Local Regression and Likelihood
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[1] M. Wand,et al. Multivariate Locally Weighted Least Squares Regression , 1994 .
[2] K. Wallis. Seasonal Adjustment and Relations Between Variables , 1974 .
[3] John M. Chambers,et al. Programming With Data , 1998 .
[4] Bernd Droge,et al. Some Comments on Cross-Validation , 1996 .
[5] J. V. Ryzin,et al. Regression Analysis with Randomly Right-Censored Data , 1981 .
[6] M. C. Jones,et al. Locally parametric nonparametric density estimation , 1996 .
[7] S. Weisberg,et al. An Introduction to Regression Graphics , 1994 .
[8] D. M. Titterington,et al. A Comparative Study of Kernel-Based Density Estimates for Categorical Data , 1980 .
[9] M. C. Jones,et al. A Brief Survey of Bandwidth Selection for Density Estimation , 1996 .
[10] N. Brinkman. Ethanol Fuel-A Single-Cylinder Engine Study of Efficiency and Exhaust Emissions , 1981 .
[11] C. Loader. Local Likelihood Density Estimation , 1996 .
[12] J. L. Jaech,et al. Statistical methods in nuclear material control , 1973 .
[13] Samuel D. Conte,et al. Elementary Numerical Analysis , 1980 .
[14] M. Bartlett. Periodogram analysis and continuous spectra. , 1950, Biometrika.
[15] David R. Cox,et al. Regression models and life tables (with discussion , 1972 .
[16] Shean-Tsong Chiu,et al. Bandwidth selection for kernel density estimation , 1991 .
[17] Hans-Georg Müller,et al. Smooth Optimum Kernel Estimators of Densities, Regression Curves and Modes , 1984 .
[18] D. M. Titterington,et al. Neural Networks: A Review from a Statistical Perspective , 1994 .
[19] W. Beggs,et al. Statistical methods for nuclear material management , 1988 .
[20] A. Izenman,et al. Philatelic Mixtures and Multimodal Densities , 1988 .
[21] L. Brown,et al. Information Inequality Bounds on the Minimax Risk (with an Application to Nonparametric Regression) , 1991 .
[22] C. J. Stone,et al. Optimal Rates of Convergence for Nonparametric Estimators , 1980 .
[23] P. Robinson. ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSION , 1988 .
[24] M. Lejeune,et al. Smooth estimators of distribution and density functions , 1992 .
[25] Jianqing Fan,et al. Adaptive Order Polynomial Fitting: Bandwidth Robustification and Bias Reduction , 1995 .
[26] Edmund Taylor Whittaker. On a New Method of Graduation , 1922, Proceedings of the Edinburgh Mathematical Society.
[27] Brian D. Ripley,et al. Flexible Non-linear Approaches to Classification , 1994 .
[28] Michael Buckley,et al. A graphical method for estimating the residual variance in nonparametric regression , 1989 .
[29] R. Gentleman,et al. Local full likelihood estimation for the proportional hazards model. , 1991, Biometrics.
[30] H. Weyl. On the Volume of Tubes , 1939 .
[31] P. Green. Penalized Likelihood for General Semi-Parametric Regression Models. , 1987 .
[32] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[33] D. W. Scott,et al. The L 1 Method for Robust Nonparametric Regression , 1994 .
[34] Satterthwaite Fe. An approximate distribution of estimates of variance components. , 1946 .
[35] Brian S. Yandell,et al. A local polynomial jump-detection algorithm in nonparametric regression , 1998 .
[36] A. Bowman,et al. Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .
[37] T. Isaksson,et al. New approach for distance measurement in locally weighted regression , 1994 .
[38] G. Wahba. Spline models for observational data , 1990 .
[39] A. Savitzky,et al. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .
[40] Terence P. Speed,et al. The Role of Statistics in Nuclear Materials Accounting: Issues and Problems , 1986 .
[41] R. Gnanadesikan,et al. Probability plotting methods for the analysis of data. , 1968, Biometrika.
[42] B. Silverman. Density estimation for statistics and data analysis , 1986 .
[43] Jeffrey D. Hart,et al. Nonparametric Smoothing and Lack-Of-Fit Tests , 1997 .
[44] T. A. Boden,et al. Trends `91: A compendium of data on global change---highlights , 1992 .
[45] Thomas M. Stoker. Smoothing bias in density derivative estimation , 1993 .
[46] Jianqing Fan,et al. Local polynomial modelling and its applications , 1994 .
[47] Vladimir Katkovnik,et al. On Multiple Window Local Polynomial Approximation with Varying Adaptive Bandwidths , 1998, COMPSTAT.
[48] V. A. Epanechnikov. Non-Parametric Estimation of a Multivariate Probability Density , 1969 .
[49] U. Grenander,et al. Statistical Spectral Analysis of Time Series Arising from Stationary Stochastic Processes , 1953 .
[50] R. Tibshirani,et al. Generalized additive models for medical research , 1986, Statistical methods in medical research.
[51] D. Siegmund,et al. Testing for a Signal with Unknown Location and Scale in a Stationary Gaussian Random Field , 1995 .
[52] David R. Cox,et al. The statistical analysis of series of events , 1966 .
[53] Adrian Bowman,et al. On the use of nonparametric regression for model checking , 1989 .
[54] R. Tibshirani,et al. Varying‐Coefficient Models , 1993 .
[55] G. V. Schiaparelli,et al. Sul modo di ricavare la vera espressione delle leggi della natura dalle curve empiriche , 1867 .
[56] J. Rice. Bandwidth Choice for Nonparametric Regression , 1984 .
[57] N. Tuma,et al. Local hazard models. , 1990, Sociological methodology.
[58] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[59] Sue Leurgans,et al. Linear models, random censoring and synthetic data , 1987 .
[60] E. Kaplan,et al. Nonparametric Estimation from Incomplete Observations , 1958 .
[61] D. W. Scott,et al. Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .
[62] James Stephen Marron,et al. A Personal View of Smoothing and Statistics , 1996 .
[63] Peter Lancaster,et al. Curve and surface fitting - an introduction , 1986 .
[64] Jerome Sacks,et al. Confidence Bands for Regression Functions , 1985 .
[65] G. Lugosi,et al. On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates , 1994 .
[66] J. Imhof. Computing the distribution of quadratic forms in normal variables , 1961 .
[67] Jeffrey S. Simonoff,et al. Probability estimation via smoothing in sparse contingency tables with ordered categories , 1987 .
[68] M. C. Jones,et al. A reliable data-based bandwidth selection method for kernel density estimation , 1991 .
[69] H. Müller,et al. Hazard rate estimation under random censoring with varying kernels and bandwidths. , 1994, Biometrics.
[70] Jiayang Sun,et al. Simultaneous confidence bands for linear regression with heteroscedastic errors , 1995 .
[71] B. Silverman,et al. Nonparametric regression and generalized linear models , 1994 .
[72] John Alan McDonald,et al. Smoothing with split linear fits , 1986 .
[73] J. Staniswalis. The Kernel Estimate of a Regression Function in Likelihood-Based Models , 1989 .
[74] Julius Shiskin,et al. The X-11 variant of the census method II seasonal adjustment program , 1965 .
[75] W. Härdle. Applied Nonparametric Regression , 1991 .
[76] Dag Tjøstheim,et al. Linearity Testing using Local Polynomial Approximation , 1998 .
[77] Theo Gasser,et al. Finite-Sample Variance of Local Polynomials: Analysis and Solutions , 1996 .
[78] E. Mammen,et al. Comparing Nonparametric Versus Parametric Regression Fits , 1993 .
[79] Jon Louis Bentley,et al. An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.
[80] H. Hotelling. Tubes and Spheres in n-Spaces, and a Class of Statistical Problems , 1939 .
[81] Lawrence D. Brown,et al. Superefficiency in Nonparametric Function Estimation , 1997 .
[82] J. Aitchison,et al. Multivariate binary discrimination by the kernel method , 1976 .
[83] R. H. Farrell. On the Best Obtainable Asymptotic Rates of Convergence in Estimation of a Density Function at a Point , 1972 .
[84] H. Akaike. A new look at the statistical model identification , 1974 .
[85] Rupert G. Miller,et al. Survival Analysis , 2022, The SAGE Encyclopedia of Research Design.
[86] Frederick Robertson Macaulay,et al. The Smoothing of Time Series , 1931 .
[87] E. F. Schuster,et al. On the Nonconsistency of Maximum Likelihood Nonparametric Density Estimators , 1981 .
[88] W. S. Meisel,et al. General Estimates of the Intrinsic Variability of Data in Nonlinear Regression Models , 1976 .
[89] Jianming Ye. On Measuring and Correcting the Effects of Data Mining and Model Selection , 1998 .
[90] Richard R. Picard,et al. Statistical methods for nuclear materials safeguards: an overview , 1982 .
[91] Jerome H. Friedman. Multivariate adaptive regression splines (with discussion) , 1991 .
[92] Brian D. Ripley,et al. Modern Applied Statistics with S-Plus Second edition , 1997 .
[93] Jerome Sacks,et al. LINEAR ESTIMATION FOR APPROXIMATELY LINEAR MODELS , 1978 .
[94] J. Friedman,et al. Projection Pursuit Regression , 1981 .
[95] W. Cleveland,et al. Computational methods for local regression , 1991 .
[96] T. Severini,et al. Quasi-Likelihood Estimation in Semiparametric Models , 1994 .
[97] A. N. Shiryaev,et al. Minimax Weights in a Trend Detection Problem of a Random Process , 1971 .
[98] H. Müller. CHANGE-POINTS IN NONPARAMETRIC REGRESSION ANALYSIS' , 1992 .
[99] C. Loader. Bandwidth selection: classical or plug-in? , 1999 .
[100] Jianqing Fan. Local Linear Regression Smoothers and Their Minimax Efficiencies , 1993 .
[101] C. Loader. CHANGE POINT ESTIMATION USING NONPARAMETRIC REGRESSION , 1996 .
[102] S. Rice. The Distribution of the Maxima of a Random Curve , 1939 .
[103] C. J. Stone,et al. Logspline Density Estimation for Censored Data , 1992 .
[104] Grace Wahba,et al. Testing the (Parametric) Null Model Hypothesis in (Semiparametric) Partial and Generalized Spline Models , 1988 .
[105] Nils Lid Hjort,et al. Dynamic Likelihood Hazard Rate Estimation , 1993 .
[106] Hilary L. Seal,et al. Graduation by piecewise cubic polynomials: A historical review , 1981 .
[107] Ørnulf Borgan,et al. On the theory of moving average graduation , 1979 .
[108] Peter Hall,et al. A Geometrical Method for Removing Edge Effects from Kernel-Type Nonparametric Regression Estimators , 1991 .
[109] C. J. Stone,et al. Optimal Global Rates of Convergence for Nonparametric Regression , 1982 .
[110] Paul L. Speckman,et al. Confidence bands in nonparametric regression , 1993 .
[111] D. W. Scott,et al. Biased and Unbiased Cross-Validation in Density Estimation , 1987 .
[112] W. Cleveland. Coplots, nonparametric regression, and conditionally parametric fits , 1994 .
[113] Day Ne,et al. A GENERAL MAXIMUM LIKELIHOOD DISCRIMINANT , 1967 .
[114] W. Cleveland,et al. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .
[115] David Ruppert,et al. Local polynomial variance-function estimation , 1997 .
[116] Jerome H. Friedman,et al. Smoothing of Scatterplots , 1982 .
[117] J. Simonoff. Smoothing Methods in Statistics , 1998 .
[118] A. Bowman. An alternative method of cross-validation for the smoothing of density estimates , 1984 .
[119] J. Anderson. Separate sample logistic discrimination , 1972 .
[120] W. Cleveland,et al. Smoothing by Local Regression: Principles and Methods , 1996 .
[121] N. Draper,et al. Applied Regression Analysis , 1966 .
[122] Mikis D. Stasinopoulos,et al. Mean and Dispersion Additive Models , 1996 .
[123] M. Rosenblatt. Remarks on Some Nonparametric Estimates of a Density Function , 1956 .
[124] E. L. De Forest,et al. On Adjustment Formulas , 1877 .
[125] William S. Cleveland,et al. Visualizing Data , 1993 .
[126] H. Müller. Weighted Local Regression and Kernel Methods for Nonparametric Curve Fitting , 1987 .
[127] J. Durbin,et al. Local trend estimation and seasonal adjustment of economic and social time series (with discussion) , 1982 .
[128] Richard A. Johnson,et al. Some Angular-Linear Distributions and Related Regression Models , 1978 .
[129] Ronald D. Snee,et al. Validation of Regression Models: Methods and Examples , 1977 .
[130] James Stephen Marron,et al. Regression smoothing parameters that are not far from their optimum , 1992 .
[131] J. Marron,et al. Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation , 1987 .
[132] Adrian Bowman,et al. On the Use of Nonparametric Regression for Checking Linear Relationships , 1993 .
[133] M. Wand,et al. EXACT MEAN INTEGRATED SQUARED ERROR , 1992 .
[134] M. Wand,et al. An Effective Bandwidth Selector for Local Least Squares Regression , 1995 .
[135] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[136] M. Stone,et al. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[137] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[138] D. Aldous. Probability Approximations via the Poisson Clumping Heuristic , 1988 .
[139] I. James,et al. Linear regression with censored data , 1979 .
[140] A. Kimber,et al. A Statistical Analysis of Batting in Cricket , 1993 .
[141] Ross Ihaka,et al. Gentleman R: R: A language for data analysis and graphics , 1996 .
[142] Raymond J. Carroll,et al. Adapting for Heteroscedasticity in Linear Models , 1982 .
[143] David Firth,et al. Model checking with nonparametric curves , 1991 .
[144] M. A. Moran,et al. PARAMETRIC AND KERNEL DENSITY METHODS IN DISCRIMINANT ANALYSIS: ANOTHER COMPARISON , 1986 .
[145] M. Woodroofe. On Choosing a Delta-Sequence , 1970 .
[146] Jerome Sacks,et al. ASYMPTOTICALLY OPTIMUM KERNELS FOR DENSITY ESTIMATION AT A POINT , 1981 .
[147] David Ruppert,et al. Fitting a Bivariate Additive Model by Local Polynomial Regression , 1997 .
[148] Thomas A. Severini,et al. Diagnostics for Assessing Regression Models , 1991 .
[149] D. Cox,et al. Analysis of Survival Data. , 1986 .
[150] C. Heyde,et al. Quasi-likelihood and its application , 1997 .
[151] B. Ismail,et al. Estimation of jump points in nonparametric regression through residual analysis , 1997 .
[152] D. Cox,et al. An Analysis of Transformations , 1964 .
[153] N. Fisher,et al. Statistical Analysis of Circular Data , 1993 .
[154] P. McCullagh,et al. Generalized Linear Models , 1972, Predictive Analytics.
[155] Andreas Krause,et al. The basics of S and S-Plus , 1997 .
[156] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[157] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[158] V. Katkovnik. Adaptive local polynomial periodogram for time-varying frequency estimation , 1996, Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96).
[159] H. Müller,et al. Kernels for Nonparametric Curve Estimation , 1985 .
[160] Stephen M. Stigler,et al. Mathematical Statistics in the Early States , 1978 .
[161] T. Gasser,et al. A Flexible and Fast Method for Automatic Smoothing , 1991 .
[162] J. W. Tukey,et al. The Measurement of Power Spectra from the Point of View of Communications Engineering , 1958 .
[163] P. Bickel,et al. On Some Global Measures of the Deviations of Density Function Estimates , 1973 .
[164] M. C. Jones,et al. On optimal data-based bandwidth selection in kernel density estimation , 1991 .
[165] D. W. Scott,et al. Kernel density estimation revisited , 1977 .
[166] James Stephen Marron,et al. Comparison of data-driven bandwith selectors , 1988 .
[167] P. J. Huber. Robust Estimation of a Location Parameter , 1964 .
[168] H. Scheffé. The Analysis of Variance , 1960 .
[169] Zhiliang Ying,et al. Large Sample Theory of a Modified Buckley-James Estimator for Regression Analysis with Censored Data , 1991 .
[170] Jeffrey S. Simonoff,et al. Smoothing categorical data , 1995 .
[171] Jianqing Fan,et al. Local polynomial kernel regression for generalized linear models and quasi-likelihood functions , 1995 .
[172] Allan R. Wilks,et al. The new S language: a programming environment for data analysis and graphics , 1988 .
[173] Jianqing Fan,et al. Censored Regression - Local Linear-approximations and Their Applications , 1994 .
[174] E. Mammen,et al. Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors , 1997 .
[175] D. Pregibon. Logistic Regression Diagnostics , 1981 .
[176] R. Cook. Detection of influential observation in linear regression , 2000 .
[177] P. Whittle. On the Smoothing of Probability Density Functions , 1958 .
[178] G. Oehlert. A note on the delta method , 1992 .
[179] B. Efron. The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis , 1975 .
[180] Vladimir Katkovnik,et al. A new method for varying adaptive bandwidth selection , 1999, IEEE Trans. Signal Process..
[181] David V. Hinkley,et al. Inference about the change-point in a sequence of binomial variables , 1970 .
[182] R. Nigel Horspool,et al. C Programming in the Berkeley Unix Environment , 1987 .
[183] H. Müller. Nonparametric regression analysis of longitudinal data , 1988 .
[184] W. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .
[185] Werner Stuetzle,et al. Some comments on the asymptotic behavior of robust smoothers , 1979 .
[186] Daniel Q. Naiman,et al. Volumes of Tubular Neighborhoods of Spherical Polyhedra and Statistical Inference , 1990 .
[187] R. Tibshirani,et al. Local Likelihood Estimation , 1987 .
[188] M. Rudemo. Empirical Choice of Histograms and Kernel Density Estimators , 1982 .
[189] Rupert G. Miller,et al. Regression with censored data , 1982 .
[190] Paul Dierckx,et al. Curve and surface fitting with splines , 1994, Monographs on numerical analysis.
[191] C. Loader. Inference for a hazard rate change point , 1991 .
[192] Lajos Horváth,et al. On $L_p$-Norms of Multivariate Density Estimators , 1991 .
[193] P. Lancaster,et al. Surfaces generated by moving least squares methods , 1981 .
[194] Jiayang Sun. Tail probabilities of the maxima of Gaussian random fields , 1993 .
[195] I. Johnstone,et al. Ideal spatial adaptation by wavelet shrinkage , 1994 .
[196] G. S. Watson,et al. Smooth regression analysis , 1964 .
[197] T. Anderson. Statistical analysis of time series , 1974 .
[198] H. Daniels. The Estimation of Spectral Densities , 1962 .
[199] Werner A. Stahel,et al. Robust Statistics: The Approach Based on Influence Functions , 1987 .
[200] Jianqing Fan,et al. Data‐Driven Bandwidth Selection in Local Polynomial Fitting: Variable Bandwidth and Spatial Adaptation , 1995 .
[201] David J. Hand,et al. Discrimination and Classification , 1982 .
[202] W. Härdle,et al. Uniform Consistency of a Class of Regression Function Estimators , 1984 .
[203] Robert P. W. Duin,et al. On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions , 1976, IEEE Transactions on Computers.
[204] Vladimir I. Piterbarg,et al. On the convergence rate of maximal deviation distribution for kernel regression estimates , 1984 .
[205] Philip C. Spector. Introduction to S and S-Plus , 1995 .
[206] Trevor Hastie,et al. Statistical Models in S , 1991 .
[207] Peter Craven,et al. Smoothing noisy data with spline functions , 1978 .
[208] E. Nadaraya. On Estimating Regression , 1964 .
[209] Charles C. Taylor,et al. Bootstrap choice of the smoothing parameter in kernel density estimation , 1989 .
[210] J. Nelder,et al. An extended quasi-likelihood function , 1987 .
[211] R. L. Eubank,et al. A bias reduction theorem with applications in nonparametric regression , 1991 .
[212] Prakasa Rao. Nonparametric functional estimation , 1983 .
[213] Ljubisa Stankovic,et al. Periodogram with varying and data-driven window length , 1998, Signal Process..
[214] Mark G. Low. Renormalizing Upper and Lower Bounds for Integrated Risk in the White Noise Model , 1993 .