Optimization of the size of nonsensitiveness regions
The problem is to determine the optimum size of nonsensitiveness regions for the level of statistical tests. This is closely connected with the problem of the distribution of quadratic forms.
Optimum experimental designs with a lack of a priori information. II. Designs for the estimation of the whole response function
Optimum experimental designs with a lack of a priori information. I. Designs for the estimation of a finite-dimensional set of functionals
Orthogonal polynomials in nonlinear analysis of regression.
Outliers in models with constraints
Outliers in univariate and multivariate regression models with constraints are under consideration. The covariance matrix is assumed either to be known or to be known only partially.
Overview of Recent Results in Growth-curve-type Multivariate Linear Models
The Extended Growth Curve Model (ECGM) is a multivariate linear model connecting different multivariate regression models in sample subgroups through common variance matrix. It has the form: Here, matrices contain subgroup division indicators, and corresponding regressors. If , we speak about (ordinary) Growth Curve Model. The model has already its age (it dates back to 1964), but it has many important applications. That is why it is still intensively studied. Many articles investigating...
Parameter estimation in cosine regression
Parameter Estimation in Linear Regression Models With Stationary Arma(p,q)-Errors Using Automatic Differentiation
Parameter estimation of S-distributions with alternating regression.
We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very fast and performs well for error-free distributions and artificial noisy data obtained as random samples generated from S-distributions, as well as for traditional statistical distributions and for actual observation data. In rare cases where the algorithm does not immediately converge, its enormous speed...
Parametric and nonparametric empirical regression models: case study of copper bromide laser generation.
Partial optimum estimator in two stage regression model with constraints and a problem of equivalence
Pattern-mixture models
Piecewise approximation and neural networks
The paper deals with the recently proposed autotracking piecewise cubic approximation (APCA) based on the discrete projective transformation, and neural networks (NN). The suggested new approach facilitates the analysis of data with complex dependence and relatively small errors. We introduce a new representation of polynomials that can provide different local approximation models. We demonstrate how APCA can be applied to especially noisy data thanks to NN and local estimations. On the other hand,...
Plans d'expérience adaptés à un champ d'hypothèses a priori. Construction. Précision globale
Pointwise representation method.
Polynomials associated with exponential regression
Fitting exponentials to data by the least squares method is discussed. It is shown how the polynomials associated with this problem can be factored. The closure of the set of this type of functions defined on a finite domain is characterized and an existence theorem derived.
Possibilities of the determination of sample size in regression analysis: a review
Posterior odds ratios for selected regression hypotheses.
Bayesian posterior odds ratios for frequently encountered hypotheses about parameters of the normal linear multiple regression model are derived and discussed. For the particular prior distributions utilized, it is found that the posterior odds ratios can be well approximated by functions that are monotonic in usual sampling theory F statistics. Some implications of these finding and the relation of our work to the pioneering work of Jeffreys and others are considered. Tabulations of odd ratios...
Precise small deviations in L 2 of some Gaussian processes appearing in the regression context
We find precise small deviation asymptotics with respect to the Hilbert norm for some special Gaussian processes connected to two regression schemes studied by MacNeill and his coauthors. In addition, we also obtain precise small deviation asymptotics for the detrended Brownian motion and detrended Slepian process.
Predictions in nonlinear regression models.