| 000 | 03857naaaa2200673uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/68972 | ||
| 005 | 20220220091748.0 | ||
| 020 | _abooks978-3-03936-647-7 | ||
| 020 | _a9783039366460 | ||
| 020 | _a9783039366477 | ||
| 024 | 7 |
_a10.3390/books978-3-03936-647-7 _cdoi |
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| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aH _2bicssc |
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| 072 | 7 |
_aJFFP _2bicssc |
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| 100 | 1 |
_aGómez Déniz, Emilio _4edt |
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| 700 | 1 |
_aGómez Déniz, Emilio _4oth |
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| 245 | 1 | 0 | _aSymmetric and Asymmetric Distributions : Theoretical Developments and Applications |
| 260 |
_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2020 |
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| 300 | _a1 electronic resource (146 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
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| 520 | _aIn recent years, the advances and abilities of computer software have substantially increased the number of scientific publications that seek to introduce new probabilistic modelling frameworks, including continuous and discrete approaches, and univariate and multivariate models. Many of these theoretical and applied statistical works are related to distributions that try to break the symmetry of the normal distribution and other similar symmetric models, mainly using Azzalini's scheme. This strategy uses a symmetric distribution as a baseline case, then an extra parameter is added to the parent model to control the skewness of the new family of probability distributions. The most widespread and popular model is the one based on the normal distribution that produces the skewed normal distribution. In this Special Issue on symmetric and asymmetric distributions, works related to this topic are presented, as well as theoretical and applied proposals that have connections with and implications for this topic. Immediate applications of this line of work include different scenarios such as economics, environmental sciences, biometrics, engineering, health, etc. This Special Issue comprises nine works that follow this methodology derived using a simple process while retaining the rigor that the subject deserves. Readers of this Issue will surely find future lines of work that will enable them to achieve fruitful research results. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
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| 546 | _aEnglish | ||
| 650 | 7 |
_aHumanities _2bicssc |
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| 650 | 7 |
_aSocial interaction _2bicssc |
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| 653 | _apositive and negative skewness | ||
| 653 | _aordering | ||
| 653 | _afitting distributions | ||
| 653 | _aEpsilon-skew-Normal | ||
| 653 | _aEpsilon-skew-Cauchy | ||
| 653 | _abivariate densities | ||
| 653 | _ageneralized Cauchy distributions | ||
| 653 | _aasymmetric bimodal distribution | ||
| 653 | _abimodal | ||
| 653 | _amaximum likelihood | ||
| 653 | _aslashed half-normal distribution | ||
| 653 | _akurtosis | ||
| 653 | _alikelihood | ||
| 653 | _aEM algorithm | ||
| 653 | _aflexible skew-normal distribution | ||
| 653 | _askew Birnbaum–Saunders distribution | ||
| 653 | _abimodality | ||
| 653 | _amaximum likelihood estimation | ||
| 653 | _aFisher information matrix | ||
| 653 | _amaximum likelihood estimates | ||
| 653 | _atype I and II censoring | ||
| 653 | _askewness coefficient | ||
| 653 | _aWeibull censored data | ||
| 653 | _atruncation | ||
| 653 | _ahalf-normal distribution | ||
| 653 | _aprobabilistic distribution class | ||
| 653 | _anormal distribution | ||
| 653 | _aidentifiability | ||
| 653 | _amoments | ||
| 653 | _apower-normal distribution | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/2740 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/68972 _70 _zDOAB: description of the publication |
| 999 |
_c78186 _d78186 |
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