波德莱尔应和论与傅立叶宇宙论

波德莱尔与傅立叶之关系问题,国内几乎无人论及。傅立叶宇宙论是波德莱尔"应和"论的哲学、美学来源之一。准确地说,波德莱尔的"应和"所对应的就是傅立叶的"情欲引力"和"普遍类比原则"(相似性)。由于傅立叶宇宙论是其历史学和政治经济学("空想社会主义")的基础,所以也被马克思恩格斯所关注和吸收。     

Matrix density estimation

The accuracy of orthogonal series types of density estimators can be conveniently measured in terms of their Mean Integrated Squared Error, or MISE. Further reduction In MISE is achieved by introducing certain weighting factors into the estimators. In this paper we consider optimal weighting matrices, and the result is a new class of density estimators, the collection of matrix density estimators.     

The use of Fourier series in the evaluation of probability distribution functions

In statistics, Fourier series have been used extensively in such areas as time series and stochastic processes. These series; however, to a large degree have been neglected with regard to their use in statistical distribution theory. This omission appears quite striking when one considers that, after the elementary functions, the trigonometric functions are the most important functions in applied mathematics. In this paper a procedure is developed for utilizing Fourier series to represent distribution functions of finite range random variables as Fourier series with coefficients easily expressible (using Chebyshev polynomials) In terms of the moments of the distribution. This method allows the evaluation of probabilities for a wide class of distributions. It is applied to the     

Tables of the cumulative non-central chi-square distribution Part 51

Some bounds and approximations for the mean of second largest order sta¬tistic from an arbitrary continuous distribution symmetric about zero are obtained. For the standard normal case, FOURIER coefficients involved in the approximation procedure are tabulated and the bounds obtained are compared with other similar methods     

Information theoretic results for circular distributions

The broad class of generalized von Mises (GvM) circular distributions has certain optimal properties with respect to information theoretic quantities. It is shown that, under constraints on the trigonometric moments, and using the Kullback–Leibler information as the measure, the closest circular distribution to any other is of the GvM form. The lower bounds for the Kullback–Leibler information in this situation are also provided. The same problem is also considered using a modified version of the Kullback–Leibler information. Finally, series expansions are given for the entropy and the normalizing constants of the GvM distribution.     

A new nonparametric procedure designed for simulation studies

This paper demonstrates how certain statistics, computed from a sample of size n (from almost any distribution) may be simulated by using a sequence of substantially less than n random normal variates. Many statistics, θ, including almost all maximum likelihood estimates, can be expressed in terms of the sample trigonometric moments, STM. The STM are asymptotically multivariate normal with a mean vector and variance-covariance matrix easily expressible in terms of equally spaced characteristic function evaluations. Thus one only needs to know the Fourier transform or equivalently the characteristic function associated with elements of any moderate to large i. i. d. sample and have access to a normal random number generator to generate a sequence of STM with distributional properties almost identical to those of STM computed from that sample. These STM can in turn be used to compute the desired statistic θ.     

A new nonlinear unit root test with Fourier function

Abstract

Traditional unit root tests display a tendency to be nonstationary in the case of structural breaks and nonlinearity. To eliminate this problem this paper proposes a new flexible Fourier form nonlinear unit root test. This test eliminates this problem to add structural breaks and nonlinearity together to the test procedure. In this test procedure, structural breaks are modeled by means of a Fourier function and nonlinear adjustment is modeled by means of an exponential smooth threshold autoregressive (ESTAR) model. The simulation results indicate that the proposed unit root test is more powerful than the Kruse and KSS tests.     

Exact factorization of the spectral density ann its application to wrf,castiilg and time series analysis

Let f be the spectral density function of a purely nondeterministic stationary stochastic process and be the optimal (canonical) fator of f. The role of the coefficients c_n and d_n (n ≥ 0) of φ and φ^?1 respectivey, in prediction, filtering and control theory is well-knwn. We show that the c_n's and d_n's can be obtained recursively in terms of the Fourier coefficients of log f. Also, recursive and updating formulae fr the kolmogorovwiener predictor similar to those Box-Jenkins are provided..