How to estimate the autocorrelation from nonuniformly spaced data Assume a continues-time random process $X(t)$ sampled nonuniformely in time to acquire discrete signal $x[n]$. The sampling times are known but the autocorrelation is not. Is there an accurate approach to estimate the autocorrelation function? This problem is challenging since at some lags there is no sample to average. 

Any suggestion even in a special case, e.g. a simple autocorrelation function with an exponential decay (continuous-time AR process of order one) would be appreciated. 



Edit

I have already read some papers, so I would like either an analytical answer or a peace of algorithm/code to address this problem for instance for an AR(1) process assuming non-uniformly spaced samples(e.g. in uniformly distributed random times).
As suggested by  Marcus Müller, interpolation in the time domain could be a solution. I never had to perform such a task, and the outcomes may depend in the nonuniformity of your sampling. I propose a use of the Wiener-Khinchin theorem:


  The Fourier transform of the autocorrelation function is the power
  spectrum, or equivalently, the autocorrelation is the inverse Fourier
  transform of the power spectrum


You may use for instance:


a non-uniform DFT (for instance NUFFT),
variants of the Lomb-Scargle periodogram (aka Least-squares spectral analysis),


and get a regularly sampled power spectrum, which I hope you could inverse Fourier back to an estimate of the autocorrelation. As I have no practical experience, if you intend to try this, I'd really appreciate you could share the outcomes, good or bad, along with the specificities of your signal:


what kind of nonuniformity (additive random, jitter, lacunary)?
type of signal and noise?


Additional references:


Power Spectral Density of Unevenly Sampled Data by Least-Square Analysis: Performance and Application to Heart Rate Signals, Laguna et al., 1998
Spectral estimates from nonuniform samples, L. Shaw, 1971