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[2] Forney G. ( 1972 ), Maximum likelihood sequence estimation of digital sequences in the presence of ISI . IEEE Transactions on Information Theory , 18

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Studia Scientiarum Mathematicarum Hungarica
Authors:
Abraão D. C. Nascimento
,
Kássio F. Silva
,
Gauss M. Cordeiro
,
Morad Alizadeh
,
Haitham M. Yousof
, and
G. G. Hamedani

Abstract

We study some mathematical properties of a new generator of continuous distributions called the Odd Nadarajah-Haghighi (ONH) family. In particular, three special models in this family are investigated, namely the ONH gamma, beta and Weibull distributions. The family density function is given as a linear combination of exponentiated densities. Further, we propose a bivariate extension and various characterization results of the new family. We determine the maximum likelihood estimates of ONH parameters for complete and censored data. We provide a simulation study to verify the precision of these estimates. We illustrate the performance of the new family by means of a real data set.

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Linear-quadratic Inventory Model: Maximum Likelihood versus Generalized Method of Moments. Journal of Monetary Economics , 35: 115–157. Schuh S. Estimating the Linear

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Acta Veterinaria Hungarica
Authors:
Katalin Ihász
,
Szilvia Marton
,
Enikő Fehér
,
Krisztián Bányai
, and
Szilvia L. Farkas

together on the phylogenetic trees with, the sequences of multiple strains named University of Helsinki vrus 3 (UVH-3). Fig. 1. Maximum-likelihood phylogenetic trees based on the nt sequence of the RNA

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functions for the evaluation of Landsat imagery, in order to extract four main classes of land use: agricultural land, vacant land, built-up land, and green vegetation. These land-use classes were extracted using maximum likelihood classification (MLC) as

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and recipient economies on the OFDI level. Our contributions to existing research are the following. First, using a dataset comprising 102 home and 67 host countries between 2001 and 2016 and applying the Poisson pseudo maximum likelihood

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Scientometrics
Authors:
J. A. García
,
Rosa Rodriguez-Sánchez
,
J. Fdez-Valdivia
, and
J. Martinez-Baena

sizes. Fuzzy maximum likelihood estimation clustering To circumvent the limitations of the fuzzy k-means, we have chosen the method of Gath and Geva ( 1989 ), sometimes referred to as fuzzy maximum likelihood

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In this article, a new four-parameter model is introduced which can be used in mod- eling survival data and fatigue life studies. Its failure rate function can be increasing, decreasing, upside down and bathtub-shaped depending on its parameters. We derive explicit expressions for some of its statistical and mathematical quantities. Some useful characterizations are presented. Maximum likelihood method is used to estimate the model parameters. The censored maximum likelihood estimation is presented in the general case of the multi-censored data. We demonstrate empirically the importance and exibility of the new model in modeling a real data set.

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robust to the normality (i.e., restricted maximum likelihood, REML) ( Pigott, 2012 ). The I 2 statistic was used to assess statistical heterogeneity, with values of 25%, 50%, and 75

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Acta Mathematica Hungarica
Authors:
Villő Csiszár
,
Péter Hussami
,
János Komlós
,
Tamás F. Móri
,
Lídia Rejtő
, and
Gábor Tusnády

Abstract

There is a uniquely defined random graph model with independent adjacencies in which the degree sequence is a sufficient statistic. The model was recently discovered independently by several authors. Here we join to the statistical investigation of the model, proving that if the degree sequence is in the interior of the polytope defined by the Erdős–Gallai conditions, then a unique maximum likelihood estimate exists.

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