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Univariate Models (univariate + models)
Selected AbstractsBivariate combined linkage and association mapping of quantitative trait lociGENETIC EPIDEMIOLOGY, Issue 5 2008Jeesun Jung Abstract In this paper, bivariate/multivariate variance component models are proposed for high-resolution combined linkage and association mapping of quantitative trait loci (QTL), based on combinations of pedigree and population data. Suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In the region, multiple markers such as single nucleotide polymorphisms are typed. Two regression models, "genotype effect model" and "additive effect model", are proposed to model the association between the markers and the trait locus. The linkage information, i.e., recombination fractions between the QTL and the markers, is modeled in the variance and covariance matrix. By analytical formulae, we show that the "genotype effect model" can be used to model the additive and dominant effects simultaneously; the "additive effect model" only takes care of additive effect. Based on the two models, F -test statistics are proposed to test association between the QTL and markers. By analytical power analysis, we show that bivariate models can be more powerful than univariate models. For moderate-sized samples, the proposed models lead to correct type I error rates; and so the models are reasonably robust. As a practical example, the method is applied to analyze the genetic inheritance of rheumatoid arthritis for the data of The North American Rheumatoid Arthritis Consortium, Problem 2, Genetic Analysis Workshop 15, which confirms the advantage of the proposed bivariate models. Genet. Epidemiol. 2008. © 2008 Wiley-Liss, Inc. [source] Predictive ability of models for calving difficulty in US HolsteinsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 3 2009E.L. De Maturana Summary The performance of alternative threshold models for analyzing calving difficulty (CD) in Holstein cows was evaluated in terms of predictive ability. Four models were considered, with CD classified into either three or four categories and analysed either as a single trait or jointly with gestation length (GL). The data contained GL and CD records from 90 393 primiparous cows, sired by 1122 bulls and distributed over 935 herd-calving year classes. Predictive ability of each model was evaluated using four criteria: mean squared error of the difference between observed and predicted CD scores; a Kullback-Leibler divergence measure between the observed and predicted distributions of CD scores; Pearson's correlation between observed and predicted CD scores and ability to correctly classify bulls as above or below average for incidence of CD. In general, the four models had similar predictive abilities. The joint analysis of CD with GL produced little, if any, improvement in predictive ability over univariate models. In light of the small difference in predictive ability between models treating CD with three or four categories and considering that a greater number of categories can provide more information, analysis of CD classified into four categories seems warranted. [source] Value-at-risk for long and short trading positionsJOURNAL OF APPLIED ECONOMETRICS, Issue 6 2003Pierre Giot In this paper we model Value-at-Risk (VaR) for daily asset returns using a collection of parametric univariate and multivariate models of the ARCH class based on the skewed Student distribution. We show that models that rely on a symmetric density distribution for the error term underperform with respect to skewed density models when the left and right tails of the distribution of returns must be modelled. Thus, VaR for traders having both long and short positions is not adequately modelled using usual normal or Student distributions. We suggest using an APARCH model based on the skewed Student distribution (combined with a time-varying correlation in the multivariate case) to fully take into account the fat left and right tails of the returns distribution. This allows for an adequate modelling of large returns defined on long and short trading positions. The performances of the univariate models are assessed on daily data for three international stock indexes and three US stocks of the Dow Jones index. In a second application, we consider a portfolio of three US stocks and model its long and short VaR using a multivariate skewed Student density. Copyright © 2003 John Wiley & Sons, Ltd. [source] Can cointegration-based forecasting outperform univariate models?JOURNAL OF FORECASTING, Issue 5 2002An application to Asian exchange rates Abstract Conventional wisdom holds that restrictions on low-frequency dynamics among cointegrated variables should provide more accurate short- to medium-term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long-term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short- and medium-term forecasting accuracy of univariate Box,Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling-window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving-average terms of order >1. ECMs perform better over medium-term time horizons for series with no moving average terms. The results suggest a need to distinguish between ,sequential' and ,synchronous' forecasting ability in such comparisons. Copyright © 2002 John Wiley & Sons, Ltd. [source] Phases of the Canadian business cycleCANADIAN JOURNAL OF ECONOMICS, Issue 3 2000Philip M. Bodman In this paper we contrast a number of univariate models of Canadian GDP. Our preferred models are used to provide a business cycle chronology for Canada, which is compared with some existing, more judgmentally determined chronologies. We find that a simple, ,two quarters of negative growth' rule for determining recession dates is the most similar to our chronology. We also find that the most recent recession in Canada was unique in both its length and the slow speed of recovery. JEL Classification: C22, C51, C52, E32 Phases du cycle d'affaires au Canada. Dans ce mémoire, les auteurs contrastent un certain nombre de modèles du PIB canadien. Les modèles préférés sont utilisés pour définir une chronologie des cycles économiques du Canada qu'on peut comparer avec d'autres chronologies existantes basées davantage sur le jugement. On découvre que la règle "deux trimestres de croissance négative" est celle qui se rapproche le plus de la chronologie proposée quand il s'agit de définir les dates de récession. On découvre aussi que la récente récession canadienne a été unique tant par sa durée que par la lenteur avec laquelle la reprise subséquente s'est amorcée. [source] Dissecting racial disparities in the treatment of patients with locoregional pancreatic cancerCANCER, Issue 4 2010A 2-Step Process Abstract BACKGROUND: Previous studies have demonstrated that black patients with pancreatic cancer are less likely to undergo resection and have worse overall survival compared with white patients. The objective of this study was to determine whether these disparities occur at the point of surgical evaluation or after evaluation has taken place. METHODS: The authors used the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data (1992-2002) to compare black patients and white patients with locoregional pancreatic cancer in univariate models. Logistic regression was used to determine the effect of race on surgical evaluation and on surgical resection after evaluation. Cox proportional hazards models were used to identify which factors influenced 2-year survival. RESULTS: Nine percent of 3777 patients were black. Blacks were substantially less likely than whites to undergo evaluation by a surgeon (odds ratio, 0.57; 95% confidence interval, 0.42-0.77) when the model was adjusted for demographics, tumor characteristics, surgical evaluation, socioeconomic status, and year of diagnosis. Patients who were younger and who had fewer comorbidities, abdominal imaging, and a primary care physician were more likely to undergo surgical evaluation. Once they were seen by a surgeon, blacks still were less likely than whites to undergo resection (odds ratio, 0.64; 95% confidence interval, 0.49-0.84). Although black patients had decreased survival in an unadjusted model, race no longer was significant after accounting for resection. CONCLUSIONS: Twenty-nine percent of black patients with potentially resectable pancreatic cancers never received surgical evaluation. Without surgical evaluation, patients cannot make an informed decision and will not be offered resection. Attaining higher rates of surgical evaluation in black patients would be the first step to eliminating the observed disparity in the resection rate. Cancer 2010. © 2010 American Cancer Society [source] | |||||||||||||