Deep Varying Coefficient Censored Regression Model
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In this study, we introduce deepVCCR, a deep learning-based method for varying coefficient censored regression. DeepVCCR harnesses the power of deep learning by combining the fundamental principles of censored regression and varying coefficient regression models. Both censored regression and varying coefficient regression models are widely used statistical techniques with diverse applications. However, we propose leveraging deep learning techniques to address the challenges inherent in high-dimensional and complex data analysis. Our experiments confirm the effectiveness of deepVCCR in managing such data and delivering superior performance compared to existing methods.
Abstract
In this study, we introduce deepVCCR, a deep learning-based method for varying coefficient censored regression. DeepVCCR harnesses the power of deep learning by combining the fundamental principles of censored regression and varying coefficient regression models. Both censored regression and varying coefficient regression models are widely used statistical techniques with diverse applications. However, we propose leveraging deep learning techniques to address the challenges inherent in high-dimensional and complex data analysis. Our experiments confirm the effectiveness of deepVCCR in managing such data and delivering superior performance compared to existing methods.
참고문헌 (30)
- LeCun Y, "Deep learning", Nature, 521, pp.436~444 (2015)
- 초예진, "Binding Affinity Prediction Using Self-supervised Learning and QP-Ensemble", Quantitative Bio-Science, 42(1), pp.57~64 (2023), 자연과학연구소[DOI]
- Goodfellow I, "Deep learning" (2016), MIT press
- Schmidhuber J, "Deep learning in neural networks : An overview", Neural Networks, 61, pp.85~117 (2015)
- Tobin J, "Estimation of relationships for limited dependent variables", Econometrica, 26, pp.24~36 (1958)
- Cox DR, "Regression models and life-tables", J R Stat Soc, 34, pp.187~220 (1972)
- Amemiya T, "Regression analysis when the dependent variable is truncated normal", Econometrica, 41, pp.997~1016 (1973)
- Buckley J, "Linear regression with censored data", Biometrika, 66, pp.429~436 (1979)
- Kaplan EL, "Nonparametric estimation from incomplete observations", J Am Stat Assoc, 66, pp.484~491 (1958)
- Wei LJ, "Linear regression analysis of censored survival data based on rank tests", Biometrika, 77, pp.845~851 (1990)
- Hastie T, "Varying-coefficient models", J Roy Stat Soc A, 55, pp.757~779 (1993)
- Hoover DR, "Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data", Biometrika, 85, pp.809~822 (1998)
- Xue X, "On estimation of the varying-coefficient censored quantile regression", Stat Probabil Lett, 130, pp.72~80 (2017)
- Fotso S, "Deep learning for survival analysis", Proc. NeurIPS Workshop on Machine Learning for Health (2018)
- Ranganath R, "The survival filter : joint survival analysis with a latent time series", arXiv preprint 2017;arXiv:1606.00931, ~ (2017)
- Katzman JL, "DeepSurv : Personalized treatment recommender system using a Cox proportional hazards deep neural network", BMC Med Res Methodol, pp.18~24 (2018)
- Jia Y, "Deep learning for quantile regression under right censoring: DeepQuantreg", Comput Stat Data An, 165, pp.107323~ (2022)[DOI]
- 심주용, "Varying Coefficient Regression Modeling using Deep Neural Network", Quantitative Bio-Science, 41(2), pp.71~76 (2022), 자연과학연구소[DOI]
- Zhou M, "M-estimation in censored linear models", Biometrika, 79, pp.837~841 (1992)
- Orbe J, "Censored partial regression", Biostatistics, 4, pp.109~121 (2003)
- Koul H, "Regression analysis with randomly right-censored data", Ann Stat, 9, pp.1276~1288 (1981)
- Li Q, "Smooth varying-coefficient estimation and inference for qualitative and quantitative data", Economet Theor, 26, pp.1607~1637 (2010)
- Lee Y, "Projection-type estimation for varying coefficient regression models", Bernoulli, 18, pp.177~205 (2012)
- Wu C, "Kernel smoothing on varying coefficient models with longitudinal dependent variable", Stat Sinica, 10, pp.433~456 (2000)
- Wang N, "Local linear estimation of spatially varying coefficient models : an improvement on the geographically weighted regression technique", Environ Plann A, 40, pp.986~1005 (2008)
- Shim J, "Varying coefficient modeling via least squares support vector regression", Neurocomp, 161, pp.254~259 (2015)
- Harrell FE, "Multivariable prognostic models:issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors", Stat Med, 15, pp.361~387 (1996)
- Miller R, "Regression with censored data", Biometrika, 69, pp.521~531 (1982)
- Ying ZL, "Survival analysis with median regression models", J Am Stat Assoc, 90, pp.178~184 (1995)
- 심주용, "Semiparametric least squares support vector machine for accelerated failure time model", Journal of the Korean Statistical Society, 40(1), pp.75~83 (2011), 한국통계학회