Probabilistic model of school-achievement test with double choice response: Variant I
To the theory of global transformation of the second order linear differential equations of finite type, special
Special dispersions of the second order linear differential equations of a finite type special and their relation to the Kummer's transformation problem
Bayesian estimation of number of the questions unfamiliar with the examinee in variant I of probabilistic model of double choice test
Statistical analysis of variant II of probabilistic model of the school-achievement test with double-choice response
On equation of finite type, 1-special, with the same central dispersion of the first kind
Bayesian estimation of parameter in variant I of probabilistic model of double choice test
Statistical analysis of probabilistic model - variant I of the school-achievement test with double-choice response
On the properties of central dispersions of linear second order differential equations being of finite type-special
Probabilistic model of school-achievement test with triple choice response
To the theory of central dispersions for the linear differential equations of a finite type-special
Statistical analysis of probabilistic model of the school-achievement test with triple-choice response
Probabilistic model of school-achievement test with the correction account of the random choice and its statistical analysis
Information analysis in variant I of probabilistic model of the school achievement test with double choice response
Variant II of probabilistic model of school-achievement test with double choice response
Weakly nonlinear regression model with constraints I: nonlinear hypothesis
The problem considered is under which conditions in weakly nonlinear regression model with constraints I a weakly nonlinear hypothesis can be tested by linear methods. The aim of the paper is to find a region around the approximate value of the regression parameter with the following property. If we are certain that the actual value of the regression parameter is in this region, then the linear method of testing can be used without any significant deterioration of the inference.
How to deal with regression models with a weak nonlinearity
If a nonlinear regression model is linearized in a non-sufficient small neighbourhood of the actual parameter, then all statistical inferences may be deteriorated. Some criteria how to recognize this are already developed. The aim of the paper is to demonstrate the behaviour of the program for utilization of these criteria.
Weakly nonlinear constraints in regression models
Confidence regions in nonlinear models with constraints
Estimation of dispersion in nonlinear regression models with constraints
Dispersion of measurement results is an important parameter that enables us not only to characterize not only accuracy of measurement but enables us also to construct confidence regions and to test statistical hypotheses. In nonlinear regression model the estimator of dispersion is influenced by a curvature of the manifold of the mean value of the observation vector. The aim of the paper is to find the way how to determine a tolerable level of this curvature.
Page 1 Next