As the global elderly population continues to rise, risk of severe crashes among drivers has become a pressing concern. This study presents comprehensive examination crash severity this demographic, employing machine learning models and data gathered from Virginia, United States America, between 2014 2021. The analysis integrates parametric models, namely logistic regression linear discriminant...

Parametric, model based, spectral estimation techniques can offer increased frequency resolution over conventional short-term fast Fourier transform methods, overcoming limitations caused by the windowing of sampled, time domain, input data. However, parametric techniques are significantly more computationally demanding than the Fourier based methods and require a wider range of arithmetic func...

Non-parametric classification is concerned with the classification of populations of objects when the underlying statistical model is the unknown parameter. The analysis of formal models of non-parametric classification involves the quantification of the stability of individual classes. This problem has not been solved in general. The problem of stability is discussed and a method for computing...

This paper focuses on the application of rapid prototyping techniques using additive manufacturing in combination with parametric design to create low-cost, yet accurate and reliable instruments. The methodology followed makes it possible to make instruments with a degree of customization until now available only to a narrow audience, helping democratize science. The proposal discusses a holist...

This paper examines parametric estimating techniques applied to technology-driven projects in various industry sectors. Parametric estimating is the use of a subset of independent variables to predict the cost of a project. These independent variables may be specifications, features, functions, or some other high level descriptive elements that are used to define the scope of the deliverables a...

Voxel-based estimation of PET images, generally referred to as parametric imaging, can provide invaluable information about the heterogeneity of an imaging agent in a given tissue. Due to high level of noise in dynamic images, however, the estimated parametric image is often noisy and unreliable. Several approaches have been developed to address this challenge, including spatial noise reduction...

the purpose of this study is estimation of daily value at risk (var) for total index of tehran stock exchange using parametric, nonparametric and semi-parametric approaches. conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated var and also to compare the performance of mentioned approaches. in most cases, based on backtesting statistics results, ...

An overview is presented of some parametric and semi-parametric models, estimators, and specification tests that can be used to analyze ordered response variables. In particular, limited dependent variable models that generalize ordered probit are compared to regression models that generalize the linear model. These techniques are then applied to analyze how self-reported satisfaction with hous...

Many compiler optimization techniques depend on the ability to calculate the number of integer values that satisfy a given set of linear constraints. This count (the enumerator of a parametric polytope) is a function of the symbolic parameters that may appear in the constraints. In an extended problem (the “integer projection” of a parametric polytope), some of the variables that appear in the ...