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In-sample forecasting: A brief review and new algorithms
- Lee, Y. K.;
- Mammen, E.;
- Nielsen, J. P.;
- Park, B. U.
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4초록
Statistical methods often distinguish between in-sample and out-of-sample approaches. In particular this is the case when time is involved. Then often time series methods are proposed that extrapolate past patterns into the future via complicated recursion formulas. Standard statistical inference is on the other hand concerned with estimating parameters within the given sample. This review paper is about a statistical methodology, where all parameters are estimated in-sample while producing a forecast out-of-sample without recursion or extrapolation. A new super-simulation algorithm ensures a faster implementation of the simplest and perhaps most important version of in-sample forecasting.
키워드
Structured nonparametric models; age-cohort models; density estimation; kernel smoothing; backfitting; Chain Ladder; UK mesothelioma mortality; PERIOD-COHORT MODELS; DOUBLE-CHAIN LADDER; MESOTHELIOMA MORTALITY; SELF-CONSISTENT; CLAIM AMOUNTS; AGE-PERIOD; TIME; REDISTRIBUTION; ESTIMATORS; PROJECTION
- 제목
- In-sample forecasting: A brief review and new algorithms
- 저자
- Lee, Y. K.; Mammen, E.; Nielsen, J. P.; Park, B. U.
- 발행일
- 2018
- 유형
- Review
- 저널명
- Alea
- 권
- 15
- 호
- 2
- 페이지
- 875 ~ 895
- 언어
- ENG
- 출판사
- IMPA
- 발행국가
- 브라질
- 분량
- 21 페이지
- ISSN
- P 1980-0436