Results 1 - 10 of 3,756

Flexible smoothing with B-splines and penalties

by Paul H. C. Eilers, Brian D. Marx - STATISTICAL SCIENCE , 1996
"... B-splines are attractive for nonparametric modelling, but choosing the optimal number and positions of knots is a complex task. Equidistant knots can be used, but their small and discrete number allows only limited control over smoothness and fit. We propose to use a relatively large number of knots ..."
Abstract - Cited by 405 (7 self) - Add to MetaCart
B-splines are attractive for nonparametric modelling, but choosing the optimal number and positions of knots is a complex task. Equidistant knots can be used, but their small and discrete number allows only limited control over smoothness and fit. We propose to use a relatively large number

Regularization paths for generalized linear models via coordinate descent

by Jerome Friedman, Trevor Hastie, Rob Tibshirani , 2009
"... We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, twoclass logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the elastic ..."
Abstract - Cited by 724 (15 self) - Add to MetaCart
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, twoclass logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the

Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

by Jianqing Fan , Runze Li , 2001
"... Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
Abstract - Cited by 948 (62 self) - Add to MetaCart
Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized

A Case for End System Multicast

by Yang-hua Chu, Sanjay G. Rao, Srinivasan Seshan, Hui Zhang - in Proceedings of ACM Sigmetrics , 2000
"... Abstract — The conventional wisdom has been that IP is the natural protocol layer for implementing multicast related functionality. However, more than a decade after its initial proposal, IP Multicast is still plagued with concerns pertaining to scalability, network management, deployment and suppor ..."
Abstract - Cited by 1290 (24 self) - Add to MetaCart
of multicast support from routers to end systems has the potential to address most problems associated with IP Multicast. However, the key concern is the performance penalty associated with such a model. In particular, End System Multicast introduces duplicate packets on physical links and incurs larger end

High dimensional graphs and variable selection with the Lasso

by Nicolai Meinshausen, Peter Bühlmann - ANNALS OF STATISTICS , 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
Abstract - Cited by 736 (22 self) - Add to MetaCart
is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for each node in the graph and is hence equivalent to variable selection for Gaussian linear models. We

The adaptive LASSO and its oracle properties

by Hui Zou - Journal of the American Statistical Association
"... The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain sc ..."
Abstract - Cited by 683 (10 self) - Add to MetaCart
scenarios where the lasso is inconsistent for variable selection. We then propose a new version of the lasso, called the adaptive lasso, where adaptive weights are used for penalizing different coefficients in the!1 penalty. We show that the adaptive lasso enjoys the oracle properties; namely, it performs

The Wage Penalty For Motherhood

by Michelle Budig, Paula England, Address Michelle, J. Budig, Paula Engl , 2000
"... Motherhood is associated with lower hourly pay, but the causes of this are not well understood. Mothers may earn less than other women because having children causes them to (1) lose job experience, (2) be less productive at work, (3) trade off higher wages for motherfriendly jobs, or (4) be discrim ..."
Abstract - Cited by 234 (6 self) - Add to MetaCart
) be discriminated against by employers. Or the relationship may be spurious rather than causal; women with lower earning potential may have children at higher rates. Using 1982-1993 NLSY data, we examine the motherhood penalty with fixed-effects models chosen to avoid spuriousness. We find penalties of 7 percent

An Automata-Theoretic Approach to Branching-Time Model Checking

by Orna Kupferman, Moshe Y. Vardi, Pierre Wolper - JOURNAL OF THE ACM , 1998
"... Translating linear temporal logic formulas to automata has proven to be an effective approach for implementing linear-time model-checking, and for obtaining many extensions and improvements to this verification method. On the other hand, for branching temporal logic, automata-theoretic techniques ..."
Abstract - Cited by 354 (66 self) - Add to MetaCart
-theoretic techniques have long been thought to introduce an exponential penalty, making them essentially useless for model-checking. Recently, Bernholtz and Grumberg have shown that this exponential penalty can be avoided, though they did not match the linear complexity of non-automata-theoretic algorithms

A constant recontracting model of sovereign debt

by Jeremy I. Bulow, Kenneth Rogoff, Jeremy Bulow, Kenneth Rogoff - Journal of Political Economy , 1989
"... Few sovereign debtors have repudiated their obligations entirely. But despite the significant sanctions at the disposal of lenders, many borrowers have been able to consistently negotiate for reduced repayments. This paper presents a model of the on-going bargaining process that determines repayment ..."
Abstract - Cited by 349 (11 self) - Add to MetaCart
Few sovereign debtors have repudiated their obligations entirely. But despite the significant sanctions at the disposal of lenders, many borrowers have been able to consistently negotiate for reduced repayments. This paper presents a model of the on-going bargaining process that determines

An effective on-chip preloading scheme to reduce data access penalty

by Jean-loup Baer, Tien-fu Chen - In Proceedings of the 1991 ACM/IEEE conference on Supercomputing, Supercomputing ’91 , 1991
"... Conventional cache prefetching approaches can be either hardware-based, generally by using a one-block-Iookahead technique, or compiler-directed, with inser-tions of non-blocking prefetch instructions. We intro-duce a new hardware scheme based on the prediction of the execution of the instruction st ..."
Abstract - Cited by 255 (4 self) - Add to MetaCart
is prevented. We evaluate our design through trace driven simulation by comparing it with a pure data cache approach under three different memory ac-cess models. Our experiments show that this scheme is very effective for reducing the data access penalty for scientific programs and that is has moderate success
Results 1 - 10 of 3,756