Limited Deviation Estimators of the Poisson Parameter.
Limiting behavior of the perturbed empirical distribution functions evaluated at -statistics for strongly mixing sequences of random variables.
Limiting properties of the k-th record values
Limiting spectral distribution of circulant type matrices with dependent inputs.
Limiting spectral distribution of XX' matrices
The methods to establish the limiting spectral distribution (LSD) of large dimensional random matrices includes the well-known moment method which invokes the trace formula. Its success has been demonstrated in several types of matrices such as the Wigner matrix and the sample covariance matrix. In a recent article Bryc, Dembo and Jiang [Ann. Probab.34 (2006) 1–38] establish the LSD for random Toeplitz and Hankel matrices using the moment method. They perform the necessary counting of terms in the...
Limits of Bayesian decision related quantities of binomial asset price models
We study Bayesian decision making based on observations () of the discrete-time price dynamics of a financial asset, when the hypothesis a special -period binomial model and the alternative is a different -period binomial model. As the observation gaps tend to zero (i. e. ), we obtain the limits of the corresponding Bayes risk as well as of the related Hellinger integrals and power divergences. Furthermore, we also give an example for the “non-commutativity” between Bayesian statistical and...
L'implication statistique entre variables modales
L'implication statistique, selon R.Gras, permet d'associer à un ensemble de variables binaires ou fréquentielles un préordre représentable par un graphe non symétrique et par une hiérarchie ascendante. L'analyse d'un questionnaire à modalités totalement ordonnées nous contraint, pour conserver l'information maximale, à étendre cette notion à des variables modales et, par suite et a fortiori, à des variables ordinales qui s'y ramènent. A la suite de cette construction, on examine les contributions...
L'implication statistique, une nouvelle méthode d'analyse de données
Si le problème de la concomitance de deux variables a et b trouve une partie de sa réponse dans l'étude symétrique de la corrélation ou dans celle de la similarité, celui de l'implication (si a alors b) passe, en revanche, par l'examen d'une relation dissymétrique. Par rapport à une problématique psychologique de complexité, R. Gras, dans sa thèse, a apporté une contribution qui a permis de nombreuses applications de ce type de relation dans des travaux de recherche en psychologie génétique et en...
Linear approximations to some non-linear AR(1) processes
Some methods for approximating non-linear AR(1) processes by classical linear AR(1) models are proposed. The quality of approximation is studied in special non-linear AR(1) models by means of comparisons of quality of extrapolation and interpolation in the original models and in their approximations. It is assumed that the white noise has either rectangular or exponential distribution.
Linear combination, product and ratio of normal and logistic random variables
The distributions of linear combinations, products and ratios of random variables arise in many areas of engineering. In this note, the exact distributions of , and are derived when and are independent normal and logistic random variables. The normal and logistic distributions have been two of the most popular models for measurement errors in engineering.
Linear comparative calibration with correlated measurements
The paper deals with the linear comparative calibration problem, i. e. the situation when both variables are subject to errors. Considered is a quite general model which allows to include possibly correlated data (measurements). From statistical point of view the model could be represented by the linear errors-in-variables (EIV) model. We suggest an iterative algorithm for estimation the parameters of the analysis function (inverse of the calibration line) and we solve the problem of deriving the...
Linear conform transformation: Errors in both coordinate systems
Linear conform transformation in the case of non-negligible errors in both coordinate systems is investigated. Estimation of transformation parameters and their statistical properties are described. Confidence ellipses of transformed nonidentical points and cross covariance matrices among them and identical points are determined. Some simulation for a verification of theoretical results are presented.
Linear discriminant analysis with a generalization of the Moore-Penrose pseudoinverse
The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates do not have full rank, and thus cannot be inverted. There are a number of ways to deal with this problem. In this paper, we propose improving LDA in this...
Linear error propagation law and nonlinear functions
Linear error propagation law (LEPL) has been using frequently also for nonlinear functions. It can be adequate for an actual situation however it need not be so. It is useful to use some rule in order to recognize whether LEPL is admissible. The aim of the paper is to find such rule.
Linear error propagation law and plug-in estimators
In mixed linear statistical models the best linear unbiased estimators need a known covariance matrix. However, the variance components must be usually estimated. Thus a problem arises what is the covariance matrix of the plug-in estimators.
Linear filtering of systems with memory and application to finance.
Linear model with inaccurate variance components
A linear model with approximate variance components is considered. Differences among approximate and actual values of variance components influence the proper position and the shape of confidence ellipsoids, the level of statistical tests and their power function. A procedure how to recognize whether these diferences can be neglected is given in the paper.
Linear model with nuisance parameters and with constraints on useful and nuisance parameters
The properties of the regular linear model are well known (see [1], Chapter 1). In this paper the situation where the vector of the first order parameters is divided into two parts (to the vector of the useful parameters and to the vector of the nuisance parameters) is considered. It will be shown how the BLUEs of these parameters will be changed by constraints given on them. The theory will be illustrated by an example from the practice.
Linear model with variances depending on the mean value
Linear models with nuisance parameters and deformation measurement