A class of copulas with piecewise linear horizontal sections

We introduce the class of bivariate copulas with piecewise linear horizontal sections whose graph is composed, at most, of two segments. It is a wide class of copulas which contains some known copulas. We study several properties of the copulas in the new class concerning absolute continuity, singular components, measures of association, concordance ordering, dependence concepts and symmetry. Finally, we provide several examples.     

Principal components analysis of nonstationary time series data

The effect of nonstationarity in time series columns of input data in principal components analysis is examined. Nonstationarity are very common among economic indicators collected over time. They are subsequently summarized into fewer indices for purposes of monitoring. Due to the simultaneous drifting of the nonstationary time series usually caused by the trend, the first component averages all the variables without necessarily reducing dimensionality. Sparse principal components analysis can be used, but attainment of sparsity among the loadings (hence, dimension-reduction is achieved) is influenced by the choice of parameter(s) (λ _1,i ). Simulated data with more variables than the number of observations and with different patterns of cross-correlations and autocorrelations were used to illustrate the advantages of sparse principal components analysis over ordinary principal components analysis. Sparse component loadings for nonstationary time series data can be achieved provided that appropriate values of λ _1,j are used. We provide the range of values of λ _1,j that will ensure convergence of the sparse principal components algorithm and consequently achieve sparsity of component loadings.     

Parameter identification for singular random fields arising in Burgers’ turbulence

The paper reports on a study of classical statistical inference problems for long-memory random fields arising as solutions of the nonlinear diffusion equation with random initial data (the Burgers’ turbulence problem).     

Singular gauss-markov models

For the general linear model Y = X$sZ + e in which e has a singular dispersion matrix $sG²A, $sG > 0, where A is n x n and singular, Mitra [2] considers the problem of testing F$sZ, where F is a known q x q matrix and claims that the sum of squares (SS) due to hypothesis is not distributed (as a x² variate with degrees of freedom (d. f.) equal to the rank of F) independent of the SS due to error, when a generalized inverse of A is chosen as (A + X'X)^–. This claim does not hold if a pseudo-inverse of A is taken to be (A + X'X)⁺ where A⁺ denotes the unique Moore-Penrose inverse (MPI) of A.     

Randomized singular spectrum analysis for long time series

ABSTRACT

Singular spectrum analysis (SSA) is a relatively new method for time series analysis and comes as a non-parametric alternative to the classical methods. This methodology has proven to be effective in analysing non-stationary and complex time series since it is a non-parametric method and do not require the classical assumptions over the stationarity or over the normality of the residuals. Although SSA have proved to provide advantages over traditional methods, the challenges that arise when long time series are considered, make the standard SSA very demanding computationally and often not suitable. In this paper we propose the randomized SSA which is an alternative to SSA for long time series without losing the quality of the analysis. The SSA and the randomized SSA are compared in terms of quality of the model fit and forecasting, and computational time. This is done by using Monte Carlo simulations and real data about the daily prices of five of the major world commodities.     

具有Riemann函数核的奇积分方程解的讨论

本文借助于偏微分方程的Riemann方法,讨论一类具有Riemann函数核的奇异积分方程的解的存在性。     

Simultaneous Canonization of Linear Models

Canonical form plays a similar role in linear models to spectral decomposition in matrix analysis. Let X = (X ₁,…, X _n )′ be a random vector with expectation Aβ and the variance–covariance matrix σV, where V is positive definite and let rank(A) = r. Then there exists a nonsingular linear transformation from X to T = (T ₁,…, T _n )′, such that ET _i  = η _i , for i = 1,…, r and zero for i > r, while cov(T _i , T _j ) = δ _ij σ. This canonical form, introduced by Ko?odziejczyk (1935 Ko?odziejczyk , S. ( 1935 ). On an important class of statistical hypotheses . Biometrika 27 : 161 – 190 .[Crossref] , [Google Scholar]), was used, among others, by Scheffé (1959 Scheffé , H. ( 1959 ). Analysis of Variance . New York : Wiley . [Google Scholar]) and by Lehmann (1959, 1986 Lehmann , E. L. (1959, 1986 ). Testing Statistical Hypotheses . New York : Wiley . [Google Scholar]). This technique is extended here for arbitrary (possibly singular) V and for simultaneous canonization of two models of this type.     

Estimation in Singular Linear Models with Stochastic Linear Restrictions and Linear Equality Restrictions

This article is concerned with the parameter estimation in a singular linear regression model with stochastic linear restrictions and linear equality restrictions simultaneously. A new estimator is introduced and it is proved that the proposed estimator is superior to the least squares estimator and singular mixed estimator in the mean squared error sense under certain conditions.     

A Tale of Two Matrix Factorizations

In statistical practice, rectangular tables of numeric data are commonplace, and are often analyzed using dimension-reduction methods like the singular value decomposition and its close cousin, principal component analysis (PCA). This analysis produces score and loading matrices representing the rows and the columns of the original table and these matrices may be used for both prediction purposes and to gain structural understanding of the data. In some tables, the data entries are necessarily nonnegative (apart, perhaps, from some small random noise), and so the matrix factors meant to represent them should arguably also contain only nonnegative elements. This thinking, and the desire for parsimony, underlies such techniques as rotating factors in a search for “simple structure.” These attempts to transform score or loading matrices of mixed sign into nonnegative, parsimonious forms are, however, indirect and at best imperfect. The recent development of nonnegative matrix factorization, or NMF, is an attractive alternative. Rather than attempt to transform a loading or score matrix of mixed signs into one with only nonnegative elements, it directly seeks matrix factors containing only nonnegative elements. The resulting factorization often leads to substantial improvements in interpretability of the factors. We illustrate this potential by synthetic examples and a real dataset. The question of exactly when NMF is effective is not fully resolved, but some indicators of its domain of success are given. It is pointed out that the NMF factors can be used in much the same way as those coming from PCA for such tasks as ordination, clustering, and prediction. Supplementary materials for this article are available online.