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1.
Nuttanan Wichitaksorn 《统计学通讯:理论与方法》2020,49(8):1801-1817
AbstractThis article proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two covariance matrices allow the extension of MtAR to a structural MtAR analysis. The proposed MtAR can also incorporate different lag orders across VAR systems that provide more flexibility to the model. The estimation results from a simulation study and an empirical study on macroeconomic application show favorable performance of our proposed models and method. 相似文献
3.
OLIVIER CAPPÉ RANDAL DOUC ERIC MOULINES & CHRISTIAN ROBERT 《Scandinavian Journal of Statistics》2002,29(4):615-635
While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker. 相似文献
4.
Philippe Huber Elvezio Ronchetti Maria-Pia Victoria-Feser 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2004,66(4):893-908
Summary. Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology. 相似文献
5.
Sets of relatively short time series arise in many situations. One aspect of their analysis may be the detection of outlying
series. We examine the performance of standard normal outlier tests applied to the means, or to simple functions of the means,
of AR(1) series, not necessarily of equal lengths. Although unequal lengths of series implies that the means have unequal
variances, that are only known approximately, it is shown that nominal significance levels hold good under most circumstances.
Thus a standard outlier test can usefully be applied, avoiding the complication of estimating the time series' parameters.
The test's power is affected by unequal lengths, being higher when the slippage occurs in one of the longer series 相似文献
6.
Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution 总被引:1,自引:0,他引:1
Adelchi Azzalini Antonella Capitanio 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2003,65(2):367-389
Summary . A fairly general procedure is studied to perturb a multivariate density satisfying a weak form of multivariate symmetry, and to generate a whole set of non-symmetric densities. The approach is sufficiently general to encompass some recent proposals in the literature, variously related to the skew normal distribution. The special case of skew elliptical densities is examined in detail, establishing connections with existing similar work. The final part of the paper specializes further to a form of multivariate skew t -density. Likelihood inference for this distribution is examined, and it is illustrated with numerical examples. 相似文献
7.
Summary:
The H–family of distributions or H–distributions, introduced by Tukey (1960; 1977), are
generated by a single transformation of the standard normal distribution and allow for leptokurtosis
represented by the parameter h. Alternatively, Haynes et al. (1997) generated leptokurtic distributions
by applying the K–transformation to the normal distribution. In this study we propose a third transformation,
the so–called J–transformation, and derive some properties of this transformation. Moreover,
so-called elongation generating functions (EGFs) are introduced. By means of EGFs we are able to
visualize the strength of tail elongation and to construct new transformations. Finally, we compare the
three transformations towards their goodness–of–fit in the context of financial return data. 相似文献
8.
The robustness of Mauchly's sphericity test criterion when sampling from a mixture of two multivariate normal distributions is studied. The distribution of the sphericity test criterion when the sample covariance matrix has a non-central Wishart density of rank one is derived in terms of Meijer's G-functions; its distribution under the mixture model is then deduced. The robustness is studied by computing actual significance levels of the test under the mixture model using the critical values under the usual normal model. 相似文献
9.
Stuart Barber Guy P. Nason 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2004,66(4):927-939
Summary. Wavelet shrinkage is an effective nonparametric regression technique, especially when the underlying curve has irregular features such as spikes or discontinuities. The basic idea is simple: take the discrete wavelet transform of data consisting of a signal corrupted by noise; shrink or remove the wavelet coefficients to remove the noise; then invert the discrete wavelet transform to form an estimate of the true underlying curve. Various researchers have proposed increasingly sophisticated methods of doing this by using real-valued wavelets. Complex-valued wavelets exist but are rarely used. We propose two new complex-valued wavelet shrinkage techniques: one based on multiwavelet style shrinkage and the other using Bayesian methods. Extensive simulations show that our methods almost always give significantly more accurate estimates than methods based on real-valued wavelets. Further, our multiwavelet style shrinkage method is both simpler and dramatically faster than its competitors. To understand the excellent performance of this method we present a new risk bound on its hard thresholded coefficients. 相似文献
10.