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Physical Sciences and Mathematics Commons™
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Articles 601 - 605 of 605
Full-Text Articles in Physical Sciences and Mathematics
Simultaneous Tolerance Intervals For Response Surface And Mixture Designs Using The Adjusted Product Set Method, Aisaku Nakamura
Simultaneous Tolerance Intervals For Response Surface And Mixture Designs Using The Adjusted Product Set Method, Aisaku Nakamura
Theses and Dissertations--Statistics
Various methods for constructing simultaneous tolerance intervals for regression models have been developed over the years, but all of them can be shown to be conservative. In this thesis, extensive simulations are conducted to evaluate the degree of conservatism with respect to their coverage probabilities. A new strategy to fit simultaneous tolerance intervals on linear models is proposed by modifying an existing method, which we call the adjusted product set (APS) method. The APS method will also be used to construct simultaneous tolerance bands on response surface and mixture designs.
A Bivariate Life Distribution And Notions Of Negative Dependence, Prajamitra Bhuyan, Shyamal Ghosh, Priyanka Majumder, Murari Mitra
A Bivariate Life Distribution And Notions Of Negative Dependence, Prajamitra Bhuyan, Shyamal Ghosh, Priyanka Majumder, Murari Mitra
Journal Articles
Bivariate life distributions used to model negative dependence typically possess certain limitations; in particular, the correlation coefficient takes values in a restricted subrange of (Formula presented.). We construct a new bivariate life distribution to remedy this. Properties of the proposed distribution are studied. It is shown that the distribution satisfies most of the popular notions of negative dependence prevalent in the literature. Stress–strength reliability bounds are obtained, and parameter estimation methodology has been discussed. Performance of the estimators are compared through a simulation study.
An Introduction To Copulas, Yifan Guo, Geng Zhang
An Introduction To Copulas, Yifan Guo, Geng Zhang
Capstone Showcase
Copulas are the mathematical functions that connect the distribution functions of univariate random variables to form multivariate distributions. We define copulas, present some of their key properties, and provide examples of their applications.
Predicting Student Success In Arcadia University’S Math Courses, Chutong Wu, Tong Zhu, Yijin Qiu
Predicting Student Success In Arcadia University’S Math Courses, Chutong Wu, Tong Zhu, Yijin Qiu
Capstone Showcase
This project examines the relative efficacy of Arcadia’s math placement test and math SAT scores in predicting student success, and explores whether SAT scores alone might suffice for certain courses.
The Role Of Topography, Soil, And Remotely Sensed Vegetation Condition Towards Predicting Crop Yield, Trenton E. Franz, Sayli Pokal, Justin P. Gibson, Yuzhen Zhou, Hamed Gholizadeh, Fatima Amor Tenorio, Daran Rudnick, Derek M. Heeren, Matthew F. Mccabe, Matteo Ziliani, Zhenong Jin, Kaiyu Guan, Ming Pan, John Gates, Brian Wardlow
The Role Of Topography, Soil, And Remotely Sensed Vegetation Condition Towards Predicting Crop Yield, Trenton E. Franz, Sayli Pokal, Justin P. Gibson, Yuzhen Zhou, Hamed Gholizadeh, Fatima Amor Tenorio, Daran Rudnick, Derek M. Heeren, Matthew F. Mccabe, Matteo Ziliani, Zhenong Jin, Kaiyu Guan, Ming Pan, John Gates, Brian Wardlow
School of Natural Resources: Faculty Publications
Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what …