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Kernel-based random effect time-varying coefficient model for longitudinal data.

, , and . Neurocomputing, (2017)

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Kernel-based random effect time-varying coefficient model for longitudinal data., , and . Neurocomputing, (2017)Doubly Regularized Kernel Regression with Heteroscedastic Censored Data., and . ICNC (1), volume 3610 of Lecture Notes in Computer Science, page 521-527. Springer, (2005)A Simple Quantile Regression via Support Vector Machine., and . ICNC (1), volume 3610 of Lecture Notes in Computer Science, page 512-520. Springer, (2005)Semiparametric spatial effects kernel minimum squared error model for predicting housing sales prices., , and . Neurocomputing, (2014)Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application, , , , , , , , , and 3 other author(s). Scientific Reports, 9 (1): 3644-- (2019)Interval Regression Analysis Using Support Vector Machine and Quantile Regression., , , , and . FSKD (1), volume 3613 of Lecture Notes in Computer Science, page 100-109. Springer, (2005)Incremental Feature Extraction Based on Empirical Kernel Map., , , , and . ISMIS, volume 2871 of Lecture Notes in Computer Science, page 440-444. Springer, (2003)Support vector quantile regression with varying coefficients., , and . Comput. Stat., 31 (3): 1015-1030 (2016)On the Tradeoff between Computation-Time and Learning-Accuracy in GAN-based Super-Resolution Deep Learning., , and . ICOIN, page 422-424. IEEE, (2021)Locally Weighted LS-SVM for Fuzzy Nonlinear Regression with Fuzzy Input-Output., , , and . CIS, volume 4456 of Lecture Notes in Computer Science, page 317-325. Springer, (2006)