• Communications for Statistical Applications and Methods
• /
• v.15 no.5
• /
• pp.675-685
• /
• 2008

This paper studies two sequential procedures to estimate a monotone convex function using $L_2$ monotonization and uniform convexification; one, denoted by FMSC, monotonizes the data first and then, convexifis the monotone estimate; the other, denoted by FCSM, first convexifies the data and then monotonizes the convex estimate. We show that two shape modifiers are not commutable and so does FMSC and FCSM. We compare them numerically in uniform error(UE) and integrated mean squared error(IMSE). The results show that FMSC has smaller uniform error(UE) and integrated mean squared error(IMSE) than those of FCSC.

• Communications for Statistical Applications and Methods
• /
• v.24 no.3
• /
• pp.271-290
• /
• 2017

We study a bivariate response regression model with arbitrary marginal distributions and joint distributions using Frank and Clayton's families of copulas. The proposed model is used for fitting dependent bivariate data with explanatory variables using the log-odd log-logistic Weibull distribution. We consider likelihood inferential procedures based on constrained parameters. For different parameter settings and sample sizes, various simulation studies are performed and compared to the performance of the bivariate odd-log-logistic-Weibull regression model. Sensitivity analysis methods (such as local and total influence) are investigated under three perturbation schemes. The methodology is illustrated in a study to assess changes on schoolchildren's oral health-related quality of life (OHRQoL) in a follow-up exam after three years and to evaluate the impact of caries incidence on the OHRQoL of adolescents.

• Journal of the Korean Mathematical Society
• /
• v.32 no.1
• /
• pp.141-149
• /
• 1995

Let N denote the set of positive integers. Fix $d_1, d_2 \in N with d = d_1 + d_2$. Let X and Y be real random variables and let ${X_i : i \in N^d_1} and {Y_j : j \in N^d_2}$ be independent families of independent identically distributed random variables with $L(X) = L(X_i) and L(Y) = L(Y_j)$, where $L(\cdot)$ denote the law of $\cdot$.

• The Korean Journal of Applied Statistics
• /
• v.6 no.1
• /
• pp.79-93
• /
• 1993

We consider adaptive L-estimation of estimating slope parameter in regression model. The proposed estimator is simple extension of trimmed least squares estimator proposed by ruppert and carroll. The efficiency of the proposed estimator is especially well compared with usual least squares estimator, least absolute value estimator, and M-estimators designed for asymmetric distributions under asymmetric error distributions.

• Journal of the Korean Data and Information Science Society
• /
• v.3 no.1
• /
• pp.25-31
• /
• 1992

Some results relating to $C_{0}(R)\;and\;L^{1}(R)$ spaces with application to kernel density estimators will be introduced. First, random elements in $C_{0}(R)\;and\;L^{1}(R)$ are discussed. Then, complete convergence limit theorems are given to show that these results can be used in establishing uniformly consistency and $L^{1}$ consistency.

• The Korean Journal of Applied Statistics
• /
• v.25 no.5
• /
• pp.803-811
• /
• 2012

For credit evaluation models, we extend the study of discriminatory power based on AUC obtained from a ROC curve when the number of defaults is small and distribution functions of the defaults and non-defaults are normal distributions. Since distribution functions do not satisfy normality in real world, the distribution functions of the defaults and non-defaults are assumed as normal mixture distributions based on results that the normal mixture could be better fitted than other distribution estimation methods for non-normal data. By using several AUC statistics, the discriminatory power under such a circumstance is explored and compared with those of normal distributions.

• Communications for Statistical Applications and Methods
• /
• v.25 no.1
• /
• pp.15-27
• /
• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

• ALGAE
• /
• v.36 no.3
• /
• pp.165-174
• /
• 2021

Radicilingua Papenfuss and Calonitophyllum Aregood are two small genera of the family Delesseriaceae that consist of only three and one taxonomically accepted species, respectively. The type species of these genera, Radicilingua thysanorhizans from England and Calonitophyllum medium from the Americas, are morphologically very similar, with the only recognized differences being vein size and procarp development. To date, only other two species were recognized inside the genus Radicilingua: R. adriatica and R. reptans. In this study, we analysed specimens of Radicilingua collected in the Adriatic and Ionian Sea (Mediterranean), including a syntype locality of R. adriatica (Trieste, northern Adriatic Sea), alongside material from near the type locality of R. thysanorhizans (Torpoint, Cornwall, UK). The sequences of the rbcL-5P gene fragment here produced represent the first molecular data available for the genus Radicilingua. Phylogenetic reconstruction showed that the specimens from the Adriatic and Ionian Seas were genetically distinct from the Atlantic R. thysanorhizans, even if morphologically overlapping with this species. A detailed morphological description of the Mediterranean specimens, together with an accurate literature search, suggested that they were distinct also from R. adriatica and R. reptans. For these reasons, a new species was here described to encompass the Mediterranean specimens investigated in this study: R. mediterranea Wolf, Sciuto & Sfriso. Moreover, in the rbcL-5P tree, sequences of the genera Radicilingua and Calonitophyllum grouped in a well-supported clade, distinct from the other genera of the subfamily Nitophylloideae, leading us to propose that Calonitophyllum medium should be transferred to Radicilingua.

• Journal of the Korean Statistical Society
• /
• v.8 no.2
• /
• pp.125-130
• /
• 1979

It is well known that $L_2(m)$ type PBIB designs have the Property A, so they are BNAS PBIB designs. However, $L_3(m)$ type PBIB designs are not of type of Property A but do have the factorial structure (Cotter, John, and Smith(1973)). In this paper, the properties of the $L_3(m)$ type PBIB designs are investigated. Extended Property A and fractional BNAS are defined and a solution formula for the treatment effects in the $L_32(m)$ type designs is obtained.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.3
• /
• pp.613-618
• /
• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.