We present multifiber echelle radial velocity results for 551 stars in the Sextans dwarf spheroidal galaxy and identify 294 stars as probable Sextans members. The projected velocity dispersion profile of the binned data remains flat to a maximum angular radius of $30^{\prime}$. We introduce a nonparametric technique for estimating the projected velocity dispersion surface, and use this to search for kinematic substructure. Our data do not confirm previous reports of a kinematically distinct stellar population at the Sextans center. Instead we detect a region near the Sextans core radius that is kinematically colder than the overall Sextans sample with 95% confidence.Comment: accepted for publication in ApJ Letters; 4 figures (2 color

Abstract:We consider the non-parametric maximum likelihood estimation in the class of Polya frequency functions of order two, viz. the densities with a concave logarithm. This is a subclass of unimodal densities and fairly rich in general. The NPMLE is shown to be the solution to a convex programming problem in the Euclidean space and an algorithm is devised similar to the iterative convex minorant algorithm by Jongbleod (1999). The estimator achieves Hellinger consistency when the true density is a PFF 2 itself.

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The supremum difference between the cumulative sum diagram, and its greatest convex minorant (GCM), in case of non-parametric isotonic regression is considered. When the regression function is strictly increasing, and the design points are unequally spaced, but approximate a positive density in even a slow rate ("n"-super- - 1/3), then the difference is shown to shrink in a very rapid (close to "n"-super- - 2/3) rate. The result is analogous to the corresponding result in case of a monotone density estimation established by Kiefer and Wolfowitz, but uses entirely different representation. The limit distribution of the GCM as a process on the unit interval is obtained when the design variables are i.i.d. with a positive density. Finally, a pointwise asymptotic normality result is proved for the smooth monotone estimator, obtained by the convolution of a kernel with the classical monotone estimator. Copyright 2006 Board of the Foundation of the Scandinavian Journal of Statistics..

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