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The Proximal Distance Principle for Constrained Estimation | Data Science
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Fortunately, the rich theory of convex optimization provides ample tools for devising novel methods. In this talk, I present applications of distance-to-set penalties to statistical learning problems. Specifically, I will focus on proximal distance algorithms, based on the MM principle, tailored to various applications such as regression and discriminant analysis. Special emphasis is given to sparsity set constraints as a compromise between exhaustive combinatorial searches and lasso penalization methods that induce shrinkage." /> <link rel="canonical" href="https://datascience.ucr.edu/news/2024/05/24/proximal-distance-principle-constrained-estimation" /> <meta property="og:site_name" content="Data Science" /> <meta property="og:type" content="website" /> <meta property="og:url" content="https://datascience.ucr.edu/news/2024/05/24/proximal-distance-principle-constrained-estimation" /> <meta property="og:title" content="The Proximal Distance Principle for Constrained Estimation" /> <meta property="og:description" content="ABSTRACT: Statistical methods often involve solving an optimization problem, such as in maximum likelihood estimation and regression. The addition of constraints, either to enforce a hard requirement in estimation or to regularize solutions, complicates matters. Fortunately, the rich theory of convex optimization provides ample tools for devising novel methods. In this talk, I present applications of distance-to-set penalties to statistical learning problems. Specifically, I will focus on proximal distance algorithms, based on the MM principle, tailored to various applications such as regression and discriminant analysis. Special emphasis is given to sparsity set constraints as a compromise between exhaustive combinatorial searches and lasso penalization methods that induce shrinkage." /> <meta property="og:updated_time" content="2024-05-28T07:46:13-0700" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:description" content="ABSTRACT: Statistical methods often involve solving an optimization problem, such as in maximum likelihood estimation and regression. The addition of constraints, either to enforce a hard requirement in estimation or to regularize solutions, complicates matters. 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Alfonso Landeros, Statistics Department, UCR</span> <div class="ucr-articles--page--title--author-info"> <span class="author-date">May 24, 2024</span> </div> </div> </div> </div> </div> <div class="grid-x grid-padding-x system-admin-controls"> </div> </div> <div id="main-content" class="grid-container full primary-content-area"> <div class="grid-x"> <div class="cell large-auto small-order-3 medium-order-3 large-order-2 pca-content"> <div> <div id="block-ucr-design-1-content" data-block-plugin-id="system_main_block"> <div class="grid-container"> <div class="grid-x grid-padding-x grid-padding-y"> <div class="cell padding-top-0"> <div class="ucr-articles--page--full"> <div class="ucr-articles--page--full--body"> <p>Statistical methods often involve solving an optimization problem, such as in maximum likelihood estimation and regression. The addition of constraints, either to enforce a hard requirement in estimation or to regularize solutions, complicates matters. Fortunately, the rich theory of convex optimization provides ample tools for devising novel methods. In this talk, I present applications of distance-to-set penalties to statistical learning problems. Specifically, I will focus on proximal distance algorithms, based on the MM principle, tailored to various applications such as regression and discriminant analysis. Special emphasis is given to sparsity set constraints as a compromise between exhaustive combinatorial searches and lasso penalization methods that induce shrinkage.</p><p><a href="https://profiles.ucr.edu/app/home/profile/alandero">Dr. Alfonso Landeros</a></p> </div> <div class="ucr-articles--page--full--sharing"> <div class="sharing-title">Share This</div><span class="a2a_kit a2a_kit_size_32 addtoany_list" data-a2a-url="https://datascience.ucr.edu/news/2024/05/24/proximal-distance-principle-constrained-estimation" data-a2a-title="The Proximal Distance Principle for Constrained Estimation"><a class="a2a_button_facebook"></a><a class="a2a_button_x"></a><a class="a2a_button_linkedin"></a><a class="a2a_button_google_plus"></a><a class="a2a_button_email"></a></a><a class="a2a_button_printfriendly"></a><a class="a2a_dd addtoany_share" aria-label="more options to share" 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