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A statistically rigorous approach for improving simulation methodology

J.J. Yi, D.J. Lilja, D.M. Hawkins
The Ninth International Symposium on High-Performance Computer Architecture, 2003. HPCA-9 2003. Proceedings.  
A formal methodology can provide a sound basis for drawing conclusions gathered from simulation results by adding statistical rigor and, consequently, can increase confidence in the simulation results.  ...  However, despite this continued dependence on simulators, statistically rigorous simulation methodologies are not typically used for computer architecture research.  ...  To improve the methodology of the first stage, we describe a statistically rigorous method of choosing the values of the processor parameters.  ... 
doi:10.1109/hpca.2003.1183546 dblp:conf/hpca/YiLH03 fatcat:jbl4ebel3vc5fpvadk7rsd5hce

Improving Computer Architecture Simulation Methodology by Adding Statistical Rigor

J.J. Yi, D.J. Lilja, D.M. Hawkins
2005 IEEE transactions on computers  
A formal methodology can provide a sound basis for drawing conclusions gathered from simulation results by adding statistical rigor and, consequently, can increase the architect's confidence in the simulation  ...  However, despite this dependence on simulators, statistically rigorous simulation methodologies are typically not used in computer architecture research.  ...  A preliminary version of this work was presented at the Ninth Annual International Symposium on High-Performance Computer Architecture [24] .  ... 
doi:10.1109/tc.2005.184 fatcat:xjsjqhvdhfcsbkdiukxs75i43q

The future of simulation: a field of dreams

J.J. Yi, L. Eeckhout, D.J. Lilja, B. Calder, L.K. John, J.E. Smith
2006 Computer  
Are simulation methodologies sufficiently defined to permit independent replication of results? How can we add scientific rigor to simulation methodologies?  ...  While there is a heavy (and growing) dependence on general simulation, architects use a wide variety of methodologies, benchmarks, and data sets.  ...  Further, by incorporating statistical rigor into simulation methodology, the researcher can run simulations efficiently, reduce the errors in the simulation methodology, and gain insight into the behavior  ... 
doi:10.1109/mc.2006.404 fatcat:yllmwsn3yjhyhgrwfbg2tx2fpu

With great responsibility comes great power: Overcoming methodological challenges for conducting power analysis in intensive longitudinal research

Ginette Lafit
2021 KU Leuven Open Science Day  
This method involves using a simulation approach that can be implemented to select the sample size for a variety of designs and research questions.  ...  We therefore discuss a methodological approach for conducting power analysis for intensive longitudinal study designs.  ... 
doi:10.21428/1192f2f8.480ec57e fatcat:xvema7o2u5afljeq45b2omn7ia

An Interdisciplinary Perspective to the Design and Decision Support of Integral Safety Systems

Christian Berger, Panagiotis Katsaros, Mahdi Bohlouli, Lefteris Angelis
2013 IFAC Proceedings Volumes  
We explore the interplay between key technology areas towards a holistic approach in the design and decision support of integral safety systems.  ...  Finally, we envision that rigorous design techniques based on models for human-machine interaction are essential for achieving adequate performance and robustness of integral safety systems.  ...  These tools offer the opportunity to combine a wide range of statistical methodologies and models that are able to cooperate for processing massive data from heterogeneous sources and producing output  ... 
doi:10.3182/20130904-3-uk-4041.00045 fatcat:w3eg54qcsnhibjumrn4bwncgre

Empirical Research in Software Process Modeling: A Systematic Literature Review

Xu Bai, He Zhang, LiGuo Huang
2011 2011 International Symposium on Empirical Software Engineering and Measurement  
Recognized as one of the powerful technologies methodology and the rigors of empirical studies.  ...  , especially in the perspectives of empirical research methodologies and rigors in reporting research.  ...  CONCLUSIONS AND FUTURE WORK This paper reports the state-of-the-practice of the empirical research in SPM based on our SLR results and suggests potential improvements for future SPM-related empirical studies  ... 
doi:10.1109/esem.2011.43 dblp:conf/esem/BaiZH11 fatcat:svyxtmcfrndgvij7y46ajviyfe

Retrofit of Heat Exchanger Networks for Optimising Crude Oil Distillation Operation

L.M. Ochoa-Estopier, M. Jobson, R. Smith
2013 Chemical Engineering Transactions  
The present work provides a new methodology for optimising crude oil distillation systems.  ...  The proposed approach determines the optimum operating conditions for the crude oil distillation unit, where the objective is maximum net profit, while proposing retrofit modifications for the heat exchanger  ...  For this purpose, a value of 1 or 0 is allocated to each scenario, depending on whether the rigorous simulation has converged.  ... 
doi:10.3303/cet1335022 doaj:78b04f4059b84ec79f8c89133e2d2775 fatcat:33wbhgnlhzhdhh7hov5tgcttnu

Page 85 of American Ceramic Society Bulletin Vol. 75, Issue 10 [page]

1996 American Ceramic Society Bulletin  
A novel teaching approach provides students with tools—factorial design, empirical model building and response surface methodology—to exercise with experimental design within a simulated laboratory environment  ...  However, it was quite common for the porosity to fluctuate at 2-4% and occa- sionally to 5%. A rigorous SPC program was introduced that, in conjunction with descriptive statistics!  ... 

Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference

E. Castro, C. Ahnert, O. Buss, N. García-Herranz, A. Hoefer, D. Porsch
2016 Annals of Nuclear Energy  
The performance of this methodology is assessed in a blind test approach, where we use measurements of a given reactor cycle to improve the prediction of the subsequent cycle.  ...  As it turns out, the resulting improvement of the prediction quality is impressive.  ...  However, the evaluation of statistical confidence bounds for nuclear safety parameters requires a consistent statistical framework to combine and propagate uncertainties in nuclear data, technological  ... 
doi:10.1016/j.anucene.2016.05.007 fatcat:wbsax5e7tfh3nk7zg4e6gvtxni

Predictive Modelling Based on Statistical Learning in Biomedicine

Olaf Gefeller, Benjamin Hofner, Andreas Mayr, Elisabeth Waldmann
2017 Computational and Mathematical Methods in Medicine  
Acknowledgments We express our appreciation to all authors for their informative contributions and to all reviewers for their support and constructive criticism making this special issue possible.  ...  Modern biomedical applications of this type of statistical learning are also sketched to provide an overview not only of recent methodologic improvements but also of practical implementation of boosting  ...  The modern term "statistical learning" for this fusion of methodology from different scientific areas could already be found in the scientific literature (see Vapnik [1, 2] ), but its meaning was slightly  ... 
doi:10.1155/2017/4041736 pmid:29093744 pmcid:PMC5637867 fatcat:pa6sd5d3n5dxnawdxwevs7i6ay

Page 204 of Geographical Analysis Vol. 38, Issue 2 [page]

2006 Geographical Analysis  
low val 1 considerable improvement in the analysis : , yR procedure prov MCP approaches with a correction for servative yroposed by Getis and Ord (2000).  ...  Currently conservative approach such as the methods of neen iddressed we propose the use of a more powerful pro and Sidak [ at r ich controls for the R (Benjamini and Hochberg 1995).  ... 

The Use of Reduced Models in the Optimisation of Energy Integrated Processes

R. Smith, L.M. Ochoa-Estopier, M. Jobson
2013 Chemical Engineering Transactions  
The importance of exploiting degrees of freedom within the process for improving energy performance has been a feature of process integration from the earliest days.  ...  A case study will be presented to demonstrate operational optimization and retrofit for energy and for optimization of heat integrated systems to maximise process profit through manipulation of feeds and  ...  granted for the development of this work.  ... 
doi:10.3303/cet1335023 doaj:bcbd9ab04ab542f594194a6cf6f2a41f fatcat:hf2cqhswdnfxfkesxtcjhwc2ba

An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors

Peyman Jafari, Francisco Azuaje
2006 BMC Medical Informatics and Decision Making  
There is a need for more rigorous reporting of important experimental factors such as statistical power and sample size, as well as the correct description and justification of statistical methods applied  ...  This paper highlights the importance of defining a minimum set of information required for reporting on statistical design and analysis of expression data.  ...  Farahmand at UU for inviting and facilitating PJ's visit. We thank the three reviewers for their comments and suggestions to help us to improve the quality of this manuscript.  ... 
doi:10.1186/1472-6947-6-27 pmid:16790051 pmcid:PMC1523197 fatcat:6bqrluwqubco7ljh27q7ioihrq

Page 763 of Journal of the Atmospheric Sciences Vol. 64, Issue 3 [page]

2007 Journal of the Atmospheric Sciences  
to more rigorously validate model simulations.  ...  The present study adopts an approach similar to that presented in Eitzen and Xu (2005, hereafter EX05) except for including simulations of more size categories  ... 

From Measurement to Analysis Reporting: Grand Challenges in Nutritional Methodology

Tapan Mehta, David B. Allison
2014 Frontiers in Nutrition  
Many of our methods have improved substantially over the years, yet there are always healthy calls for ever greater rigor in nutrition research (1) (2) (3) .  ...  Novel and valid analytic approaches need to be proposed to address statistical issues to overcome the limitations of existing statistical approaches (26) .  ...  Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest  ... 
doi:10.3389/fnut.2014.00006 pmid:25590036 pmcid:PMC4290856 fatcat:cd72dct2yrhljnvfpsnheq3o5q
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