1,968 research outputs found
A comparative study of monotone nonparametric kernel estimates
In this paper we present a detailed numerical comparison of three monotone nonparametric kernel regression estimates, which isotonize a nonparametric curve estimator. The first estimate is the classical smoothed isotone estimate of Brunk (1958). The second method has recently been proposed by Hall and Huang (2001) and modifies the weights of a commonly used kernel estimate such that the resulting estimate is monotone. The third estimate was recently proposed by Dette, Neumeyer and Pilz (2003) and combines density and regression estimation techniques to obtain a monotone curve estimate of the inverse of the isotone regression function. The three concepts are briefly reviewed and their finite sample properties are studied by means of a simulation study. Although all estimates are first order asymptotically equivalent (provided that the unknown regression function is isotone) some differences for moderate samples are observed. --isotonic regression,order restricted inference,Nadaraya-Watson estimator,local linear regression,monte carlo simulation
A simple nonparametric estimator of a monotone regression function
In this paper a new method for monotone estimation of a regression function is proposed. The estimator is obtained by the combination of a density and a regression estimate and is appealing to users of conventional smoothing methods as kernel estimators, local polynomials, series estimators or smoothing splines. The main idea of the new approach is to construct a density estimate from the estimated values ˆm(i/N) (i = 1, . . . ,N) of the regression function to use these “data” for the calculation of an estimate of the inverse of the regression function. The final estimate is then obtained by a numerical inversion. Compared to the conventially used techniques for monotone estimation the new method is computationally more efficient, because it does not require constrained optimization techniques for the calculation of the estimate. We prove asymptotic normality of the new estimate and compare the asymptotic properties with the unconstrained estimate. In particular it is shown that for kernel estimates or local polynomials the monotone estimate is first order asymptotically equivalent to the unconstrained estimate. We also illustrate the performance of the new procedure by means of a simulation study. --isotonic regression,order restricted inference,Nadaraya-Watson estimator,local linear regression
A note on nonparametric estimation of the effective dose in quantal bioassay
For the common binary response model we propose a direct method for the nonparametric estimation of the effective dose level ED? (0Binary response model,effective dose level,nonparametric regression,isotonic regression,order restricted inference,local linear regression
A note on the invariant distribution of a quasi-birth-and-death process
The aim of this paper is to give an explicit formula of the invariant
distribution of a quasi-birth-and-death process in terms of the block entries
of the transition probability matrix using a matrix-valued orthogonal
polynomials approach. We will show that the invariant distribution can be
computed using the squared norms of the corresponding matrix-valued orthogonal
polynomials, no matter if they are or not diagonal matrices. We will give an
example where the squared norms are not diagonal matrices, but nevertheless we
can compute its invariant distribution
Solution of a Generalized Stieltjes Problem
We present the exact solution for a set of nonlinear algebraic equations
. These
were encountered by us in a recent study of the low energy spectrum of the
Heisenberg ferromagnetic chain \cite{dhar}. These equations are low
(density) ``degenerations'' of more complicated transcendental equation of
Bethe's Ansatz for a ferromagnet, but are interesting in themselves. They
generalize, through a single parameter, the equations of Stieltjes,
, familiar from Random Matrix theory.
It is shown that the solutions of these set of equations is given by the
zeros of generalized associated Laguerre polynomials. These zeros are
interesting, since they provide one of the few known cases where the location
is along a nontrivial curve in the complex plane that is determined in this
work.
Using a ``Green's function'' and a saddle point technique we determine the
asymptotic distribution of zeros.Comment: 19 pages, 4 figure
D-optimal designs via a cocktail algorithm
A fast new algorithm is proposed for numerical computation of (approximate)
D-optimal designs. This "cocktail algorithm" extends the well-known vertex
direction method (VDM; Fedorov 1972) and the multiplicative algorithm (Silvey,
Titterington and Torsney, 1978), and shares their simplicity and monotonic
convergence properties. Numerical examples show that the cocktail algorithm can
lead to dramatically improved speed, sometimes by orders of magnitude, relative
to either the multiplicative algorithm or the vertex exchange method (a variant
of VDM). Key to the improved speed is a new nearest neighbor exchange strategy,
which acts locally and complements the global effect of the multiplicative
algorithm. Possible extensions to related problems such as nonparametric
maximum likelihood estimation are mentioned.Comment: A number of changes after accounting for the referees' comments
including new examples in Section 4 and more detailed explanations throughou
Anisotropic Radial Layout for Visualizing Centrality and Structure in Graphs
This paper presents a novel method for layout of undirected graphs, where
nodes (vertices) are constrained to lie on a set of nested, simple, closed
curves. Such a layout is useful to simultaneously display the structural
centrality and vertex distance information for graphs in many domains,
including social networks. Closed curves are a more general constraint than the
previously proposed circles, and afford our method more flexibility to preserve
vertex relationships compared to existing radial layout methods. The proposed
approach modifies the multidimensional scaling (MDS) stress to include the
estimation of a vertex depth or centrality field as well as a term that
penalizes discord between structural centrality of vertices and their alignment
with this carefully estimated field. We also propose a visualization strategy
for the proposed layout and demonstrate its effectiveness using three social
network datasets.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Risk estimators for choosing regularization parameters in ill-posed problems - Properties and limitations
This paper discusses the properties of certain risk estimators that recently regained popularity for choosing regularization parameters in ill-posed problems, in particular for sparsity regularization. They apply Stein’s unbiased risk estimator (SURE) to estimate the risk in either the space of the unknown variables or in the data space. We will call the latter PSURE in order to distinguish the two different risk functions. It seems intuitive that SURE is more appropriate for ill-posed problems, since the properties in the data space do not tell much about the quality of the reconstruction. We provide theoretical studies of both approaches for linear Tikhonov regularization in a finite dimensional setting and estimate the quality of the risk estimators, which also leads to asymptotic convergence results as the dimension of the problem tends to infinity. Unlike previous works which studied single realizations of image processing problems with a very low degree of ill-posedness, we are interested in the statistical behaviour of the risk estimators for increasing ill-posedness. Interestingly, our theoretical results indicate that the quality of the SURE risk can deteriorate asymptotically for ill-posed problems, which is confirmed by an extensive numerical study. The latter shows that in many cases the SURE estimator leads to extremely small regularization parameters, which obviously cannot stabilize the reconstruction. Similar but less severe issues with respect to robustness also appear for the PSURE estimator, which in comparison to the rather conservative discrepancy principle leads to the conclusion that regularization parameter choice based on unbiased risk estimation is not a reliable procedure for ill-posed problems. A similar numerical study for sparsity regularization demonstrates that the same issue appears in non-linear variational regularization approaches
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