10 results
Search Results
2. Robust Circular Logistic Regression Model and Its Application to Life and Social Sciences.
- Author
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CASTILLA, ELENA
- Subjects
- *
LOGISTIC regression analysis , *REGRESSION analysis , *MONTE Carlo method , *MAXIMUM likelihood statistics - Abstract
This paper presents robust estimators for binary and multinomial circular logistic regression, where a circular predictor is related to the response. An extensive Monte Carlo Simulation Study clearly shows the robustness of proposed methods. Finally, three numerical examples of Botany, Crime and Meteorology illustrate the application of these methods to Life and Social Sciences. Although in the Botany data the proposed method showed little improvement, in the Crime and Meteorological data an increment up to 5% and 4% of accuracy, respectively, is achieved. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Likelihood-Based Inference for the Asymmetric Exponentiated Bimodal Normal Model.
- Author
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MARTÍNEZ-FLÓREZ, GUILLERMO, PACHECO-LÓPEZ, MARIO, and TOVAR-FALÓN, ROGER
- Subjects
- *
MAXIMUM likelihood statistics , *SKEWNESS (Probability theory) , *STOCHASTIC convergence , *STOCHASTIC matrices , *KURTOSIS - Abstract
Asymmetric probability distributions have been widely studied by various authors in recent decades. Special interest has been had families of flexible distributions with the capability to have into account degree of skewness and kurtosis greater than the classical distributions widely known in statistical theory. While, most of the new distributions _t unimodal data, and a few fit bimodal data, in the bimodal proposals, singularity problems have been found in the information matrices. Therefore, in this paper, extensions of the alpha-power family of distributions are developed, which have non-singular information matrix. The new proposals are based on the bimodal-normal and bimodal elliptical skew-normal distributions. These new extensions allow modeling asymmetric bimodal data, which are commonly found in several areas of scientific interest. The properties of these new distributions of probability are also studied in detail, and the statistical inference process is carried out to estimate the parameters of the proposed models. The stochastic convergence for the maximum likelihood estimator (MLE) vector can be found due to the non-singularity of the expected information matrix in the corresponding support. We also introduced extensions of the asymmetric bimodal normal and bimodal elliptical skew-normal models for the situations in which the data present censorship. A small simulation study to evaluate the properties of the MLE is also presented and, finally, two applications to real data set are presented for illustrative purposes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Some Inferential Problems from Log Student's T-distribution and its Multivariate Extension.
- Author
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OLOSUNDE, AKINLOLU and OLOFINTUADE, SYLVESTER
- Subjects
- *
MARGINAL distributions , *CUMULATIVE distribution function , *DISTRIBUTION (Probability theory) , *MAXIMUM likelihood statistics , *SKEWNESS (Probability theory) , *GOODNESS-of-fit tests - Abstract
Assumption of normality in statistical analysis had been a common practice in many literature, but in the event where small sample is obtainable, then normality assumption will lead to erroneous conclusion in the statistical analysis. Taking a large sample had been a serious concern in practice due to various factors. In this paper, we further derived some inferential properties for log student's t-distribution (simply log-t distribution) which makes it more suitable as substitute to log-normal when carrying out analysis on right-skewed small sample data. Mathematical and Statistical properties such as the moments, cumulative distribution function, survival function, hazard function and log-concavity are derived. We further extend the results to case of multivariate log-t distribution; we obtained the marginal and conditional distributions. The parameters estimation was done via maximum likelihood estimation method, consequently its best critical region and information matrix were derived in order to obtain the asymptotic confidence interval. The applications of log-t distribution and goodness-of-fit test was carried out on two dataset from literature to show when the model is most appropriate. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. Maximum likelihood classification of soil remote sensing image based on deep learning.
- Author
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Shujun Liang, Jing Cheng, and Jianwei Zhang
- Subjects
- *
REMOTE sensing , *DEEP learning , *MAXIMUM likelihood statistics , *IMAGE denoising , *MACHINE learning - Abstract
Soil remote sensing image classification is the most difficult in the National Soil Census work. Current soil remote sensing image classification methods based on deep learning and maximum likelihood estimation are challenging to meet the actual needs. Therefore, this paper combines deep learning with maximum likelihood estimation and proposes a maximum likelihood classification method for soil remote sensing images based on deep learning. The method is divided into four parts. Firstly, the pretreatment of soil remote sensing image is carried out, including three processes: image gray, image denoising, and image correction; secondly, the target of soil remote sensing image is detected by deep learning algorithm; thirdly, the maximum likelihood algorithm is used to classify soil remote sensing image; finally, the classification performance is tested by an example. The results show that this method can effectively segment the remote sensing image of soil, and the segmentation accuracy is high, which proves the effectiveness and superiority of the method. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
6. On the Alpha Power Kumaraswamy Distribution: Properties, Simulation and Application.
- Author
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AHMED, MOHAMED ALI
- Subjects
- *
PARAMETER estimation , *ORDER statistics - Abstract
Adding new parameters to classical distributions becomes one of the most important methods for increasing distributions flexibility, especially, in simulation studies and real data sets. In this paper, alpha power transformation (APT) is used and applied to the Kumaraswamy (K) distribution and a proposed distribution, so called the alpha power Kumaraswamy (AK) distribution, is presented. Some important mathematical properties are derived, parameters estimation of the AK distribution using maximum likelihood method is considered. A simulation study and a real data set are used to illustrate the flexibility of the AK distribution compared with other distributions. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
7. Two Useful Discrete Distributions to Model Overdispersed Count Data.
- Author
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MAZUCHELI, JOSMAR, BERTOLI, WESLEY, and OLIVEIRA, RICARDO
- Subjects
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CONTINUOUS distributions , *INFINITE series (Mathematics) , *MONTE Carlo method , *POISSON distribution , *DISCRETIZATION methods - Abstract
The methods to obtain discrete analogs of continuous distributions have been widely considered in recent years. In general, the discretization process provides probability mass functions that can be competitive with the traditional model used in the analysis of count data, the Poisson distribution. The discretization procedure also avoids the use of continuous distribution in the analysis of strictly discrete data. In this paper, we seek to introduce two discrete analogs for the Shanker distribution using the method of the infinite series and the method based on the survival function as alternatives to model overdispersed datasets. Despite the difference between discretization methods, the resulting distributions are interchangeable. However, the distribution generated by the method of the infinite series method has simpler mathematical expressions for the shape, the generating functions, and the central moments. The maximum likelihood theory is considered for estimation and asymptotic inference concerns. A simulation study is carried out in order to evaluate some frequentist properties of the developed methodology. The usefulness of the proposed models is evaluated using real datasets provided by the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
8. On Reliability in a Multicomponent Stress-Strength Model with Power Lindley Distribution.
- Author
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PAK, ABBAS, GUPTA, ARJUN KUMAR, and KHOOLENJANI, NAYEREH BAGHERI
- Subjects
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MECHANICAL stress analysis , *RELIABILITY in engineering , *MAXIMUM likelihood statistics , *CONFIDENCE intervals , *MARKOV chain Monte Carlo - Abstract
In this paper we study the reliability of a multicomponent stress-strength model assuming that the components follow power Lindley model. The maximum likelihood estimate of the reliability parameter and its asymptotic con- fidence interval are obtained. Applying the parametric Bootstrap technique, interval estimation of the reliability is presented. Also, the Bayes estimate and highest posterior density credible interval of the reliability parameter are derived using suitable priors on the parameters. Because there is no closed form for the Bayes estimate, we use the Markov Chain Monte Carlo method to obtain approximate Bayes estimate of the reliability. To evaluate the performances of different procedures, simulation studies are conducted and an example of real data sets is provided. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
9. Form-Invariance of the Non-Regular Exponential Family of Distributions.
- Author
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GHORBANPOUR, S., CHINIPARDAZ, R., and REZA ALAVI, SAYED MOHAMMAD
- Subjects
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MATHEMATICAL symmetry , *MAXIMUM likelihood statistics , *FISHER information , *PROBABILITY density function , *RANDOM variables - Abstract
The weighted distributions are used when the sampling mechanism records observations according to a nonnegative weight function. Sometimes the form of the weighted distribution is the same as the original distribution except possibly for a change in the parameters that is called the form-invariant weighted distribution. In this paper, by identifying a general class of weight functions, we introduce an extended class of form-invariant weighted distributions belonging to the non-regular exponential family which included two common families of distribution: exponential family and non-regular family as special cases. Some properties of this class of distributions such as the suficient and minimal suficient statistics, maximum likelihood estimation and the Fisher information matrix are studied. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
10. Alpha-Skew Generalized t Distribution.
- Author
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ACITAS, SUKRU, SENOGLU, BIRDAL, and ARSLAN, OLCAY
- Subjects
- *
SKEWNESS (Probability theory) , *KURTOSIS , *DISTRIBUTION (Probability theory) , *MAXIMUM likelihood statistics , *PROBABILITY density function - Abstract
The alpha-skew normal (ASN) distribution has been proposed recently in the literature by using standard normal distribution and a skewing ap- proach. Although ASN distribution is able to model both skew and bimodal data, it is shortcoming when data has thinner or thicker tails than normal. Therefore, we propose an alpha-skew generalized t (ASGT) by using the gen- eralized t (GT) distribution and a new skewing procedure. From this point of view, ASGT can be seen as an alternative skew version of GT distribution. However, ASGT differs from the previous skew versions of GT distribution since it is able to model bimodal data sest as well as it nests most commonly used density functions. In this paper, moments and maximum likelihood estimation of the parameters of ASGT distribution are given. Skewness and kurtosis measures are derived based on the first four noncentral moments. The cumulative distribution function (cdf) of ASGT distribution is also ob- tained. In the application part of the study, two real life problems taken from the literature are modeled by using ASGT distribution. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
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