Profile Likelihood and Incomplete Data

Gmm and Empirical Likelihood with Incomplete Data

In applied work economists often encounter data generating mechanisms that produce censored or truncated observations. These dgp’s induce a probability distribution on the realized observations that differs from the underlying distribution for which inference is to be made. If this dichotomy between the target and realized populations is not taken into account, statistical inference can be seve...

Pseudo-likelihood Estimation for Incomplete Data

In statistical practice, incomplete measurement sequences are the rule rather than the exception. Fortunately, in a large variety of settings, the stochastic mechanism governing the incompleteness can be ignored without hampering inferences about the measurement process. While ignorability only requires the relatively general missing at random assumption for likelihood and Bayesian inferences, ...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Maximum Likelihood Estimation of Incomplete Genomic Spectrum from HTS Data

High-throughput sequencing makes possible to process samples containing multiple genomic sequences and then estimate their frequencies or even assemble them. The maximum likelihood estimation of frequencies of the sequences based on observed reads can be efficiently performed using expectationmaximization (EM) method assuming that we know sequences present in the sample. Frequently, such knowle...