دانلود مقاله ISI انگلیسی شماره 24566
ترجمه فارسی عنوان مقاله

کمینه محلی در رگرسیون چند طبقه ای

عنوان انگلیسی
Local minima in categorical multiple regression
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
24566 2006 17 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computational Statistics & Data Analysis, Volume 50, Issue 2, 30 January 2006, Pages 446–462

ترجمه کلمات کلیدی
متناوب حداقل مربعات - برگشت اتصالات - داده های - کمینه محلی - خطی - نواخت - رگرسیون چندگانه - رگرسیون غیر خطی - غیر یکنواخت - پوسته پوسته شدن مطلوب - کمی - شروع چندگانه نظام مند - دگرگونی -
کلمات کلیدی انگلیسی
Alternating least squares, Backfitting, Categorical data, CATREG, Local minima, Linearization, Monotonic, Multiple regression, Nonlinear regression, Nonmonotonic, Optimal scaling, Quantification, Multiple systematic starts, Transformation
پیش نمایش مقاله
پیش نمایش مقاله  کمینه محلی در رگرسیون چند طبقه ای

چکیده انگلیسی

CATREG is a program for categorical multiple regression, applying optimal scaling methodology to quantify categorical variables, including the response variable, simultaneously optimizing the multiple regression coefficient. The scaling levels that can be applied are nominal, nonmonotonic spline, ordinal, monotonic spline or numerical. When ordinal or monotonic spline scaling levels are applied, local minima can occur. With ordinal or monotonic spline scaling levels, the transformations are required to be monotonically increasing, but this can also be achieved by reflecting a monotonic decreasing transformation. A monotonic transformation is obtained by restricting a nonmonotonic transformation, but the direction of the monotonic restriction (increasing or decreasing) is undefined, and it will be shown that this is the cause of local minima. Several strategies to obtain the global minimum for the ordinal scaling level will be presented. Also, results of a simulation study to assess the performance of these strategies are given. The simulation study is also used to identify data conditions under which local minima are more likely to occur and are more likely to be severe. It was found that local minima more often occur with low to moderately low R2R2 values, with higher number of categories and with higher multicollinearity.