R.A. Fisher and the making of maximum likelihood 1912-1922

Aldrich, John

doi:10.1214/ss/1030037906

Cited by 487 publications

(255 citation statements)

References 46 publications

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“…Where the variance is given as: (17) and, ω, φ and β are model parameters whose estimation can be by maximizing the sum of the function Maximum Likelihood Estimation (MLE) which is given by the expression similar to that of Aldrich (1997): (18) Where is the residual value of the return; is the residual value squared and is the unconditional variance in period t.…”

Section: The Garch Volatility Modelmentioning

confidence: 99%

The contribution of the African capital markets in the diversification of investment global portfolios

Barreto¹

2017

Afr. J. Bus. Manage.

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This study aims to evaluate the contribution of the African capital markets in the diversification of investment global portfolios. The study used the methodology based on the application of optimization models like mean variance (MV), resample michaud (RM), semi variance (SV), mean absolute deviation (MAD), and filtered historical simulation (FHS). In-sample and out-of-sample approaches were used to analyze the data. The study results suggested the existence of a strong correlation between some African capital markets and global capital markets, that is, they tend to move in the same direction. The most important being the diversification of global portfolio with assets of African capital markets generate benefits for both types of investors, risk averse and taker investors; that is, it provides benefits in the return and reduce investment risk. Still, the study results suggested that the foreign investors should look for African capital markets with a chance to maximize their wealth and diversify the investment risk in their portfolios. In the same order, the study result went further to elaborate on the advantages of the international diversification and furthermore contributes to the literature through application of the FHS method in the optimization portfolio. This methodology in addition to producing good results, is more restrained in the composition of investment portfolios than the other methods.

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Section: The Garch Volatility Modelmentioning

confidence: 99%

The contribution of the African capital markets in the diversification of investment global portfolios

Barreto¹

2017

Afr. J. Bus. Manage.

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“…A BC sequence-based decision over the HMM can be made in a statistically optimum fashion -in the maximum likelihood sense [37] -by employing the Viterbi algorithm (VA) [38]. As outlined in the preceding sections, this algorithm seeks to identify the path through the trellis over N time steps whose path metric Γ (i.e.…”

Section: Viterbi Detectionmentioning

confidence: 99%

Embedded CMOS basecalling for nanopore DNA sequencing

Wang

Zheng

Magierowski

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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DNA sequencing is undergoing a profound evolution into a mobile technology.Unfortunately the effort needed to process the data emerging from this new sequencing technology requires a compute power only available to traditional desktop or cloud-based machines. To empower the full potential of portable DNA solutions a means of efficiently carrying out their computing needs in an embedded format will certainly be required. This thesis presents the design of a custom fixed-point VLSI hardware implementation of an HMM-based multi-channel DNA sequence processor. A 4096 state (6-mer nanopore sensor) basecalling architecture is designed in a 32-nm CMOS technology with the ability to process 1 million DNA base pairs per second per channel. Over a 100 mm 2 silicon footprint the design could process the equivalent of one human genome every 30 seconds at a power consumption of around 5 W.ii

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“…W l , W r , W h , a l , a r , and b) can be fitted, in theory, by using Maximum Likelihood [19]. Due to the difficulty of computing the derivative of the log-likelihood gradients, Hinton proposed an approximation method called Contrastive Divergence (CD) [20].…”

Section: Learning and Update Rulesmentioning

confidence: 99%

Deep learning for objective quality assessment of 3D images

Mocanu

Exarchakos

Liotta

2014

2014 IEEE International Conference on Image Processing (ICIP)

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Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging proposition, mainly due to our limited knowledge of 3D image Quality Assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. In this paper we tackle this issue from a new angle, using deep learning to make predictions on the user's QoE rather than trying to measure it through deterministic algorithms. We benchmark our method, dubbed Quality of Experience for 3D images through Factored Third Order Restricted Boltzmann Machine (Q3D-RBM), with subjective QoE methods, to determine its accuracy for different types of 3D images. The outcome is a Reduced Reference QoE assessment process for automatic image assessment and has significant potential to be extended to work on 3D video assessment.

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R.A. Fisher and the making of maximum likelihood 1912-1922

Cited by 487 publications

References 46 publications

The contribution of the African capital markets in the diversification of investment global portfolios

The contribution of the African capital markets in the diversification of investment global portfolios

Embedded CMOS basecalling for nanopore DNA sequencing

Deep learning for objective quality assessment of 3D images

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