• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.41-51
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• 1994

We consider nonparametric procedures for the selection and ranking problems. Tukey's generalized lambda distribution is condidered as the distribution for the score function because the distribution can approximate many well-known contionuous distributions. Also we compare these procedures in terms of efficiency, defined by the ratio of a probability of a correct selection divided by the expected selected subset size.

• Journal of the Korean Statistical Society
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• v.11 no.1
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• pp.1-11
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• 1982

Srivastava and Anderson(1970) illustrate a method of obtaining Balanced (but not orthogonal) Resolution $IV^*$ designs starting with a BIB design. The incidence matrix of a BIB design with parameters (v, b, r, k, and $\lambda$) is utilized to obtain Balanced Resolution $IV^*$ designs with m factors and n=2b runs, where $m \leq v$. In this paper, the same idea is extended to the case of PBIB designs to obtain Partially Balanced Resolution $IV^*$ designs. In the designs obtained here the variances are balanced and the covariances are partially balanced with respect to the main effects. A proof of this property of Partially Balanced Resoultion $IV^*$ designs is given. The efficiency of Partially Balanced Resolution $IV^*$ designs is also considered and examples of Partially Balanced Resoultion $IV^*$ designs are included.