Applications of an Inequality Involving Conditional Expectations.
Applications of intentionally biased bootstrap methods.
Applications of monotonic dependence functions: descriptive aspects
Applications of Random Sampling in Computational Geometry, II.
Applications of regime-switching models based on aggregation operators
A synthesis of recent development of regime-switching models based on aggregation operators is presented. It comprises procedures for model specification and identification, parameter estimation and model adequacy testing. Constructions of models for real life data from hydrology and finance are presented.
Applications of simulation methods to barrier options driven by Lévy processes.
Applications of the theory of partially ordered sets to cluster analysis
Applied statistics in designing special organic mixtures.
Applying a theorem of Fernique
Applying Coefficients of Preference in Ranking (CPR)
Apport de l'analyse factorielle à l'étude d'un processus
Approaches to Identification of Linear Relations from Compound Noisy and Noise-Free Data
Approche non paramétrique en théorie de la fiabilité : revue bibliographique
Approximate bias for first-order autoregressive model with uniform innovations. Small sample case
The first-order autoregressive model with uniform innovations is considered. The approximate bias of the maximum likelihood estimator (MLE) of the parameter is obtained. Also, a formula for the approximate bias is given when a single outlier occurs at a specified time with a known amplitude. Simulation procedures confirm that our formulas are suitable. A small sample case is considered only.
Approximate construction of a two-dimensional confidence region
Approximate Distributions of Order Statistics, with Applications to Nonparametric Statistics - R. D. Reiss.
Approximate Distributions of the Maximum Deviation of Histograms.
Approximate maximum likelihood estimation for a spatial point pattern.
Several authors have proposed stochastic and non-stochastic approximations to the maximum likelihood estimate for a spatial point pattern. This approximation is necessary because of the difficulty of evaluating the normalizing constant. However, it appears to be neither a general theory which provides grounds for preferring a particular method, nor any extensive empirical comparisons. In this paper, we review five general methods based on approximations to the maximum likelihood estimate which have...
Approximate polynomial expansion for joint density
Let (X,Y) be a random vector with joint probability measure σ and with margins μ and ν. Let and be two bases of complete orthonormal polynomials with respect to μ and ν, respectively. Under integrability conditions we have the following polynomial expansion: . In this paper we consider the problem of changing the margin μ into μ̃ in this expansion. That is the case when μ is the true (or estimated) margin and μ̃ is its approximation. It is shown that a new joint probability with new margins...
Approximated maximum likelihood estimation of parameters of discrete stable family
In this article we propose a method of parameters estimation for the class of discrete stable laws. Discrete stable distributions form a discrete analogy to classical stable distributions and share many interesting properties with them such as heavy tails and skewness. Similarly as stable laws discrete stable distributions are defined through characteristic function and do not posses a probability mass function in closed form. This inhibits the use of classical estimation methods such as maximum...