On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks

LF Hoogerheide; JF (Johan) Kaashoek; Herman van Dijk

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks

LF Hoogerheide
, JF (Johan) Kaashoek
, Herman van Dijk

Research output: Book/Report/Inaugural speech/Farewell speech › Report › Academic

Original language	Undefined/Unknown
Place of Publication	3000 DR Rotterdam
Number of pages	27
Edition	EI Reprint Reeks EI-1450
Publication status	Published - 2007

Publication series

Series	EI Reprint Reeks
Volume	EI-1450

Research programs

EUR ESE 31

Cite this

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title = "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks",

author = "LF Hoogerheide and Kaashoek, \{JF (Johan)\} and \{van Dijk\}, Herman",

year = "2007",

language = "Undefined/Unknown",

series = "EI Reprint Reeks",

edition = "EI Reprint Reeks EI-1450",

}

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks. / Hoogerheide, LF; Kaashoek, JF (Johan); van Dijk, Herman.
EI Reprint Reeks EI-1450 ed. 3000 DR Rotterdam, 2007. 27 p. (EI Reprint Reeks, Vol. EI-1450).

Research output: Book/Report/Inaugural speech/Farewell speech › Report › Academic

TY - BOOK

T1 - On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks

AU - Hoogerheide, LF

AU - Kaashoek, JF (Johan)

AU - van Dijk, Herman

PY - 2007

Y1 - 2007

M3 - Report

T3 - EI Reprint Reeks

BT - On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks

CY - 3000 DR Rotterdam

ER -