• 충청수학회지
• /
• 제27권1호
• /
• pp.145-149
• /
• 2014

I consider the optimal consumption and portfolio selection problem with nonnegative wealth constraints using the dynamic programming approach. I use the constant relative risk aversion (CRRA) utility function and disutility to derive the closed-form solutions.

• 한국환경복원기술학회지
• /
• 제15권5호
• /
• pp.31-48
• /
• 2012

The purpose of this study is to select indicators by a methodical approach and to establish a functional assessment system as a basic study for planning and constructing green space of forest. The types of green space were divided into 6 classes based on theoretical reviews of literature and the functions of green space were restricted to 'nature-ecological function', 'environment-control function' and 'usage function'. As a result of the selection of indicators, 35 indicators were initially selected by theoretical review and these indicators were reduced to 29 through brainstorming. Also, these indicators were classified into three functions such as 12 indicators (nature-ecological function), 8 indicators (environment-control function), 6 indicators (usage function) by analysis of suitability. According to the result of selection of the optimum indicators using MCB (Multiple Comparisons with the Best treatment) analysis, the optimum indicators of 7, 5, and 4 respectively by each function were selected for forest green space. The results of AHP (Analytic Hierarchy Process) for the establishment of the assessment system in forest, the weight of nature-ecological function was evaluated highest at 0.558, while the weight of environment-control and usage function were calculated at each 0.277, 0.165. 'Naturality (0.189)', 'Carbon sink (0.235)', and 'Accessibility (0.354)' among indicators showed highest by each function. The weight of indicator and assessment system may be used as a valuable guideline in case of assessing synthetically green space within urban planning.

• Communications for Statistical Applications and Methods
• /
• 제22권1호
• /
• pp.41-54
• /
• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Communications for Statistical Applications and Methods
• /
• 제31권3호
• /
• pp.337-347
• /
• 2024

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions.

• 디지털융복합연구
• /
• 제15권1호
• /
• pp.247-253
• /
• 2017

피처선택, 혹은 변수 선택은 피처의 수가 매우 많은 고차원 데이터에서 주어진 주제와 연관성이 높은 피처를 선별하는 과정으로서, 데이터의 차원수를 낮추어 군집분석이나 분류 분석 등을 용이하게 하는데 중요한 기법이다. 많은 수의 피처들 중에서 일부의 피처를 선별하기 위해서는 피처들을 평가하기 위한 도구가 필요하다. 현재까지 제안된 도구들은 대부분 확률이론이나 정보이론에 기초하여 만들어졌기 때문에 하나의 피처, 즉 1차원 데이터만을 평가할 수 있다. 그러나 피처들 간에는 상호작용이 있기 때문에 하나의 피처를 평가하기 보다는 여러 피처들의 집합, 즉 다차원 데이터를 평가할 수 있어야 효과적인 피처 선택이 가능하다. 본 연구에서는 확장된 거리 함수를 이용하여 1차원 데이터 평가용으로 제안된 FSDD 평가 함수를 다차원 데이터에 대한 평가가 가능하도록 개선하는 방법에 대해 제안하였다. 본 연구에서 제안한 접근법은 다른 1차원 데이터 평가함수에도 적용이 될 수 있을 것으로 기대된다.

• 조명전기설비학회논문지
• /
• 제28권5호
• /
• pp.122-133
• /
• 2014

This paper deals with the rapid charger which is the mid-type between the slow and fast chargers in the aspect of charging time. In its functions, it can perform the Active Power Filter(APF) function without changing the topology besides the charging function. In addition, to perform the charging and APF function, this paper proposes the mode selection algorithm. The operation of the charger that has APF function and the mode selection algorithm are verified by the simulation and experiment.

• Communications for Statistical Applications and Methods
• /
• 제17권6호
• /
• pp.909-916
• /
• 2010

A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.

• 한국보육지원학회지
• /
• 제20권2호
• /
• pp.145-167
• /
• 2024

Objective: This study aimed to investigate the moderated-moderated mediating effects of children's media content selection and parental restrictive media mediation on the relationship between children's screen media time and executive function difficulties. Methods: A total of 693 parents of children aged 5~6 years participated in this study and were asked to answer all survey questions. The data were analyzed by descriptive statistics and correlation analysis using SPSS 27.0. Model 11 of PROCESS macro 4.3 was used to examine the moderated-moderated mediation model. Children's gender, age, childcare enrollment status, and household income were included in the analyses as covariates. Results: The moderated-moderated mediating effects of children's media content selection and parental restrictive media mediation were found to be significant. Specifically, bedtime mediated the relationship between screen media time and executive function difficulties only when parents did not appropriately implement restrictive mediation and children freely selected media content. Conclusion/Implications: It is recommended that parents understand the importance of implementing restrictive media mediation and selecting appropriate media contents for their child to prevent executive function difficulties in early childhood. Also, child education or day-care centers should offer education program about appropriate media use to reach more parents.

• 한국컴퓨터정보학회논문지
• /
• 제26권7호
• /
• pp.1-7
• /
• 2021

본 논문에서는 다중 레이블 분류를 위한 특징 선별 기법을 제안한다. 기존 많은 특징 선별 기법들은 상호정보척도 등을 이용하여 특징과 레이블 사이의 연관성을 계산하여 특징을 선별하였다. 하지만 상호정보척도는 결합 확률을 요구하기 때문에 실제 전제 특징 집합에서 결합 확률을 계산하는 것은 어렵다. 따라서 소수의 특징만 계산이 가능하여 지역적 최적화만 가능하다는 단점을 가진다. 이런 지역적 최적화 문제를 피해, 주어진 특징 전체 공간에서 저랭크 공간을 구성하고, 희소성을 가진 특징들을 선별할 수 있는 특징 선별 기법을 제안한다. 이를 위해 뉴클리어 노름을 이용해 회귀 기반의 목적함수를 설계하였고, 이 목적 함수의 최적화 문제를 풀기 위한 경사하강법 방식의 알고리즘을 제안하였다. 4가지의 데이터와 3가지 다중 레이블 분류 성능을 기준으로 다중 레이블 분류 실험 결과를 통해 제안하는 방법론이 기존 특징 선별 기법보다 좋은 성능을 나타내는 것을 보였다. 또한 제안하는 목적함수의 파라미터 값 변화에도 성능 변화가 둔감한 것을 실험적인 결과로 확인하였다.

• Journal of the Korean Data and Information Science Society
• /
• 제20권6호
• /
• pp.1191-1197
• /
• 2009

Most Bayesian approaches to model selection of wavelet analysis have drawbacks that computational cost is expensive to obtain accuracy for the fitted unknown function. To overcome the drawback, this article introduces loss functions which are criteria for level dependent threshold selection in wavelet based Bayesian methods with arbitrary size and regular design points. We demonstrate the utility of these criteria by four test functions and real data.