This article describes asciker and bsciker, two programs that enrich the possibility for density analysis using Stata. asciker and bsciker compute asymptotic and bootstrap confidence intervals for kernel density estimation, respectively, based on the theory of kernel density confidence intervals estimation developed in Hall (1992b)and Horowitz (2001). asciker and bsciker allow several options and are compatible with Stata 7 and Stata 8, using the appropriate graphics engine under both versions.
DavidsonR., and MacKinnonJ.G.2003. Econometric Theory and Methods. NewYork: Oxford University Press.
2.
HallP.1992a. The Bootstrap and Edgeworth Expansion.New York: Springer.
3.
HallP.1992b. Effect of bias estimation on coverage accuracy of bootstrap confidence intervals for a probability density. Annals of Statistics20: 675–694.
4.
HärdleW.1991. Smoothing Techniques. With implementation inS. New York: Springer.
5.
HorowitzJ. L.2001. The bootstrap. In Handbook of Econometrics, ed. HeckmanJ. J., and LeamerE., vol. 5, 3159–3228. Amsterdam: North-Holland.
6.
Salgado-UgarteI. H., and Pérez-HernándezM. A.2003. Exploring the use of variable bandwidth kernel density estimators. Stata Journal3(2): 133–147.
7.
Salgado-UgarteI. H., ShimizuM., and TaniuchiT.1993. snp6: Exploring the shape of univariate data using kernel density estimators. Stata Technical Bulletin16: 8–19. In Stata Technical Bulletin Reprints, vol. 3, 155–173. College Station, TX: Stata Press.
8.
Salgado-UgarteI. H.1995a. snp6.1: ASH, WARPing, and kernel density estimation for univariate data. Stata Technical Bulletin26: 23–31. In Stata Technical Bulletin Reprints, vol. 5, 161–172. College Station, TX: Stata Press.
9.
Salgado-UgarteI. H.1995b. snp6.2: Practical rules for bandwidth selection in univariate density estimation. Stata Technical Bulletin27: 5–19. In Stata Technical Bulletin Reprints, vol. 5, 172–190. College Station, TX: Stata Press.
10.
ScottD. W.1992. Multivariate Density Estimation.New York: John Wiley & Sons.
11.
SilvermanB. W.1986. Density Estimation for Statistics and Data Analysis.London: Chapman & Hall.
12.
Van KermP.2003. Adaptive kernel density estimation. Stata Journal3(2): 148–156.