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Fingerprint Dive into the research topics where John Lamb is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Data envelopment analysis Engineering & Materials Science
Data Envelopment Analysis Mathematics
Location Problem Mathematics
Center Problem Mathematics
Cycle Mathematics
Resampling Mathematics
Cloud computing Engineering & Materials Science
Regression analysis Engineering & Materials Science

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Research Output 2001 2019

23 Downloads (Pure)

Making Cornish-Fisher fit for risk measurement

Lamb, J. D., Monville, M. E. & Tee, K-H., Jun 2019, In : Journal of Risk. 21, 5, p. 53–81 29 p.

Research output: Contribution to journalArticle

File
Risk measurement
Skewness
Kurtosis
Conditional value at risk
Estimation error

A management view of security and cloud computing

Anomelechi, N., Cooper, W., Duncan, B. & Lamb, J., Feb 2018, The Ninth International Conference on Cloud Computing, GRIDs, and Virtualization. Duncan, B., Lee, Y. W. & Olmsted, A. (eds.). IARIA, p. 25-30 6 p. (Cloud Computing 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Open Access
Cloud computing
Data privacy
Costs
Industry

Making Cornish–Fisher Distributions Fit

Lamb, J. D., Monville, M. E. & Tee, K-H., 2016, 59 p.

Research output: Working paper

File
Skewness
Kurtosis
Conditional value at risk
Mean-variance
Goodness of fit test

Data Envelopment Analysis (DEA) Integrated Risk Assessment Technique on Hedge Funds Investment: Theory and Practical Application

Lamb, J. & Tee, K-H., Oct 2012, Risk Assessment and Management. Zhang, Z. (ed.). United States: Academy Publish, p. 73–84 12 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

38 Citations (Scopus)

Data envelopment analysis models of investment funds

Lamb, J. D. & Tee, K-H., 1 Feb 2012, In : European Journal of Operational Research. 216, 3, p. 687–696 9 p.

Research output: Contribution to journalArticle

Data envelopment analysis
Data Envelopment Analysis
Diversification
Stochastic Dominance
Returns to Scale