The mRMR variable selection method: a comparative study for functional data
نویسندگان
چکیده
منابع مشابه
The mRMR variable selection method: a comparative study for functional data
The use of variable selection methods is particularly appealing in statistical problems with functional data. The obvious general criterion for variable selection is to choose the ‘most representative’ or ‘most relevant’ variables. However, it is also clear that a purely relevanceoriented criterion could lead to select many redundant variables. The mRMR (minimum Redundance Maximum Relevance) pr...
متن کاملApplication of the Kudryashov method and the functional variable method for the complex KdV equation
In this present work, the Kudryashov method and the functional variable method are used to construct exact solutions of the complex KdV equation. The Kudryashov method and the functional variable method are powerful methods for obtaining exact solutions of nonlinear evolution equations.
متن کاملa comparative study of the relationship between self-, peer-, and teacher-assessments in productive skills
تمایل به ارزیابی جایگزین و تعویض آن با آزمون سنتی مداد و کاغذ در سالهای اخیر افزایش یافته است. اکثر زبان آموزان در کلاس های زبان از نمره نهایی که استاد تعیین میکند ناراضی اند. این تحقیق جهت بررسی ارزیابی در کلاس های زبان انگلیسی به هدف رضایتمندی زبان آموزان از نمره هایشان انجام گرفته است که در آن نمرات ارائه شده توسط سه گروه ارزیاب (ارزیابی خود دانشجو، همسالان واستاد) در مهارت های تولید (تکل...
15 صفحه اولa study on insurer solvency by panel data model: the case of iranian insurance market
the aim of this thesis is an approach for assessing insurer’s solvency for iranian insurance companies. we use of economic data with both time series and cross-sectional variation, thus by using the panel data model will survey the insurer solvency.
Variable selection in functional data classification: a maxima hunting proposal
Variable selection is considered in the setting of supervised binary classification with functional data {X(t), t ∈ [0, 1]}. By “variable selection” we mean any dimensionreduction method which leads to replace the whole trajectory {X(t), t ∈ [0, 1]}, with a low-dimensional vector (X(t1), . . . , X(td)) still keeping a similar classification error. Our proposal for variable selection is based on...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Computation and Simulation
سال: 2015
ISSN: 0094-9655,1563-5163
DOI: 10.1080/00949655.2015.1042378