Text

Simulation-Based Prediction

This paper is concerned with the use of simulation in computing predictors in settings in which real-world observations are collected. A major challenge is that the state description underlying the simulation will typically include information that is not observed in the real system. This makes it challenging to initialize simulations that are aligned with the most recent observation collected in the real-world system, especially when the simulation does not visit the most recently observed value frequently. Our estimation methodology involves the use of “splitting,” so that multiple simulations are launched from states that are closely aligned with the most recently collected real-world observation. We provide estimators both in the setting that the observed real-world values are discrete and are continuous, with kernel smoothing methods being systematically exploited in the continuous setting

Ketersediaan

Informasi Detail

Judul Seri

OPERATIONS RESEARCH; Vol.71 No.1 January-February 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 23-46

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SIMULATION
ANALYTICS
RISKINESS INDEX
DATA-DRIVEN OPTIMIZATION
BED SHORTAGES
COHERENT METRIC

Info Detail Spesifik

https://doi.org/10.1287/opre.2021.2229

Pernyataan Tanggungjawab

Eunji Lim, Peter W. Glynn

Versi lain/terkait

Lampiran Berkas

Komentar