16,775 research outputs found
Averaged Singular Integral Estimation as a Bias Reduction Technique
This paper proposes an averaged version of singular integral estimators, whose bias achieves higher rates of convergence under smoothing assumptions. We derive exact bias bounds, without imposing smoothing assumptions, which are a basis for deriving the rates of convergence under differentiability assumptions.Publicad
Optimal Uniform Convergence Rates for Sieve Nonparametric Instrumental Variables Regression
We study the problem of nonparametric regression when the regressor is
endogenous, which is an important nonparametric instrumental variables (NPIV)
regression in econometrics and a difficult ill-posed inverse problem with
unknown operator in statistics. We first establish a general upper bound on the
sup-norm (uniform) convergence rate of a sieve estimator, allowing for
endogenous regressors and weakly dependent data. This result leads to the
optimal sup-norm convergence rates for spline and wavelet least squares
regression estimators under weakly dependent data and heavy-tailed error terms.
This upper bound also yields the sup-norm convergence rates for sieve NPIV
estimators under i.i.d. data: the rates coincide with the known optimal
-norm rates for severely ill-posed problems, and are power of
slower than the optimal -norm rates for mildly ill-posed problems. We then
establish the minimax risk lower bound in sup-norm loss, which coincides with
our upper bounds on sup-norm rates for the spline and wavelet sieve NPIV
estimators. This sup-norm rate optimality provides another justification for
the wide application of sieve NPIV estimators. Useful results on
weakly-dependent random matrices are also provided
Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation
problems based on partial observations. Existing matrix factorization is based
on least squares and aims to yield a low-rank matrix to interpret the
conditional sample means given the observations. However, in many real
applications with skewed and extreme data, least squares cannot explain their
central tendency or tail distributions, yielding undesired estimates. In this
paper, we propose \emph{expectile matrix factorization} by introducing
asymmetric least squares, a key concept in expectile regression analysis, into
the matrix factorization framework. We propose an efficient algorithm to solve
the new problem based on alternating minimization and quadratic programming. We
prove that our algorithm converges to a global optimum and exactly recovers the
true underlying low-rank matrices when noise is zero. For synthetic data with
skewed noise and a real-world dataset containing web service response times,
the proposed scheme achieves lower recovery errors than the existing matrix
factorization method based on least squares in a wide range of settings.Comment: 8 page main text with 5 page supplementary documents, published in
AAAI 201
Numerical experience with a class of algorithms for nonlinear optimization using inexact function and gradient information
For optimization problems associated with engineering design, parameter estimation, image reconstruction, and other optimization/simulation applications, low accuracy function and gradient values are frequently much less expensive to obtain than high accuracy values. Here, researchers investigate the computational performance of trust region methods for nonlinear optimization when high accuracy evaluations are unavailable or prohibitively expensive, and confirm earlier theoretical predictions when the algorithm is convergent even with relative gradient errors of 0.5 or more. The proper choice of the amount of accuracy to use in function and gradient evaluations can result in orders-of-magnitude savings in computational cost
Semi-nonparametric IV estimation of shape-invariant Engel curves
This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identification and estimation of both the nonparametric shapes of the Engel curves and the parametric specification of the demographic scaling parameters. The identification condition relates to the bounded completeness and the estimation procedure applies the sieve minimum distance estimation of conditional moment restrictions, allowing for endogeneity. We establish a new root mean squared convergence rate for the nonparametric instrumental variable regression when the endogenous regressor could have unbounded support. Root-n asymptotic normality and semiparametric efficiency of the parametric components are also given under a set of "low-level" sufficient conditions. Our empirical application using the U.K. Family Expenditure Survey shows the importance of adjusting for endogeneity in terms of both the nonparametric curvatures and the demographic parameters of systems of Engel curves
Long's Vortex Revisited
The conical self-similar vortex solution of Long (1961) is reconsidered, with
a view toward understanding what, if any, relationship exists between Long's
solution and the more-recent similarity solutions of Mayer and Powell (1992),
which are a rotational-flow analogue of the Falkner-Skan boundary-layer flows,
describing a self-similar axisymmetric vortex embedded in an external stream
whose axial velocity varies as a power law in the axial (z) coordinate, with
phi=r/z^n being the radial similarity coordinate and n the core growth rate
parameter. We show that, when certain ostensible differences in the
formulations and radial scalings are properly accounted for, the Long and
Mayer-Powell flows in fact satisfy the same system of coupled ordinary
differential equations, subject to different kinds of outer-boundary
conditions, and with Long's equations a special case corresponding to conical
vortex core growth, n=1 with outer axial velocity field decelerating in a 1/z
fashion, which implies a severe adverse pressure gradient. For pressure
gradients this adverse Mayer and Powell were unable to find any
leading-edge-type vortex flow solutions which satisfy a basic physicality
criterion based on monotonicity of the total-pressure profile of the flow, and
it is shown that Long's solutions also violate this criterion, in an extreme
fashion. Despite their apparent nonphysicality, the fact that Long's solutions
fit into a more general similarity framework means that nonconical analogues of
these flows should exist. The far-field asymptotics of these generalized
solutions are derived and used as the basis for a hybrid spectral-numerical
solution of the generalized similarity equations, which reveal the existence of
solutions for more modestly adverse pressure gradients than those in Long's
case, and which do satisfy the above physicality criterion.Comment: 30 pages, including 16 figure
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