• Corpus ID: 233481929

Generalizing the normality: a novel towards different estimation methods for skewed information

@inproceedings{Nascimento2021GeneralizingTN,
  title={Generalizing the normality: a novel towards different estimation methods for skewed information},
  author={Diego Carvalho do Nascimento and Pedro Luiz Ramos and David Elal-Olivero and Milton Cortes-Araya and Francisco Louzada},
  year={2021}
}
Normality is the most often mathematical supposition used in data modeling. Nonetheless, even based on the law of large numbers (LLN), normality is a strong presumption given that the presence of asymmetry and multi-modality in real-world problems is expected. Thus, a flexible modification in the Normal distribution proposed by Elal-Olivero [12] adds a skewness parameter, called Alpha-skew Normal (ASN) distribution, enabling bimodality and fat-tail, if needed, although sometimes not trivial to… 
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References

SHOWING 1-10 OF 40 REFERENCES

Estimating Parameters in Continuous Univariate Distributions with a Shifted Origin

SUMMARY A general method of estimating parameters in continuous univariate distributions is proposed. It is especially suited to cases where one of the parameters is an unknown shifted origin. This

Least-squares estimation of distribution functions in johnson's translation system

Compared to traditional methods of distribution fitting based on moment matching, percentile matching, L 1 estimation, and L ⌆ estimation, the least-squares technique is seen to yield fits of similar accuracy and to converge more rapidly and reliably to a set of acceptable parametre estimates.

A Novel Method for Regional Short-Term Forecasting of Water Level

The water level forecasting system represented by the hydrodynamic model relies too much on the input data and the forecast value of the boundary, therefore introducing uncertainty in the prediction

How Organization Models Impact the Governing of Industrial Symbiosis in Public Wastewater Management. An Explorative Study in Sweden

The industrial symbiosis (IS) landscape is evolving rapidly. While previous studies have argued for the importance of municipalities participating in the governing of IS, research on the implications

NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots

ABSTRACT Land degradation in semi-arid natural environments is usually associated with climate vulnerability and anthropic pressure, leading to devastating social, economic and environmental impacts.

Modeling traumatic brain injury lifetime data: Improved estimators for the Generalized Gamma distribution under small samples

This paper considers an amount of three real data sets related to traumatic brain injury caused by traffic accidents to demonstrate that the Generalized Gamma distribution is a simple alternative to be used in this type of applications for different occurrence rates and risks, and in the presence of small samples.

Precipitation From Persistent Extremes is Increasing in Most Regions and Globally

Extreme precipitation often persists for multiple days with variable duration but has usually been examined at fixed duration. Here we show that considering extreme persistent precipitation by

The Multivariate Alpha Skew Gaussian Distribution

  • Anderson AraF. Louzada
  • Mathematics, Computer Science
    Bulletin of the Brazilian Mathematical Society, New Series
  • 2019
A new class of probability distributions, so called multivariate alpha skew normal distribution, which can accommodate up to two modes and generalizes the distribution proposed by Elal-Olivero in its marginal components is proposed.