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Parametric statistics

About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.


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Proceedings ArticleDOI
07 Jun 2015
TL;DR: A substantial improvement in the representational power of the model is shown, while maintaining the efficiency of a linear shape basis, and it is shown that hand shape variation can be represented using only a small number of basis components.
Abstract: We describe how to learn a compact and efficient model of the surface deformation of human hands. The model is built from a set of noisy depth images of a diverse set of subjects performing different poses with their hands. We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model. The model simultaneously accounts for variation in subject-specific shape and subject-agnostic pose. Specifically, hand shape is parameterized as a linear combination of a mean mesh in a neutral pose with a small number of offset vectors. This mesh is then articulated using standard linear blend skinning (LBS) to generate the control mesh of a subdivision surface. We define an energy that encourages each depth pixel to be explained by our model, and the use of a smooth subdivision surface allows us to optimize for all parameters jointly from a rough initialization. The efficacy of our method is demonstrated using both synthetic and real data, where it is shown that hand shape variation can be represented using only a small number of basis components. We compare with other approaches including PCA and show a substantial improvement in the representational power of our model, while maintaining the efficiency of a linear shape basis.

141 citations

Journal ArticleDOI
TL;DR: The paper shows the improvement that occurs in verification by considering optimisation phases, the appropriateness of using new techniques of feedback, and the influence of optimisation parameters.
Abstract: This paper presents a new method for volumetric verification of machine tools. Beyond the consideration of a particular machine, a general verification methodology is presented based on the type of machine to verify the number and movement of axes and different techniques that can be used. A scheme and kinematic model with the inclusion of the measurement system depending on the kinematics of the machine are presented. The model describes the geometry and kinematics of a real milling machine based on a parametric synthetic data generator, which generates a test with known geometric errors and noise to enable a study of different optimisation techniques and models. Similarly, different errors identification techniques and volumetric verification models are presented and analysed. The paper shows the improvement that occurs in verification by considering optimisation phases, the appropriateness of using new techniques of feedback, and the influence of optimisation parameters. Chebyshev polynomials and its characteristics are presented, as well as a regression function for the new verification model. The new developed technique allows the characterisation of the different errors in the whole workspace of the machine and in less time than direct verification methods.

141 citations

Journal ArticleDOI
TL;DR: In this article, the authors advocate the use of semi-parametric models for distributions, where the mean vector μ and covariance Σ are parametric components and the so-called density generator (function) g is the nonparametric component.
Abstract: The benchmark theory of mathematical finance is the Black-Scholes-Merton theory, based on Brownian motion as the driving noise process for asset prices. Here the distributions of returns of the assets in a portfolio are multivariate normal. The two most obvious limitations here concern symmetry and thin tails, neither being consistent with real data. The most common replacements for the multinormal are parametric—stable, generalized hyperbolic, variance gamma. In this paper we advocate the use of semi-parametric models for distributions, where the mean vector μ and covariance Σ are parametric components and the so-called density generator (function) g is the non-parametric component. We work mainly within the family of elliptically contoured distributions, focusing particularly on normal variance mixtures with self-decomposable mixing distributions. We show how the parametric cases can be treated in a unified, systematic way within the non-parametric framework and obtain the density generators fo...

141 citations

Journal ArticleDOI
TL;DR: The effect of deviating from the normal distribution assumption when considering the power of two many-sample location test procedures: ANOVA (parametric) and Kruskal-Wallis (non-parametric).
Abstract: This paper studies the effect of deviating from the normal distribution assumption when considering the power of two many-sample location test procedures: ANOVA (parametric) and Kruskal-Wallis (non-parametric). Power functions for these tests under various conditions are produced using simulation, where the simulated data are produced using MacGillivray and Cannon's [10] recently suggested g-and-k distribution. This distribution can provide data with selected amounts of skewness and kurtosis by varying two nearly independent parameters.

141 citations

Book ChapterDOI
26 Mar 2011
TL;DR: Using a new version of parametric probabilistic model checking, it is shown how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique.
Abstract: We introduce the problem of Model Repair for Probabilistic Systems as follows Given a probabilistic system M and a probabilistic temporal logic formula φ such that M fails to satisfy φ, the Model Repair problem is to find an M′ that satisfies v and differs from M only in the transition flows of those states in M that are deemed controllable Moreover, the cost associated with modifying M's transition flows to obtain M′ should be minimized Using a new version of parametric probabilistic model checking, we show how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique We demonstrate the practical utility of our approach by applying it to a number of significant case studies, including a DTMC reward model of the Zeroconf protocol for assigning IP addresses, and a CTMC model of the highly publicized Kaminsky DNS cache-poisoning attack

140 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20252
20242
20233,966
20227,822
20211,968
20202,033