11,745 research outputs found
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration
In this paper, we propose a new framework to remove parts of the systematic
errors affecting popular restoration algorithms, with a special focus for image
processing tasks. Generalizing ideas that emerged for regularization,
we develop an approach re-fitting the results of standard methods towards the
input data. Total variation regularizations and non-local means are special
cases of interest. We identify important covariant information that should be
preserved by the re-fitting method, and emphasize the importance of preserving
the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we
provide an approach that has a "twicing" flavor and allows re-fitting the
restored signal by adding back a local affine transformation of the residual
term. We illustrate the benefits of our method on numerical simulations for
image restoration tasks
Appraisal Using Generalized Additive Models
Many of the results from real estate empirical studies depend upon using a correct functional form for their validity. Unfortunately, common parametric statistical tools cannot easily control for the possibility of misspecification. Recently, semiparametric estimators such as generalized additive models (GAMs) have arisen which can automatically control for additive (in price) or multiplicative (in ln(price)) nonlinear relations among the independent and dependent variables. As the paper shows, GAMs can empirically outperform naive parametric and polynomial models in ex-sample predictive behavior. Moreover, GAMs have well-developed statistical properties and can suggest useful transformations in parametric settings.
Convex and non-convex regularization methods for spatial point processes intensity estimation
This paper deals with feature selection procedures for spatial point
processes intensity estimation. We consider regularized versions of estimating
equations based on Campbell theorem derived from two classical functions:
Poisson likelihood and logistic regression likelihood. We provide general
conditions on the spatial point processes and on penalty functions which ensure
consistency, sparsity and asymptotic normality. We discuss the numerical
implementation and assess finite sample properties in a simulation study.
Finally, an application to tropical forestry datasets illustrates the use of
the proposed methods
Local Adaptive Grouped Regularization and its Oracle Properties for Varying Coefficient Regression
Varying coefficient regression is a flexible technique for modeling data
where the coefficients are functions of some effect-modifying parameter, often
time or location in a certain domain. While there are a number of methods for
variable selection in a varying coefficient regression model, the existing
methods are mostly for global selection, which includes or excludes each
covariate over the entire domain. Presented here is a new local adaptive
grouped regularization (LAGR) method for local variable selection in spatially
varying coefficient linear and generalized linear regression. LAGR selects the
covariates that are associated with the response at any point in space, and
simultaneously estimates the coefficients of those covariates by tailoring the
adaptive group Lasso toward a local regression model with locally linear
coefficient estimates. Oracle properties of the proposed method are established
under local linear regression and local generalized linear regression. The
finite sample properties of LAGR are assessed in a simulation study and for
illustration, the Boston housing price data set is analyzed.Comment: 30 pages, one technical appendix, two figure
Efficient estimation of Banach parameters in semiparametric models
Consider a semiparametric model with a Euclidean parameter and an
infinite-dimensional parameter, to be called a Banach parameter. Assume: (a)
There exists an efficient estimator of the Euclidean parameter. (b) When the
value of the Euclidean parameter is known, there exists an estimator of the
Banach parameter, which depends on this value and is efficient within this
restricted model. Substituting the efficient estimator of the Euclidean
parameter for the value of this parameter in the estimator of the Banach
parameter, one obtains an efficient estimator of the Banach parameter for the
full semiparametric model with the Euclidean parameter unknown. This hereditary
property of efficiency completes estimation in semiparametric models in which
the Euclidean parameter has been estimated efficiently. Typically, estimation
of both the Euclidean and the Banach parameter is necessary in order to
describe the random phenomenon under study to a sufficient extent. Since
efficient estimators are asymptotically linear, the above substitution method
is a particular case of substituting asymptotically linear estimators of a
Euclidean parameter into estimators that are asymptotically linear themselves
and that depend on this Euclidean parameter. This more general substitution
case is studied for its own sake as well, and a hereditary property for
asymptotic linearity is proved.Comment: Published at http://dx.doi.org/10.1214/009053604000000913 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Inference in Additively Separable Models With a High-Dimensional Set of Conditioning Variables
This paper studies nonparametric series estimation and inference for the
effect of a single variable of interest x on an outcome y in the presence of
potentially high-dimensional conditioning variables z. The context is an
additively separable model E[y|x, z] = g0(x) + h0(z). The model is
high-dimensional in the sense that the series of approximating functions for
h0(z) can have more terms than the sample size, thereby allowing z to have
potentially very many measured characteristics. The model is required to be
approximately sparse: h0(z) can be approximated using only a small subset of
series terms whose identities are unknown. This paper proposes an estimation
and inference method for g0(x) called Post-Nonparametric Double Selection which
is a generalization of Post-Double Selection. Standard rates of convergence and
asymptotic normality for the estimator are shown to hold uniformly over a large
class of sparse data generating processes. A simulation study illustrates
finite sample estimation properties of the proposed estimator and coverage
properties of the corresponding confidence intervals. Finally, an empirical
application to college admissions policy demonstrates the practical
implementation of the proposed method
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