{"673568":{"#nid":"673568","#data":{"type":"event","title":"ISyE Statistic Seminar - Jiming Jiang","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EThat Prasad-Rao is Robust: Estimation of MSPE of OBP under Potential Model Misspecification\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u003Cspan\u003E\u003Cspan\u003EWe consider estimation of the mean squared prediction error (MSPE) of the observed best\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Epredictor (OBP) in small area estimation under an area-level model with potential model\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emisspecification. It was previously thought that the traditional Prasad-Rao (P-R) linearization\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emethod could not be used, because it is derived under the assumption that the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is correctly specified. However, we show that, when it comes to estimating the\u0026nbsp;unconditional MSPE, the PR estimator, derived for estimating the MSPE of OBP assuming\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ethat the underlying model is correct, remains first-order unbiased even when the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is misspecified in its mean function. A second-order unbiased estimator of the MSPE\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Eis derived by modifying the PR MSPE estimator. The PR and modified PR estimators also\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ehave much smaller variation compared to the existing MSPE estimators for OBP. The\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Etheoretical findings are supported by empirical results including simulation studies and\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ereal-data applications. This work is joint with Xiaohui Liu and Haiqiang Ma of Jiangxi\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003EUniversity of Finance and Economics.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EWe consider estimation of the mean squared prediction error (MSPE) of the observed best\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Epredictor (OBP) in small area estimation under an area-level model with potential model\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emisspecification. It was previously thought that the traditional Prasad-Rao (P-R) linearization\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emethod could not be used, because it is derived under the assumption that the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is correctly specified. However, we show that, when it comes to estimating the\u0026nbsp;unconditional MSPE, the PR estimator, derived for estimating the MSPE of OBP assuming\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ethat the underlying model is correct, remains first-order unbiased even when the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is misspecified in its mean function. A second-order unbiased estimator of the MSPE\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Eis derived by modifying the PR MSPE estimator. The PR and modified PR estimators also\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ehave much smaller variation compared to the existing MSPE estimators for OBP. The\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Etheoretical findings are supported by empirical results including simulation studies and\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ereal-data applications. This work is joint with Xiaohui Liu and Haiqiang Ma of Jiangxi\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003EUniversity of Finance and Economics.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"That Prasad-Rao is Robust: Estimation of MSPE of OBP under Potential Model Misspecification"}],"uid":"34977","created_gmt":"2024-03-18 11:21:47","changed_gmt":"2024-03-18 11:21:46","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-26T11:00:00-04:00","event_time_end":"2024-03-26T12:00:00-04:00","event_time_end_last":"2024-03-26T12:00:00-04:00","gmt_time_start":"2024-03-26 15:00:00","gmt_time_end":"2024-03-26 16:00:00","gmt_time_end_last":"2024-03-26 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","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":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}