Capacity Investment under Bayesian Information Updates at Reporting Periods: Model and Application

2021 ◽  
Author(s):  
Aditya Vedantam ◽  
Ananth Iyer
Keyword(s):  
Capacity Investment ◽  
Information Updates

The Timing of Capacity Investment by Start-Ups and Established Firms in New Markets

2010 ◽  
Author(s):  
Robert Swinney ◽  
Gerard Cachon ◽  
Serguei Netessine
Keyword(s):  
Capacity Investment ◽  
New Markets ◽  
Established Firms ◽  
Start Ups

Optimal Risk Adoption and Capacity Investment in Disruptive Innovations

2020 ◽  
Author(s):  
Lars H. Sendstad ◽  
Michail Chronopoulos ◽  
Verena Hagspiel
Keyword(s):  
Capacity Investment ◽  
Disruptive Innovations

scholarly journals Optimal Risk Adoption and Capacity Investment in Technological Innovations

2021 ◽  
pp. 1-14
Author(s):  
Lars H. Sendstad ◽  
Michail Chronopoulos ◽  
Verena Hagspiel
Keyword(s):  
Technological Innovations ◽  
Capacity Investment

The Impact of Duplicate Orders on Demand Estimation and Capacity Investment

2005 ◽  
Vol 51 (10) ◽  
pp. 1505-1518 ◽  
Author(s):  
Mor Armony ◽  
Erica L. Plambeck
Keyword(s):  
Demand Estimation ◽  
Capacity Investment ◽  
On Demand ◽  
The Impact

scholarly journals Comparison of models for missing pedigree in single-step genomic prediction

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yutaka Masuda ◽  
Shogo Tsuruta ◽  
Matias Bermann ◽  
Heather L Bradford ◽  
Ignacy Misztal
Keyword(s):  
Density Function ◽  
Single Step ◽  
Joint Density ◽  
Pedigree Information ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Breeding Values ◽  
Joint Density Function ◽  
Information Updates ◽  
Genetic Trends

Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas–Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g′). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g′)=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.

scholarly journals Strategic capacity investment under uncertainty

2015 ◽  
Vol 46 (2) ◽  
pp. 376-408 ◽  
Author(s):  
Kuno J.M. Huisman ◽  
Peter M. Kort
Keyword(s):  
Capacity Investment ◽  
Investment Under Uncertainty ◽  
Strategic Capacity
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