Optimal Bayesian estimation of the median effective dose

Optimal Bayesian estimation of the median effective dose

Journal of Statistical Planning and Inference 69 18 (1988) 69-81 North-Holland OPTIMAL BAYESIAN EFFECTIVE DOSE M.K. ESTIMATION OF THE MEDIAN ...

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Journal

of Statistical

Planning

and Inference

69

18 (1988) 69-81

North-Holland

OPTIMAL BAYESIAN EFFECTIVE DOSE M.K.

ESTIMATION

OF THE MEDIAN

KHAN

Department of Mathematical Sciences, Kent State University, Kent, OH 44242, USA Received

6 June

Recommended

1986; revised manuscript

Abstract: This paper quanta1

response

Fisher

information

positive

received

22 December

1986

by S. Zacks

function

provides

bioassay,

some properties

attribute

function

(arising

and dilution

in Probit,

of order 2 and consequently

as well as a function

of the Fisher

life testing

Logit

unimodal

of the design level. A method

information

function

assay models.

and extreme

value

as a function

of the unknown

is provided

for obtaining

arising

It is shown models)

in

that the

is a totally parameter

estimates

of the shift

parameter (median effective dose when scale parameter is known for symmetric tolerance densities) in these models from Bayesian and adaptive Bayesian points of view. We assume that the prior

distribution

belongs

to a class containing

line besides the rectangular Fisher information under certain the median

densities.

function.

conditions effective

Polya

Optimality

densities

is defined

It is shown that the optimal

Bayesian

on the model and the prior distributions.

AMS Subject Classification: Primary Key words and phrases: Attribute bioassays;

2 defined

estimates Optimal

over the real the expected

exist and are unique Bayesian

estimates

dose for a two stage design set up for Logit model are provided

prior distributions. It is shown that the tables of optimal uniform prior can be used for logistic prior as well.

response

of order

in terms of maximizing

62K99;

Secondary

life testing;

dilution

Bayesian

estimates

obtained

of

for some for the

62F15. assays;

Fisher

information;

quanta1

total positivity.

1. Introduction In quanta1 response bioassay (QRB), dilution assays (DA) and attribute life testing (ALT) models the only observable random variable is the number of responses, J(X), having a binomial distribution with parameters N, F(x, 0) =p, where F is assumed to be a known cumulative distribution function (called the tolerance distribution) and 8 is an unknown parameter which needs to be estimated. The variable x is under the control of the experimenter - called a design level and usually represents the log of the dose administered in QRB, log of the amount of dilution in DA and the log of time of inspection in ALT. In dilution assays x represents the natural log of the amount of dilution and 8 is related to the parameter of interest, namely, the concentration of the virus (or other organism under investigation) in the 0378.3758/88/$3.50

0

1988, Elsevier

Science Publishers

B.V. (North-Holland)

70

M.K. Khan / Optimal Bayesian estimation

solution or sample. N represents the total number of identical units over which the experiment was observed and is also specified before the experiment is conducted. Essentially, QRB differs from both ALT and DA due to different choice of F. In QRB, F is usually assumed to be the normal, logistic or some other symmetric cumulative distribution function (CDF) (Finney (1978), Cox (1970)). In ALT and DA, F could be taken as the extreme value distribution (Fraser (1979), Fisher (1922)). The parameter 19is a pair (/3e, &) such that F(x, 19)= F(po+/31x). In DA, j3, is taken to be 1. Marks (1962) and Freeman (1970) studied QRB models and assumed that fir is a known constant. We will assume this as well throughout this paper. The aim is to estimate the median effective dose LD,,, i.e., F(LD,,, 0) = + where 8 is -&, and pi = 1. Marks (1962) considered the Probit model and assumed that &, had a normal prior distribution while Freeman (1970) used the Logit model and assumed that &, had a logistic prior distribution. We will keep the class of prior distributions and the choice of F to contain the above mentioned distributions. In the next section, some properties of the Fisher information function are obtained when the parameter of interest is the shift parameter. A necessary and sufficient condition (for symmetric tolerance distributions) is provided under which the Fisher information function has a finite global point of maximum when considered as a function of the design level. The use of the totally positive functions of order 2 (TP2) [and their sub class called Polya functions of order 2 (PF2)] in reliability theory (Barlow and Proschan (1975), Barlow and Marshall (1964)), Economics (Karlin (1959)), approximation theory and many other branches of mathematics (Karlin (1968)) is well known. In Section 2 it is shown that the Fisher information functions associated with QRB, DA and ALT models are TP2 as a function of the parameter and the design level. This, besides other aspects, shows that the Fisher information function (as a function of the design level) is unimodal. In Section 3 the optimality criterion is defined. In Section 4 the problem of design of experiment is discussed. It is shown that when a prior knowledge of the parameter is available, a one point Bayesian design provides more expected information than a sequence of ad hoc designs for the same number of experimental units. Furthermore, we provide the existence and uniqueness results of optimal Bayesian estimates. In Section 5, a two stage adaptive design is obtained whose first stage coincides with the optimal Bayesian experimental design. Some examples are provided for the Logit model when the prior distribution is uniform and logistic density respectively. It is shown that one can use the table of optimal estimates corresponding to the uniform density for the logistic prior as well. Finally, a short discussion is provided in the last section.

2. Properties

of the Fisher information

function

Maximization of the Fisher information function is often used as the criterion of optimality for non-linear models (Chernoff (1951) and Zacks (1977)). In the fol-

M.K. Khan / Optimal Bayesian estimation

lowing

we define the optimality

criterion.

Since we assume that /3i is a known

11

con-

stant,wecantakeittobel.AnddefineF(x,8)=F(x-8);-oo
- e)( 1 - F(X - e))].

(1)

One example of the maximization of Z(e, x) in ALT models (for the case of negative exponential distribution) was considered by Zacks (1977) in relation to applications in reliability theory. It was assumed that B (8 being the scale parameter) had a gamma prior distribution. Khan (1984) considered the same problem of maximization of Z(e, x) from a non-Bayesian point of view. Note that due to our assumptions that the density f(u) is continuously differentiable and unimodal, Z(u)+0 as The following lemma characterizes the unimodality of the Z(x- 0) as a IxI+m. function of x (and fixed t9) for the symmetric tolerance densities. Lemma 1. Let f(u) be a symmetric (about 0) and unimodal density. The Fisher information function Z(u) as given in (1) has a global point of maximum at u =0 if and only if

(F(u) -+)2+(1

-f2(u)/f2(0))

Vu>O.

(2)

Proof. Note that if F(u) is the CDF then by symmetry F(u)( 1 -F(u)) = $ - (F(u) - +)2. Now, Z(U)SZ(O) for all u>O if and only if +f 2(u>if2(0)F(u)(1 -F(u)), which proves the lemma. Example 1. Logit and Probit (Finney (1978)). The Probit

models are two of the most widely used models in QRB model takes ‘11

F(u) = (27~~ 1’2

exp(-+t2)dt,

--03
Sm while the Logit model

assumes

F(u)=(l+exp(-u)))‘,

that -m
For a comparison of these and some other models see Finney (1978). For the Logit model it is trivial to show that the Fisher information function is unimodal. For the Probit model, the inequality in Lemma (1) can be used to prove the unimodality of the Fisher information function. Indeed (Johnson and Kotz (1970)), @(x)5 +[l + (1 - exp(-x2))“2] for all x, this implies the condition of Lemma 1.

72

A4.K. Khan / Optimal Bayesian estimation

To study the unimodality (not necessarily symmetric) Definition

1 (Karlin

variables ranging if for all x,
and other properties of the Fisher information function given in (1) we use the concept of TP2 functions.

(1968) p. 11). A real non-negative

function

Z(X,y) of two

over linearly ordered sets X and Y, respectively, is said to be TP2 yl
Z(%,Y,)

4X,,Y,)

>.

4X29Y1)

Z(X29Y2)

-

*

An important specialization occurs if Z(x, y) can be written as Z(x-y) where X and Y are both the real line. In this case Z(U) is called a PF2. Another characterization of PF2 functions, which is sometimes easier to verify for differentiable functions, is that log(Z(u)) be a concave function (see Barlow and Proschan (1975)). One of the properties of the PF2 functions is their unimodality. The following lemma will be needed in the sequel. Its proof follows by showing that log(Z(u)) is concave. Lemma 2. For the,Z(& x) as given in (I), where f(u) is the density, the following

two

statements are equivalent: (i) I(@ x) is TP2 as a function of x and 13. (ii) 2f’(x)/f(x) -f(x)/F(x) + r (x ) IS ’ d ecreasing Vx, where r(x) is the failure rate associated with the tolerance density f(x), and f’(x) is the first derivative of f(x). Example 2. The Fisher information functions associated with the Logit and Probit models are TP2. It is simple to show that the Fisher information function for the Logit model is TP2. In order to show that the same is true for the Probit model, let r(x) [email protected](x)/(l - Q(x)) be the failure rate of the standard normal density. It is proved by Sampford (1953) that the failure rate is convex and 0
of the important

results

related

to the TP2

functions

is their

diminishing property (VDP) (Karlin (1968) pp. 20-22). We use this property tion 4 to show that the optimal Bayesian estimates exist and are unique.

variation in Sec-

(1975) p. 93). Let K(x, y) be a TP2 function defined on the Euclidean plane. Let h(x) be a bounded and Bore1 measurable function on the real line. Let the transformation Lemma 3 (Barlow and Proschan

co H(x) = .r--m

K(x, y)h(y) dy

be finite for each x in (-

03,

00). Then the number of sign changes of H is less than

M.K. Khan / Optimal Bayesian estimation

or equal to the of h is at most number of sign their respective

13

number of sign changes of h provided the number of sign changes one. Moreover, if the number of sign changes of h is equal to the changes of H, then h and H exhibit the same sequence of signs when arguments traverse the domain of definition from left to right.

In the following the non-existence of the MLE is discussed. In classical statistical analysis, if we observe responses Ji at the design level Xi from Ni, i= 1,2, . . . , n, units respectively, then the MLE of 0 is the solution of the likelihood equation $, f(Xi-e)(Ji-NiF(Xi-B))/[F(Xi-8)(1-F(Xi-8))]=0 where, f is the tolerance density. Let B be the union of the events { C Ji = 0} and w h ere M= C N, is fixed. Clearly, the probability of the event B is {CJi=M} fi

FN’(Xi

-

8) +

i=l

ie,

(1 - F(xi -

e)>N’

Now it is simple to see that the likelihood equation has no finite solution over the event B. We should add that the non-existence of the MLE may be of minor practical importance since P(B) is rather small for any reasonably chosen design levels when some prior

3. Optimality

knowledge

criterion

of B is available.

and experimental

designs

An experimenter faces two problems for estimating B or any function of it. First problem is the design of experiment when some prior knowledge is available. The second problem is how to estimate the parameter when an experiment has been conducted over some design levels. In the following we study how the amount of Fisher information is affected by the choices of the number of design levels n, the design vector x=(x,, . . . . x,,) and the allocation vector N=(N,, . . ..N.,), when M=N,+~~~+N, is fixed. Clearly, the Fisher information function varies with the parameter 8. Therefore, we overcome this problem by either considering locally optimal vectors (i.e., design vectors which are optimal in a neighborhood of 0) or from Bayesian point of view. units to be used to Let x1, . . . . x, be the design levels with N,, . . . , N, experimental obtain J,, . . . , J, responses respectively. Let Z(B 1n, x, A’) be the total Fisher information associated with the experiment. By independence, Z(e 1n, x, IV) = Cl=, Z(e, Xi) where Z(0, Xi) is given in equation (1) in which N is replaced by N,. Theorem 1 in the next section indicates, when a prior knowledge of the parameter is available, it is better to use one ‘good’ Bayesian design level than to use many ad hoc design levels which might be far away from the optimal design. However, experimenters prefer using more than one design levels due to incomplete prior knowledge. Section 5 shows how one could use adaptive designs in such situations.

14

M.K.

Khan

/ Optimal

Bayesian

estimation

Now we consider estimation from a fixed sample point of view. After performing n experiments at a set of predetermined design levels x1, . . . , x,, (n 2 l), we would like to estimate 8. By Lemma 1, for symmetric densities, the LD,, is the point of maximum of the Fisher information function as well. The point of maximum of the Fisher information function for non-symmetric tolerance densities is different from the LD,,,. However, due to the simple relationship between the percentiles of a distribution, one can use the estimate of one to estimate the other. Indeed, let the point of maximum of Z(U) and d be a real number such that F(d) =p. Then most informative point for estimating 19is x* = c + f3 and LDP = d + 8. Our aim use the most informative design levels to estimate LDP, where p is not close or 1. Definition

2. Let .Z,, Jz, . . . , J,, be the observed

x1,x2,**., x,, respectively. Bayesian (OB) if

An estimate

E{Z(O, 2)] rE{Z(O,x)}

number

of x* (denoted

for - 00
03,

c be the is to to 0

of responses

at design levels

by 2) is defined

to be optimal

n =0, 1, . . . ,

(3)

where the expectations are taken with respect to the posterior distribution (prior distribution if n =0) of 0 given the J1, J2, . . . , J, and xl, x2, . . . , x,,. Once x* is estimated by .?‘, the OB estimate of LDP is given by d - c +k

4. Main results The following theorem describes expected Fisher information.

the Bayesian

design which maximizes

the total

Theorem 1. Let Z(u) be a continuously differentiable unimodal function representing the Fisher information function (l), and let g be a PF2 density of 0. Then the point of maximum of E(Z(O / n, x, N)) is obtained for n = 1, N, =M, x=2, where 2 is the unique solution of the equation E((a/ax)z(e,

x)) = 0

and the expectation is with respect to the distribution of 0. Proof. Since E(Z(x- 0)) is a continuous and bounded function it has at least one point of maximum. Clearly, 0 as lxl’oo,

E(Z(O 1n, x, N)) = i N; m G(X; i=l d-0D

of x converging

to

e)g(e)de,

where G(xj - 0) represents Z(x; - e)/Ni. Now, note that due to the fact that Z(u) is a continuously differentiable and unimodal function, IZ'(u) 1-+O and as / u[ -00. This implies that Z’(U) is bounded. Hence, by the M-test for uniform convergence

M.K.

of the integral

Khan

/ Optimal

we can interchange

(a/ax)E(G(x-

Bayesian

the derivative

75

estimation

and the integral,

i.e.,

0)) = ‘m (J/ax) G(x - @g(B) de. I -to

By the fact that Z(U) is unimodal, G’ has exactly one sign change and is from positive to negative. Since g is PF2, by Lemma (3), the number of sign changes of ‘m

103 G’(u)g(x;

- U) du =

G’(x; [email protected](0)

d0

1_a

I_,

is one and is from positive to negative. Therefore, E(Z(O,x)) has exactly one point of maximum and is achieved at the unique point A?given by (4). Define a random variable U taking values 1, . . . , n with probabilities Nj/it4, i = 1, . . . , n, respectively. Now,

ace

lc0

E(Z(O j n, x, N))/M

=E

G(x,-

tV)g(B) d0 I

I\ -02

I! -co

where the expectation is with respect to the discrete random by M proves the theorem.

G(g-

variable

e)s(Q

de,

CT.Multiplying

Remark. Note that for the Logit model Z(u) =f(u) and therefore, Z(U) itself is a PF2 density function. For such models when Z(U) is an integrable PF2 function, E(Z(O,x)) itself becomes a PF2 function. This follows by a convolution property of PF2 densities (Karlin (1968) pp. 332-333). Indeed, if X, Y are two independent random variables with PF2 densities f and g defined over the real line respectively, then Z = X+ Y has a PF2 density as well. Hence, for integrable unimodal PF2 Z(U) (such as Probit and Logit models), E(Z(O, x)) itself is a PF2 function when g is a PF2 density. This would give a much simpler proof as to why (4) has a unique root. In order to characterize the fixed sample OB estimates we proceed as follows. Let design j=(j,,j,, . . . . j,) be the observed number of responses at the predetermined levelsx=(x,,x, ,..., x,)out ofN,,N, ,..., N, units respectively. The posterior distribution 0 given j,x is: g,(e)

= K(x, j) i

i=l

where K(x, j) does not depend the prior density g.

P(x,-

e)(i -F(x;

- ep$(e)

on 8. For n = 0, the posterior

(3 density

(5) reduces

to

Theorem 2. Let Z(B,x) be as given in (1) such that Z(u) is unimodal. Zf the prior density g(Q), the tolerance CDF F(u) and 1 -F(u) are PF2 then the OB estimate of x* is the unique solution of the equation

E((~?/ax)Z(@,x)) = 0,

(6)

where the expectation is taken with respect to the posterior density given in (5).

M.K. Khan / Optimal Bayesian estimation

16

Proof. @1(0,x)) = c j_“, 1(x- @g,(O) de, where c is bounded, by the same argument as given in the at least one point of maximum of E(Z(O,x)). Now gral sign (which is justified by the same argument change of variable we have

does not depend on x. Since Z(u) proof of Theorem 1, there exists by differentiating under the inteas provided in Theorem 1) and

>oo (a/l3x)E(z(O,x))

= c

Z’(y)g,(x-_v)

(7)

dy.

1 --to Since the product of PF2 functions is PF2, g, is a PF2 and consequently g,(x-y) is a TP2 as a function of x, y. By Lemma 3, the number of sign changes of the left hand side in (7) is less than or equal to the number of sign changes of Z’(U). Due to the unimodality of Z(U), the number of sign changes of Z’(U) is one and from positive to negative. Therefore, by Lemma 3 the sign of the left side in (7) changes from positive to negative, which proves the theorem. Corollary. As special cases, we see that the OB estimates are characterized by (6) for Probit, Logit, Extreme models.

5. Adaptive

designs

In the following

we outline

a two stage design

of experiment.

First stage design Although Theorem 2 characterizes the first stage design (for n = 0), the characterization requires less assumptions. In fact for symmetric densities, one does not need the PF2 properties as shown in Theorem 3 below. Let 0 represent the random variable having the prior density g(0). The first stage optimal Bayesian estimate (OBl) of 19 is a real number x which maximizes cc

E(Z(O, x)) =

(8)

Z(& x)g(@ de.
To motivate the concepts, lets assume that 0 has a uniform prior distribution over the interval [a, b]. If Z(0,x) follows the conditions of Lemma 1 and decreasing for x>0, (8) reduces to x-a

E(Z(O, x)) = N/(b - a) i x-b

f 2(y)/[F(y)(l

-F(Y))]

dy

b-a/2 I

N/(b - a)

.i -b-a/2 That is, OBl estimate

of 6’is the median

f 2(y)/[F(y)(l

-F(Y))] dy

of the prior distribution.

For general

prior

II

M.K. Khan / Optimal Bay&an estimation

distributions 0f

we have the following

two theorems

to characterize

the OBl estimates

e.

Theorem 3. Let Z(t?,x) be the Fisher information function

as given by (1) such that I(& x) is decreasing for x> 8, and f (x) is symmetric about 0. Zf 0 has a prior density g(0 - A) decreasing for 0 > ,I, where g(u) is symmetric about 0, then the OB 1 estimate of 8 is 8, =A. Proof.

Without

loss of generality we can assume A= 0. For a fixed x> 0, define h(&x)=Z(e,O)-Z(B,x). For anyy>O, h(+x-y,x)=-h(+x+y,x). Therefore, cc

‘co

I

h(e,x)g(e)

de

< -cc

=

I

h(~x-y,x)[g(3x-y)-g(tx+y)l

dy.

(9)

.O

We will show that the integrand on the right in (9) is a product of two non-negative functions. Since, x>O, y>O, therefore 0< I+x-yi <+x+y. By the symmetry of g and the fact that g(u) is decreasing for u > 0, g(+x -y) - g(+x+y) > 0 Vy > 0, where x>O is fixed. Similarly, h(+x-y,x) =Z(+x-y)-Z(+x+y) and by similar reasons h(+x-y,x)>O for all y>O, where x>O is fixed. Therefore E(h(O,x))>O Vx>O. Hence, by symmetry, E(Z(O,O))~E(Z(O,x)) VX, which proves the theorem. Note that in Theorem 3 as one would expect, .Ct = A is a solution of the equation E((8/ax)Z(O,x)) = 0. For the cases when the tolerance density or the prior density are not necessarily symmetric we can use the following theorem. Theorem

4. The OBl

estimate of x* is the unique solution of the equation

E((a/ax)z(O,x))

= 0,

(10)

where the expectation is taken with respect to the prior density of 0, provided either (i) or (ii) hold, where (i) Z(x- 8) = Z(u) where x- t9= u is such that Z(u) is unimodal and 0 has a PF2 prior density g. (ii) Z(x - 8) = Z(u) where x - 13= u is such that Z(u) is an integrable PF2 function and 0 has a continuously differentiable unimodalprior density g(0 - A) =g(t) where 0 - A= t is such that g(t) obtains its mode at t = 0. Proof. to the Now be the

The proof of(i) follows from Theorem 2, since the posterior density reduces prior density. to prove (ii), without loss of generality we can assume that A =O. Let g’(e) derivative of g(e). Since ‘rn

(a/ax)E(z(O,

x)) =

(\ -co

Z(X -

e) g’(e) de,

78

M.K. Khan / Optimal Bayesian estimation

by similar argument theorem follows.

as given after

(7) and interchanging

the role of Z and g’ the

Second stage design The most informative use its estimate

design level to use is the x* itself. Since, it is unknown,

obtained

from the first stage, namely

we

2,) as the design level for the

next experiment. After performing the experiment at the first design level we observe .Z(,?t) =j. Clearly, given f3, J(i,) is a binomial random variable with parameters Nt , F(_2, - 0). The posterior density of 0 given ,i?t and .Z(_?,)=j is g,(e

1 j,a,)

= zqi,,.j)(~(.q

- e))j(i

4y.2,

- epg(e)

(11)

where K(i,, j) does not depend on 8. The second stage optimal Bayesian (OB2) estimate of 6’is defined to be a real number which maximizes the posterior expected Fisher information function given 2, and J(g,) =j. Theorem 2 provides the 0B2 estimate of x* as well, since the posterior density is of the same form as (5) where xi is replaced by 2,. We will use the notation &(j,N) to represent the OB2 estimate of x*. Clearly, when f and g are symmetric and N is even in Theorem 2, we have _x?~(+N,N) = ,i?, =A, where L is the median of g. Furthermore, intuitively one expects that the following should hold in general: -m<_?~(N,N)I_?*(N-l,N)I.**1.?~(0,N)<03.

(12)

The finiteness of all the _i?zis a consequence of Theorem 2. Numerical calculations for the examples given below do satisfy (12) in which we consider the uniform and logistic prior densities for the Logit model. However, we do not have a proof (or counter example) for (12). Example 3 (Logit model, Llniform prior). For tne Logit model, let 0 have a uniform density over (-b, b). Then &(j,N) is the point of maximum of (13) By numerical integration (Shampine (1973)) we can obtain the values of the OB2 estimates. Table 1 and Table 2 provide the values of &(j, N) for N= 1,2, . . . ,8 and j=O, 1, . . . . [+N] when b= 10,20. Example 4 (Logit model, Logistic prior). Due to a simple relationship between the uniform and logistic prior models, one does not need to provide new tables to obtain &(j,N) for the logistic prior. Indeed, for the logistic prior we have, E(Z(O,x))

= bum lim ./~I.z(r-e)(~)-+2e-o(i+1)de.

(14)

79

M.K. Khan / Optimal Bayesian estimation Table

1

Logit

model

N

and uniform

0

(-10;

10) prior

1

2

1

5.68

2

6.00

0.00

3 4

6.20

0.84

6.30

1.30

0.00

5

6.40

1.60

0.46

6 7

6.50 6.56

1.86

0.78

0.00

2.06

0.32

8

6.62

2.36

1.02 1.38

0.76

Therefore, from (13) and (14) we conclude that if &Jj, estimate of 8 for the uniform (-6,6) prior and -12,,,(j,N) mate of 19for the logistic prior then for large b, %,,(j,Jv As Tables

= $,&+

1 and 2 indicate

4

3

0.00

N) represents the OB2 represents the OB2 esti-

l,N+2). the equality

(15) in (15) holds

for br 10.

6. Discussion One can use classical estimation techniques, such as maximum likelihood method (Robbins, Monro (1951), or sequential estimation methods, e.g., Robbins-Monro Wetherill(1963), Wetherill(1975)), Up-and-Down method (Dixon and Mood (1948)) or Spearman-Karber type (Spearman (1908), Karber (1931), Brown (1959)) nonparametric estimation techniques. As is commonly the case in non-linear models the maximum likelihood estimates can be biased and the bias can become serious if the

Table 2 Logit model

and uniform

N

0

(-20; 20) prior

1

1

11.10

2

11.48

0.00

3 4

11.64 12.05

0.84 1.30

5 6 I

10.66

1.60

9.14

1.86 2.06

8

8.06 7.24

2.36

2

3

4

0.00 0.46 0.78

0.00

1.02

0.32

1.38

0.76

0.00

80

M.K. Khan / Optimal Bayesian estimation

design levels are chosen ‘far’ away from some central percentile such as LD,, for the symmetric tolerance densities. The sequential techniques have asymptotically optimal properties, however, for very small number of design levels their convergence is rather slow if the initial (starting value) design is not close to the ‘optimal’ design. In QRB, DA and ALT, it is common that the experimenter has some prior knowledge about 8 due to the past experiences. The amount of prior information can range from some specific probability density for 0 (this phenomenon seems unlikely) or just that 8 falls in some interval [a, 61 uniformly (this information is quite often available). One must add that when a design is planned for estimation purposes, either for maximum likelihood or for other methods of estimation, an intrinsic assumption is made about the location of the parameter to be estimated. It is well known that the MLEs and other estimators perform rather badly if the design span misses the LD,,. In fact, as was shown in Section 2, the MLE do not exist with positive probability for any design. From Bayesian point of view relatively few articles are available in the literature. Both Marks (1962) and Freeman (1970) consider minimization of an appropriate cost function as the criterion of optimality from Bayesian point of view. One of the problems (as far as their applications are concerned) has been the analytical interactability of the expressions involved after a few stages of the experiment. When the cost of the experiment or units is not of paramount concern one can use other functions for defining optimality. In this article we showed that the usual maximization of the Fisher information function as a criterion of optimality could also be used in QRB, DA and ALT. The technique, however, is general and could be modified for minimization of some risk functions as well. It was shown that with the use of Fisher information function, the problem is analytically relatively simple. The Fisher information function has some properties which should be of independent interest. We studied a general class of distributions (including the symmetric tolerance distributions such as Probit and Logit models). Our aim is to design an experiment for n design levels (n 2 1) to obtain an estimate of LD,, where 0


M.K. Khan / Optimal Bay&an estimation

81

Acknowledgement I would Rolletschek

like to thank Professors Phil Boland, Shelemyahu and the referee for their contributions to this paper.

Zacks

and

H.

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