Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A REPLICATION APPROACH TO INTERVAL ESTIMATION IN SIMULATION
Date
1991-12-11
Author
Köksalan, Mustafa Murat
BASOZ, N
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
192
views
0
downloads
Cite This
The authors modify an earlier approach developed for reducing the bias of the estimator for the mean response in simulation caused by the initial conditions. They try to balance the bias of the estimator in a simulation run by imposing a bias in the opposite direction in a companion run by suitably setting its initial conditions. They present analytical results for the bias of the estimator for AR(1) and M/M/s processes. They suggest making independent replications of the pairs of runs to construct a confidence interval for the mean response. They present some empirical results on the coverages and precisions of the confidence intervals. The results suggest that the idea of balancing a bias with a bias in the opposite direction is promising.
Subject Keywords
Steady-state
,
Industrial engineering
,
State estimation
,
Nails
,
Engineering management
,
Statistical analysis
,
Random variables
,
Analytical models
,
Statistics
,
Testing
URI
https://hdl.handle.net/11511/57722
DOI
https://doi.org/10.1109/wsc.1991.185719
Collections
Department of Industrial Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
A genetic algorithm for 2d shape optimization
Chen, Wei Hang; Oral, Süha; Department of Mechanical Engineering (2008)
In this study, an optimization code has been developed based on genetic algorithms associated with the finite element modeling for the shape optimization of plane stress problems. In genetic algorithms, constraints are mostly handled by using the concept of penalty functions, which penalize infeasible solutions by reducing their fitness values in proportion to the degrees of constraint violation. In this study, An Improved GA Penalty Scheme is used. The proposed method gives information about unfeasible ind...
A comparison of orthogonal cutting data from experiments with three different finite element models
Bil, H; Kilic, SE; Tekkaya, AE (Elsevier BV, 2004-07-01)
The aim of this study is to compare various simulation models of orthogonal cutting process with each other as well as with the results of various experiments. Commercial implicit finite element codes MSC.Marc, Deform2D and the explicit code Thirdwave AdvantEdge have been used. In simulations, a rigid tool is advanced incrementally into the deformable workpiece which is remeshed whenever needed. In simulations with MSC.Marc and Thirdwave AdvantEdge, there is no separation criterion defined since chip format...
A computational approach to nonparametric regression: bootstrapping cmars method
Yazıcı, Ceyda; Batmaz, İnci; Department of Statistics (2011)
Bootstrapping is a resampling technique which treats the original data set as a population and draws samples from it with replacement. This technique is widely used, especially, in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a special case of nonparametric regression, Conic Multivariate Adaptive Regression Splines (CMARS). Here, the CMARS method, which uses conic quadr...
A LINEAR MATHEMATICAL-MODEL FOR THE SEISMIC INPLANE BEHAVIOR OF BRICK MASONRY WALLS .2. DETERMINATION OF MODEL PARAMETERS THROUGH OPTIMIZATION USING EXPERIMENTAL-DATA
Sucuoğlu, Haluk; McNiven, Hugh (Wiley, 1984-01-01)
The parameters appearing in the mixture and effective modulus models proposed in Part 1 are determined through optimization by matching theoretical and experimental responses. The optimization analysis is performed in frequency space. The response quantities chosen to be matched are the complex frequency response functions (experimental and theoretical) relating the Fourier transforms of top and base accelerations of the wall. Computations in optimization analysis are carried out by introducing an object (e...
A regime switching model for temperature modeling and applications to weather derivatives pricing
Turkvatan, Aysun; Hayfavi, Azize; Omay, Tolga (2020-01-01)
In this study, we propose a regime-switching model for temperature dynamics, where the parameters depend on a Markov chain. We improve upon the traditional models by modeling jumps in temperature dynamics via the chain itself. Moreover, we compare the performance of the proposed model with the existing models. The results indicate that the proposed model outperforms in the short time forecast horizon while the forecast performance of the proposed model is in line with the existing models for the long time h...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. M. Köksalan and N. BASOZ, “A REPLICATION APPROACH TO INTERVAL ESTIMATION IN SIMULATION,” 1991, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57722.