• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.213-224
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• 2014

In this paper, we study the problem of transforming to normality. We propose to estimate the transformation parameter by minimizing a weighted squared distance between the empirical characteristic function of transformed data and the characteristic function of the normal distribution. Our approach also allows for other symmetric target characteristic functions. Asymptotics are established for a random sample selected from an unknown distribution. The proofs show that the weight function $t^{-2}$ needs to be modified to have thinner tails. We also propose the method to compute the influence function for M-equation taking the form of U-statistics. The influence function calculations and a small Monte Carlo simulation show that our estimates are less sensitive to a few outliers than the maximum likelihood estimates.

• Kyungpook Mathematical Journal
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• v.48 no.3
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• pp.443-455
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• 2008

For an extension $A\;{\subseteq}\;B$ of commutative rings, we present a sufficient conditio for the ring $[[A^{S,\;\leq}]]$ of generalized power series to be weakly normal (resp., stronglyt-closed) in $[[B^{S,\;\leq}]]$, where (S, $\leq$) be a torsion-free cancellative strictly ordered monoid. As a corollary, it can be applied to the ring of power series in infinitely many indeterminates as well as in finite indeterminates.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.127-139
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• 1995

This paper is concerned with the asymptotic properties of the least absolute deviation estimators for nonlinear regression models. The simple and practical sufficient conditions for the strong consistency and the asymptotic normality of the least absolute deviation estimators are given. It is confirmed that the extension of these properties to wide class of regression functions can be established by imposing some condition on the input values. A confidence region based on the least absolute deviation estimators is proposed and some desirable asymptotic properties including the asymptotic relative efficiency also discussed for various error distributions. Some examples are given to illustrate the application of main results.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.385-392
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• 2000

Many tests for multivariate normality are based on the spherical coordinates of the scaled residuals of multivariate observations. Moore and Stubblebine's (1981) Pearson chi-square test is based on the radii of the scaled residuals, or equivalently the sample Mahalanobis distances of the observations from the sample mean vector. The chi-square statistic does not have a limiting chi-square distribution since the unknown parameters are estimated from ungrouped data. We will derive a simple closed form of the Rao-Robson chi-square test statistic and provide a self-contained proof that it has a limiting chi-square distribution. We then provide an illustrative example of application to a real data with a simulation study to show the accuracy in finite sample of the limiting distribution.

• Journal of Environmental Health Sciences
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• v.17 no.1
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• pp.32-38
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• 1991

Airborne suspended particulate concentration in drilling sites of underground coal mines in Taebaek area was evaluated. And respirable coal dust exposure level was evaluated. Airborne suspended particulate mass include total suspended particle(TSP) and thoracic particle(TPM). TSP (by open-face filter holder) and TPM(by elutriator) concentration were determined by low volume air samplers. Personal air samplers were attached to the coal workers including drillers, coal cutters, and their assistants. Normality and log-normality of TSP, TPM, and respirable dust(RPM) concentration were tested by Kolmogorov-Smirnov one-sample test. Differences of means of TSP, TPM, and RPM concentration were tested by paired t-test. Relation between TSP, TPM, and RPM with pairs were tested by regression test and Pearson's correlation.

• Proceedings of the Korea Concrete Institute Conference
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• 1997.10a
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• pp.546-551
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• 1997

New typology of failure mechanisms for uniform compression fields are presented based on the classical theory of plasticity, in particular th normality rule, and the limit theorem. The concrete is assumed as a rigid-perfectly plastic material obeying the modified Coulomb failure criteria with zero tension cut-off. The failure mechanisms are capable of explaining flexural types of crushing failure in uniaxial uniform compression stress fields which are called struts in truss models. The failure mechanisms consist of sliding failure along straight failure lines or hyperbolic failure curves and rigid body rotation. The failure mechanisms involving straight failure lines are explained by constant strain expansion in the first principal direction and rigid body rotation motion. The failure mechanisms presented are applied to the explanation of bond failure of bar combined with concrete crushing failure and flexural crushing failure of concrete.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.6
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• pp.642-647
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• 1998

A Poisson error model as a generalized linear mixed model (GLMM) has been suggested for genetic analysis of counted observations. One of the assumptions in this model is the normality for random effects. Since this assumption is not always appropriate, a more flexible model is needed. For count traits, a Poisson hierarchical generalized linear model (HGLM) that does not require the normality for random effects was proposed. In this paper, a Poisson-Gamma HGLM was examined along with corresponding analytical methods. While a difficulty arises with Poisson GLMM in making inferences to the expected values of observations, it can be avoided with the Poisson-Gamma HGLM. A numerical example with simulated embryo yield data is presented.

• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1373-1384
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• 2016

In this paper, we research the normality of sequences of meromorphic functions concerning the sequence of omitted functions. The main result is listed below. Let {$f_n(z)$} be a sequence of functions meromorphic in D, the multiplicities of whose poles and zeros are no less than k + 2, $k{\in}\mathbb{N}$. Let {$b_n(z)$} be a sequence of functions meromorphic in D, the multiplicities of whose poles are no less than k + 1, such that $b_n(z)\overset{\chi}{\Rightarrow}b(z)$, where $b(z({\neq}0)$ is meromorphic in D. If $f^{(k)}_n(z){\neq}b_n(z)$, then {$f_n(z)$} is normal in D. And we give some examples to indicate that there are essential differences between the normal family concerning the sequence of omitted functions and the normal family concerning the omitted function. Moreover, the conditions in our paper are best possible.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.53-58
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• 2009

We unite the two con concepts - normality We unite the two con concepts - normality and congruence - in an intuitionistic fuzzy subgroup setting. In particular, we prove that every intuitionistic fuzzy congruence determines an intuitionistic fuzzy subgroup. Conversely, given an intuitionistic fuzzy normal subgroup, we can associate an intuitionistic fuzzy congruence. The association between intuitionistic fuzzy normal sbgroups and intuitionistic fuzzy congruences is bijective and unigue. This leads to a new concept of cosets and a corresponding concept of guotient.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.229-235
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• 2001

Let {X$\_$n/, n$\geq$1] be a stationary sequence of negatively associated random variables with distribution function F(x)=P(X$_1$$\leq$x). The empirical distribution function F$\_$n/(x) based on X$_1$, X$_2$,....., X$\_$n/ is proposed as an estimator for F$\_$n/(x). Strong consistency and asymptotic normality of F$\_$n/(x) are studied. We also apply these ideas to estimation of the survival function.