• Bulletin of the Korean Mathematical Society
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• v.35 no.2
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• pp.279-292
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• 1998

We derive maximal inequalities of linearly positive quadrant dependent (LPQD) random variables for d = 1,2. With an application we also obtain the weak convergence for 2-parameter arrays of LPQD random variables.

• Journal of the Chungcheong Mathematical Society
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• v.24 no.1
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• pp.75-83
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• 2011

In this paper the Baum-Katz law of large numbers for negatively orthant dependent random variables is studied. The complete convergence of negatively orthant dependent random variables under some conditions of uniform integrablity is also obtained.

• Journal of the Chungcheong Mathematical Society
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• v.23 no.4
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• pp.721-730
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• 2010

In this paper we consider some results on convergence for arrays of rowwise and pairwise negatively quadrant dependent random variables.

• Bulletin of the Korean Mathematical Society
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• v.48 no.2
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• pp.343-351
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• 2011

In this paper, we establish a Marcinkiewicz-Zygmund type strong law for sequences of blockwise and pairwise m-dependent random variables. The sharpness of the results is illustrated by an example.

• Journal of Applied Reliability
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• v.12 no.4
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• pp.265-274
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• 2012

We in this paper discuss the strong law of large numbers for weighted sums of arrays of rowwise LNQD random variables by using a new exponential inequality of LNQD r.v.'s under suitable conditions and we obtain one of corollary.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.455-463
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• 2004

In this paper, we derive sharp upper and lower expectation bounds on the extreme order statistics from possibly dependent random variables whose marginal distributions are only known. The marginal distributions of the considered random variables may not be the same and the expectation bounds are completely determined by the marginal distributions only.

• Journal of Families and Better Life
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• v.10 no.1 s.19
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• pp.131-138
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• 1992

Regression analysis has become a standard statistical tool in the behavioral science. Because of its widespread popularity. regression has been often misused. Such is the case when the dependent variable is a qualitative measure rather than a continuous, interval measure. Regression estimates with a qualitative dependent variable does not meet the assumptions underlying regression. It can lead to serious errors in the standard statistical inference. Logit model is recommended as alternatives to the regression model for qualitative dependent variables. Researchers can employ this model to measure the relationship between independent variables and qualitative dependent variables without assuming that logit model was derived from probabilistic choice theory. Coefficients in logit model are typically estimated by the method of Maximum Likelihood Estimation in contrast to ordinary regression model which estimated by the method of Least Squares Estimation. Goodness of fit in logit model is based on the likelihood ratio statistics and the t-statistics is used for testing the null hypothesis.

• Journal of applied mathematics & informatics
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• v.37 no.3_4
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• pp.207-217
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• 2019

We are presented of several basic properties for negatively superadditive dependent(NSD) random variables. By using this concept we are obtained complete convergence for maximum partial sums of rowwise NSD random variables. These results obtained in this paper generalize a corresponding ones for independent random variables and negatively associated random variables.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.603-610
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• 2006

In this paper we discuss the complete convergence of the linear process generated by negatively orthant dependent random variables under suitable conditions.

• Journal of the Korean Mathematical Society
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• v.42 no.5
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• pp.949-957
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• 2005

For weighted sum of a sequence {X, X$\_{n}$, n $\geq$ 1} of identically distributed, negatively orthant dependent random variables such that |r| > 0, has a finite moment generating function, a strong law of large numbers is established.