List

The smoothAPC package implements smoothing of demographic data. The method uses bivariate thin plate splines, bivariate lasso-type regularization, and allows for both period and cohort effects. Thus the mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors.

The methods are described in Dokumentov, A., and Hyndman, R.J. (2014) Bivariate data with ridges: two-dimensional smoothing of mortality rates.

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  Tag: nonparametric smoothing

13 posts
September 20th, 2016

smoothAPC package for R

The smoothAPC package implements smoothing of demographic data. The method uses bivariate thin plate splines, bivariate lasso-type regularization, and allows […]

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

Thomas Url1, Rob J Hyndman2, Alexander Dokumentov2 Vienna University of Economics and Business, Vienna, Austria Monash Business School, Monash University, […]

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

International Symposium on Forecasting Riverside, California   I will describe and demonstrate a new open-source R package that implements the […]

June 19th, 2015

Probabilistic forecasting of peak electricity demand

Southern California Edison Rosemead, California   Electricity demand forecasting plays an important role in short-term load allocation and long-term planning […]

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

By Alex Dokumentov and Rob J Hyndman

December 24th, 2014

Bivariate data with ridges: two-dimensional smoothing of mortality rates

By Alexander Dokumentov and Rob J Hyndman

December 17th, 2014

MEFM package for R

The MEFM package for R includes a set of tools for implementing the Monash Electricity Forecasting Model.

June 5th, 2014

Low-dimensional decomposition, smoothing and forecasting of sparse functional data

By Alexander Dokumentov and Rob J Hyndman

October 31st, 2013

Nonparametric and semiparametric response surface methodology: a review of designs, models and optimization techniques

Laura Villanova, Rob J Hyndman, Kate Smith-Miles, Irene Poli Abstract: Since the introduction of Response Surface Methodology in the 1950s, there […]

October 19th, 2013

hdrcde package for R

The hdrcde package provides tools for computation of highest density regions in one and two dimensions, kernel estimation of univariate […]

February 1st, 2012

Short-term load forecasting based on a semi-parametric additive model

Shu Fan and Rob J Hyndman Revised 10 January 2011 IEEE Transactions on Power Systems (2012), 27(1), 134-141. Abstract Short-term […]

October 3rd, 2011

Forecasting electricity demand distributions using a semiparametric additive model

Talk given at University of Melbourne, 11 October 2011. University of Adelaide, 16 March 2012 Monash University, 16 May 2012 […]

July 21st, 2010

Short-term load forecasting based on a semi-parametric additive model

Shu Fan and Rob J Hyndman 20th Australasian Universities Power Engineering Conference 5-8 December 2010, University of Canterbury, Christchurch, New […]

February 8th, 2010

Functionalization of microarray devices: process optimization using a multiobjective PSO and multiresponse MARS modeling

L. Villanova, P. Falcaro, D. Carta, I. Poli, R. J. Hyndman, K. Smith-Miles 2010 IEEE Congress on Evolutionary Computation, July […]

January 3rd, 2010

Density forecasting for long-term peak electricity demand

Rob J Hyndman and Shu Fan IEEE Transactions on Power Systems, 2010, 25(2), 1142-1153 Abstract: Long-term electricity demand forecasting plays […]

July 13th, 2009

Nonparametric time series forecasting with dynamic updating

March 19th, 2007

Kernel estimation of ROC curves

Download R code (Code corrections made: 19 March 2007) DESCRIPTION Functions for kernel estimation of ROC curves. Bandwidth selection based […]

September 17th, 2006

Projection pursuit estimator for multivariate conditional densities

Azhong Ye and Rob J. Hyndman (2006) J. Fuzhou Univ. Nat. Sci. Ed. 34(6), 794–797. (Chinese).

July 20th, 2006

A Bayesian approach to bandwidth selection for multivariate kernel density estimation

Computational Statistics & Data Analysis (2006), 50(11), 3009-3031. Xibin Zhang, Maxwell L King and Rob J. Hyndman Abstract: Kernel density […]

May 1st, 2006

Local linear multivariate regression with variable bandwidth in the presence of heteroscedasticity

Azhong Ye1 , Rob J Hyndman2 and Zinai Li3 College of Management, Fuzhou University, Fuzhou, 350002, China. Department of Econometrics […]

January 16th, 2005

Local linear forecasts using cubic smoothing splines

Australian and New Zealand Journal of Statistics (2005), 47(1), 87-99. Rob J. Hyndman1, Maxwell L. King1, Ivet Pitrun1 and Baki […]

July 16th, 2004

Nonparametric confidence intervals for receiver operating characteristic curves

Biometrika (2004), 91(3), 743-750. Peter Hall1, Rob J. Hyndman2 and Yanan Fan3 Centre for Mathematics and its Applications, Australian National […]

January 16th, 2004

Spline interpolation for demographic variables: the monotonicity problem

Journal of Population Research (2004), 21(1), 95-98. Len Smith1, Rob J. Hyndman2 and Simon N. Wood3 Australian Centre for Population […]

February 16th, 2003

Improved methods for bandwidth selection when estimating ROC curves

Statistics and Probability Letters (2003), 64 181-189. Peter G. Hall1 and Rob J. Hyndman1,2 Centre for Mathematics and its Applications, […]

November 16th, 2002

Mixed model-based hazard estimation

Journal of Computational and Graphical Statistics (2002), 11(4), 784-798. T. Cai1, R.J. Hyndman2 and M.P. Wand3 Department of Biostatistics, University […]

July 16th, 2002

Nonparametric estimation and symmetry tests for conditional density functions

Journal of Nonparametric Statistics (2002), 14(3), 259-278. Rob J Hyndman1 and  Qiwei Yao2 Department of Econometrics and Business Statistics, Monash University, […]

June 16th, 2001

Bandwidth selection for kernel conditional density estimation

Computational Statistics and Data Analysis (2001), 36(3), 279-298. David Bashtannyk and Rob J Hyndman Abstract: We consider bandwidth selection for […]

July 16th, 2000

Pagan and Ullah. Nonparametric econometrics

July 16th, 1998

Smoothing non-Gaussian time series with autoregressive structure

Computational Statistics and Data Analysis (1998), 28, 171-191. Gary Grunwald1 and Rob J Hyndman2 Center for Human Nutrition, Box C225, […]

July 15th, 1998

Simonoff. Smoothing methods in Statistics

December 16th, 1997

Nonparametric autocovariance function estimation

Australian Journal of Statistics (1997), 39, 313-325. Rob J Hyndman1 and Matt Wand2 Department of Econometrics and Business Statistics, Monash […]

July 16th, 1996

Estimating and visualizing conditional densities

Journal of Computational and Graphical Statistics (1996), 5 315-336. Rob J Hyndman1 and David Bashtannyk1 Abstract: We consider the kernel […]

July 15th, 1996

Wand and Jones. Kernel smoothing