  This paper derives central limit theorems (CLTs) for general linear spectral
statistics (LSS) of three important multi-spiked Hermitian random matrix
ensembles. The first is the most common spiked scenario, proposed by Johnstone,
which is a central Wishart ensemble with fixed-rank perturbation of the
identity matrix, the second is a non-central Wishart ensemble with fixed-rank
noncentrality parameter, and the third is a similarly defined non-central $F$
ensemble. These CLT results generalize our recent work to account for multiple
spikes, which is the most common scenario met in practice. The generalization
is non-trivial, as it now requires dealing with hypergeometric functions of
matrix arguments. To facilitate our analysis, for a broad class of such
functions, we first generalize a recent result of Onatski to present new
contour integral representations, which are particularly suitable for computing
large-dimensional properties of spiked matrix ensembles. Armed with such
representations, our CLT formulas are derived for each of the three spiked
models of interest by employing the Coulomb fluid method from random matrix
theory along with saddlepoint techniques. We find that for each matrix model,
and for general LSS, the individual spikes contribute additively to yield a
$O(1)$ correction term to the asymptotic mean of the linear statistic, which we
specify explicitly, whilst having no effect on the leading order terms of the
mean or variance.
