  In a spiked population model, the population covariance matrix has all its
eigenvalues equal to units except for a few fixed eigenvalues (spikes).
Determining the number of spikes is a fundamental problem which appears in many
scientific fields, including signal processing (linear mixture model) or
economics (factor model). Several recent papers studied the asymptotic behavior
of the eigenvalues of the sample covariance matrix (sample eigenvalues) when
the dimension of the observations and the sample size both grow to infinity so
that their ratio converges to a positive constant. Using these results, we
propose a new estimator based on the difference between two consecutive sample
eigenvalues.
