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Background: Introduction to nonparametric regression and classification
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List of papers:
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### Shrinkage

- Tibshirani (1996) Regression Shrinkage and Selection via the Lasso.
- Efron et al (2004) Least Angle Regression.
- Chen at al (1998) Atomic Decomposition by Basis Pursuit.
- Osborne at al (2000) On the LASSO and Its Dual.
- Meinshausen and Yu (2009) LASSO-Type Recovery of Sparse Representations for High-Dimensional Data.
- Yuan and Lin (2007) Model Selection and Estimation in Regression with Grouped Variables.
- Zou and Hastie (2005) Regularization and Variable Selection via the Elastic Net.

### Smoothing Parameter Selection

- Craven and Wabha (1979) Smoothing noisy data with spline functions.
- Golub et al (1979) Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter.

### Spline Models

- Eilers and Marx (1996) Flexible Smoothing with B-splines and Penalties.
- O'Sullivan (1986) A Statistical Perspective on Ill-Posed Inverse Problems.
- Sun and Loader (1994) Confidence Bands for Linear Regression and Smoothing.
- Knaft et al (1985) Confidence Bands for Regression Functions.
- Crainiceanu et al (2005) Bayesian Analysis for Penalized Spline Regression using WinBUGS.
- Crainiceanu et al (2008) Bayesian Analysis for Penalized Spline Regression using WinBUGS.
- Fong et al (2009) Bayesian Inference for Generalized Linear Models.

### Kernel Methods

- Fan (1992) Design-Adaptive Nonparametric Regression.
- Fan (1993) Local Linear Regression Smoothers and Their Minimax Efficiencies.
- Nadaraya (1964) On Estimating Regression.
- Watson (1964) Smooth Regression Analysis.

### Variance Estimation

- Rice (1984) Bandwidth Choice for Nonparametric Regression.
- Gasser et al (1986) Residual Variance and Residual Pattern in Nonlinear Regression.

### Bias, variance and the curse of dimensionality

- Kohavi and Wolpert (1996) Bias Plus Variance Decomposition for Zero-One Loss Functions
- Friedman (1997) On Bias, Variance, 0/1‚ÄîLoss, and the Curse-of-Dimensionality.
- Bengio et al (2005) The Curse of Dimensionality for Local Kernel Machines

### Model selection

- Lindley (1968) The Choice of Variables in Multiple Regression
- Mallows (1973) Some comments on Cp.
- Stone (1974) Cross-Validatory Choice and Assessment of Statistical Predictions.
- Stone (1977) An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike's Criterion .
- Schwartz (1978) Estimating the Dimension of a Model.
- Akaike (1979) Maximum Likelihood Identification of Gaussian Autoregressive Moving Average Models.
- Craven and Wahba(1979) Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by methods of generalized cross-validation.
- Efron and Gong (1983) A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation
- Efron (1983) Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
- Hutchinson and de Hoog (1985) Smoothing noisy data with spline functions.
- Efron (1986) How Biased is the Apparent Error Rate of a Prediction Rule?
- Breiman (1992) The Little Bootstrap and Other Methods for Dimensionality Selection in Regression: X-Fixed Prediction Error
- Breiman and Spector (1992) Submodel Selection and Evaluation in Regression. The X-Random Case
- Kohavi (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection
- Breiman (1996) Heuristics of instability and stabilization in model selection.
- Hurvich et al (1998) Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion.
- Friedman and Popescu (2004) Gradient Directed Regularization
- Bartlett et al (2006) Convexity, Classification and Risk Bounds
- Bengio and Grandvalet No Unbiased Estimator of the Variance of K-Fold Cross-Validation

### The Lasso

- Knight and Fu (2000) Asymptotics for Lasso-type estimators

### Bayesian methods for model selection

- Kass and Raftery (1996) Bayes factors
- Raftery (1996) Approximate Bayes Factors and Accounting for Model Uncertainty in Generalised Linear Models
- Spiegelhalter et al (2002) Bayesian Measures of Model Complexity and Fit.
- Raftery and Dean (2006) Variable Selection for Model-Based Clustering
- Casella and Moreno (2006) Objective Bayesian Variable Selection
- Casella et al (2007) Consistency of Bayesian Procedures for Variable Selection
- Park and Casella (2007) The Bayesian Lasso

### Bootstrap methods

- Efron (1979) Bootstrap Methods: Another Look at the Jackknife.
- Efron and Tibshirani (1999) Improvements on Cross-Validation: The .632+ Bootstrap Method

### Smoothing and spline models

- Friedman and Silverman (1989) Flexible Parsimonious Smoothing and Additive Modeling
- Stone et al (1997) Polynomial Splines and their Tensor Products in Extended Linear Modeling
- Denison et al (1998) Automatic Bayesian Curve Fitting
- DiMatteo et al (2001) Bayesian curve-fitting with free-knot splines
- Pearce and Wand (2006) Penalized splines and reproducing kernel methods
- Wand (2006) Smoothing and mixed models

### Kernel-based methods and local regression

- Priestly and Chao (1972) Non-parametric function fitting
- Benedetti (1977) On the nonparametric estimation of regression functions
- Cleveland (1979) Robust locally weighted regression and smoothing scatterplots
- Stone (1980) Optimal rates of convergence for nonparametric estimators
- Silverman (1984) Spline smoothing: The equivalent kernel method
- Cleveland and Delvin (1988) Locally weighted regression: An approach to regression analysis by local fitting
- Altman (1992) An introduction to kernel and nearest-neighbor nonparametric regression
- Fan and Gijbels (1992) Variable bandwidth and local linear regression smoothers
- Ruppert et al (1995) An effective bandwidth selector for local least squares regression
- Jones et al (1996) A brief survey of bandwidth selection for density estimation
- Sheather (2004) Density estimation
- Schucany (2004) Kernel smoothers: An overview of curve estimators for the first graduate course in nonparametric statistics
- Duong (2007) Using ks for bivariate density estimation
- Turlach Bandwidth selection in kernel density estimation: A review
- Hastie and Loader Local Regression: Automatic Kernel Carpentry.