Douglas Nychka

Voice (303) 497-1711, FAX (303) 497-2483 Cell (303) 725-3199    Email nychka "at" ucar "dot" edu
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I am the Director of the Institute for Mathematics Applied to Geosciences (November, 2004 - present) (IMAGe) and also a Senior Scientist in Geophysical Statistics Project (GSP). My main task is to enrich the scientific and educational activity at NCAR through mathematical methods and models. Also, I use the large scientific projects at NCAR to engage the mathematical science communities in new applications and to motivate new mathematics. Before directing IMAGe I was the GSP project leader (August,1997 - June, 2006)

Some current personal research interests are nonparametric regression (mostly splines), statistical computing, spatial statistics and spatial designs. I was a Math/Physics undergrad at Duke, with Robert Wolpert as my advisor, and received my Ph D ('83) from the University of Wisconsin under Grace Wahba. Although my original thesis work was on splines and inverse problems, over time I have become more interested in spatial statistics and Bayesian methods for curve and surface fitting. This change of course came about by the chance to work at the National Institute of Statistical Sciences (1994-1996) on an EPA grant. During this time I wrote the first version of the Krig function (at about 11PM the night before an EPA presentation) in the S language. Krig was first part of the funfits package, coauthored with Perry Haaland and Barb Bailey and it is now in the fields package -- completely different code but still with the same name. I guess this is like my career!

I came to GSP/NCAR in 1997 after spending 14 enjoyable years as a faculty member in the Statistics Department at North Carolina State University. This was supposed to be an interesting leave of absence for just two years ... Well, people will not dispute that time moves differently in Boulder. My most recent research interests are spatial statistics for large data sets with Steve Sain, Tia Lerud and Dorit Hammerling and an application of hierarchical models to the reconstruction of past climate with Bo Li, Martin Tingley and Suz Tolwinski.

My wife Helen Nychka is a speech language pathologist serving autistic children. We have three kids: Allie (22), Everett (19) and Grace (16). Some interests outside work are skiing the trees at Mary Jane, trail running -- and remodeling bathrooms (the one who dies with the most tools wins)! Everett recommends that you check out New Schoolers for the latest in what can be done with skis.

Come visit us at the Mesa Lab

Short course on Statistics and Regional Climate

Institute of Mathematical Sciences, National University of Singapore, Feb 28 - March 4 2011

NARCCAP Companion

Useful datasets and source for interpreting the NARCCAP model output..

Daily Surface Ozone Eastern US 1995-1999

An example of a nonstationary spatial process and spatial extremes .

A case study dataset on Colorado climate:

Regional climate model and observed precipitation data for the Colorado Front Range.

R packages

LatticeKrig is a spatial method for large data sets that builds on compactly supported basis functions, Markov random fields and sparse matrix methods.

Downloading LatticeKrig:

fields (home page) is a collection of programs based in R for curve and function fitting with an emphasis on spatial data and flexible covariance functions for Kriging.

Downloading Fields:

Short course CD A directory with the lectures, source code, R binaries and R packages used in the ENAR short course, MAR 2009.

spam is a collection of functions based in R/Fortran for sparse matrix algebra. Written by Reinhard Furrer with the attention to detail that have made the Swiss famous! The fields pacakage uses these functions for spatial analysis of large datasets. Current CRAN version: Version 0.23 SEP-2010.

Major fields functions:
Tps: Thin Plate spline regression
Krig: Spatial process estimate (Kriging)
This function allows you to supply a covariance function as R code, uses sparse matrix methods from the spam package and can handle large data sets.
mKrig (micro Krig ) and fastTps
Fast spatial prediction that can take advantage of compactly supported covariance functions and handle big data sets
cover.design: Finds a space filling design
as.image, image.plot, quilt.plot, crop.image, average.image, designer.colors: Some useful functions for working with image data on 2-d grids and color scales
sreg, qsreg : 1-d smoothing splines and 1-d quantile splines

There are also generic functions that support these methods such as:
plot diagnostic plots of fit
summary statistical summary of fit
surface graphical display of fitted surface
predict, predict.se evaluation fit and prediction error at arbitrary points

nnreg

Neural Networks Package. Estimates a function using a single hidden layer neural network by nonlinear least squares. The fitting algorithm is both robust and accurate. Has supporting functions for diagnostics, GCV and graphing.
Doug Nychka (contact), Stephen Ellner and Barbara Bailey nnreg_1.1.tar.gz (59K)

lenns

Lyapunov Exponents fit by Neural Networks
Fits nonlinear autoregressive maps to multivariate time series data and estimates global and local Lyapunov Exponents. Doug Nychka (contact), Stephen Ellner and Barbara Bailey lenns_1.0.tar.gz (25K)