Probabilistic model of school-achievement test with triple choice response
Statistical analysis of probabilistic model - variant I of the school-achievement test with double-choice response
Probabilistic model of school-achievement test with the correction account of the random choice and its statistical analysis
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
Bayesian estimation of number of the questions unfamiliar with the examinee in variant I of probabilistic model of double choice test
Probabilistic model of school-achievement test with double choice response: Variant I
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
On equation of finite type, 1-special, with the same central dispersion of the first kind
To the theory of global transformation of the second order linear differential equations of finite type, special
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
Variant II of probabilistic model of school-achievement test with double choice response
On the properties of central dispersions of linear second order differential equations being of finite type-special
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.
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.
The Type A Uncertainty
If in the model of measurement except useful parameters, which are to be determined, other auxiliary parameters occur as well, which were estimated from another experiment, then the type A and B uncertainties of measurement results must be taken into account. The type A uncertainty is caused by the new experiment and the type B uncertainty characterizes an accuracy of the parameters which must be used in estimation of useful parameters. The problem is to estimate of the type A uncertainty in the...
Underparametrization of weakly nonlinear regression models
A large number of parameters in regression models can be serious obstacle for processing and interpretation of experimental data. One way how to overcome it is an elimination of some parameters. In some cases it need not deteriorate statistical properties of estimators of useful parameters and can help to interpret them. The problem is to find conditions which enable us to decide whether such favourable situation occurs.
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