Description Usage Arguments Value References Examples

Give a sequence of chi-squared statistic values, the function computes the posterior mean, variance, and skewness of the noncentrality parameter given the data.

1 2 3 4 5 6 7 |

`x` |
a sequence of chi-squared test statistics |

`df` |
the degrees of freedom |

`qq` |
the quantiles used in spline basis |

`method` |
LS: parametric least-squares; PLS: penalized least-squares; g-model: g-modeling |

`mixture` |
default is FALSE: there is no point mass at zero. |

a list: posterior mean, variance, and skewness estimates

Du and Hu (2020), *An Empirical Bayes Method for Chi-Squared Data*, *Journal of American Statistical Association*, forthcoming.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
p = 1000
k = 7
# the prior distribution for lambda
alpha = 2
beta = 10
# lambda
lambda = rep(0, p)
pi_0 = 0.8
p_0 = floor(p*pi_0)
p_1 = p-p_0
lambda[(p_0+1):p] = rgamma(p_1, shape = alpha, rate=1/beta)
# Generate a Poisson RV
J = sapply(1:p, function(x){rpois(1, lambda[x]/2)})
X = sapply(1:p, function(x){rchisq(1, k+2*J[x])})
qq_set = seq(0.01, 0.99, 0.01)
out = EB_CS(X, k, qq=qq_set, method='LS', mixture = TRUE)
E = out$E_lambda
V = out$V_lambda
S = out$S_lambda
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.