By Jack P.C. Kleijnen
This is a brand new variation of Kleijnen’s complicated expository booklet on statistical equipment for the layout and research of Simulation Experiments (DASE). Altogether, this re-creation has nearly 50% new fabric no longer within the unique ebook. extra particularly, the writer has made major adjustments to the book’s association, together with putting the bankruptcy on Screening Designs instantly after the chapters on vintage Designs, and reversing the order of the chapters on Simulation Optimization and Kriging Metamodels. The latter chapters mirror how lively the examine has been in those areas.
The validation part has been moved into the bankruptcy on vintage Assumptions as opposed to Simulation perform, and the bankruptcy on Screening now has a piece on making a choice on the variety of replications in sequential bifurcation via Wald’s sequential likelihood ration try out, in addition to a bit on sequential bifurcation for a number of different types of simulation responses. while all references within the unique version have been put on the finish of the ebook, during this version references are positioned on the finish of every chapter.
From experiences of the 1st Edition:
“Jack Kleijnen has once more produced a state-of-the-art method of the layout and research of simulation experiments.” (William E. BILES, JASA, June 2009, Vol. 104, No. 486)
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Extra info for Design and Analysis of Simulation Experiments
The use of transformations illustrates that simulation analysts should be guided by knowledge of the real system and—if available— corresponding analytical models. 2 Linear Regression First we discuss basic linear regression analysis. Next we discuss slightly advanced linear regression analysis, which uses several F -statistics. These F -statistics are known to be sensitive to the classic regression assumptions; namely, the outputs are independently and normally distributed with a common variance.
This issue will be discussed Sect. 4. In this chapter we assume that the simulation outputs wi (i = 1, . . , n) are indeed normally and independently distributed with the same variance 2 ; obviously, these wi may have diﬀerent means in diﬀerent in(say) σw put combinations i. Let us initially—until the discussion after Eq. 23)— assume that the linear regression model deﬁned in Eq. 10) is a “valid” metamodel, which is deﬁned as follows. 4 A metamodel is valid if and only if its residual has zero mean: E(e) = 0.
Hence, some readers may wish to skip this advanced analysis, and proceed to Sect. 3. 10) where y = (y1 , . . , yn ) denotes the n-dimensional vector with the dependent variable and n denotes the number of simulated input combinations (runs, observations); X = (xi;j ) denotes the n × q matrix of independent (explanatory) regression variables with xi;j denoting the value of independent variable j in combination i (i = 1, . . , n; j = 1, . . , q); β = (β1 , . . , βq ) denotes the q-dimensional vector with regression parameters; and e = (e1 , .
Design and Analysis of Simulation Experiments by Jack P.C. Kleijnen