Estimation of a semiparametric multiplicative density model

Lee, Young Kyung

doi:10.1016/j.jkss.2016.06.001

Journal of the Korean Statistical Society

2016

DOI: 10.1016/j.jkss.2016.06.001

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Estimation of a semiparametric multiplicative density model

Young Kyung Lee

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2018

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“…It is of course not necessary that all entering functions are nonparametric. Lee (2016) pointed out that parametrising one component could stabilise estimation and forecast at the cost of introducing a model bias in case the semiparametric model assumption is violated.…”

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confidence: 99%

In-sample forecasting: A brief review and new algorithms

Lee¹,

Mammen²,

Nielsen³

et al. 2018

ALEA

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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.

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mentioning

confidence: 99%

In-sample forecasting: A brief review and new algorithms

Lee¹,

Mammen²,

Nielsen³

et al. 2018

ALEA

View full text Add to dashboard Cite

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Estimation of a semiparametric multiplicative density model

Cited by 1 publication

References 11 publications

In-sample forecasting: A brief review and new algorithms

In-sample forecasting: A brief review and new algorithms

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