{"id":211,"date":"2020-01-30T07:55:13","date_gmt":"2020-01-30T06:55:13","guid":{"rendered":"https:\/\/site.uit.no\/nordstat2020\/?page_id=211"},"modified":"2021-03-14T17:47:33","modified_gmt":"2021-03-14T16:47:33","slug":"invited-sessions","status":"publish","type":"page","link":"https:\/\/site.uit.no\/nordstat2021\/invited-sessions\/","title":{"rendered":"Invited sessions"},"content":{"rendered":"

Tentative overview:<\/p>\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Invited sessions<\/th>Organizer<\/th>Speakers<\/th>Topics<\/th>\n<\/tr>\n<\/thead>\n
I1: Model evaluation<\/a><\/td>Thordis Thorarinsdottir<\/td>Claudio Heinrich<\/td>Proper scoring rules for point processes<\/a><\/td>\n<\/tr>\n
Jonas Wallin<\/td>Locally scale invariant proper scoring rules<\/a><\/td>\n<\/tr>\n
I2: Causal learning<\/a><\/td>Niels Richard Hansen <\/td>Ingeborg Waernbaum<\/td>Properties of calibration estimators of the average causal effect - a comparative study of balancing approaches<\/a><\/td>\n<\/tr>\n
Leonard Henckel<\/td>Graphical tools for selecting efficient conditional instrumental sets<\/a><\/td>\n<\/tr>\n
Lasse Petersen<\/td>Combining the partial copula with quantile regression to test conditional independence<\/a><\/td>\n<\/tr>\n
I3: Network analysis<\/a><\/td>Lasse Leskel\u00e4
\n<\/td>		Maximilien Dreveton<\/td>Estimation of static community memberships from multiplex and temporal network data<\/a><\/td>\n<\/tr>\n
Alex Jung<\/td>Networked Federated Multi-Task Learning<\/a><\/td>\n<\/tr>\n
Fanny Villers<\/td>Multiple testing of paired null hypotheses using a latent graph model<\/a><\/td>\n<\/tr>\n
I4: Biostatistics<\/a><\/td>Iain Johnston<\/td>Matti Pirinen<\/td>Variable selection using summary statistics<\/a><\/td>\n<\/tr>\n
Alvaro K\u00f6hn-Luque<\/td>Deconvolution of drug-response heterogeneity in cancer cell populations<\/a><\/td>\n<\/tr>\n
Owen Thomas<\/td>LilleBror for misspecification-robust likelihood free inference in high dimensions<\/a><\/td>\n<\/tr>\n
I5: Modeling spatial data -porous materials, proteins and networks<\/a><\/td>Aila S\u00e4rkk\u00e4<\/td>Sandra Barman<\/td>Porous materials: spatial models of 3D geometries with specific global connectivity structures & new methods for capturing the connectivity<\/a>
\n<\/td>\n<\/tr>\n
Louis Gammelgaard Jensen<\/td>Semiparametric point process modeling of blinking artifacts in photoactivated localization microscopy<\/a><\/td>\n<\/tr>\n
Mohammad Mehdi Moradi<\/td>P\u200boint patterns on linear networks: a focus on intensity estimation<\/a><\/td>\n<\/tr>\n
I6: Statistics in forestry<\/a><\/td>Lauri Meht\u00e4talo<\/td>Juha Lappi<\/td>Between-group and within group effects and intra-class correlation<\/a><\/td>\n<\/tr>\n
Mari Myllym\u00e4ki<\/td>Nonparametric graphical tests of significance for the functional general linear model with application to forestry data<\/a><\/td>\n<\/tr>\n
Lauri Meht\u00e4talo<\/td>Finding hidden trees in remote sensing of forests by using stochastic geometry, sequential spatial point processes and the HT-estimator<\/a><\/td>\n<\/tr>\n
I7: Teaching and communicating statistics<\/a><\/td>Mette Langaas
\n<\/td>		Thea Bj\u00f8rnland<\/td>Developing an introductory statistics course for engineering students at NTNU<\/a>
\n<\/td>\n<\/tr>\n
Sam Clifford<\/td>Project-based learning for statistics in practice - collaboration, computation, communication<\/a><\/td>\n<\/tr>\n
Mine \u00c7etinkaya-Rundel<\/td>The art and science of teaching data science<\/a><\/td>\n<\/tr>\n
I8: Recent advances in causal inference<\/a><\/td>Juha Karvanen<\/td>Tetiana Gorbach<\/td>Contrasting identification criteria of average causal effects: Asymptotic variances and semiparametric estimators<\/a><\/td>\n<\/tr>\n
Niklas Pfister<\/td>Stabilizing variable selection and regression<\/a><\/td>\n<\/tr>\n
Juha Karvanen<\/td>Identifying causal effects via context-specific independence relations<\/a><\/td>\n<\/tr>\n
I9: Opening the black box<\/a><\/td>Martin Jullum<\/td>Martin Jullum<\/td>Efficient Shapley value explanation through feature groups<\/a><\/td>\n<\/tr>\n
Kary Fr\u00e4mling<\/td>Why explainable AI should move from influence to contextual importance and utility<\/a><\/td>\n<\/tr>\n
Homayun Afrabandpey <\/td>Model interpretability in Bayesian framework<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n","protected":false},"excerpt":{"rendered":"

Tentative overview:<\/p>\n","protected":false},"author":1010,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-211","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/pages\/211","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/users\/1010"}],"replies":[{"embeddable":true,"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/comments?post=211"}],"version-history":[{"count":8,"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/pages\/211\/revisions"}],"predecessor-version":[{"id":448,"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/pages\/211\/revisions\/448"}],"wp:attachment":[{"href":"https:\/\/site.uit.no\/nordstat2021\/wp-json\/wp\/v2\/media?parent=211"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}