R: Initial Sequence Estimators

initseq {mcmc} | R Documentation

## Initial Sequence Estimators

### Description

Variance of sample mean of functional of reversible Markov chain using methods of Geyer (1992).

### Usage

    initseq(x)

### Arguments

`x` | a numeric vector that is a scalar-valued functional of a reversible Markov chain.

### Details

Let

gamma[k] = cov(x[i], x[i + k])

considered as a function of the lag k be the autocovariance function of the input time series. Define

Gamma[k] = gamma[2 k] + gamma[2 k + 1]

the sum of consecutive pairs of autocovariances. Then Theorem 3.1 in Geyer (1992) says that Gamma[k] considered as a function of k is strictly positive, strictly decreasing, and strictly convex, assuming the input time series is a scalar-valued functional of a reversible Markov chain. All of the MCMC done by this package is reversible. This R function estimates the “big gamma” function, Gamma[k] considered as a function of k, subject to three different constraints, (1) nonnegative, (2) nonnegative and nonincreasing, and (3) nonnegative, nonincreasing, and convex. It also estimates the variance in the Markov chain central limit theorem (CLT)

- gamma0 + 2 * sum(gamma) = - gamma0 + 2 * sum(Gamma)

Note: The batch means provided by `metrop` are also scalar functionals of a reversible Markov chain. Thus these initial sequence estimators applied to the batch means give valid standard errors for the mean of the match means even when the batch length is too short to provide a valid estimate of asymptotic variance. One does, of course, have to multiply the asymptotic variance of the batch means by the batch length to get the asymptotic variance for the unbatched chain.

### Value

a list containing the following components:

`gamma0` | the scalar gamma[0], the marginal variance of `x`.
`Gamma.pos` | the vector Gamma, estimated so as to be nonnegative, where, as always, R uses one-origin indexing so `Gamma.pos[1]` is Gamma[0].
`Gamma.dec` | the vector Gamma, estimated so as to be nonnegative and nonincreasing, where, as always, R uses one-origin indexing so `Gamma.dec[1]` is Gamma[0].
`Gamma.con` | the vector Gamma, estimated so as to be nonnegative and nonincreasing and convex, where, as always, R uses one-origin indexing so `Gamma.con[1]` is Gamma[0].
`var.pos` | the scalar `- gamma0 + 2 * sum(Gamma.pos)`, which is the asymptotic variance in the Markov chain CLT. Divide by `length(x)` to get the approximate variance of the sample mean of `x`.
`var.dec` | the scalar `- gamma0 + 2 * sum(Gamma.dec)`, which is the asymptotic variance in the Markov chain CLT. Divide by `length(x)` to get the approximate variance of the sample mean of `x`.
`var.con` | the scalar `- gamma0 + 2 * sum(Gamma.con)`, which is the asymptotic variance in the Markov chain CLT. Divide by `length(x)` to get the approximate variance of the sample mean of `x`.

### Bugs

Not precisely a bug, but `var.pos`, `var.dec`, and `var.con` can be negative. This happens only when the chain is way too short to estimate the variance, and even then rarely. But it does happen.

### References

Geyer, C. J. (1992) Practical Markov Chain Monte Carlo. Statistical Science 7 473–483.

### See Also

`metrop`

### Examples

    n <- 2e4
    rho <- 0.99
    x <- arima.sim(model = list(ar = rho), n = n)
    out <- initseq(x)
    ## Not run:
    plot(seq(along = out$Gamma.pos) - 1, out$Gamma.pos,
       xlab = "k", ylab = expression(Gamma[k]), type = "l")
    lines(seq(along = out$Gamma.dec) - 1, out$Gamma.dec, col = "red")
    lines(seq(along = out$Gamma.con) - 1, out$Gamma.con, col = "blue")

    ## End(Not run)
    # asymptotic 95% confidence interval for mean of x
    mean(x) + c(-1, 1) * qnorm(0.975) * sqrt(out$var.con / length(x))
    # estimated asymptotic variance
    out$var.con
    # theoretical asymptotic variance
    (1 + rho) / (1 - rho) * 1 / (1 - rho^2)
    # illustrating use with batch means
    bm <- apply(matrix(x, nrow = 5), 2, mean)
    initseq(bm)$var.con * 5

[Package mcmc version 0.9-7 Index]