The estimation of the regular regression model requires several assumptions to be satisfied such as "linearity". One problem occurs by partitioning the regression curve into two (or more) parts and then joining them by threshold point(s). This situation is regarded as a linearity violation of regression. Therefore, the multiphase regression model is received increasing attention as an alternative approach which describes the changing of the behavior of the phenomenon through threshold point estimation. Maximum likelihood estimator "MLE" has been used in both model and threshold point estimations. However, MLE is not resistant against violations such as outliers' existence or in case of the heavy-tailed error distribution. The main goal of this paper is to suggest a new hybrid estimator obtained by an ad-hoc algorithm which relies on data driven strategy that overcomes outliers. While the minor goal is to introduce a new employment of an unweighted estimation method named "winsorization" which is a good method to get robustness in regression estimation via special technique to reduce the effect of the outliers. Another specific contribution in this paper is to suggest employing "Kernel" function as a new weight (in the scope of the researcher's knowledge).Moreover, two weighted estimations are based on robust weight functions named "Cauchy" and "Talworth". Simulations have been constructed with contamination levels (0%, 5%, and 10%) which associated with sample sizes (n=40,100). Real data application showed the superior performance of the suggested method compared with other methods using RMSE and R2 criteria.
Often times, especially in practical applications, it is difficult to obtain data that is not tainted by a problem that may be related to the inconsistency of the variance of error or any other problem that impedes the use of the usual methods represented by the method of the ordinary least squares (OLS), To find the capabilities of the features of the multiple linear models, This is why many statisticians resort to the use of estimates by immune methods Especially with the presence of outliers, as well as the problem of error Variance instability, Two methods of horsepower were adopted, they are the robust weighted least square(RWLS)& the two-step robust weighted least square method(TSRWLS), and their performance was verifie
... Show MoreUtilizing the Turbo C programming language, the atmospheric earth model is created from sea level to 86 km. This model has been used to determine atmospheric Earth parameters in this study. Analytical derivations of these parameters are made using the balancing forces theory and the hydrostatic equation. The effects of altitude on density, pressure, temperature, gravitational acceleration, sound speed, scale height, and molecular weight are examined. The mass of the atmosphere is equal to about 50% between sea level and 5.5 km. g is equal to 9.65 m/s2 at 50 km altitude, which is 9% lower than 9.8 m/s2 at sea level. However, at 86 km altitude, g is close to 9.51 m/s2, which is close to 15% smaller than 9.8 m/s2. These resu
... Show MoreIn this work, radius of shock wave of plasma plume (R) and speed of plasma (U) have been calculated theoretically using Matlab program.
In the analysis of multiple linear regression, the problem of multicollinearity and auto-correlation drew the attention of many researchers, and given the appearance of these two problems together and their bad effect on the estimation, some of the researchers found new methods to address these two problems together at the same time. In this research a comparison for the performance of the Principal Components Two Parameter estimator (PCTP) and The (r-k) class estimator and the r-(k,d) class estimator by conducting a simulation study and through the results and under the mean square error (MSE) criterion to find the best way to address the two problems together. The results showed that the r-(k,d) class estimator is the best esti
... Show MoreThe problem of Multicollinearity is one of the most common problems, which deal to a large extent with the internal correlation between explanatory variables. This problem is especially Appear in economics and applied research, The problem of Multicollinearity has a negative effect on the regression model, such as oversized variance degree and estimation of parameters that are unstable when we use the Least Square Method ( OLS), Therefore, other methods were used to estimate the parameters of the negative binomial model, including the estimated Ridge Regression Method and the Liu type estimator, The negative binomial regression model is a nonline
... Show MoreThe charge transfer at C23H17F8N8O2PRu, C44H30BF4N5O4Ru, C56H52CL5N5OOsP2 and C76H88F80N24O11P10Ru4 nitrosyl complexes are investigation and studies theoretically using the quantum consideration. Charge transfer behavior largely rely to the electric properties of nitrosyl complexes system whose depending on the main important parameters for the transmission rate constant such that: orientation transition energy, overlapping coupling coefficient, driving force energy, height barrier and Temperature T (K). Data results have been evaluated using a MATLAB program. Results show that rate of charge transfer increases due to increases the orientation transition energy.
Background: Chronic myeloid leukemia is a cancer of the white blood cells characterized by the increased and unregulated growth of predominantly myeloid cells in the bone marrow. This study aimed to determine the effect of chronic myeloid leukemia on Dental caries and Oral health status including Gingivitis, Loss of attachment, Plaque index and Calculus index as well as evaluation of salivary flow rate and salivary interleukins-6 and tumor necrosis factor-?. Material and methods: Study group consisted of (75) subjects, (25) were newly diagnosed with chronic myeloid leukemia, (25) were taking medications (Glevic), and (25) were control subjects, all ag
... Show MoreIn this paper, we will study non parametric model when the response variable have missing data (non response) in observations it under missing mechanisms MCAR, then we suggest Kernel-Based Non-Parametric Single-Imputation instead of missing value and compare it with Nearest Neighbor Imputation by using the simulation about some difference models and with difference cases as the sample size, variance and rate of missing data.