Conditional mode estimation for functional stationary ergodic data with responses missing at random

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

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HEVC Fast Intra-Mode Selection Using World War II Technique

Electronics ◽

10.3390/electronics10090985 ◽

2021 ◽

Vol 10 (9) ◽

pp. 985

Author(s):

Junaid Tariq ◽

Ammar Armghan ◽

Fayadh Alenezi ◽

Amir Ijaz ◽

Saad Rehman ◽

...

Keyword(s):

World War Ii ◽

High Efficiency ◽

Mode Selection ◽

Estimation Algorithm ◽

Minimum Variance ◽

World War ◽

High Efficiency Video Coding ◽

Mode Estimation ◽

Compression Time ◽

Variance Estimates

High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is presented here based on the classic World War II (WW2) technique known as the ‘German Tanks Problem’ (GTP). This not only is the first article to use GTP for early estimation of intra mode, but also expedites the estimation process of GTP. Secondly, the various elements of the intra process are efficiently mapped to the elements of GTP estimation. Finally, the two variations of GPT are modeled and are also minimum-variance estimates. These experimental results indicate that proposed GTP-based fast estimation reduced the compression time of HEVC from 23.88% to 31.44%.

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Resilience Against Data Manipulation in Distributed Synchrophasor-Based Mode Estimation

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2021.3067303 ◽

2021 ◽

pp. 1-1

Author(s):

Arezoo Rajabi ◽

Rakesh B. Bobba

Keyword(s):

Data Manipulation ◽

Mode Estimation

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Evaluating the Observed Log-Likelihood Function in Two-Level Structural Equation Modeling with Missing Data: From Formulas to R Code

Psych ◽

10.3390/psych3020017 ◽

2021 ◽

Vol 3 (2) ◽

pp. 197-232

Author(s):

Yves Rosseel

Keyword(s):

Structural Equation ◽

Structural Equation Models ◽

Likelihood Function ◽

Likelihood Estimation ◽

Missing At Random ◽

Equation Modeling ◽

Computationally Efficient ◽

Log Likelihood ◽

Efficient Expression ◽

Special Case

This paper discusses maximum likelihood estimation for two-level structural equation models when data are missing at random at both levels. Building on existing literature, a computationally efficient expression is derived to evaluate the observed log-likelihood. Unlike previous work, the expression is valid for the special case where the model implied variance–covariance matrix at the between level is singular. Next, the log-likelihood function is translated to R code. A sequence of R scripts is presented, starting from a naive implementation and ending at the final implementation as found in the lavaan package. Along the way, various computational tips and tricks are given.

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