Weighted exponential and generalized exponential functions are used in life time data analysis. Both of these functions can be applied as a growth curve. Locally D-optimal designs for weighted exponential and generalized exponential models are investigated. These designs are minimally supported.
The investigation of environment impact have important role to development of a city. The application of the artificial intelligence in form of computational models can be used to analyze the data. One of them is rough set theory. The utilization of data clustering method, which is a part of rough set theory, could provide a meaningful contribution on the decision making process. The application of this method could come in term of selecting the attribute of environment impact. This paper examine the application of variable precision rough set model for selecting attribute of environment impact. This mean of minimum error classification based approach is applied to a survey dataset by utilizing variable precision of attributes. This paper demonstrates the utilization of variable precision rough set model to select the most important impact of regional development. Based on the experiment, The availability of public open space, social organization and culture, migration and rate of employment are selected as a dominant attributes. It can be contributed on the policy design process, in term of formulating a proper intervention for enhancing the quality of social environment.
The modeling between predictors and response in statistics sometimes deals with more than one response or multiresponse situation. Furthermore, it can be happen that some predictors have linear relationship with the responses and the others predictor have unknown relationship. To overcome this modeling problem we proposed multiresponse semiparametric regression model. This model has more than one response and contains both parametric and nonparametric model. This study focuses on how to estimate parameter in multiresponse semiparametric regression. The weighted penalized least squares method is used to fit the model. This method produce partial spline estimator for nonparametric model and by applying some assumptions the estimator is polynomial natural spline. The performance of this estimator depends on smoothing parameter. So, we also proposed G criteria as modification of generalized cross validation in the context of multiresponse semiparametric regression to choose the optimal smoothing parameter. Using simulation data, it can be shown that this model can work well to describe relationship between some predictors and several responses.
Exponential model is widely used in biology, chemistry, pharmacokinetics and microbiology. Doptimal criteria is criteria with the purpuse to minimize the variance of the estimator of parameters in the model. In this paper will discuss the D-optimal design for the generalized exponential model with homoscedastics errore assumtion. We used minimally supported design with the proportion of each design point is uniform. The optimization is used modified Newton, and the results obtained that the design points are interior points of the design region.
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