The Experts below are selected from a list of 171060 Experts worldwide ranked by ideXlab platform
Matteo Bottai - One of the best experts on this subject based on the ideXlab platform.
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a penalized approach to covariate selection through quantile Regression Coefficient models
Statistical Modelling, 2020Co-Authors: Gianluca Sottile, Paolo Frumento, Marcello Chiodi, Matteo BottaiAbstract:The Coefficients of a quantile Regression model are one-to-one functions of the order of the quantile. In standard quantile Regression (QR), different quantiles are estimated one at a time. Another...
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Parametric modeling of quantile Regression Coefficient functions with censored and truncated data.
Biometrics, 2017Co-Authors: Paolo Frumento, Matteo BottaiAbstract:Quantile Regression Coefficient functions describe how the Coefficients of a quantile Regression model depend on the order of the quantile. A method for parametric modeling of quantile Regression Coefficient functions was discussed in a recent article. The aim of the present work is to extend the existing framework to censored and truncated data. We propose an estimator and derive its asymptotic properties. We discuss goodness-of-fit measures, present simulation results, and analyze the data that motivated this article. The described estimator has been implemented in the R package qrcm.
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parametric modeling of quantile Regression Coefficient functions
Biometrics, 2016Co-Authors: Paolo Frumento, Matteo BottaiAbstract:type="main" xml:lang="en"> Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile Regression is foremost among the utilized methods. The Coefficients of a quantile Regression model depend on the order of the quantile being estimated. For example, the Coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the Regression Coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package.
Michael L Johnson - One of the best experts on this subject based on the ideXlab platform.
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Regression Coefficient based scoring system should be used to assign weights to the risk index
Journal of Clinical Epidemiology, 2016Co-Authors: Hemalkumar B Mehta, Vinay Mehta, Cynthia J Girman, Deepak Adhikari, Michael L JohnsonAbstract:OBJECTIVE Some previously developed risk scores contained a mathematical error in their construction: risk ratios were added to derive weights to construct a summary risk score. This study demonstrates the mathematical error and derived different versions of the Charlson comorbidity score (CCS) using Regression Coefficient-based and risk ratio-based scoring systems to further demonstrate the effects of incorrect weighting on performance in predicting mortality. STUDY DESIGN AND SETTING This retrospective cohort study included elderly people from the Clinical Practice Research Datalink. Cox proportional hazards Regression models were constructed for time to 1-year mortality. Weights were assigned to 17 comorbidities using Regression Coefficient-based and risk ratio-based scoring systems. Different versions of CCS were compared using Akaike information criteria (AIC), McFadden's adjusted R2, and net reclassification improvement (NRI). RESULTS Regression Coefficient-based models (Beta, Beta10/integer, Beta/Schneeweiss, Beta/Sullivan) had lower AIC and higher R2 compared to risk ratio-based models (HR/Charlson, HR/Johnson). Regression Coefficient-based CCS reclassified more number of people into the correct strata (NRI range, 9.02-10.04) compared to risk ratio-based CCS (NRI range, 8.14-8.22). CONCLUSION Previously developed risk scores contained an error in their construction adding ratios instead of multiplying them. Furthermore, as demonstrated here, adding ratios fail to even work adequately from a practical standpoint. CCS derived using Regression Coefficients performed slightly better than in fitting the data compared to risk ratio-based scoring systems. Researchers should use a Regression Coefficient-based scoring system to develop a risk index, which is theoretically correct.
Paolo Frumento - One of the best experts on this subject based on the ideXlab platform.
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a penalized approach to covariate selection through quantile Regression Coefficient models
Statistical Modelling, 2020Co-Authors: Gianluca Sottile, Paolo Frumento, Marcello Chiodi, Matteo BottaiAbstract:The Coefficients of a quantile Regression model are one-to-one functions of the order of the quantile. In standard quantile Regression (QR), different quantiles are estimated one at a time. Another...
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Parametric modeling of quantile Regression Coefficient functions with censored and truncated data.
Biometrics, 2017Co-Authors: Paolo Frumento, Matteo BottaiAbstract:Quantile Regression Coefficient functions describe how the Coefficients of a quantile Regression model depend on the order of the quantile. A method for parametric modeling of quantile Regression Coefficient functions was discussed in a recent article. The aim of the present work is to extend the existing framework to censored and truncated data. We propose an estimator and derive its asymptotic properties. We discuss goodness-of-fit measures, present simulation results, and analyze the data that motivated this article. The described estimator has been implemented in the R package qrcm.
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parametric modeling of quantile Regression Coefficient functions
Biometrics, 2016Co-Authors: Paolo Frumento, Matteo BottaiAbstract:type="main" xml:lang="en"> Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile Regression is foremost among the utilized methods. The Coefficients of a quantile Regression model depend on the order of the quantile being estimated. For example, the Coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the Regression Coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package.
C Y Lin - One of the best experts on this subject based on the ideXlab platform.
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selection for milk production and persistency using eigenvectors of the random Regression Coefficient matrix
Journal of Dairy Science, 2006Co-Authors: K Togashi, C Y LinAbstract:The purpose of this study was to investigate the relationships of the eigenvectors of the additive genetic random Regression Coefficient matrix (K) to selection responses and to determine how many eigenvectors are necessary in the breeding goal to explain the variation. The construction of various eigenvector indexes was based on the K matrix estimated from test-day records of Japanese Holstein cattle. The first (leading) eigenvector index produced constant responses for each day of lactation, indicating that the first eigenvector is responsible for scaling the lactation curve without altering its shape. Daily genetic responses to the second eigenvector index increased linearly as DIM increased. Genetic responses to the third eigenvector index were negative in mid-lactation but were positive in early and late lactation (concave curve). Genetic responses to the fourth and fifth eigenvector indexes hovered around zero across the lactation. The results suggest that both second and third eigenvectors account for the change in the shape of the lactation curve and there is little utility of the fourth and fifth eigenvectors in improving lactation milk or persistency. When the goal is to increase lactation milk yield alone, the index based on the first eigenvector produced a similar response to the index based on all 5 eigenvectors. When the goal is to improve both lactation milk yield and persistency, the index based on the first 3 eigenvectors achieved more than 99.9% of the genetic response to an index based on all 5 eigenvectors. The advantage of an eigenvector index over conventional selection based on total lactation milk yield increases with increasing economic weight assigned to persistency.
Kenneth A. Frank - One of the best experts on this subject based on the ideXlab platform.
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Impact of a confounding variable on a Regression Coefficient
Sociological Methods and Research, 2000Co-Authors: Kenneth A. FrankAbstract:Regression Coefficients cannot be interpreted as causal if the relationship can be attributed to an alternate mechanism. One may control for the alternate cause through an experiment (e.g., with random assignment to treatment and control) or by measuring a corresponding confounding variable and including it in the model. Unfortunately, there are some circumstances under which it is not possible to measure or control for the potentially confounding variable. Under these circumstances, it is helpful to assess the robustness of a statistical inference to the inclusion of a potentially confounding variable. In this article, an index is derived for quantifying the impact of a confounding variable on the inference of a Regression Coefficient. The index is developed for the bivariate case and then generalized to the multivariate case, and the distribution of the index is discussed. The index also is compared with existing indexes and procedures. An example is presented for the relationship between socioeconomic background and educational attainment, and a reference distribution for the index is obtained. The potential for the index to inform causal inferences is discussed, as are extensions.
