Contains bibliographical references (p. 9-11).
|Statement||Roger W. Koenker ; Pin Ng ; Stephen Portnoy|
|Series||BEBR faculty working paper -- no. 93-0130, BEBR faculty working paper -- no. 93-0130.|
|Contributions||Ng, Pin, Portnoy, Stephen, University of Illinois at Urbana-Champaign. Bureau of Economic and Business Research|
|The Physical Object|
|Pagination||11, 7 ;|
|Number of Pages||11|
I think this is a great book on smoothing splines that one should treasure like Wahba and Gu. ―Pang Du, Biometrics, December a readable text that focuses on methodology, computation, implementation, software, and application. The book is lavishly illustrated with real examples and incorporates many figures which clearly demonstrate Cited by: For quantile smoothing splines, the problem of computing a family of solutions for various λ is greatly eased by the fact that the problem is a parametric linear program in the parameter λ. is a platform for academics to share research papers. CENTRALCIRCULATION BOOKSTACKS Thepersonchargingthis material:- theHDrary latestDatestamped feeof$foreachlost book. fordUclpUnaryactionand mayresult SEP APR Whenrenewingbyphone,writenewdue datebelow previousduedate.
FacultyWorkingPaper B sCOPY QuantileSmoothingSplines Thelibraryofme MOV,6W1 Universityo\m*te oturbana-Chanf.*^ RogerKoenker DepartmentofEconomics. TY - JOUR. T1 - An algorithm for quantile smoothing splines. AU - Ng, Pin T. PY - /7/1. Y1 - /7/1. N2 - For p = 1 and ∞, Koenker, Ng and Portnoy (Statistical Data Analysis Based on the L1 Norm and Related Methods (North-Holland, New York, ); Biometrika, 81 ()) proposed the τth Lp quantile smoothing spline, ĝτ,Lp, defined to solve min "fidelity" + λ "Lp roughness" g Cited by: Quantile smoothing splines provide nonparametric estimation of conditional quantile functions. Like other nonparametric smoothing techniques, the choice of smoothing parameters considerably affects the performance of quantile smoothing splines. The robust cross-validation (RCV) has beenFile Size: KB. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends.
The goal of this paper is to provide a statistically based definition of employment subcenters for multicentric urban areas. In particular, we examine the shape of the employment density function using quantile smoothing splines as a nonparametric empirical by: Full text of "Quantile smoothing splines" See other formats Faculty Working Paper B s COPY Quantile Smoothing Splines The library of me MOV, 6 W1 University o\ m*te ot urbana-Chanf.*^ Roger Koenker Department of Economics University of Illinois Pin Ng Department of Economics University of Houston Bureau of Economic and Business Research College of Commerce . Quantile smoothing splines provide nonparametric estimation of conditional quantile functions. Like other nonparametric smoothing techniques, the choice of smoothing parameters considerably affects the performance of quantile smoothing splines. The robust cross-validation (RCV) has been commonly used as a tuning criterion in by: The computation of a “quantile regression with smoothing splines” – the terminology is a bit confusing here, especially in view of “quantile smoothing splines” to come below; we thus adopt the term of Bosch et al. – via is then reduced to a quadratic programming by: 1.