1 / 39

Further Statistical Issues

Further Statistical Issues. Chapter 12. Last revision June 9, 2003. What We’ll Do. Random-number generation Generating random variates Nonstationary Poisson processes Variance reduction Sequential sampling Designing and executing simulation experiments. Backup material:

wade-mcintosh
Download Presentation

Further Statistical Issues

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Further Statistical Issues Chapter 12 Last revision June 9, 2003 Chapter 12 – Further Statistical Issues

  2. What We’ll Do ... • Random-number generation • Generating random variates • Nonstationary Poisson processes • Variance reduction • Sequential sampling • Designing and executing simulation experiments • Backup material: • Appendix C: A Refresher on Probability and Statistics • Appendix D: Arena’s Probability Distributions Chapter 12 – Further Statistical Issues

  3. f(x) 1 x 1 0 Random-Number Generators (RNGs) • Algorithm to generate independent, identically distributed draws from the continuous UNIF (0, 1) distribution • These are called random numbersin simulation • Basis for generating observations from all other distributions and random processes • Transform random numbers in a way that depends on the desired distribution or process (later in this chapter) • It’s essential to have a good RNG • There are a lot of bad RNGs — this is very tricky • Methods, coding are both tricky Chapter 12 – Further Statistical Issues

  4. The Nature of RNGs • Recursive formula (algorithm) • Start with a seed (or seed vector) • Do something weird to the seed to get the next one • Repeat … generate the same sequence • Will eventually repeat (cycle) … want long cycle length • Not really “random” as in unpredictable • Does this matter? Philosophically? Practically? • Want to “design” RNGs • Long cycle length • Good statistical properties (uniform, independent) -- tests • Fast • Streams (subsegments) – many and long (for variance reduction … later) • This is not easy! • Doing something weird isn’t enough Chapter 12 – Further Statistical Issues

  5. Linear Congruential Generators(LCGs) • The most common of several different methods • But not the one in Arena (though it’s related) … more later • Generate a sequence of integers Z1, Z2, Z3, … via the recursion Zi = (a Zi–1 + c) (mod m) • a, c, and m are carefully chosen constants • Specify a seed Z0 to start off • “mod m” means take the remainder of dividing by m as the next Zi • All the Zi’s are between 0 and m – 1 • Return the ith “random number” as Ui = Zi / m Chapter 12 – Further Statistical Issues

  6. Example of a “Toy” LCG • Parameters m = 63, a = 22, c = 4, Z0 = 19: Zi = (22 Zi–1 + 4) (mod 63), seed with Z0 = 19 i 22 Zi–1+4 ZiUi 0 19 1 422 44 0.6984 2 972 27 0.4286 3 598 31 0.4921 4 686 56 0.8889 : : : : 61 158 32 0.5079 62 708 15 0.2381 63 334 19 0.3016 64 422 44 0.6984 65 972 27 0.4286 66 598 31 0.4921 : : : : • Cycling — will repeat forever • Cycle length £m • (could be << m depending • on parameters) • Pick mBIG • But that might not be enoughfor good statistical properties Chapter 12 – Further Statistical Issues

  7. Issues with LCGs • Cycle length: <m • Typically, m = 2.1 billion (= 231 – 1) or more • Which used to be a lot … more later • Other parameters chosen so that cycle length = m or m – 1 • Statistical properties • Uniformity, independence • There are many tests of RNGs • Empirical tests • Theoretical tests — “lattice” structure (next slide …) • Speed, storage — both are usually fine • Must be carefully, cleverly coded — BIG integers • Reproducibility — streams (long internal subsequences) with fixed seeds Chapter 12 – Further Statistical Issues

  8. Issues with LCGs (cont’d.) • “Regularity” of LCGs (and other kinds of RNGs): For the earlier “toy” LCG … • “Design” RNGs: dense lattice in high dimensions • Other kinds of RNGs — longer memory in recursion, combination of several RNGs Plot of Ui vs. i Plot of Ui+1 vs. Ui “Random Numbers Fall Mainly in the Planes” — George Marsaglia Chapter 12 – Further Statistical Issues

  9. The Original (1983) Arena RNG • LCG with: m = 231 – 1 = 2,147,483,647 a = 75 = 16,807 c = 0 • Cycle length = m – 1 = 2.1  109 • Ten different automatic streams with fixed seeds • In its day, this was a good, well-tested generator in an efficient code • But current computer speed make this cycle length inadequate • Exhaust in 10 minutes on 2 GHz PC if we just generate • Can get this generator (not recommended) • Place a Seeds module (Elements panel) in your model Chapter 12 – Further Statistical Issues

  10. Zn / 4294967088 if Zn > 0 4294967087 / 4294967088 if Zn = 0 Un = The Current (2000) Arena RNG • Uses some of the same ideas as LCG • Modulo division, recursive on earlier values • But is not an LCG • Combines two separate component generators • Recursion involves more than just the preceding value • Combined multiple recursive generator (CMRG) An = (1403580 An-2 – 810728 An-3) mod 4294967087 Bn = (527612 Bn-1 – 1370589 Bn-3) mod 4294944443 Zn = (An – Bn) mod 4294967087 Seed = a six-vector of first three An’s, Bn’s Two simultaneous recursions Combine the two The next random number Chapter 12 – Further Statistical Issues

  11. The Current (2000) Arena RNG – Properties • Extremely good statistical properties • Good uniformity in up to 45-dimensional hypercube • Cycle length = 3.1  1057 • To cycle, all six seeds must match up • On 2 GHz PC, would take 2.8  1040 millennia to exhaust • Under Moore’s law, it will be 216 years until this generator can be exhausted in a year of nonstop computing • Only slightly slower than old LCG • And RNG is usually a minor part of overall computing time Chapter 12 – Further Statistical Issues

  12. The Current (2000) Arena RNG – Streams and Substreams • Automatic streams and substreams • 1.8  1019 streams of length 1.7  1038 each • Each stream further divided into 2.3  1015 substreams of length 7.6  1022 each • 2 GHz PC would take 669 million years to exhaust a substream • Default stream is 10 (historical reasons) • Also used for Chance-type Decide module • To use a different stream, append its number after a distribution’s parameters • For example, EXPO(6.7, 4) to use stream 4 • When using multiple replications, Arena automatically advances to next substream in each stream for the next replication • Helps synchronize for variance reduction Chapter 12 – Further Statistical Issues

  13. Generating Random Variates • Have: Desired input distribution for model (fitted or specified in some way), and RNG (UNIF (0, 1)) • Want: Transform UNIF (0, 1) random numbers into “draws” from the desired input distribution • Method: Mathematical transformations of random numbers to “deform” them to the desired distribution • Specific transform depends on desired distribution • Details in online Help about methods for all distributions • Do discrete, continuous distributions separately Chapter 12 – Further Statistical Issues

  14. –2 0 3 Generating from Discrete Distributions • Example: probability mass function • Divide [0, 1] • Into subintervals of length 0.1, 0.5, 0.4 • Generate U ~ UNIF (0, 1) • See which subinterval it’s in • Return X = corresponding value Chapter 12 – Further Statistical Issues

  15. Discrete Generation: Another View • Plot cumulative distribution function; generate U and plot on vertical axis; read “across and down” • Inverting the CDF • Equivalent to earlier method Chapter 12 – Further Statistical Issues

  16. Generating from Continuous Distributions • Example: EXPO (5) distribution Density (PDF) Distribution (CDF) • General algorithm (can be rigorously justified): 1. Generate a random number U ~ UNIF(0, 1) 2. Set U = F(X) and solve for X = F–1(U) • Solving analytically for X may or may not be simple (or possible) • Sometimes use numerical approximation to “solve” Chapter 12 – Further Statistical Issues

  17. Generating from Continuous Distributions (cont’d.) • Solution for EXPO (5) case: Set U = F(X) = 1 – e–X/5 e–X/5 = 1 – U –X/5 = ln (1 – U) X = – 5 ln (1 – U) • Picture (inverting the CDF, as in discrete case): Intuition (garden hose): More U’s will hit F(x) where it’s steep This is where the density f(x) is tallest, and we want a denser distribution of X’s Chapter 12 – Further Statistical Issues

  18. Nonstationary Poisson Processes • Many systems have externally originating events affecting them — e.g., arrivals of customers • If process is stationary over time, usually specify a fixed interevent-time distribution • But process could vary markedly in its rate • Fast-food lunch rush • Freeway rush hours • Ignoring nonstationarity can lead to serious model and output errors • Already seen this — automotive repair shop,Chapter 5 Chapter 12 – Further Statistical Issues

  19. Nonstationary Poisson Processes – Definition • Usual model: nonstationary Poisson process: • Have a rate functionl(t) • Number of events in [t1, t2] ~ Poisson with mean • Issues: • How to estimate rate function? • Given an estimate, how to generate during simulation? l(t) t Chapter 12 – Further Statistical Issues

  20. Nonstationary Poisson Processes – Estimating the Rate Function • Estimation of the rate function • Probably the most practical method is piecewise constant • Decide on a time interval within which rate is fixed • Estimate from data the (constant) rate during each interval • Be careful to get the units right • Other (more complicated) methods exist in the literature Chapter 12 – Further Statistical Issues

  21. Nonstationary Poisson Processes – Generation • Arena has a built-in method to generate, assuming a piecewise-constant rate function • ArrivalSchedule in Create module – auto repair (Model 5-2) • Method is to invert a rate-one stationary Poisson process against the cumulate rate function L • Similar to inverting CDF for continuous random-variable generation • Exploits some speed-up possibilities • Details in Help topic “Non-Stationary Exponential Distribution” • Alternative method: “thinning” of a stationary Poisson process at the peak rate Chapter 12 – Further Statistical Issues

  22. Variance Reduction • Random input Þ random output (RIRO) • In other words, output has variance • Higher output variance means less precise results • Would like to eliminate or reduce output variance • One (bad) way to eliminate: replace all input random variables by constants (like their mean) • Will get rid of random output, but will also invalidate model • Thus, best hope is to reduce output variance • Easy (brute-force) variance reduction: just simulate some more • Terminating: additional replications • Steady-state: additional replications or a longer run Chapter 12 – Further Statistical Issues

  23. Variance Reduction (cont’d.) • But sometimes can reduce variance without more runs — free lunch (?) • Key: unlike physical experiments, can control randomness in computer-simulation experiments via manipulating the RNG • Re-use the same “random” numbers either as they were, in some opposite sense, or for a similar but simpler model • Several different variance-reduction techniques • Classified into categories — common random numbers, antithetic variates, control variates, indirect estimation, … • Usually requires thorough understanding of model, “code” • Will look only at common random numbers in detail Chapter 12 – Further Statistical Issues

  24. Common Random Numbers (CRN) • Applies when objective is to compare two (or more) alternative configurations or models • Interest is in difference(s) of performance measure(s) across alternatives • Model 7-2 (small mfg. system), total avg. WIP output — two alternatives A. Base case (as is) B. 3.5% increase in business (interarrival-time mean falls from 13 to 12.56 minutes) • Same run conditions, but change model into Model 12-1: • Remove Output File Total WIP History.dat • Add entry to Statistic module to compute and save to a .dat file the total avg. WIP on each replication Chapter 12 – Further Statistical Issues

  25. The “Natural” Comparison • Run case A, make the change to get to case B and run it, then Compare Means via Output Analyzer: • Difference is not statistically significant • Were the runs of A and B statistically independent? • Did we use the same random numbers running A and B? • Did we use the same random numbers intelligently? Chapter 12 – Further Statistical Issues

  26. CRN Intuition • Get sharper comparison if you subject all alternatives to the same “conditions” • Then observed differences are due to model differences rather than random differences in the “conditions” • Small mfg. system: For both A and B runs, cause: • The “same” parts arrive at the same times • Be assigned same attributes (job type) • Have the same process times at each step • Then observed differences will be attributable to system differences, not random bounce • There isn’t any random bounce Chapter 12 – Further Statistical Issues

  27. Synchronization of Random Numbers in CRN • Generally, get CRN by using the same RNG, seed, stream(s) for all alternatives • Already are using the same stream, default = stream 10 • But its usage generally gets mixed up across alternatives • Must use the same random numbers for the same purposes across the alternatives — synchronization of random-number usage • Usually requires some work, understanding of model • Usually use different streams in the RNG • Usually different ways to do this in a given model • Sometimes can’t synchronize completely for complex models — settle for partial synchronization Chapter 12 – Further Statistical Issues

  28. Synchronization of Random Numbers in CRN (cont’d.) • Synchronize by source of randomness (we’ll do) • Assign stream to each point of variate generation • Separate random-number “faucets” … extra parameter in r.v. calls • Model 12-1: 14 sources of randomness, separate stream for each (see book for details), modify into Model 12-2 • Fairly simple but might not ensure complete synchronization; still usually get some benefit from this • Synchronize by entity (won’t do — see Exercises) • Pre-generate every possible random variate an entity might need when it arrives, assign to attributes, used downstream • Better synchronization insurance but uses more memory • Across replications, RNG automatically goes to next substream within each stream • Maintains synchronization if alternatives disagree on number of random numbers used per stream per replication Chapter 12 – Further Statistical Issues

  29. Effect of CRN • CRN was no more computational effort • Effect here is fairly dramatic, but it will not always be this strong • Depends on “how similar” A and B are “Natural” Comparison Synchronized CRN Chapter 12 – Further Statistical Issues

  30. = 0 if indep > 0 with CRN CRN Statistical Issues • In Output Analyzer, Analyze > Compare Means option, have choice of Paired-t or Two-Sample-t for c.i. test on difference between means • Paired-t subtracts results replication by replication — must use this if doing CRN • Two-Sample-t treats the samples independently — can use this if doing independent sampling, often better than Paired-t • Mathematical justification for CRN • Let X = output r.v. from alternative A, Y = output from B Var(X – Y) = Var(X) + Var(Y) – 2 Cov(X, Y) Chapter 12 – Further Statistical Issues

  31. Other Variance-Reduction Techniques • For single-system simulation, not comparisons • Antithetic variates: make pairs of runs • Use “U’s” on first run of pair, “1 – U’s” on second run of pair • Take average of two runs: negatively correlated, reducing variance of average • Like CRN, must take care to synchronize • Control variates • Use internal variate generation to “control” results up, down • Indirect estimation • Simulation estimates something other than what you want, but related to what you want by a fixed formula Chapter 12 – Further Statistical Issues

  32. Sequential Sampling • Always try to quantify imprecision in results • If imprecision is “small enough,” you’re done • If not, need to do something to increase precision • Just saw one way: variance-reduction techniques • Obvious way to increase precision: keep simulating one more “step” at a time, quit when you achieve desired precision • Terminating models: “step” = another replication • Cannot extend length of replications — that’s part of the model • Steady-state models: • “step” = another replication if using truncated replications, or • “step” = some extension of the run if using batch means Chapter 12 – Further Statistical Issues

  33. Sequential Sampling — Terminating Models • Modify Model 12-2 (small mfg., base case, with random-number streams) into Model 12-3 • Made 25 replications, get 95% c.i. on expected average Total WIP as 13.03 ± 1.28 • Suppose the ±1.28 is too big — want to reduce to ±0.5 • Approximate formulas from Sec. 6.3: need 124 or 164 total replications (depending on which formula) rather than 25 • Instead, just make one more at a time, re-compute c.i., stop as soon as half-width is less than 0.5 • “Trick” Arena to keep making more replications until c.i. half-width < tolerance = 0.5 Chapter 12 – Further Statistical Issues

  34. Sequential Sampling — Terminating Models (cont’d.) • Recall: With > 1 replication, automatically get cross-replication 95% c.i.’s for expected values in Category Overview report • Related internal Arena variables: • ORUNHALF(Output ID) = half-width of 95% c.i. using completed replications (Output ID = Avg Total WIP) • MREP = total number of replications asked for (initially, MREP = Number of Replications in Run > Setup > Replication Parameters) • NREP = replication number now in progress (= 1, 2, 3, …) • Use, manipulate these variables Chapter 12 – Further Statistical Issues

  35. Sequential Sampling — Terminating Models (cont’d.) • Initially set MREP to huge value in NumberofReplications in Run>Setup>Replication Parameters • Keep replicating until we cut off when half-width £ 0.5 • Add a logic (in a submodel) to sense when done • Create one “control” entity at beginning of each replication • Control entity immediately checks to see if: • NREP£ 2: This is beginning of 1st or 2nd replication • ORUNHALF(Avg Total WIP) > 0.5: c.i. on completed replications is still too big In either case, keep going with this replication (and the next one too); control entity is Disposed and takes no action • If both conditions are false, Control entity Assigns MREP = NREP to stop after this replication, and is Disposed Chapter 12 – Further Statistical Issues

  36. Sequential Sampling — Terminating Models (cont’d.) • Details • This overshoots required number of replications by one • In Assign module setting MREP to NREP, have to select Type = Other since MREP is a built-in Arena variable • Results: Stopped with 232 total replications, yielding half width = 0.49699 (barely less than 0.5) • Different from earlier number-of-replications approximations (they’re just that) • Generalizations • Precision demands on several outputs • Relative-width stopping: (half-width) / (pt. estimate) small Chapter 12 – Further Statistical Issues

  37. Sequential Sampling — Steady-State Models • If doing truncated-replications approach to steady-state statistical analysis • Same strategy as above for terminating models • Warm-up Period specified in Run > Setup > Replication Parameters • Err on the side of too much warmup • Point-estimator bias is especially dangerous in sequential sampling • Getting tight c.i. centered in the wrong place • The tighter the c.i. demand, the worse the coverage probability Chapter 12 – Further Statistical Issues

  38. Sequential Sampling — Steady-State Models (cont’d.) • Batch-means approach • Model 12-4: modification of Model 7-4 (small mfg. system) – want half-width on E(average WIP) to be < 1 • Keep extending the run to reduce c.i. half-width • Use automatic run-time batch-means 95% c.i.’s • Stopping criterion: Terminating Condition field of Run > Setup > Replication Parameters • Half-width variables are THALF(Tally ID) or DHALF(Dstat ID) • For us, condition is DHALF(Total WIP) < 1 • Remove all other stopping devices from model • If “Insuf” or “Corr” would be returned because of too little data, half-width variables set to huge value — keep going • Could demand multiple smallness criteria, relative precision (use TAVG, DAVG variables for point-estimate denominator) Chapter 12 – Further Statistical Issues

  39. Designing and Executing Simulation Experiments • Think of a simulation model as a convenient “testbed” or laboratory for experimentation • Look at different output responses • Look at effects, interaction of different input factors • Apply classical experimental-design techniques • Factorial experiments — main effects, interactions • Fractional-factorial experiments • Factor-screening designs • Response-surface methods, “metamodels” • CRN is “blocking” in experimental-design terminology • Process Analyzer (PAN) provides a convenient way to carry out a designed experiment • See Chapt. 6 for an example of using PAN for a factorial experiment Chapter 12 – Further Statistical Issues

More Related