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Efficient Secure Ridge Regression from Randomized Gaussian Elimination
multiparty computation privacy-preserving machine learning secure ridge regression
2019/7/8
In this paper we present a practical protocol for secure ridge regression. We develop the necessary secure linear algebra tools, using only basic arithmetic over prime fields. In particular, we will s...
Privacy-Preserving Ridge Regression over Distributed Data from LHE
LHE real-world datasets
2017/10/10
Linear regression with 2-norm regularization (i.e., ridge regression) is an important statistical technique that models the relationship between some explanatory values and an outcome value using a li...
Ridge regression is an algorithm that takes as input a large number of data points and finds the best-fit linear curve through these points. It is a building block for many machine-learning operations...
Privacy-Preserving Ridge Regression on Distributed Data
linear regression distributed data privacy-preserving system
2017/7/26
Linear regression is an important statistical tool that models the relationship between some explanatory values and an outcome value using a linear function. In many current applications (e.g. predict...
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Divide and Conquer Kernel Ridge Regression A Distributed Algorithm Minimax Optimal Rates
2013/6/14
We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subse...
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Ridge Regression dimensional subspace
2011/6/16
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which
one simply projects the data onto a finite dimensional subspace (as specified by a Principal
Component...
Effect of W，LR，and LM Tests on the Performance of Preliminary Test Ridge Regression Estimators
Lagrangian multiplier likelihood ratio test preliminary test ridge regression risk superiority Wald test
2009/3/10
The preliminary test ridge regression estimators (PTRRE) based on the Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are considered in this paper. Using risks, the regions of opt...
Anomalies in the Foundations of Ridge Regression
Constrained optimization incomplete use of LaGrange’s method nonsingular distributions alternative foundations
2010/4/27
Anomalies persist in the foundations of ridge regression as set forth in Hoerl
and Kennard (1970) and subsequently. Conventional ridge estimators and their properties
do not follow on constraining l...
LOGISTIC RIDGE REGRESSION FOR CLINICAL DATA ANALYSIS (A CASE STUDY)
logit multicollinearity bootstrap restless legs
2010/3/24
This paper focuses on regression with binomial response data. In these cases logit regression is the most used model. An example is a retrospective biomedical problem, where multicollinearity occurs,t...
The Economic Value of Irrigation Water in the Western United States: An Application to Ridge Regression
Irrigation Water Western United States An Application Ridge Regression
2013/9/11
Reliable estimates of the demand characteristics of irrigation water are crucial to successful water policy formulation in the West. Although various studies concerning irrigation water demand exist i...