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Nonlinear Regression With Censored Data
| Content Provider | Scilit |
|---|---|
| Author | Heuchenne, Cédric Keilegom, Ingrid Van |
| Copyright Year | 2007 |
| Description | Suppose that the random vector (X, Y) satisfies the regression model Y = m(X) + σ(X)ϵ, where m (·) = E(Y||·) belongs to some parametric class ${m_{θ}$(·):θ∈} of regression functions, $σ^{2}$(·) = var(Y||·) is unknown, and ϵ is independent of X. The response Y is subject to random right censoring, and the covariate X is completely observed. A new estimation procedure for the true, unknown parameter vector $θ_{0}$ is proposed that extends the classical least squares procedure for nonlinear regression to the case where the response is subject to censoring. The consistency and asymptotic normality of the proposed estimator are established. The estimator is compared through simulations with an estimator proposed by Stute in 1999, and both methods are also applied to a fatigue life dataset of strain-controlled materials. |
| Related Links | https://orbi.uliege.be/bitstream/2268/11279/1/mainart2.pdf |
| Ending Page | 44 |
| Page Count | 11 |
| Starting Page | 34 |
| ISSN | 00401706 |
| e-ISSN | 15372723 |
| DOI | 10.1198/004017006000000417 |
| Journal | Technometrics |
| Issue Number | 1 |
| Volume Number | 49 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2007-02-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Technometrics Statistics and Probability Regression Model Kernel Method Survival Analysis Least Squares Estimation Nonlinear Regression Data Quality Censored Data Bootstrap Nonparametric Regression Right Censoring Least Square |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Statistics and Probability Modeling and Simulation |
