an efficient sequential design for sensitivity experiments yubin tian school of science, beijing...
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An Efficient Sequential Design for Sensiti
vity Experiments Yubin Tian
School of Science, Beijing Institute of Technology
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Sensitivity experiments:
experimental settings in which each experimental unit has a critical stimulus level that cannot be observed directly.
Introduction
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Response Curve
2121 ,0),();( xGxF
F(x) is the distribution of the critical stimulus levels over the test specimens.
where, G is a known distribution function
The general version of F(x) is
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Observations
stimulus settings x1,…,xn, and corresponding response results y1,…,yn.
When xi is at or above the critical level of the ith unit, it responds and yi=1; otherwise, it does not respond and yi=0, i=1,…,n.
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The interested parameter
We are often interested in pth quantile of F(x), p ,
)()/1()/( 1112 pGp
Our goal
Make inference for p using small samples.
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Historic Data
In China, for explosives and materials, there
often exists a data set from the documented
method -------- up-and-down procedure for
analyzing the sensitivity.
When considering the availability, interest wo
uld aim to estimate the extreme quantile p by
limited sample size n.
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inappropriate for the existed methods
Based on such kind of historic data set, we can use Wu’s method, Neyer’s method, or Joseph’s method to estimate p.
However, for this directly using, there will cast many units in the subsequent sequential procedure to modify the characteristic of centering on 0.5 of this historic data set before quickly converging to the interest parameter p.
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Proposed methods
Our purpose is to develop a new sequential procedure that can make the estimate for p more precisely by quickly and efficiently exploiting the information in the tested data and known knowledge.
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Specification of the Prior
)()( 21 xGxF
Let =2/1, =1/1. Thus
)(
x
G
It is natural to assume that the prior distribution of is normal, the prior distribution of is lognormal, and they are independent.
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For the historic data set, the experiment settings
is a realization of the Markov chain with finite
state space { a0,…,ak-1} and transition
probability matrix
11
22
22
11
00
10000000
10000000
000000000
00000100
00000010
00000001
),(
kk
kk
pp
pp
pp
pp
pp
P
1,...,0),(
kja
Gp jj
where
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)ˆ,ˆ( Let be the MLE of (, ) based on
historic data set.
)ˆ,ˆ( P
1** ,...,1),ˆ,ˆ( miii
Now, with as transition probability, generate m1 Markov chain realizations, and obtain estimates of (, ).
)ˆ,ˆ( **iiP
2**,
**, ,...,1),ˆ,ˆ( mjjiji
Then with as transition probability, generate the m2 second-level Markov chain realizations and obtain estimates
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After the transformation from (, ) to (1,2), we specify the prior distribution for (1,2).
Let From.ˆ/ˆ1 2
1
***,
2
M
jijii M
)/ˆ,ˆ(),...,/ˆ,ˆ(111
**1
*1
*1 MMM
we calculate their means and variances and then specify the prior distribution of and .
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Assume we have collected data (x1, y1),…, ( xt, yt) (This includes the case of not having any data, for which t=0).
The New Sequential Design
Our objective: select a new level x* so that if we run there and obtain the result yx, the average posterior variance of with respect to yx, but conditioned on (x1, y1),…,(xt, yt), En[Var(|y1,…,yt,yx)], is minimized
Set xt+1=x*, make a run there and obtain the observation yt+1.
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Estimation of
After a certain fixed number, n, of observationsis obtained, we use the posterior expectation of
to estimate it.
),...,|( 1 nyyE
Let be the parameter space for =(1,2) and let xi(i1) take values in A. Let the subset of x1–2 for xA, be B.
Some notations
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High Order ApproximationTheorem 1. Suppose the following conditions hold:
(i) There exists an integer m such that the data set
{(xi, yi): i=1,…,m} has an overlap in responses and nonresponses.
(ii) -lnG(u) and –ln[1-G(u)] are convex. G(u) is strictly increasing at every u satisfying 0<G(u)<1.
(iii) The derivatives of five orders of G(u) are bounded over {u: 0<G(u)<1} .
(iv) A is bounded and B{u: 0<G(u)<1}. Also, B is compact and connected.
)}( ])}3[)(ˆ({6
])ˆ[(2
ˆ),...,|( 20,
10
0
10
1
nOkkhkn
khn
yyE uln
ijnllijun
ijnijijn
Then for nm Laplace formula produces the approximation
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Consistency of the posterior estimator
),...,|( 1 nyyE
nasBBBxnxnPn
ii
n
ii ,1}),{( 321
1
1
1
210
Theorem 2 Under conditions of Theorem 1
and following condition
is consistent.B1={(u,v): u=1, |v|<1<1},
B2={(u,v): u1, u>2>0,v=0},
B3={(u,v): u1, v0, u>31>0, h(u,v)> 32>0}
}4
)4)1(|1(|
1
12
1
2{
)4)1(|1(|4
4),(
2
22222
2222
2
v
vuu
u
uv
u
vu
vuuv
vvuh
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Comparisons in Simulation
We now compare the performance of ourmethod with other four methods through simulations. Under comparison are (a) the up and down method recommended
by ASTM (1995); (b) Wu’s recursive design (1985); (c) Neyer’s method (1994); (d) the modified Robbins-Monro procedure proposed by Joseph (2004).
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We use the logit model,
as the true distribution, where 1=1/2, 2=5.
)52
1exp(1
)52
1exp(
)(
x
xxF
We first simulate the historic data set with sample size 20 recommended by standards . Then simulate the data by using our method and other four methods with sample size n . We repeat above processes 500 times and obtain 500 estimates of .
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Simulating ResultsThe results of Monte Carlo mean and mean squared error of 500 estimates are given in following table.
0.1 (true value is 5.61) 0.2 (true value is 7.23)
Design n n
10 15 10 15
Mean MSE Mean MSE Mean MSE Mean MSE
Proposed method
5.68 1.32 5.62 1.28 7.31 0.95 7.24 0.90
Wu’s method 5.90 1.50 5.81 1.36 7.45 1.06 7.40 0.93
Neyer’s method 6.04 1.53 5.95 1.35 7.56 1.07 7.49 0.96
Roshan’s method
6.07 1.49 5.88 1.32 7.54 1.06 7.46 0.92
Up-and–down method
5.59 3.00 5.63 2.61 7.21 2.26 7.22 1.89
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The simulation study demonstrates the superior performance of our method over the other methods. Our method did a good job for commonly used extreme quantiles under the requested sample size.
Conclution
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Thank you for your attendance.
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All involved integrals are two dimensional integrals,
In this paper, we use the algorithm which is efficient and easy to implement given by James C. Fu and Liquan Wang (2002) to calculate this kind of integral. Here we don’t use MCMC algorithms because for the low dimensional integral MCMC does not perform very well and the functional forms of full conditionals of posterior distributions is not clear.
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Voelkel J. G.