{"620492":{"#nid":"620492","#data":{"type":"event","title":"ISyE Statistic Seminar - Qiong Zhang","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003ESequential Selection for Accelerated Life Testing via Approximate Bayesian Inference\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EApproximate Bayesian inference (Chen and Ryzhov, 2019) has been proposed to construct computationally tractable statistical learning procedures for incomplete or censored data. In this talk, I will discuss a sequential model-updating procedure via approximate Bayesian inference for the Log-normal model with censored observations. We show that the proposed procedure leads to a consistent model parameter estimation. The developed model updating procedure also\u0026nbsp;enables a closed form expression of a sequential design criterion. The proposed procedure is applied to accelerated life testing experiments, which aims at determining the material alternative with the best reliability performance.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EDr.\u0026nbsp;Qiong\u0026nbsp;Zhang is\u0026nbsp;an assistant professor in the School of Mathematical and Statistical Sciences at Clemson University. Previously, she was an assistant professor of statistics at Virginia Commonwealth University in 2014\u0026ndash;2018.\u0026nbsp;Dr. Zhang received a B.S. degree in statistics from Nankai University and an M.S. degree in statistics from Peking University in 2007 and 2009, respectively.\u0026nbsp;She received her Ph.D. degree in statistics from University of Wisconsin-Madison in 2014. Dr.\u0026nbsp;Zhang\u0026rsquo;s research interests include the interface between information collection and statistical modeling, design and analysis of computer experiment, and uncertainty quantification.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EApproximate Bayesian inference (Chen and Ryzhov, 2019) has been proposed to construct computationally tractable statistical learning procedures for incomplete or censored data. In this talk, I will discuss a sequential model-updating procedure via approximate Bayesian inference for the Log-normal model with censored observations. We show that the proposed procedure leads to a consistent model parameter estimation. The developed model updating procedure also\u0026nbsp;enables a closed form expression of a sequential design criterion. The proposed procedure is applied to accelerated life testing experiments, which aims at determining the material alternative with the best reliability performance.\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Sequential Selection for Accelerated Life Testing via Approximate Bayesian Inference"}],"uid":"34977","created_gmt":"2019-04-16 19:02:18","changed_gmt":"2019-04-16 19:02:18","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-04-22T15:00:00-04:00","event_time_end":"2019-04-22T16:00:00-04:00","event_time_end_last":"2019-04-22T16:00:00-04:00","gmt_time_start":"2019-04-22 19:00:00","gmt_time_end":"2019-04-22 20:00:00","gmt_time_end_last":"2019-04-22 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}