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Resampling

About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.


Papers
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Journal ArticleDOI
TL;DR: It is shown that interpolated signals and their derivatives contain specific detectable periodic properties, and a blind, efficient, and automatic method capable of finding traces of resampling and interpolation is proposed.
Abstract: In this paper, we analyze and analytically describe the specific statistical changes brought into the covariance structure of signal by the interpolation process. We show that interpolated signals and their derivatives contain specific detectable periodic properties. Based on this, we propose a blind, efficient, and automatic method capable of finding traces of resampling and interpolation. The proposed method can be very useful in many areas, especially in image security and authentication. For instance, when two or more images are spliced together, to create high quality and consistent image forgeries, almost always geometric transformations, such as scaling, rotation, or skewing are needed. These procedures are typically based on a resampling and interpolation step. By having a method capable of detecting the traces of resampling, we can significantly reduce the successful usage of such forgeries. Among other points, the presented method is also very useful in estimation of the geometric transformations factors.

304 citations

Journal ArticleDOI
TL;DR: In this article, a general and simple resampling method for inferences about a finite-dimensi onal parameter vector based on pivotal estimating functions is proposed, which can be easily and efficiently implemented with existing statistical software.
Abstract: SUMMARY Suppose that, under a semiparametric model setting, one is interested in drawing inferences about a finite-dimensi onal parameter vector /? based on an estimating function. Generally a consistent point estimator /J for /?0, the true value for /J, can be easily obtained by finding a root of the corresponding estimating equation. To estimate the variance of ft, however, may involve complicated and subjective nonparametric functional estimates. In this paper, a general and simple resampling method for inferences about jS0 based on pivotal estimating functions is proposed. The new procedure is illustrated with the quantile and rank regression models. For both cases, our proposal can be easily and efficiently implemented with existing statistical software.

303 citations

Book
01 Dec 2010
TL;DR: The capital asset pricing model, factor models and principal components, and nonparametric regression and splines are presented.
Abstract: Introduction.- Returns.- Fixed income securities.- Exploratory data analysis.- Modeling univariate distributions.- Resampling.- Multivariate statistical models.- Copulas.- Time series models: basics.- Time series models: further topics.- Portfolio theory.- Regression: basics.- Regression: troubleshooting.- Regression: advanced topics.- Cointegration.- The capital asset pricing model.- Factor models and principal components.- GARCH models.- Risk management.- Bayesian data analysis and MCMC.- Nonparametric regression and splines.

299 citations

Journal ArticleDOI
TL;DR: In this article, a Bayesian decision theoretic framework was proposed for optimal portfolio selection using a skew normal distribution, which has many attractive features for modeling multivariate returns. But, it is important to incorporate higher order moments in portfolio selection, which leads to higher expected utility than the traditional Markowitz approach.
Abstract: We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the traditional Markowitz approach: the ability to handle higher moments and parameter uncertainty. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portfolio selection. Further, our comparison to other methods where parameter uncertainty is either ignored or accommodated in an ad hoc way, shows that our approach leads to higher expected utility than competing methods, such as the resampling methods that are common in the practice of finance.

296 citations

Journal ArticleDOI
TL;DR: The problems of replication stability, model complexity, selection bias and an overoptimistic estimate of the predictive value of a model are discussed together with several proposals based on resampling methods, which favour greater simplicity of the final regression model.
Abstract: Summary. The number of variables in a regression model is often too large and a more parsimonious model may be preferred. Selection strategies (e.g. all-subset selection with various penalties for model complexity, or stepwise procedures) are widely used, but there are few analytical results about their properties. The problems of replication stability, model complexity, selection bias and an overoptimistic estimate of the predictive value of a model are discussed together with several proposals based on resampling methods. The methods are applied to data from a case-control study on atopic dermatitis and a clinical trial to compare two chemotherapy regimes by using a logistic regression and a Cox model. A recent proposal to use shrinkage factors to reduce the bias of parameter estimates caused by model building is extended to parameterwise shrinkage factors and is discussed as a further possibility to illustrate problems of models which are too complex. The results from the resampling approaches favour greater simplicity of the final regression model.

294 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
20242
2023377
2022759
2021275
2020279