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271.
272.
Grover G. Huebner 《Journal of the American Statistical Association》2013,108(86):516-518
273.
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275.
Jan G. De Gooijer 《Journal of applied statistics》2007,34(4):371-381
We compare and investigate Neyman's smooth test, its components, and the Kolmogorov-Smirnov (KS) goodness-of-fit test for testing the uniformity of multivariate forecast densities. Simulations indicate that the KS test lacks power when the forecast distributions are misspecified, especially for correlated sequences of random variables. Neyman's smooth test and its components work well in samples of size typically available, although there sometimes are size distortions. The components provide directed diagnosis regarding the kind of departure from the null. For illustration, the tests are applied to forecast densities obtained from a bivariate threshold model fitted to high-frequency financial data. 相似文献
276.
James P. McDermott G. Jogesh Babu John C. Liechty Dennis K. J. Lin 《Statistics and Computing》2007,17(4):311-321
We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In
this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic.
We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve
that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed
for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation.
For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation
study to perform well and to have several distinct advantages over existing methods. 相似文献
277.
For semiparametric models, interval estimation and hypothesis testing based on the information matrix for the full model is a challenge because of potentially unlimited dimension. Use of the profile information matrix for a small set of parameters of interest is an appealing alternative. Existing approaches for the estimation of the profile information matrix are either subject to the curse of dimensionality, or are ad-hoc and approximate and can be unstable and numerically inefficient. We propose a numerically stable and efficient algorithm that delivers an exact observed profile information matrix for regression coefficients for the class of Nonlinear Transformation Models [A. Tsodikov (2003) J R Statist Soc Ser B 65:759-774]. The algorithm deals with the curse of dimensionality and requires neither large matrix inverses nor explicit expressions for the profile surface. 相似文献
278.
The article presents the results of a survey on statistical consulting at German universities, where
the survey focused on obtaining information on when, where and to whom statistical consulting is provided.
We investigate the financial frame of the activity and question the advantages and disadvantages from a consultant’s
point of view. 相似文献
279.
The paper and the special issue focus on the activity of statistical consulting and its varieties.
This includes academic consulting, consulting to and in industry as well as statistics in public media. 相似文献
280.
To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs. 相似文献