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Unbiased Prediction (unbiased + prediction)
Kinds of Unbiased Prediction Selected AbstractsA Bayesian analysis of response to selection for uterine capacity in rabbitsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2 2001Blasco A divergent, eight generation selection experiment on uterine capacity in rabbits was performed. Rabbit does were ovariectomized unilaterally before puberty, and selected for increased and decreased litter size by ,best linear unbiased prediction' using data from up to four parities. Two different analyses were performed to estimate the response to selection. The first was based on least squares analysis; the second was based on Bayesian methods using Gibbs sampling techniques. Three different priors were used for variance components, but these had little influence on the results. Posterior means of heritabilities for uterine capacity, varied from 0.09 to 0.12, and repeatabilities from 0.18 to 0.22. The response to eight generations of selection was symmetrical and led to a divergence of 0.16 young rabbits per generation, which amounts to about 2% of the average litter size of the base population per generation. The pattern of response however, was not linear: a high initial response was followed by a period where little further response was observed, and a final burst of response was obtained during the last two cycles of selection. Análisis Bayesiona de la Respuesta a la selección por capacidad uterina en conejos Se llevó a cabo un experimento de selección divergente por capacidad uterina en conejos. Las conejas fueron ovariectomizadas unilateralmente antes de la pubertad, y fueron seguidamente seleccionadas para aumentar y disminuir el tamaño de camada mediante un BLUP que incluía datos de hasta el cuarto parto. Se realizaron dos tipos de análisis para estimar la respuesta, el primero basado en mínimos cuadrados y el segundo en técnicas bayesianas con la ayuda de muestreo de Gibbs. Aunque tres a prioris diferentes fueron usados para las componentes de varianza, esto apenas influyó en los resultados, pues las medias posteriores para las heredabilidades variarton de 0.09 a 0.12 y las medias de las repetibilidades variaron de 0.18 a 0.22. La respuesta a ocho generaciones de selección fue simétrica y condujo a una diverjencia de 0.16 conejos por generación, lo que representa un 2% de la media de la población base por generación. La respuesta no fue, sin embargo, lineal: una fuerte respuesta inicial fue seguida de un periodo de estancamiento y una nueva respuesta en los dos últimos ciclos de selección. Eine Bayes Analyse zu Auswirkungen der Selektion auf uterine Kapazität beim Kaninchen Ein Selektionsexperiment mit divergierender Selektion wurde über acht Generationen durchgeführt. Die Zibben wurden vor der Geschlechtreife einseitig ovarektomiert und auf höhere und niedrigere Wurfgröße über Best Linear Unbiased Prediction (BLUP) selektiert. Daten von bis zu vier Trächtigkeiten wurden verwendet. Zwei verschiedene Analysen wurden verwendet um den Selektionserfolg zu bestimmen. Die erste basierte auf der Least Square Analyse, die zweite auf der Bayes Methode des Gibbs Sampling. Drei verschiedene Priors wurden in der Varianzkomponentenschätzung verwendet, aber sie hatten wenig Einfluss auf die Endergebnisse. Die a posteriori Ergebnisse für die Heritabilitäten der uterinen Kapazität variierten von 0,09 bis 0,12, die Wiederholbarkeiten von 0, 18 bis 0,22. Die Selektionserfolg über acht Generationen war symmetrisch und führte zu einem Unterschied von 0,16 Jungtieren pro Generation, was ca. 2% der durchschnittlichen Wurfgröße, bezogen auf die Basispopulation je Generation, entspricht. Das Bild des Zuchtfortschritts war nicht linear: nach einem hohen Anfangserfolg wurde eine Periode geringen Erfolges beobachtet und ein Ende des Zuchtfortschrittes wurde in den letzten zwei Generationen erreicht. [source] Barley adaptation and improvement in the Mediterranean basinPLANT BREEDING, Issue 6 2008A. Pswarayi Abstract To study barley adaptation and improvement in the Mediterranean basin, a collection of 188 entries comprising landraces and old genotypes and current modern varieties from the Mediterranean basin and elsewhere was tested on moisture-contrasted environments in seven Mediterranean countries, during 2004 and 2005 harvest seasons. The experimental design consisted of an unreplicated trial for all entries, augmented by four repeated checks to which a partial replicate containing a quarter of the entries was added. Best Linear Unbiased Predictions (BLUPs) representing adjusted genotypic means were generated for individual trials using a mixed model. BLUPs were used for genotype by environment interaction analysis using main effect plus genotype by environment interaction (GGE) biplots of yield ranked data and for comparisons of landraces, old and modern genotypes using analysis of variance. Mean yields ranged from near crop failure to 6 t/ha. Local landraces were better adapted to environments yielding below 2 t/ha, thus breeding has mostly benefited environments yielding above 2 t/ha where modern genotypes out yielded landraces and old cultivars by 15%. Current barley selection is leading to specifically adapted genotypes. [source] Spatial sampling design under the infill asymptotic framework,ENVIRONMETRICS, Issue 4 2006Zhengyuan Zhu Abstract We study optimal sample designs for prediction with estimated parameters. Recent advances in the infill asymptotic theory provide a deeper understanding of the finite sample behavior of prediction and estimation. By incorporating these known asymptotic results, we modify some existing design criteria for estimation of covariance function and best linear unbiased prediction. These modified criteria could significantly reduce the computation time necessary for finding an optimal design. We illustrate our approach through both a real experiment in agriculture and simulation. Copyright © 2005 John Wiley & Sons, Ltd. [source] Application of robust procedures for estimation of breeding values in multiple-trait random regression test-day modelJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2007J. Jamrozik Summary Robust procedures for estimation of breeding values were applied to multiple-trait random regression test-day (TD) model to reduce the influence of outliers on inferences. Robust estimation methods consisted of correcting selected observations (defined as outliers) in the process of solving mixed-model equations in such a way that ,new' observations gave residuals (actual observation minus predicted) within k residual standard deviations for a given day in milk in 305-day lactation. Data were 980 503 TD records on 63 346 Canadian Jersey cows. Milk, fat, protein and somatic cell score in the first three lactations were analysed jointly in the model that included fixed herd-TD effect and regressions within region,age,season of calving, and regressions with random coefficients for animal genetic and permanent environmental effects. All regressions were orthogonal polynomials of order 4. Robust procedures for k = 1.5, 2.0, 2.5, 2.75 and 3.0 were contrasted with the regular best linear unbiased prediction (BLUP) method in terms of numbers and distributions of outliers, and estimated breeding values (EBV) of animals. Distributions of outliers were similar across traits and lactations. Early days in milk (from 5 to 15) were associated with larger frequency of outliers compared with the remaining part of lactation. Several, computationally simple, robust methods (for k > 2.0) reduced the influence of outlier observations in the model and improved the overall model performance. Differences in rankings of animals from robust evaluations were small compared with the regular BLUP method. No clear associations between changes in EBV (rankings) of top animals from different methods and the occurrence of outliers were detected. [source] Prediction of accuracy of estimated Mendelian sampling termsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2005S. Avendaño Summary This study describes a general framework for predicting the accuracy of Mendelian sampling terms when truncation selection is applied on best linear unbiased prediction (BLUP) estimated breeding values. A selection index approach is followed. The pseudo-BLUP index is extended to include terms related to the Mendelian sampling term. Predicted accuracies are compared with those obtained through stochastic computer simulation. Good predictions for the accuracy of the Mendelian sampling term were obtained both at selection time and at convergence of long-term contributions of selected candidates for a range of heritabilities and population structures. The prediction approach developed provides a key tool for obtaining predictions of genetic response from quadratic optimization that maximizes the rate of genetic progress while restricting the rate of inbreeding. [source] Top down preselection using marker assisted estimates of breeding values in dairy cattleJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2004Jörn Bennewitz Summary Top down preselection of young bulls before entering progeny testing has been proposed as a practicable form of marker-assisted selection (MAS), especially in dairy cattle populations with large male paternal half-sib families. Linkage phase between the superior (Q) and the inferior (q) QTL alleles of heterozygous sires (Qq at the QTL) with informative markers is established within each paternal half-sib family and may be used for selection among grand-progeny. If, additionally to sires, bulldams are also genotyped and data from consecutive generations are used, then a marker-assisted best linear unbiased prediction (MA-BLUP) model can be employed to connect the information of all generations and families of a top down design, and to select across all families. A customized ,augmented' sire model (with sires and dams of sires as random effects) is introduced for this purpose. Adapted formulae for the mixed model equations are given and their equivalence to a corresponding animal model and to a certain variant of previously proposed reduced animal models is shown. The application of the augmented sire model in MA-BLUP estimation from daughter-yield deviations and effective daughter contributions is presented. Zusammenfassung Die Top down Vorselektion von jungen Bullen vor der Nachkommenschaftsprüfung ist bekannt als eine praktikable Form der markergestützten Selektion in Milchrinderpopulationen. Die Kopplungsphasen zwischen dem günstigen (Q) und dem ungünstigen (q) Allel eines QTL heterozygoten Vaters (Qq am QTL) mit den Allelen gekoppelter genetischer Marker werden innerhalb Familien festgestellt und können zur Vorselektion von Enkeln genutzt werden. Wenn zusätzlich zu den Vätern die Mütter genotypisiert sind und Daten von mehreren Generationen vorliegen, können MA-BLUP Modelle genutzt werden, um Informationen von mehreren Familien und mehreren Generationen eines Top down Designs zusammenzuführen und um eine Vorselektion über Familien hinweg vorzunehmen. Hierfür wird ein geeignetes ,erweitertes' Vatermodell eingeführt, welches die Väter und zusätzlich die Mütter der Väter als zufällige Effekte enthält. Angepasste Formeln für die gemischten Modell Gleichungen werden beschrieben. Die Gleichheit dieses erweiterten Vatermodells mit einem entsprechenden Tiermodell und mit einer Variante des reduzierten Tiermodells wird gezeigt. Die Anwendung des erweiterten Vatermodells zur MA-BLUP Schätzung mit daughter yield deviations und effective daughter contributions ist beschrieben. [source] A comparison of restricted selection procedures to control genetic gainsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2 2004S. Ieiri Summary Using Monte Carlo simulation, two schemes of restricted selection were compared under various combinations of genetic parameters and constraints on the genetic gains. The first selection scheme is the combination of best linear unbiased prediction (BLUP) evaluation and linear programming technique (BLUP + LP), and the second one is based on the restricted BLUP selection (RBLUP). Selection for two traits was supposed, in which animals were selected to maximize the genetic gain in trait 2 (,g2) under a proportional restriction on the genetic gain in trait 1 (,g1) to satisfy the intended ratio (,g1:,g2). Little differences were found between the two selection schemes with respect to the genetic gains averaged over replicates. However, in all the cases studied, the variance of genetic gains among replicates under BLUP + LP selection was smaller and less sensitive to the genetic parameters and the intended restriction than RBLUP selection. Under the situations of antagonistic selection, the difference tended to be larger. When the heritabilities of the two traits were different, RBLUP selection remarkably increased the variance of genetic gain in a trait with a higher heritability. These results suggest that BLUP + LP selection should always be preferable to RBLUP selection because of the smaller risk of selection. This choice is especially important for the situation where the genetic parameters act as limiting factors for the achievement of intended genetic gains. Zusammenfassung Unter Verwendung von Monte Carlo Simulation wurden zwei verschiedene Selektionsstrategien mit verschiedenen Kombinationen genetischer Parameter und Beschränkungen des genetischen Fortschritts verglichen. Die erste Selektionsstrategie stellt eine Kombination von BLUP-Schätzung und linearer Programmiertechnik (BLUP + LP) dar, die zweite Strategie basiert auf einer reduzierten BLUP-Selektion (RBLUP). Die Selektion wurde auf zwei Merkmalen basierend durchgeführt in welchen Tiere ausgewählt wurden, um den genetischen Fortschritt in Merkmal 2 zu maximieren (,g2) unter proportionaler Restriktion des genetischen Fortschritts in Merkmal 1 (,g1), um die beabsichtigte Beziehung zu erreichen (,g1:,g2). Kleine Unterschiede wurden zwischen den beiden Selektionsstrategien in Bezug auf den mittleren genetischen Fortschritt über Wiederholungen gefunden. Wie auch immer, in allen untersuchten Fällen war die Varianz des genetischen Fortschritts zwischen Wiederholungen mit der BLUP + LP-Selektion geringer und weniger abhängig in Bezug auf die genetischen Parameter und die beabsichtigte Restriktion im Vergleich zur RBLUP-Selektion. Unter den Gegebenheiten der gegenläufigen Selektion schien der Unterschied größer zu werden. Wenn die Heritabilitäten der beiden Merkmale unterschiedlich waren, stieg die Varianz des genetischen Fortschritts bei RBLUP in einem Merkmal mit höherer Heritabilität außergewöhnlich. Diese Ergebnisse deuten an, dass BLUP + LP-Selektion gegenüber RBLUP aufgrund des geringeren Selektionsrisikos stets bevorzugt werden sollte. Diese Wahl ist vornehmlich wichtig für die Situation, in der die genetischen Parameter ein limitierender Faktor zur Erreichung von genetischem Fortschritt sind. [source] Forecasting with panel data,JOURNAL OF FORECASTING, Issue 2 2008Badi H. Baltagi Abstract This paper gives a brief survey of forecasting with panel data. It begins with a simple error component regression model and surveys the best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper also surveys how these forecasts have been used in panel data applications, running horse races between heterogeneous and homogeneous panel data models using out-of-sample forecasts. Copyright © 2008 John Wiley & Sons, Ltd. [source] On a family of finite moving-average trend filters for the ends of seriesJOURNAL OF FORECASTING, Issue 2 2002Alistair G. Gray Abstract A family of finite end filters is constructed using a minimum revisions criterion and based on a local dynamic model operating within the span of a given finite central filter. These end filters are equivalent to evaluating the central filter with unavailable future observations replaced by constrained optimal linear predictions. Two prediction methods are considered: best linear unbiased prediction and best linear biased prediction where the bias is time invariant. The properties of these end filters are determined. In particular, they are compared to X-11 end filters and to the case where the central filter is evaluated with unavailable future observations predicted by global ARIMA models as in X-11-ARIMA or X-12-ARIMA. Copyright © 2002 John Wiley & Sons, Ltd. [source] Genetic evaluation of dairy cattle using a simple heritable genetic groundJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 11 2010Josef Pribyl Abstract The evaluation of an animal is based on production records, adjusted for environmental effects, which gives a reliable estimation of its breeding value. Highly reliable daughter yield deviations are used as inputs for genetic marker evaluation. Genetic variability is explained by particular loci and background polygenes, both of which are described by the genomic breeding value selection index. Automated genotyping enables the determination of many single-nucleotide polymorphisms (SNPs) and can increase the reliability of evaluation of young animals (from 0.30 if only the pedigree value is used to 0.60 when the genomic breeding value is applied). However, the introduction of SNPs requires a mixed model with a large number of regressors, in turn requiring new algorithms for the best linear unbiased prediction and BayesB. Here, we discuss a method that uses a genomic relationship matrix to estimate the genomic breeding value of animals directly, without regressors. A one-step procedure evaluates both genotyped and ungenotyped animals at the same time, and produces one common ranking of all animals in a whole population. An augmented pedigree,genomic relationship matrix and the removal of prerequisites produce more accurate evaluations of all connected animals. Copyright © 2010 Society of Chemical Industry [source] Estimation of breeding values from large-sized routine carcass data in Japanese Black cattle using Bayesian analysisANIMAL SCIENCE JOURNAL, Issue 6 2009Aisaku ARAKAWA ABSTRACT Volumes of official data sets have been increasing rapidly in the genetic evaluation using the Japanese Black routine carcass field data. Therefore, an alternative approach with smaller memory requirement to the current one using the restricted maximum likelihood (REML) and the empirical best linear unbiased prediction (EBLUP) is desired. This study applied a Bayesian analysis using Gibbs sampling (GS) to a large data set of the routine carcass field data and practically verified its validity in the estimation of breeding values. A Bayesian analysis like REML-EBLUP was implemented, and the posterior means were calculated using every 10th sample from 90 000 of samples after 10 000 samples discarded. Moment and rank correlations between breeding values estimated by GS and REML-EBLUP were very close to one, and the linear regression coefficients and the intercepts of the GS on the REML-EBLUP estimates were substantially one and zero, respectively, showing a very good agreement between breeding value estimation by the current GS and the REML-EBLUP. The current GS required only one-sixth of the memory space with REML-EBLUP. It is confirmed that the current GS approach with relatively small memory requirement is valid as a genetic evaluation procedure using large routine carcass data. [source] Comparison of genetic improvement for litter size at birth by direct and indirect selection in swine herdANIMAL SCIENCE JOURNAL, Issue 6 2006Masahiro SATOH ABSTRACT Responses to selection for number of piglets born alive (NBA) by the total number of piglets born (TNB), the NBA, and the NBA plus number of piglets born dead (NBD) were compared using the accuracy of selection and expected genetic gain calculated from the selection index with family information and the real response to selection, using data generated by Monte Carlo computer simulation. The accuracy of selection for NBA selected by TNB was higher than that by NBA only if the genetic correlation between TNB and NBA was close to 1.0, or the value of heritability for the TNB was much larger than that for the NBA. The accuracy of selection for the NBA selected by the combination of the TNB and the NBA was generally highest in the three selection methods in each family structure. Selection by the TNB resulted in the greatest expected genetic gain for the TNB among the selection methods. In the best linear unbiased prediction (BLUP) selection, the genetic gain for the NBA accumulated by the NBA tended to be similar to that accumulated by the combination of the NBA and the NBD, and both genetic gains at generation 10 were significantly larger than that by the TNB (P < 0.001). The accumulated responses selected by the two-trait animal model BLUP estimated from genetic parameters with errors were similar to those estimated from the true parameters, and there was no significant difference between them. These results indicate that selection by the NBA or by the NBA and the NBD gives more genetic improvement in the NBA than that by the TNB. [source] Improving management for higher reproduction accelerates genetic improvement in closed herd of swineANIMAL SCIENCE JOURNAL, Issue 6 2004Masahiro SATOH ABSTRACT The present study compared responses to selection at different conception rates and litter sizes at weaning in a simulated closed herd in a swine breeding program. The base population consisted of 10 males and 50 females, and 10 generations of selection was practiced by using individual phenotype or best linear unbiased prediction of breeding values for a trait with heritability (h2) of either 0.2 or 0.5. The probability of conception in a single mating was assumed to be 0.8, 0.9 or 1.0. Litter size at weaning was sampled randomly from a normal distribution with mean 8, 10 or 12 and variance 8.1225. Genetic response increased by approximately 6% for h2 = 0.2 and approximately 5% for h2 = 0.5 at generation 10 when conception rate was increased from 0.8 to 1.0. However, litter size at weaning did not affect response to selection. In conclusion, improving conception rate by environmental management increases genetic response indirectly in a breeding program of a closed swine herd. [source] Including an additional systematic environmental effect within a generation in an evaluation model improves accuracy of prediction of breeding values in a closed herd of pigsANIMAL SCIENCE JOURNAL, Issue 2 2004Masahiro SATOH ABSTRACT The present study evaluated the advantage of mixed-model techniques over a selection index under different magnitudes of an additional systematic environmental effect (ASEE) in terms of accuracy of prediction and expected genetic gain. The data attempted to simulate a closed herd in a pig breeding program. The base population (G0) consisted of 10 males and 50 females. Six generations (G0 to G5) were selected by using a selection index of three traits without overlapping. Additional systematic environmental constants with four levels in a generation were assigned from a uniform distribution at different ranges. Breeding values of animals in the last generation (G5) were estimated on the basis of an index of individual phenotype (SI-U), SI-U adjusted for ASEE using a least-squares mean (SI-A), best linear unbiased prediction using an animal model excluding ASEE (AM-E), and an animal model including ASEE (AM-I). Accuracy of prediction and expected genetic gain were larger by the animal model than by the selection index, even if heritability of the traits selected was high and ASEE was set to zero. When ASEE was zero, the accuracy of prediction and expected genetic gain given by SI-U and AM-I were similar to those given by SI-A and AM-E, respectively. However, the differences in accuracy and expected gain between SI-U and AI-A and between AM-I and AM-E increased as the range of ASEE increased. It was concluded that selection based on an animal model was more effective than index selection, even if the herd environment was uniform and traits with high heritability were selected, and that it should be always included in an evaluation model, however slight any systematic environmental effect may be in a closed herd. [source] Multilevel Mixture Cure Models with Random EffectsBIOMETRICAL JOURNAL, Issue 3 2009Xin Lai Abstract This paper extends the multilevel survival model by allowing the existence of cured fraction in the model. Random effects induced by the multilevel clustering structure are specified in the linear predictors in both hazard function and cured probability parts. Adopting the generalized linear mixed model (GLMM) approach to formulate the problem, parameter estimation is achieved by maximizing a best linear unbiased prediction (BLUP) type log-likelihood at the initial step of estimation, and is then extended to obtain residual maximum likelihood (REML) estimators of the variance component. The proposed multilevel mixture cure model is applied to analyze the (i) child survival study data with multilevel clustering and (ii) chronic granulomatous disease (CGD) data on recurrent infections as illustrations. A simulation study is carried out to evaluate the performance of the REML estimators and assess the accuracy of the standard error estimates. [source] A Bayesian analysis of response to selection for uterine capacity in rabbitsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2 2001Blasco A divergent, eight generation selection experiment on uterine capacity in rabbits was performed. Rabbit does were ovariectomized unilaterally before puberty, and selected for increased and decreased litter size by ,best linear unbiased prediction' using data from up to four parities. Two different analyses were performed to estimate the response to selection. The first was based on least squares analysis; the second was based on Bayesian methods using Gibbs sampling techniques. Three different priors were used for variance components, but these had little influence on the results. Posterior means of heritabilities for uterine capacity, varied from 0.09 to 0.12, and repeatabilities from 0.18 to 0.22. The response to eight generations of selection was symmetrical and led to a divergence of 0.16 young rabbits per generation, which amounts to about 2% of the average litter size of the base population per generation. The pattern of response however, was not linear: a high initial response was followed by a period where little further response was observed, and a final burst of response was obtained during the last two cycles of selection. Análisis Bayesiona de la Respuesta a la selección por capacidad uterina en conejos Se llevó a cabo un experimento de selección divergente por capacidad uterina en conejos. Las conejas fueron ovariectomizadas unilateralmente antes de la pubertad, y fueron seguidamente seleccionadas para aumentar y disminuir el tamaño de camada mediante un BLUP que incluía datos de hasta el cuarto parto. Se realizaron dos tipos de análisis para estimar la respuesta, el primero basado en mínimos cuadrados y el segundo en técnicas bayesianas con la ayuda de muestreo de Gibbs. Aunque tres a prioris diferentes fueron usados para las componentes de varianza, esto apenas influyó en los resultados, pues las medias posteriores para las heredabilidades variarton de 0.09 a 0.12 y las medias de las repetibilidades variaron de 0.18 a 0.22. La respuesta a ocho generaciones de selección fue simétrica y condujo a una diverjencia de 0.16 conejos por generación, lo que representa un 2% de la media de la población base por generación. La respuesta no fue, sin embargo, lineal: una fuerte respuesta inicial fue seguida de un periodo de estancamiento y una nueva respuesta en los dos últimos ciclos de selección. Eine Bayes Analyse zu Auswirkungen der Selektion auf uterine Kapazität beim Kaninchen Ein Selektionsexperiment mit divergierender Selektion wurde über acht Generationen durchgeführt. Die Zibben wurden vor der Geschlechtreife einseitig ovarektomiert und auf höhere und niedrigere Wurfgröße über Best Linear Unbiased Prediction (BLUP) selektiert. Daten von bis zu vier Trächtigkeiten wurden verwendet. Zwei verschiedene Analysen wurden verwendet um den Selektionserfolg zu bestimmen. Die erste basierte auf der Least Square Analyse, die zweite auf der Bayes Methode des Gibbs Sampling. Drei verschiedene Priors wurden in der Varianzkomponentenschätzung verwendet, aber sie hatten wenig Einfluss auf die Endergebnisse. Die a posteriori Ergebnisse für die Heritabilitäten der uterinen Kapazität variierten von 0,09 bis 0,12, die Wiederholbarkeiten von 0, 18 bis 0,22. Die Selektionserfolg über acht Generationen war symmetrisch und führte zu einem Unterschied von 0,16 Jungtieren pro Generation, was ca. 2% der durchschnittlichen Wurfgröße, bezogen auf die Basispopulation je Generation, entspricht. Das Bild des Zuchtfortschritts war nicht linear: nach einem hohen Anfangserfolg wurde eine Periode geringen Erfolges beobachtet und ein Ende des Zuchtfortschrittes wurde in den letzten zwei Generationen erreicht. [source] Near-Term Travel Speed Prediction Utilizing Hilbert,Huang TransformCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 8 2009Khaled Hamad In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert,Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters. [source] Prediction of lethal/effective concentration/dose in the presence of multiple auxiliary covariates and components of varianceENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 9 2007Steve Gutreuter Abstract Predictors of the percentile lethal/effective concentration/dose are commonly used measures of efficacy and toxicity. Typically such quantal-response predictors (e.g., the exposure required to kill 50% of some population) are estimated from simple bioassays wherein organisms are exposed to a gradient of several concentrations of a single agent. The toxicity of an agent may be influenced by auxiliary covariates, however, and more complicated experimental designs may introduce multiple variance components. Prediction methods lag examples of those cases. A conventional two-stage approach consists of multiple bivariate predictions of, say, medial lethal concentration followed by regression of those predictions on the auxiliary covariates. We propose a more effective and parsimonious class of generalized nonlinear mixed-effects models for prediction of lethal/effective dose/concentration from auxiliary covariates. We demonstrate examples using data from a study regarding the effects of pH and additions of variable quantities 2',5'-dichloro-4'-nitrosalicylanilide (niclosamide) on the toxicity of 3-trifluoromethyl-4-nitrophenol to larval sea lamprey (Petromyzon marinus). The new models yielded unbiased predictions and root-mean-squared errors (RMSEs) of prediction for the exposure required to kill 50 and 99.9% of some population that were 29 to 82% smaller, respectively, than those from the conventional two-stage procedure. The model class is flexible and easily implemented using commonly available software. [source] | |||||||||||||