1,180 research outputs found
Consistency of cross validation for comparing regression procedures
Theoretical developments on cross validation (CV) have mainly focused on
selecting one among a list of finite-dimensional models (e.g., subset or order
selection in linear regression) or selecting a smoothing parameter (e.g.,
bandwidth for kernel smoothing). However, little is known about consistency of
cross validation when applied to compare between parametric and nonparametric
methods or within nonparametric methods. We show that under some conditions,
with an appropriate choice of data splitting ratio, cross validation is
consistent in the sense of selecting the better procedure with probability
approaching 1. Our results reveal interesting behavior of cross validation.
When comparing two models (procedures) converging at the same nonparametric
rate, in contrast to the parametric case, it turns out that the proportion of
data used for evaluation in CV does not need to be dominating in size.
Furthermore, it can even be of a smaller order than the proportion for
estimation while not affecting the consistency property.Comment: Published in at http://dx.doi.org/10.1214/009053607000000514 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Maximum L-likelihood estimation
In this paper, the maximum L-likelihood estimator (MLE), a new
parameter estimator based on nonextensive entropy [Kibernetika 3 (1967) 30--35]
is introduced. The properties of the MLE are studied via asymptotic analysis
and computer simulations. The behavior of the MLE is characterized by the
degree of distortion applied to the assumed model. When is properly
chosen for small and moderate sample sizes, the MLE can successfully trade
bias for precision, resulting in a substantial reduction of the mean squared
error. When the sample size is large and tends to 1, a necessary and
sufficient condition to ensure a proper asymptotic normality and efficiency of
MLE is established.Comment: Published in at http://dx.doi.org/10.1214/09-AOS687 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Sparsity Oriented Importance Learning for High-dimensional Linear Regression
With now well-recognized non-negligible model selection uncertainty, data
analysts should no longer be satisfied with the output of a single final model
from a model selection process, regardless of its sophistication. To improve
reliability and reproducibility in model choice, one constructive approach is
to make good use of a sound variable importance measure. Although interesting
importance measures are available and increasingly used in data analysis,
little theoretical justification has been done. In this paper, we propose a new
variable importance measure, sparsity oriented importance learning (SOIL), for
high-dimensional regression from a sparse linear modeling perspective by taking
into account the variable selection uncertainty via the use of a sensible model
weighting. The SOIL method is theoretically shown to have the
inclusion/exclusion property: When the model weights are properly around the
true model, the SOIL importance can well separate the variables in the true
model from the rest. In particular, even if the signal is weak, SOIL rarely
gives variables not in the true model significantly higher important values
than those in the true model. Extensive simulations in several illustrative
settings and real data examples with guided simulations show desirable
properties of the SOIL importance in contrast to other importance measures
Forecast Combination Under Heavy-Tailed Errors
Forecast combination has been proven to be a very important technique to
obtain accurate predictions. In many applications, forecast errors exhibit
heavy tail behaviors for various reasons. Unfortunately, to our knowledge,
little has been done to deal with forecast combination for such situations. The
familiar forecast combination methods such as simple average, least squares
regression, or those based on variance-covariance of the forecasts, may perform
very poorly. In this paper, we propose two nonparametric forecast combination
methods to address the problem. One is specially proposed for the situations
that the forecast errors are strongly believed to have heavy tails that can be
modeled by a scaled Student's t-distribution; the other is designed for
relatively more general situations when there is a lack of strong or consistent
evidence on the tail behaviors of the forecast errors due to shortage of data
and/or evolving data generating process. Adaptive risk bounds of both methods
are developed. Simulations and a real example show superior performance of the
new methods
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