• 기술혁신학회지
• /
• 제6권3호
• /
• pp.279-297
• /
• 2003

The purpose of this paper is to analyze the determinants of company R＆D investment with zero observations by using the data of R＆D Scoreboard published by Ministry of Science and Technology(2002). Conventional parametric approach to dealing with zero investments is not robust to heteroscedastic and/or non-normal error structure. Thus, this study applies symmetrically trimmed least squares(STLS) estimation as a semi-parametric approach to dealing with zero R&D investments. The result of specification test indicates the semi-parametric approach outperforms the parametric approach significantly. Moreover, the results of the study provide various implications as summarized below. The R&D investment of IT company is larger than that of non-IT company. The R&D investment has a positive relation to foreigners' investment ratio. The higher degree of financial self-reliance is, the larger the R&D investment is. Firm size variables such as sales amount and the number of workers are positively related to R&D investment. The sales elasticity of R&D investment is larger than one. However, the workers elasticity of R&D investment is smaller than one.

• Communications for Statistical Applications and Methods
• /
• 제26권5호
• /
• pp.519-526
• /
• 2019

Response dimension reduction in a sufficient dimension reduction (SDR) context has been widely ignored until Yoo and Cook (Computational Statistics and Data Analysis, 53, 334-343, 2008) founded theories for it and developed an estimation approach. Recent research in SDR shows that a semi-parametric approach can outperform conventional non-parametric SDR methods. Yoo (Statistics: A Journal of Theoretical and Applied Statistics, 52, 409-425, 2018) developed a semi-parametric approach for response reduction in Yoo and Cook (2008) context, and Yoo (Journal of the Korean Statistical Society, 2019) completes the semi-parametric approach by proposing an unstructured method. This paper theoretically discusses and provides insightful remarks on three versions of semi-parametric approaches that can be useful for statistical practitioners. It is also possible to avoid numerical instability by presenting the results for an orthogonal transformation of the response variables.

• Communications for Statistical Applications and Methods
• /
• 제29권5호
• /
• pp.615-627
• /
• 2022

Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.

• Journal of the Korean Data and Information Science Society
• /
• 제27권5호
• /
• pp.1389-1397
• /
• 2016

Semi-parametric frailty model with nonparametric baseline hazards has been widely used for the analyses of clustered survival-time data. The frailty models can be fitted via an auxiliary Poisson hierarchical generalized linear model (HGLM). For the inferences of the frailty model marginal likelihood, which gives MLE, is often used. The marginal likelihood is usually obtained by integrating out random effects, but it often requires an intractable integration. In this paper, we propose to obtain the MLE via Laplace approximation using a Poisson HGLM approach for semi-parametric frailty model. The proposed HGLM approach uses hierarchical-likelihood (h-likelihood), which avoids integration itself. The proposed method is illustrated using a numerical study.

• 한국기술혁신학회:학술대회논문집
• /
• 한국기술혁신학회 2006년도 추계학술대회
• /
• pp.411-416
• /
• 2006

• 한국통신학회:학술대회논문집
• /
• 한국통신학회 2006년도 하계종합학술발표회 논문 초록집
• /
• pp.304-304
• /
• 2006

• 자원ㆍ환경경제연구
• /
• 제10권4호
• /
• pp.515-545
• /
• 2001

This paper introduces the least absolute deviations estimation of the contingent valuation model, which corresponds to the semi-parametric estimation of discrete choice models by Manski (1975, 1985) and Lee (1992). The least absolute deviations estimation is more robust to mis-specified distributional assumptions in the estimation of the contingent valuation model, compared to the maximum likelihood estimation. The full identification and strong consistency of the estimation are proved and its application to different formats of contingent valuation survey data is discussed. Simulation studies are designed to evaluate its operational characteristics including computational strategies, small sample properties and the efficiency gain of a follow-up question. The bias and efficiency of least absolute deviations and maximum likelihood estimation are compared in the presence of heteroskedasticity.

• 자원ㆍ환경경제연구
• /
• 제14권2호
• /
• pp.447-474
• /
• 2005

응답자들에 따라 지불의사액(willingness to pay : WTP) 조사에서 응답한 WTP에 대한 확신도, 즉 선호의 강도가 다를 수 있다. 본 연구는 선호강도의 정도에 대한 정보를 얻기 위해 응답자가 응답한 WTP에 대해 선호강도가 어떤지에 대한 응답을 이끌어 내었다. 선호강도를 반영한 WTP 자료의 분석을 위해 본 논문에서는 Type 3 토빗모형의 적용을 고려한다. 이 모형을 추정하기 위해서는 통상 동분산 및 이변량 정규성을 만족하는 오차항 구조를 가정한 모수적 2단계 추정법을 적용한다. 하지만 이 가정들이 만족되지 않는다면 추정치는 비일치적이게 된다. 동분산과 정규성 가설에 대해 검정한 결과 유의수준 1%에서 두 가정은 모두 기각되었다. 따라서 모수적 Type 3 토빗모형을 추정하는데 요구되는 가정은 너무 제약적이라 할 수 있다. 본 연구에서는 이 모수적 모형에 대한 대안으로 준모수적 Type 3 토빗모형을 적용한다. 분석결과 준모수적 추정은 모수적 추정보다 유의하게 우수하였으며, 더욱더 중요하게는 모수적 모형으로부터 계산된 평균 WTP 추정치는 준모수적 모형으로부터 계산된 것과 유의하게 다름을 알 수 있었다.

• 한국전자파학회논문지
• /
• 제23권1호
• /
• pp.85-95
• /
• 2012

본 논문에서는 주파수 변조 단속 지속파(Frequency Modulated Interrupted Continuous Wave: FMICW) 시스템을 기반으로 한 고해상도(HRR: High Range Resolution) 레이더 탐색기에서 발생하는 스퓨리어스(Spurious)를 제거하기 위한 스펙트럼 분석 기법에 대해서 연구하고 새로운 제거 기법을 제안한다. 주파수 변조 지속파(Frequency Modulated Continuous Wave: FMCW)를 기반으로 하는 고해상도 레이더 시스템과 다르게 FMICW를 사용한 시스템은 주기적으로 나타나는 비연속적 IF(Intermediate Frequency) 신호에 의해 스펙트럼 상에서 스퓨리어스가 생기게 된다. 이러한 스퓨리어스를 제거하기 위해서 대역 통과 필터(band pass filter)를 사용하면 FMICW 시스템의 정확도가 이전 추정된 거리 값에 의존적이 되고 random interrupted sequence를 사용하면 noise floor가 증가하며, staggering process를 사용하면 중복된 정보를 위해 여러 개의 파형을 송신해야 되는 단점이 있다. 최근 소개된 IAA(Iterative Adaptive Approach) 또는 SPICE(SemiParametric Iterative Covariance-based Estimation method)와 같은 스펙트럼 분석 기법을 이용하면 이러한 단점 없이 FMICW 시스템에서의 스퓨리어스를 효과적으로 제거할 수 있다. IAA 또는 SPICE를 사용하기 위해서는 신뢰할 수 있는 데이터(reliable data)와 신뢰할 수 없는 데이터(unreliable data)를 구분하고, 신뢰할 수 있는 데이터만 이용하여야 하는데, 이를 위해서 STFT(Short Time Fourier Transform)가 적용된다.

• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.721-733
• /
• 2022

In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.