• Journal of the Chungcheong Mathematical Society
• /
• v.22 no.2
• /
• pp.171-175
• /
• 2009

Let $A_1,\;A_2,\;{\cdots},\;A_n$ be a sequence of events on a given probability space. Let $m_n$ be the number of those $A_{i}{^{\prime}}s$ which occur. We establish an improved lower bounds of Univariate Bonferroni-Type inequality by using the linearity of binomial moments $S_1,\;S_2,\;S_3,\;S_4$ and$S_5$.

• Journal of the Korea Safety Management & Science
• /
• v.1 no.1
• /
• pp.79-90
• /
• 1999

In this paper, we suggested system reliability inequality used by failure rate distribution and developed new theorem-reliability function is increasing function. Also we calculated failure probability of coherent system used by variable transformation. Several examples are illustrated.

• Journal of Environmental Impact Assessment
• /
• v.22 no.6
• /
• pp.713-724
• /
• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

• Honam Mathematical Journal
• /
• v.30 no.3
• /
• pp.479-486
• /
• 2008

Let {${\Omega}$, F, P} be a probability space and {$X_n{\mid}n{\geq}1$} be a sequence of random variables defined on it. We study the Hajeck-Renyi-type inequality for p..mixing random variable sequences and obtain the strong law of large numbers by using this inequality. We also consider the strong law of large numbers for weighted sums of ${\tilde{\rho}}$-mixing sequences.

• Communications of the Korean Mathematical Society
• /
• v.12 no.2
• /
• pp.393-401
• /
• 1997

Let $A_1,A_2,\cdots,A_m$ and $B_1,B_2,\cdots,B_n$ be two sequences of events on a given probability space. Let $X_m$ and $Y_n$, respectively, be the number of those $A_i$ and $B_j$, which occur we establish new upper and lower bounds on the probability $P(X=r, Y=t)$ which improve upper bounds and classical lower bounds in terms of the bivariate binomial moment $S_{r,t},S_{r+1,t},S_{r,t+1}$ and $S_{r+1,t+1}$.

• Communications of the Korean Mathematical Society
• /
• v.18 no.4
• /
• pp.725-736
• /
• 2003

Let $A_1,{\;}A_2,...,A_n$ be a sequence of events on a given probability space. Let $m_n$ be the number of those $A'_{j}s$ which occur. Upper bounds of P($m_n{\;}\geq{\;}1) are obtained by means of probability of consecutive terms which reduce the number of terms in binomial moments $S_2,n,S_3,n$ and $S_4,n$.

• The Korean Journal of Applied Statistics
• /
• v.15 no.1
• /
• pp.73-84
• /
• 2002

Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.

• Bulletin of the Korean Mathematical Society
• /
• v.32 no.2
• /
• pp.329-336
• /
• 1995

Let $A_1, A_2, \ldots, A_n$ be a sequence of events on a given probability space and let $m_n$ be the number of those A's which occur. Put $S_{0,n} = 1$ and $$ S_{k,n} = \Sigma P(A_i_1 \cap A_i_2 \cap \cdots \cap A_i_k), (a \leq k)$$ where the summation is over all subscripts satisfying $1 \let i_1 < i_2 < \cdots < i_k \leq n$.

• Communications for Statistical Applications and Methods
• /
• v.6 no.1
• /
• pp.261-266
• /
• 1999

We present a geometric interpretation of Chebyshev type inequalities. This uses a simple diagram which illustrates the functional bound for the indicator function of the event whose probability we want to assess. We also give a geometric interpretation of the inequalities in terms of volume in a Euclidean space of appropriate dimension. Markov's inequality and Chebyshev's inequality are treated in more detail.

• Journal of the Korean Mathematical Society
• /
• v.53 no.6
• /
• pp.1275-1292
• /
• 2016

The Marcinkiewicz-Zygmund strong law of large numbers for conditionally independent and conditionally identically distributed random variables is an existing, but merely qualitative result. In this paper, for the more general cases where the conditional order of moment belongs to (0, ${\infty}$) instead of (0, 2), we derive results on convergence rates which are quantitative ones in the sense that they tell us how fast convergence is obtained. Furthermore, some conditional probability inequalities are of independent interest.