Consistent bandwidth selection for kernel binary regression
From MaRDI portal
Publication:1299420
DOI10.1016/S0378-3758(97)00176-6zbMath0937.62039MaRDI QIDQ1299420
Naomi S. Altman, K. Brenda MacGibbon
Publication date: 14 June 2000
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)
Related Items (3)
Prediction Error Estimation Under Bregman Divergence for Non‐Parametric Regression and Classification ⋮ Survival function estimation when lifetime and censoring time are dependent ⋮ Asymptotically minimax non–parametric function estimation with positivity constraints I
Cites Work
- Linear smoothers and additive models
- Central limit theorem for integrated square error of multivariate nonparametric density estimators
- Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
- Consistent nonparametric regression. Discussion
- Choosing a kernel regression estimator. With comments and a rejoinder by the authors
- The choice of weights in kernel regression estimation
- Some diagnostics for binary logistic regression via smoothing
- How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
- On the use of nonparametric regression for model checking
- A Unifying Approach to Nonparametric Regression Estimation
- Morse Theory. (AM-51)
- A completely automatic french curve: fitting spline functions by cross validation
- Martingale Central Limit Theorems
- Some Comments on C P
- A Class of Statistics with Asymptotically Normal Distribution
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Consistent bandwidth selection for kernel binary regression
