{"id":453,"date":"2017-05-16T10:20:38","date_gmt":"2017-05-16T09:20:38","guid":{"rendered":"http:\/\/blogs.kent.ac.uk\/seak\/?p=453"},"modified":"2017-05-16T10:20:38","modified_gmt":"2017-05-16T09:20:38","slug":"biometrics-paper-hidden-markov-models-for-extended-batch-data","status":"publish","type":"post","link":"https:\/\/blogs.kent.ac.uk\/seak\/2017\/05\/16\/biometrics-paper-hidden-markov-models-for-extended-batch-data\/","title":{"rendered":"Biometrics Paper: Hidden Markov Models for Extended Batch Data"},"content":{"rendered":"<p><span style=\"color: black;font-family: 'Calibri',sans-serif\">The paper Hidden Markov Models for Extended Batch Data\u00a0<\/span><span style=\"color: black;font-family: 'Calibri',sans-serif\">by\u00a0<\/span><span style=\"color: black;font-family: 'Calibri',sans-serif\">Laura L. E. Cowen,\u00a0Panagiotis Besbeas,\u00a0Byron J. T. Morgan\u00a0<\/span><span style=\"color: black;font-family: 'Calibri',sans-serif\">and Carl J. Schwarz\u00a0has been published\u00a0online early in\u00a0Biometrics <\/span><\/p>\n<p><span style=\"color: black;font-family: 'Calibri',sans-serif\">Summary. Batch marking provides an important and efficient way to estimate the survival probabilities and population\u00a0sizes of wild animals. It is particularly useful when dealing with animals that are difficult to mark individually. For the\u00a0first time, we provide the likelihood for extended batch-marking experiments. It is often the case that samples contain\u00a0individuals that remain unmarked, due to time and other constraints, and this information has not previously been\u00a0analysed.\u00a0We provide ways of modelling such information, including an open N-mixture approach. We demonstrate that models for both\u00a0marked and unmarked individuals are hidden Markov models; this provides a unified approach, and is the key to developing\u00a0methods for fast likelihood computation and maximisation. Likelihoods for marked and unmarked individuals can easily be\u00a0combined using integrated population modelling. This allows the simultaneous estimation of population size and immigration,\u00a0in addition to survival, as well as efficient estimation of standard errors and methods of model selection and evaluation,\u00a0using standard likelihood techniques. Alternative methods for estimating population size are presented and compared. An\u00a0illustration is provided by a weather-loach data set, previously analysed by means of a complex procedure of constructing a\u00a0pseudo likelihood, the formation of estimating equations, the use of sandwich estimates of variance, and piecemeal estimation\u00a0of population size. Simulation provides general validation of the hidden Markov model methods developed and demonstrates<\/span><span style=\"color: black;font-family: 'Calibri',sans-serif\">their excellent performance and efficiency. This is especially notable due to the large numbers of hidden states that may be\u00a0typically required\u200b<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper Hidden Markov Models for Extended Batch Data\u00a0by\u00a0Laura L. E. Cowen,\u00a0Panagiotis Besbeas,\u00a0Byron J. T. Morgan\u00a0and Carl J. Schwarz\u00a0has been published\u00a0online early in\u00a0Biometrics Summary. Batch marking provides an important and efficient way to estimate the survival probabilities and population\u00a0sizes of wild animals. It is particularly useful when dealing with animals that are difficult to mark [&hellip;]<\/p>\n","protected":false},"author":40695,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[597],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/posts\/453"}],"collection":[{"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/users\/40695"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/comments?post=453"}],"version-history":[{"count":1,"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/posts\/453\/revisions"}],"predecessor-version":[{"id":454,"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/posts\/453\/revisions\/454"}],"wp:attachment":[{"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/media?parent=453"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/categories?post=453"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/seak\/wp-json\/wp\/v2\/tags?post=453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}