• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.411-423
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• 2009

본 논문은 삼각 분할표 자료의 예측문제에 있어 Verrall (1990)의 발생연도효과와 경과년도효과만 있는 베이지안 선형모형을 절대연도효과가 있는 모형으로 확장한 모형을 제시하고 이에 대한 추정 방법으로 마르코프 연쇄 몬테칼로 방법을 제안한다. 제안된 모형과 추정 방법은 세 가지 실제 예를 통하여 기존의 방법들에 비해서 일반적으로 작은 상대 예측오차를 제공함을 보였다.

• Communications for Statistical Applications and Methods
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• 제15권4호
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• pp.551-562
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• 2008

보험분야에서 지급준비금(loss reserve)을 추정할 때에는 보험사고의 발생년도와 사건발생 이후의 경과년도에 따라 지급된 보험금을 자료로 사용하게 되는데, 이것은 흔히 삼각분할표(run-off triangular table)의 형태로 주어진다. 이러한 삼각분할표 자료에 대하여 지급준비금 추정에 주로 사용되는 방법으로 사다리법(chain-ladder method)이 있는데, 이것은 사고발생년도부터 보험금이 정산되는 시점까지의 경과기간동안 지급된 누적 보험금의 변화율(진전계수)을 추정함으로써 지급준비금을 추정하는 것이다. 이러한 사다리법은 보험사고의 발생년도에 따른 진전계수의 변화가 없다는 가정을 기본전제로 하고 있다. 그러나 여러 가지 사회 환경적 요인으로 인하여 시간이 지남에 따라 지급보험금의 진전패턴이 달라질 수 있고, 본 논문에서는 사건의 변화에 따른 구조적 변화점 유무를 검정할 수 있는 검정법을 제안하고자 한다. 또한 이를 실제 예제에 적용 고찰해 보고자 한다.

• Journal of applied mathematics & informatics
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• 제32권3_4호
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• pp.343-357
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• 2014

In many non-life insurance applications past data are given in a form known as the run-off triangle. Smoothing such data using parametric crisp regression models has long served as the basis of estimating future claim amounts and the reserves set aside to protect the insurer from future losses. In this article a fuzzy counterpart of the Hoerl curve, a well-known claim reserving regression model, is proposed to analyze the past claim data and to determine the reserves. The fuzzy Hoerl curve is more flexible and general than the one considered in the previous fuzzy literature in that it includes a categorical variable with multiple explanatory variables, which requires the development of the fuzzy analysis of covariance, or fuzzy ANCOVA. Using an actual insurance run-off claim data we show that the suggested fuzzy Hoerl curve based on the fuzzy ANCOVA gives reasonable claim reserves without stringent assumptions needed for the traditional regression approach in claim reserving.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2007년도 학술대회 3부
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• pp.306-317
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• 2007

We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At run-time, as the underlying models deform over time, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30~100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE [4] and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB culling algorithm [2] and observed about two times improvement.