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
Probabilistic model of school-achievement test with the correction account of the random choice and its statistical analysis
Statistical analysis of probabilistic model - variant I of the school-achievement test with double-choice response
Variant II of probabilistic model of school-achievement test with double choice response
Probabilistic model of school-achievement test with double choice response: Variant I
Information analysis in variant I of probabilistic model of the school achievement test with double choice response
Special dispersions of the second order linear differential equations of a finite type special and their relation to the Kummer's transformation problem
On equation of finite type, 1-special, with the same central dispersion of the first kind
To the theory of central dispersions for the linear differential equations of a finite type-special
Bayesian estimation of parameter 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
Statistical analysis of probabilistic model of the school-achievement test with triple-choice response
To the theory of global transformation of the second order linear differential equations of finite type, special
Bayesian estimation of number of the questions unfamiliar with the examinee in variant I of probabilistic model of double choice test
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.
Confidence regions in nonlinear models with constraints
Weakly nonlinear constraints in regression models
Variance components and nonlinearity
Unknown parameters of the covariance matrix (variance components) of the observation vector in regression models are an unpleasant obstacle in a construction of the best estimator of the unknown parameters of the mean value of the observation vector. Estimators of variance componets must be utilized and then it is difficult to obtain the distribution of the estimators of the mean value parameters. The situation is more complicated in the case of nonlinearity of the regression model. The aim of the...
Page 1 Next