Optimal Composite Response Surface Designs

Here we consider the design space is k-dimensional ball (k-ball), i.e. . Fix the first-order design and find added points by D-optimal criterion. Under these assumptions, we study the D-optimal composite designs and minimal-point second-order designs.

Methodology

For the cases of two and three factors, the close forms of |XX| are computed, and the optimal added supports are found by maximizing these determinants directly. For the cases of more than 3 factors, the determinant of the information matrix is too complicate to write down its close form. Then a simulated annealing algorithm with a MCMC sampling method is proposed to get the corresponding numerical results. In this works, the number of center point is assumed to be 1.

For simplicity, we let , where p=k(k-1)/2 is the number of rotation angles. The |X’X| is the function of θi, i=1, …, p, d(θi). Since the objective function is d(θi) that we want to maximize, we denote a density

,

where T(t) is the “temperature” at time t and is a decreasing function from initial temperature, T(0) > 0, to 0+. The SA algorithm is described in the following:

Step1. Initialize ay any arbitrary configuration θ(0) and temperature level T(0).

Step2. Sample θ from πT(t)(θ) by a MCMC method.

Step3. Increase t to t+1.

For more information about SA algorithm, please see Chapter 10 in Liu (2001). The MCMC method applied for sampling  is the systematic scan Gibbs sampler as follows:

1. Select the initial value θ(0) = ().

2. At the sth iteration of Gibbs sampler, we sample  from the conditional distribution,

where θi=(θ1, θ2, …, θi-1, θi+1, …θp).

We summarize the algorithm that we use to maximize |XX|, and this algorithm is called the Best Angle (BA) sampler:

Step1. Select the initial angles, , i=1, …, p.       

Step2. Run Nt iterations of Gibbs sampler to sample θ(s) fromπT(t)(s)) and at each iteration of Gibbs sampler, for i=1, … , p, draw   fromπT(t)( θi|)  by inversion method.

Step3. Set t to t+1.

 

1. D-optimal composite designs

2. Minimal-point second-order designs

3. Near D-optimal designs