Fuzzy regression is considered one of the most important regression models, and recently the fuzzy regression model has become a powerful tool for conducting statistical operations, however, the above model also faces some problems and violations, including (when the data is skewed, or no-normal, .....) and thus leads to incorrect results, so it is necessary to find a model to deal with such violations and problems suffered by the regular fuzzy regression models and at the same time be more powerful and immune than the fuzzy regression model called the semi-parametric fuzzy quantile regression. This model is characterized by containing two parts, the first is the fuzzy parametric part (fuzzy inputs and crisp parameters) and the second is the fuzzy nonparametric part for fuzzy triangular numbers, and the semiparametric fuzzy quantile regression is estimated. To demonstrate the effectiveness of our combining model, we will utilize the following Akbari and Hesamian (2019) dataset that was used as a reference case study. Estimate Fuzzy Quantile Regression Model: (FQRM), Fuzzy semi-parametric quantile regression: (FSPQRM), Fuzzy Support Vector Machine: (FSVM), Combining FQRM-FSVR (Comb), Combining FSPQRM-FSVR. Using a new metric measure Jensen–Shannon Distance: (JS) based on fuzzy belonging functions. Two criteria MSM and G1 were used in comparison.
In this paper, the Monte-Carlo simulation method was used to compare the robust circular S estimator with the circular Least squares method in the case of no outlier data and in the case of the presence of an outlier in the data through two trends, the first is contaminant with high inflection points that represents contaminant in the circular independent variable, and the second the contaminant in the vertical variable that represents the circular dependent variable using three comparison criteria, the median standard error (Median SE), the median of the mean squares of error (Median MSE), and the median of the mean cosines of the circular residuals (Median A(k)). It was concluded that the method of least squares is better than the
... Show MoreAbstract The wavelet shrink estimator is an attractive technique when estimating the nonparametric regression functions, but it is very sensitive in the case of a correlation in errors. In this research, a polynomial model of low degree was used for the purpose of addressing the boundary problem in the wavelet reduction in addition to using flexible threshold values in the case of Correlation in errors as it deals with those transactions at each level separately, unlike the comprehensive threshold values that deal with all levels simultaneously, as (Visushrink) methods, (False Discovery Rate) method, (Improvement Thresholding) and (Sureshrink method), as the study was conducted on real monthly data represented in the rates of theft crimes f
... Show MoreThis study aims to analyze the spatial distribution of the epidemic spread and the role of the physical, social, and economic characteristics in this spreading. A geographically weighted regression (GWR) model was built within a GIS environment using infection data monitored by the Iraqi Ministry of Health records for 10 months from March to December 2020. The factors adopted in this model are the size of urban interaction areas and human gatherings, movement level and accessibility, and the volume of public services and facilities that attract people. The results show that it would be possible to deal with each administrative unit in proportion to its circumstances in light of the factors that appe
Semi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.
We compare two methods Bayesian and . Then the results were compared using MSe criteria.
A simulation had been used to study the empirical behavior for the Logistic model , with different sample sizes and variances. The results using represent that the Bayesian method is better than the at small samples sizes.
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The research examined with the importance banking merger to address the situation of Troubled banks in Iraq, Through The use of Logistic Regression Model. . The study attempted to present a conceptual aspect of banking merger and logistic regression, as well as the applied aspect which includes a sample consisting of six private Iraqi banks, and the hypothesis of the study is that the promotion of mergers among banks has positive impacts on improving the efficiency of performance of troubled banks, which contributes to the increase of banking services, raise of their financial indicators and the high liquidity and profits of the new banking entity as it is a way to overcome the prevailing banking crises.
... Show MoreThis paper presents a parametric audio compression scheme intended for scalable audio coding applications, and is particularly well suited for operation at low rates, in the vicinity of 5 to 32 Kbps. The model consists of two complementary components: Sines plus Noise (SN). The principal component of the system is an. overlap-add analysis-by-synthesis sinusoidal model based on conjugate matching pursuits. Perceptual information about human hearing is explicitly included into the model by psychoacoustically weighting the pursuit metric. Once analyzed, SN parameters are efficiently quantized and coded. Our informal listening tests demonstrated that our coder gave competitive performance to the-state-of-the- art HelixTM Producer Plus 9 from
... Show MoreIn this paper, preliminary test Shrinkage estimator have been considered for estimating the shape parameter α of pareto distribution when the scale parameter equal to the smallest loss and when a prior estimate α0 of α is available as initial value from the past experiences or from quaintance cases. The proposed estimator is shown to have a smaller mean squared error in a region around α0 when comparison with usual and existing estimators.
Let R be a commutative ring with 1 and M be a (left) unitary R – module. This essay gives generalizations for the notions prime module and some concepts related to it. We termed an R – module M as semi-essentially prime if annR (M) = annR (N) for every non-zero semi-essential submodules N of M. Given some of their advantages characterizations and examples, and we study the relation between these and some classes of modules.
Let