Multiple quantile regressionNon-crossingSmoothing functionNeural tangent kernelVariable selectionIn this paper, we present the DNN-NMQR estimator, an approach that utilizes a deep neural network structure to so
Originally introduced for two-sample comparisons, the win ratio has been extended to regression, with the proportional win-fractions (PW) model serving as the primary framework [5]. In this setting, the win ratio is modeled as a multiplicative function of covariate differences, allowing a well-r...
More Regression Methods Method BTS Yet another approach to comparing two explanatory variables is to estimate the strength of the association based on the Theil–Sen estimator, and then use a percentile bootstrap method to test H0:η12=η22, where now ηj2 is explanatory power when using predi...
The unobserved-heterogeneity model for both the conditional fixed-effects as well as the random- effects estimator can be written in utility-maximization form as = x β + + Assuming we have a panel dataset with repeated observations from individuals, is the utility of the th individual toward ...
"Without clustering": lasso-penalized logistic regression model; "HC clustering": group elastic-net model with hierarchical clustering using linear correlation measured between variables; "Information clustering": information-based clustering21; "MI-DP clustering": group elastic-net model with variable ...
L. Devroye, On the almost everywhere convergence of nonparametric regression function estimates. Ann. Stat. 9, 1310–1319 (1981a) Article MathSciNet MATH Google Scholar L. Devroye, On the inequality of Cover and Hart in nearest neighbor discrimination. IEEE Trans. Pattern Anal. Mach. Intell...
Explain what you understand by precision of an estimator? What is the difference between average and marginal analysis? What is the relationship between sample size and margin of error? Evaluate the measurement scales used in the USTA survey. Which level of measurement conveys the most ...
the maximum margin. Refs. [139–142] provides comprehensive overviews on the basic principles of the standard SVM. This technique was originally used in pattern recognition studies and was not applied to nonlinear regression estimation and time series forecasting until the introduction of Vapnik’s ...
prevalence and recidivism incidence at 1, 3, and 5 years after release from prison.7 The analysis of both prevalence—as well as incidence measures of recidivism allows us to differentiate between criminal involvement at both the extensive—(recidivism status) and intensive margin (number of ...
However, when the first stage regression model is fitted to a left-truncated sample, it estimates ( | , , > ̃ ), which is generally different from ( | , ), even if the truncation is independent. Similarly, for a left-truncated sample the G-estimator ψ in model (2) solves the ...