Maximum Likelihood Estimation

By In Jae Myung. Encyclopedia of Biometrics, 2012

396 citations recorded by the source corpus.

A method is provided for the direction of arrival (DOA) tracking of at least one source, along a single azimuth axis or along both azimuth and elevation axes. The method includes the steps of selecting all the high peaks from a DOA function as potential track points, converting the potential track points into a plurality of tracks and selecting a true track from the plurality of tracks.

View Paper: https://www.semanticscholar.org/paper/b000a4849972ffd2389f5cca4372c8a997bf6c3e

Citations in the corpus

The MERRA-2 Aerosol Reanalysis, 1980 - onward, Part I: System Description and Data Assimilation Evaluation. (2017, unknown): “Also shown are the multimodel average results from phase I of the AeroCom intercomparison project (Kinne et al. 2006), as well as both model and observational estimates from Yu et al. (2006). Yu et al. (2006) attempt to account for satellite clear-sky biases by combining MODIS and MISR observations ”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, unknown): “Early studies in Dee (1995), Blanchet et al. (1997), Mitchell and Houtekamer (2000) and Liang et al. (2012) proposed ﬁnding the optimal Q and R that maximize the current innovation likelihood at time k .”
Latent Profile Analysis (2017, unknown): “ML estimation attempts different model parameter estimates using the sample data and choose the estimates associated with the highest likelihood of coming from that sample (see Enders, 2005 or McLachlan & Peel, 2000, for an explanation of ML).”
Bias-corrected method of moments estimators for dynamic panel data models (2021, unknown): “Since E[y −1,iui] = −bT (α0)σ2, replacing the last term with −bT (α) yields the245 nonlinear moment conditions from the previous section.246 Hsiao et al. (2002) treat the initial observations as a random variable with247 E[∆yi1] = b and E[(∆yi1 − b)2] = ωσ2, where ∆ is the first-difference…”
A Study of Reinforcement Learning for Neural Machine Translation (2018, unknown): “Such training approach is referred as maximum likelihood estimation (MLE) (Scholz, 1985).”