An Efficient Shrinkage Estimators For Generalized Inverse Rayleigh Distribution Based On Bounded And Series Stress-Strength Models

The reliability of the stress-strength model attracted many statisticians for several years owing to its applicability in different and diverse parts such as engineering, quality control, and economics. In this paper, the system reliability estimation in the stress-strength model containing Kth parallel components will be offered by four types of shrinkage methods: constant Shrinkage Estimation Method, Shrinkage Function Estimator, Modified Thompson Type Shrinkage Estimator, Squared Shrinkage Estimator. The Monte Carlo simulation study is compared among proposed estimators using the mean squared error. The result analyses of the shrinkage estimation methods showed that the shrinkage functions estimator was the best since it has a minor mean squared error than the other methods followed by the additional shrinkage estimator. The stress and strength belong to the In verse Kumaraswamy distribution

Section: Estimation Methods For System Reliability 41 Shrinkage Estim...mentioning

confidence: 99%

Estimation of a Parallel Stress-strength Model Based on the Inverse Kumaraswamy Distribution

Kalaf

Hameed²,

Salman

et al. 2023

“…Eq (1) is linear in terms of the new space that ɸ (x) maps the data to non-linear in the space, see Figure 3. The most common kernels are: linear, polynomial, sigmoid or Multi-Layer Perceptron (MLP) and Gaussian or Radial Basis Function (RPF) [11][12][13]. Their expressions are as follows: we define the kernel function as K(𝑥 𝑖 , 𝑥 𝑗 ) =< ɸ(𝑥 𝑖 ), ɸ(𝑥 𝑗 ) >= ɸ(𝑥 𝑖 ) 𝑇 ɸ(𝑥 𝑗 ) where ɸ is a mapping from input space to output space, see Figure 4.…”

Section: Kernels Transformationmentioning

confidence: 99%

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Mahdi,

Seror Faeq Mohammed,

Md Kamrul Hasan Khan

2024

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a very high accuracy and is quite robust. ESGD-SVM is used to analyze the heart disease dataset downloaded from Harvard Dataverse. The entire analysis was performed using the program R version 4.3.

“…Suppose 𝑇 is a random variable following Inverse Generalized Rayleigh distribution with two parameters θ and λ. Then p.d.f and c.d.f functions of inverse Generalized Rayleigh distribution are given for equations ( 3) and ( 4), respectively, by [29]; [30], When t=1 in equation ( 6)…”

Section: Truncated Inverse Generalized Rayleigh Distributionmentioning

confidence: 99%

“…In addition, it happens when we are unable to detect or record events that take place inside or outside of a predetermined range or below or above a given threshold. The truncation can be from the left side, the right side, or both sides [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Truncated Inverse Generalized Rayleigh Distribution and Some Properties

Kalaf,

Abdul Ameer Jabar,

Madaki

2023

Self Cite

Truncated distributions arise naturally in many practical situations. It’s a conditional distribution that develops when the parent distribution's domain is constrained to a smaller area. The distribution of a right truncated is one of the types of a single truncated that is restricted within a specific field and usually occurs when the specified period for the study is complete. Hence, this paper introduces Right Truncated Inverse Generalized Rayleigh Distribution (RTIGRD) with two parameters is introduced. Then, provided some properties such as; (probability density function, cumulative distribution function (CDF), survival function, hazard function, ‎rth moment, mean, variance, Moment Generating Function, Skewness, kurtosis, Median, and Mode for Right Truncated Inverse Generalized Rayleigh Distribution on [0,1].