic regression analysis method for mixed data
FU Hui-min; YUE Xiao-rui
2011-01-01
The heteroscedastic regression model was established and the heteroscedastic regression analysis method was presented for mixed data composed of complete data, type- I censored data and type- Ⅱ censored data from the location-scale distribution. The best unbiased estimations of regression coefficients, as well as the confidence limits of the location parameter and scale parameter were given. Furthermore, the point estimations and confidence limits of percentiles were obtained. Thus, the traditional multiple regression analysis method which is only suitable to the complete data from normal distribution can be extended to the cases of heteroscedastic mixed data and the location-scale distribution. So the presented method has a broad range of promising applications.
Probabilistic Signal Recovery and Random Matrices
2016-12-08
that classical methods for linear regression (such as Lasso) are applicable for non- linear data. This surprising finding has already found several...we studied the complexity of convex sets. In numerical linear algebra , we analyzed the fastest known randomized approximation algorithm for...and perfect matchings In numerical linear algebra , we studied the fastest known randomized approximation algorithm for computing the permanents of
A brief introduction to regression designs and mixed-effects modelling by a recent convert
Balling, Laura Winther
2008-01-01
This article discusses the advantages of multiple regression designs over the factorial designs traditionally used in many psycholinguistic experiments. It is shown that regression designs are typically more informative, statistically more powerful and better suited to the analysis of naturalistic tasks. The advantages of including both fixed and random effects are demonstrated with reference to linear mixed-effects models, and problems of collinearity, variable distribution and variable sele...
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Isabel. I love having you in my arms, and although you are still too young to understand what a hug is, your warmth has given me the strength and...squares and the quantile regression models adjust to changes in the data set, denoted by the red dots. Notice that the observa- tions are moved upwards...model hardly changes. If we change this observation in red even further upwards, we would notice no more changes in the quantile regression function
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Boosted regression tree, table, and figure data
Spreadsheets are included here to support the manuscript Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition. This dataset is associated with the following publication:Golden , H., C. Lane , A. Prues, and E. D'Amico. Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition. JAWRA. American Water Resources Association, Middleburg, VA, USA, 52(5): 1251-1274, (2016).
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.
Discriminative Elastic-Net Regularized Linear Regression.
Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen
2017-03-01
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.
Spontaneous regression of metastatic Merkel cell carcinoma.
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
The Infinite Hierarchical Factor Regression Model
Rai, Piyush
2009-01-01
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.
Marginal longitudinal semiparametric regression via penalized splines.
Kadiri, M Al; Carroll, R J; Wand, M P
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
ajansen; kwhitefoot; panteltje1; edprochak; sudhakar, the
2014-07-01
In reply to the physicsworld.com news story “How to make a quantum random-number generator from a mobile phone” (16 May, http://ow.ly/xFiYc, see also p5), which describes a way of delivering random numbers by counting the number of photons that impinge on each of the individual pixels in the camera of a Nokia N9 smartphone.
Multiple-Instance Regression with Structured Data
Wagstaff, Kiri L.; Lane, Terran; Roper, Alex
2008-01-01
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.
[Iris movement mediates pupillary membrane regression].
Morizane, Yuki
2007-11-01
In the course of mammalian lens development, a transient capillary meshwork called as the pupillary membrane (PM) forms. It is located in the pupil area to nourish the anterior surface of the lens, and then regresses to clear the optical path. Although the involvement of the apoptotic process has been reported in PM regression, the initiating factor remains unknown. We initially found that regression of the PM coincided with the development of iris motility, and that iris movement caused cessation and resumption of blood flow within the PM. Therefore, we investigated whether the development of the capacity of the iris to constrict and dilate can function as an essential signal that induces apoptosis in the PM. Continuous inhibition of iris movement with mydriatic agents suppressed apoptosis of the PM and resulted in the persistence of PM in rats. The distribution of apoptotic cells in the regressing PM was diffuse and showed no apparent localization. These results indicated that iris movement induced regression of the PM by changing the blood flow within it. This study suggests the importance of the physiological interactions between tissues-in this case, the iris and the PM-as a signal to advance vascular regression during organ development.
Post-processing through linear regression
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Post-processing through linear regression
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Drzewiecki, Wojciech
2016-12-01
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
Quantifying the potential for dose reduction with visual grading regression.
Smedby, O; Fredrikson, M; De Geer, J; Borgen, L; Sandborg, M
2013-01-01
Objectives To propose a method to study the effect of exposure settings on image quality and to estimate the potential for dose reduction when introducing dose-reducing measures. Methods Using the framework of visual grading regression (VGR), a log(mAs) term is included in the ordinal logistic regression equation, so that the effect of reducing the dose can be quantitatively related to the effect of adding post-processing. In the ordinal logistic regression, patient and observer identity are treated as random effects using generalised linear latent and mixed models. The potential dose reduction is then estimated from the regression coefficients. The method was applied in a single-image study of coronary CT angiography (CTA) to evaluate two-dimensional (2D) adaptive filters, and in an image-pair study of abdominal CT to evaluate 2D and three-dimensional (3D) adaptive filters. Results For five image quality criteria in coronary CTA, dose reductions of 16-26% were predicted when adding 2D filtering. Using five image quality criteria for abdominal CT, it was estimated that 2D filtering permits doses were reduced by 32-41%, and 3D filtering by 42-51%. Conclusions VGR including a log(mAs) term can be used for predictions of potential dose reduction that may be useful for guiding researchers in designing subsequent studies evaluating diagnostic value. With appropriate statistical analysis, it is possible to obtain direct numerical estimates of the dose-reducing potential of novel acquisition, reconstruction or post-processing techniques.
Regression Test Selection for C# Programs
Nashat Mansour
2009-01-01
Full Text Available We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
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.
Hyperglycemia impairs atherosclerosis regression in mice.
Gaudreault, Nathalie; Kumar, Nikit; Olivas, Victor R; Eberlé, Delphine; Stephens, Kyle; Raffai, Robert L
2013-12-01
Diabetic patients are known to be more susceptible to atherosclerosis and its associated cardiovascular complications. However, the effects of hyperglycemia on atherosclerosis regression remain unclear. We hypothesized that hyperglycemia impairs atherosclerosis regression by modulating the biological function of lesional macrophages. HypoE (Apoe(h/h)Mx1-Cre) mice express low levels of apolipoprotein E (apoE) and develop atherosclerosis when fed a high-fat diet. Atherosclerosis regression occurs in these mice upon plasma lipid lowering induced by a change in diet and the restoration of apoE expression. We examined the morphological characteristics of regressed lesions and assessed the biological function of lesional macrophages isolated with laser-capture microdissection in euglycemic and hyperglycemic HypoE mice. Hyperglycemia induced by streptozotocin treatment impaired lesion size reduction (36% versus 14%) and lipid loss (38% versus 26%) after the reversal of hyperlipidemia. However, decreases in lesional macrophage content and remodeling in both groups of mice were similar. Gene expression analysis revealed that hyperglycemia impaired cholesterol transport by modulating ATP-binding cassette A1, ATP-binding cassette G1, scavenger receptor class B family member (CD36), scavenger receptor class B1, and wound healing pathways in lesional macrophages during atherosclerosis regression. Hyperglycemia impairs both reduction in size and loss of lipids from atherosclerotic lesions upon plasma lipid lowering without significantly affecting the remodeling of the vascular wall.
Mehta, Madan Lal
1990-01-01
Since the publication of Random Matrices (Academic Press, 1967) so many new results have emerged both in theory and in applications, that this edition is almost completely revised to reflect the developments. For example, the theory of matrices with quaternion elements was developed to compute certain multiple integrals, and the inverse scattering theory was used to derive asymptotic results. The discovery of Selberg's 1944 paper on a multiple integral also gave rise to hundreds of recent publications. This book presents a coherent and detailed analytical treatment of random matrices, leading
Competing Risks Quantile Regression at Work
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2017-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We us...... into the distribution of transitions out of maternity leave. It is found that cumulative incidences implied by the quantile regression model differ from those implied by a proportional hazards model. To foster the use of the model, we make an R-package (cmprskQR) available....... large-scale maternity duration data with multiple competing risks derived from German linked social security records to analyse how public policies are related to the length of economic inactivity of young mothers after giving birth. Our results show that the model delivers detailed insights...
SMOOTH TRANSITION LOGISTIC REGRESSION MODEL TREE
RODRIGO PINTO MOREIRA
2008-01-01
Este trabalho tem como objetivo principal adaptar o modelo STR-Tree, o qual é a combinação de um modelo Smooth Transition Regression com Classification and Regression Tree (CART), a fim de utilizá-lo em Classificação. Para isto algumas alterações foram realizadas em sua forma estrutural e na estimação. Devido ao fato de estarmos fazendo classificação de variáveis dependentes binárias, se faz necessária a utilização das técnicas empregadas em Regressão Logística, dessa forma a estimação dos pa...
Unsupervised K-Nearest Neighbor Regression
Kramer, Oliver
2011-01-01
In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or for face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regression framework for learning of low-dimensional manifolds. Similar to related approaches that are mostly based on kernel methods, unsupervised K-nearest neighbor (UKNN) regression optimizes latent variables w.r.t. the data space reconstruction error employing the K-nearest neighbor heuristic. The problem of optimizing latent neighborhoods is difficult to solve, but the UKNN formulation allows an efficient strategy of iteratively embedding latent points to fixed neighborhood topologies. The approaches will be tested experimentally.
LINEAR REGRESSION WITH R AND HADOOP
Bogdan OANCEA
2015-07-01
Full Text Available In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies. For storing the data we used Hadoop and for computation we used R. The interface between R and Hadoop is the open source library RHadoop. We present the main features of the Hadoop and R software systems and the way of interconnecting them. We then show how the least squares solution for the linear regression problem could be expressed in terms of map-reduce programming paradigm and how could be implemented using the Rhadoop library.
Rapidly Regressive Unilateral Fetal Pleural Effusion
Tuncay Yuce
2015-03-01
Full Text Available Intrauterine pleural effusion of fetal lungs rarely regresses without intervention. In our case we treated a women at 32th weeks of gestation. Her pregnancy was complicated with fetal pleural effusion and polyhydramniosis. A therapeutic thoracocentesis was planned and she received two courses of betamethasone prior to procedure. On the day of planned procedure, a substantial regression of pleural effusion was observed and procedure was postponed. During her antenatal follow-up a complete regression of pleural effusion was observed. After delivery pleural effusion did not relapse. These findings hint there may be a role of antenatal steroids in treatment of fetal pleural effusion, which is known to be resistant to treatment modalities both during antenatal and postnatal period. [Cukurova Med J 2015; 40(Suppl 1: 25-28
KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS
HANY DEVITA
2015-02-01
Full Text Available Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR as an extension of Generalized Ridge Regression (GRR for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
On Solving Lq-Penalized Regressions
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
A tutorial on Bayesian Normal linear regression
Klauenberg, Katy; Wübbeler, Gerd; Mickan, Bodo; Harris, Peter; Elster, Clemens
2015-12-01
Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view. Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach. These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.
无
2007-01-01
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study,measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity office brown spot using hyperspectral reflectance data at the leaf level.
Removing Malmquist bias from linear regressions
Verter, Frances
1993-01-01
Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.
Multicollinearity in cross-sectional regressions
Lauridsen, Jørgen; Mur, Jesùs
2006-10-01
The paper examines robustness of results from cross-sectional regression paying attention to the impact of multicollinearity. It is well known that the reliability of estimators (least-squares or maximum-likelihood) gets worse as the linear relationships between the regressors become more acute. We resolve the discussion in a spatial context, looking closely into the behaviour shown, under several unfavourable conditions, by the most outstanding misspecification tests when collinear variables are added to the regression. A Monte Carlo simulation is performed. The conclusions point to the fact that these statistics react in different ways to the problems posed.
Federico A. Sturzeneger
1992-03-01
Full Text Available Currency Substitution and the Regressivity of Inflationary Taxation The purpose of this paper is to show that in the presence of financial adaptation or currency substitution. the inflation tax is extremely regressive. This regressivity arises from the existence of a fixed cost of switching to inflation-proof transactions technologies. This fixed cost makes it optimal only for those agents with sufficiently high incomes to switch out of domestic currency. The effects are illustrated and quantified for a particular case.
CARR,ROBERT D.; VEMPALA,SANTOSH
2000-01-25
The authors present a new technique for the design of approximation algorithms that can be viewed as a generalization of randomized rounding. They derive new or improved approximation guarantees for a class of generalized congestion problems such as multicast congestion, multiple TSP etc. Their main mathematical tool is a structural decomposition theorem related to the integrality gap of a relaxation.
Demonstration of a Fiber Optic Regression Probe
Korman, Valentin; Polzin, Kurt A.
2010-01-01
The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for
A Skew-Normal Mixture Regression Model
Liu, Min; Lin, Tsung-I
2014-01-01
A challenge associated with traditional mixture regression models (MRMs), which rest on the assumption of normally distributed errors, is determining the number of unobserved groups. Specifically, even slight deviations from normality can lead to the detection of spurious classes. The current work aims to (a) examine how sensitive the commonly…
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
The Geometry of Enhancement in Multiple Regression.
Waller, Niels G
2011-10-01
In linear multiple regression, "enhancement" is said to occur when R (2)=b'r>r'r, where b is a p×1 vector of standardized regression coefficients and r is a p×1 vector of correlations between a criterion y and a set of standardized regressors, x. When p=1 then b≡r and enhancement cannot occur. When p=2, for all full-rank R xx≠I, R xx=E[xx']=V Λ V' (where V Λ V' denotes the eigen decomposition of R xx; λ 1>λ 2), the set [Formula: see text] contains four vectors; the set [Formula: see text]; [Formula: see text] contains an infinite number of vectors. When p≥3 (and λ 1>λ 2>⋯>λ p ), both sets contain an uncountably infinite number of vectors. Geometrical arguments demonstrate that B 1 occurs at the intersection of two hyper-ellipsoids in ℝ (p) . Equations are provided for populating the sets B 1 and B 2 and for demonstrating that maximum enhancement occurs when b is collinear with the eigenvector that is associated with λ p (the smallest eigenvalue of the predictor correlation matrix). These equations are used to illustrate the logic and the underlying geometry of enhancement in population, multiple-regression models. R code for simulating population regression models that exhibit enhancement of any degree and any number of predictors is included in Appendices A and B.
Regression Segmentation for M³ Spinal Images.
Wang, Zhijie; Zhen, Xiantong; Tay, KengYeow; Osman, Said; Romano, Walter; Li, Shuo
2015-08-01
Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M(3)). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S(3)). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M(3) spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M(3) images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M(3) diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M(3) spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases.
A Spline Regression Model for Latent Variables
Harring, Jeffrey R.
2014-01-01
Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…
Assessing risk factors for periodontitis using regression
Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa
2013-10-01
Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.
Selecting a Regression Saturated by Indicators
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain...
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.
Finite Algorithms for Robust Linear Regression
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Finite Algorithms for Robust Linear Regression
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Modeling confounding by half-sibling regression
Schölkopf, Bernhard; Hogg, David W; Wang, Dun
2016-01-01
We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as "half-sibling regression," is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both...
Selecting a Regression Saturated by Indicators
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain...
Structural Break Tests Robust to Regression Misspecification
Abi Morshed, Alaa; Andreou, E.; Boldea, Otilia
2016-01-01
Structural break tests developed in the literature for regression models are sensitive to model misspecification. We show - analytically and through simulations - that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the
Panel data specifications in nonparametric kernel regression
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
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…
Genetic Programming Transforms in Linear Regression Situations
Castillo, Flor; Kordon, Arthur; Villa, Carlos
The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.
Macroeconomic Forecasting Using Penalized Regression Methods
Smeekes, Stephan; Wijler, Etiënne
2016-01-01
We study the suitability of lasso-type penalized regression techniques when applied to macroeconomic forecasting with high-dimensional datasets. We consider performance of the lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumption underlying penalized regressio
Deriving the Regression Line with Algebra
Quintanilla, John A.
2017-01-01
Exploration with spreadsheets and reliance on previous skills can lead students to determine the line of best fit. To perform linear regression on a set of data, students in Algebra 2 (or, in principle, Algebra 1) do not have to settle for using the mysterious "black box" of their graphing calculators (or other classroom technologies).…
Prediction of dynamical systems by symbolic regression
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Shobo, Yetty; Wong, Jen D.; Bell, Angie
2014-01-01
Regression discontinuity (RD), an "as good as randomized," research design is increasingly prominent in education research in recent years; the design gets eligible quasi-experimental designs as close as possible to experimental designs by using a stated threshold on a continuous baseline variable to assign individuals to a…
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 in
Uh, Hae-Won; Hartgers, Franca C; Yazdanbakhsh, Maria; Houwing-Duistermaat, Jeanine J
2008-10-17
The statistical analysis of immunological data may be complicated because precise quantitative levels cannot always be determined. Values below a given detection limit may not be observed (nondetects), and data with nondetects are called left-censored. Since nondetects cannot be considered as missing at random, a statistician faced with data containing these nondetects must decide how to combine nondetects with detects. Till now, the common practice is to impute each nondetect with a single value such as a half of the detection limit, and to conduct ordinary regression analysis. The first aim of this paper is to give an overview of methods to analyze, and to provide new methods handling censored data other than an (ordinary) linear regression. The second aim is to compare these methods by simulation studies based on real data. We compared six new and existing methods: deletion of nondetects, single substitution, extrapolation by regression on order statistics, multiple imputation using maximum likelihood estimation, tobit regression, and logistic regression. The deletion and extrapolation by regression on order statistics methods gave biased parameter estimates. The single substitution method underestimated variances, and logistic regression suffered loss of power. Based on simulation studies, we found that tobit regression performed well when the proportion of nondetects was less than 30%, and that taken together the multiple imputation method performed best. Based on simulation studies, the newly developed multiple imputation method performed consistently well under different scenarios of various proportion of nondetects, sample sizes and even in the presence of heteroscedastic errors.
Cardiorespiratory fitness and laboratory stress: a meta-regression analysis.
Jackson, Erica M; Dishman, Rod K
2006-01-01
We performed a meta-regression analysis of 73 studies that examined whether cardiorespiratory fitness mitigates cardiovascular responses during and after acute laboratory stress in humans. The cumulative evidence indicates that fitness is related to slightly greater reactivity, but better recovery. However, effects varied according to several study features and were smallest in the better controlled studies. Fitness did not mitigate integrated stress responses such as heart rate and blood pressure, which were the focus of most of the studies we reviewed. Nonetheless, potentially important areas, particularly hemodynamic and vascular responses, have been understudied. Women, racial/ethnic groups, and cardiovascular patients were underrepresented. Randomized controlled trials, including naturalistic studies of real-life responses, are needed to clarify whether a change in fitness alters putative stress mechanisms linked with cardiovascular health.
Robust linear regression with broad distributions of errors
Postnikov, Eugene B
2015-01-01
We consider the problem of linear fitting of noisy data in the case of broad (say $\\alpha$-stable) distributions of random impacts ("noise"), which can lack even the first moment. This situation, common in statistical physics of small systems, in Earth sciences, in network science or in econophysics, does not allow for application of conventional Gaussian maximum-likelihood estimators resulting in usual least-squares fits. Such fits lead to large deviations of fitted parameters from their true values due to the presence of outliers. The approaches discussed here aim onto the minimization of the width of the distribution of residua. The corresponding width of the distribution can either be defined via the interquantile distance of the corresponding distributions or via the scale parameter in its characteristic function. The methods provide the robust regression even in the case of short samples with large outliers, and are equivalent to the normal least squares fit for the Gaussian noises. Our discussion is il...
Genuer, Robin; Poggi, Jean-Michel; Tuleau-Malot, Christine; Villa-Vialaneix, Nathalie
2017-01-01
Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based on decision trees combined with aggregation and bootstrap ideas, random forests were introduced by Bre...
Tapiero, Charles S.; Vallois, Pierre
2016-11-01
The premise of this paper is that a fractional probability distribution is based on fractional operators and the fractional (Hurst) index used that alters the classical setting of random variables. For example, a random variable defined by its density function might not have a fractional density function defined in its conventional sense. Practically, it implies that a distribution's granularity defined by a fractional kernel may have properties that differ due to the fractional index used and the fractional calculus applied to define it. The purpose of this paper is to consider an application of fractional calculus to define the fractional density function of a random variable. In addition, we provide and prove a number of results, defining the functional forms of these distributions as well as their existence. In particular, we define fractional probability distributions for increasing and decreasing functions that are right continuous. Examples are used to motivate the usefulness of a statistical approach to fractional calculus and its application to economic and financial problems. In conclusion, this paper is a preliminary attempt to construct statistical fractional models. Due to the breadth and the extent of such problems, this paper may be considered as an initial attempt to do so.
Logistic regression when binary predictor variables are highly correlated.
Barker, L; Brown, C
Standard logistic regression can produce estimates having large mean square error when predictor variables are multicollinear. Ridge regression and principal components regression can reduce the impact of multicollinearity in ordinary least squares regression. Generalizations of these, applicable in the logistic regression framework, are alternatives to standard logistic regression. It is shown that estimates obtained via ridge and principal components logistic regression can have smaller mean square error than estimates obtained through standard logistic regression. Recommendations for choosing among standard, ridge and principal components logistic regression are developed. Published in 2001 by John Wiley & Sons, Ltd.
PREDICTIONG OF EUCALYPTUS WOOD BY COKRIGING, KRIGING AND REGRESSION
Wellington Jorge Cavalcanti Lundgren
2015-06-01
Full Text Available In the Gypsum Pole of Araripe, semiarid zone of Pernambuco, where is produces 97% of the plaster consumed in Brazil, a forest experiment with 1875 eucalyptus was cut off and all the trees were rigorously cubed by the Smalian method. The location of each tree was marked on a Cartesian plane, and a sample of 200 trees was removed by entirely random process. In the 200 sample trees, three estimation methods for variable volume timber, regression analysis, kriging and cokriging were used. To cokriging method, the secondary variable was the DBH (Diameter at Breast Height, and for the regression model of Spurr or the combined variable, it uses two explanatory variables: total height of the tree (H and the DBH. The variables volume and DBH showed spatial dependency. To compare de methods it was used the coefficient of determination (R2 and the residual distribution of the errors (real x estimated data. The best results were achieved with the Spurr equation R2 = 0.82 and total volume estimated 166.25 m3. The cokriging provided and R2 = 0.72 with total volume estimated of 164.14 m3 and kriging had R2 = 0.32 and the total volume estimated of 163.21 m3. The real volume of the experiment was 166.14 m3. Key words: Forest inventory, Volume of timber, Geostatistics.
Model selection in kernel ridge regression
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Online support vector regression for reinforcement learning
Yu Zhenhua; Cai Yuanli
2007-01-01
The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critical. Furthermore, the samples in value functions are sequentially obtained. Therefore, an online support vector regression (OSVR) is set up, which is a function approximator to estimate value functions in reinforcement learning. OSVR updates the regression function by analyzing the possible variation of support vector sets after new samples are inserted to the training set. To evaluate the OSVR learning ability, it is applied to the mountain-car task. The simulation results indicate that the OSVR has a preferable convergence speed and can solve continuous problems that are infeasible using lookup table.
Constrained regression models for optimization and forecasting
P.J.S. Bruwer
2003-12-01
Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.