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| 7 | +<meta name="description" content="Active and proposed Statlib formalization projects."> |
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| 55 | +<body class="site-page"> |
| 56 | + |
| 57 | +<nav> |
| 58 | + <a href="index.html" class="brand">Statlib</a> |
| 59 | + <div class="links"> |
| 60 | + <a href="index.html">About</a> |
| 61 | + <a href="tutorial/index.html">Tutorial</a> |
| 62 | + <a href="roadmap.html">Roadmap</a> |
| 63 | + <a href="projects.html" class="active">Projects</a> |
| 64 | + <a href="contribute.html">Contribute</a> |
| 65 | + </div> |
| 66 | +</nav> |
| 67 | + |
| 68 | +<main> |
| 69 | + <div class="wrap"> |
| 70 | + <h1>Projects</h1> |
| 71 | + |
| 72 | + <p class="intro"> |
| 73 | + We welcome researchers in sub-areas of statistics to propose projects, contribute projects, and help evolve the library! These projects connect concrete formalization work with the broader Statlib <a href="roadmap.html">roadmap</a>. |
| 74 | + </p> |
| 75 | + |
| 76 | + <section class="project" id="semiparametric-efficiency-theory"> |
| 77 | + <h2>1. Semiparametric Efficiency Theory</h2> |
| 78 | + <p class="project-label">Description.</p> |
| 79 | + <p> |
| 80 | + This project formalizes foundational results in semiparametric efficiency theory, especially the asymptotic theory underlying efficient estimation in statistical models. |
| 81 | + </p> |
| 82 | + <p class="project-label">Milestones.</p> |
| 83 | + <ul class="items"> |
| 84 | + <li>Contiguity</li> |
| 85 | + <li>Local asymptotic normality, or LAN</li> |
| 86 | + <li>Hájek–Le Cam Local Asymptotic Minimax Theorem</li> |
| 87 | + <li>Convolution theorem</li> |
| 88 | + <li>Argmax Theorem</li> |
| 89 | + </ul> |
| 90 | + <p class="project-label">Maintainer.</p> |
| 91 | + <p>Rajarshi Mukherjee; Aaron Lin</p> |
| 92 | + <p class="project-link"><strong>Roadmap.</strong> <a href="roadmap.html#sec-1-4">1.4 Semiparametric Efficiency Theories</a></p> |
| 93 | + </section> |
| 94 | + |
| 95 | + <section class="project" id="id-algorithm-and-causal-inference"> |
| 96 | + <h2>2. ID Algorithm and Causal Inference</h2> |
| 97 | + <p class="project-label">Description.</p> |
| 98 | + <p> |
| 99 | + This project focuses on formalizing tools from causal inference, especially graphical models and identification theory. A central goal is to formalize the ID algorithm and related results about causal effects. |
| 100 | + </p> |
| 101 | + <p class="project-label">Milestones.</p> |
| 102 | + <ul class="items"> |
| 103 | + <li>Graphical models</li> |
| 104 | + <li>Causal diagrams</li> |
| 105 | + <li>Identification of causal effects</li> |
| 106 | + <li>ID algorithm</li> |
| 107 | + </ul> |
| 108 | + <p class="project-label">Maintainer.</p> |
| 109 | + <p>Qingyuan Zhao</p> |
| 110 | + <p class="project-link"><strong>Roadmap.</strong> <a href="roadmap.html#sec-1-12">1.12 Causal Identification Theories</a></p> |
| 111 | + </section> |
| 112 | + |
| 113 | + <section class="project" id="decision-theory-and-statistical-experiments"> |
| 114 | + <h2>3. Decision Theory and Statistical Experiments</h2> |
| 115 | + <p class="project-label">Description.</p> |
| 116 | + <p> |
| 117 | + This project studies the formalization of statistical experiments, their comparisons, and convergence, along with other decision-theoretic foundations such as the minimax theorem and complete class theorem. |
| 118 | + </p> |
| 119 | + <p class="project-label">Milestones.</p> |
| 120 | + <ul class="items"> |
| 121 | + <li>Convergence of Statistical Experiments</li> |
| 122 | + <li>Sufficiency, Completion, and Ancillarity of Sigma Fields</li> |
| 123 | + <li>Minimax theorem</li> |
| 124 | + <li>Complete Class Theorem</li> |
| 125 | + </ul> |
| 126 | + <p class="project-label">Maintainer.</p> |
| 127 | + <p>Zixiao Wang</p> |
| 128 | + <p class="project-link"><strong>Roadmap.</strong> <a href="roadmap.html#sec-1-1">1.1 Fundamentals of Decision Theory</a>; <a href="roadmap.html#sec-1-2">1.2 Comparison of Experiments</a></p> |
| 129 | + </section> |
| 130 | + |
| 131 | + <section class="project" id="empirical-process-foundations"> |
| 132 | + <h2>4. Empirical Process Foundations</h2> |
| 133 | + <p class="project-label">Description.</p> |
| 134 | + <p> |
| 135 | + This project develops the foundations of empirical process theory needed for asymptotic statistics. |
| 136 | + </p> |
| 137 | + <p class="project-label">Milestones.</p> |
| 138 | + <ul class="items"> |
| 139 | + <li>Maximal inequalities</li> |
| 140 | + <li>Glivenko–Cantelli lemma</li> |
| 141 | + <li>Donsker's theorem</li> |
| 142 | + </ul> |
| 143 | + <p class="project-label">Maintainer.</p> |
| 144 | + <p>Debarghya Mukherjee</p> |
| 145 | + <p class="project-link"><strong>Roadmap.</strong> <a href="roadmap.html#sec-1-13">1.13 Core Probabilistic Toolbox</a></p> |
| 146 | + </section> |
| 147 | + |
| 148 | + <section class="project" id="propose-a-project"> |
| 149 | + <h2>Propose a Project</h2> |
| 150 | + <p> |
| 151 | + If you would like to propose a new project, please contact Rajarshi Mukherjee at <a href="mailto:ram521@mail.harvard.edu">ram521@mail.harvard.edu</a>. |
| 152 | + </p> |
| 153 | + </section> |
| 154 | + |
| 155 | + </div> |
| 156 | +</main> |
| 157 | + |
| 158 | +</body> |
| 159 | +</html> |
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