{"action":"created","issue":{"url":"https://api.github.com/repos/statsmodels/statsmodels/issues/5410","repository_url":"https://api.github.com/repos/statsmodels/statsmodels","labels_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/5410/labels{/name}","comments_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/5410/comments","events_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/5410/events","html_url":"https://github.com/statsmodels/statsmodels/issues/5410","id":388273244,"node_id":"MDU6SXNzdWUzODgyNzMyNDQ=","number":5410,"title":"ENH linalg tools weighted orthogonalization","user":{"login":"josef-pkt","id":440735,"node_id":"MDQ6VXNlcjQ0MDczNQ==","avatar_url":"https://avatars1.githubusercontent.com/u/440735?v=4","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"labels":[{"id":163677,"node_id":"MDU6TGFiZWwxNjM2Nzc=","url":"https://api.github.com/repos/statsmodels/statsmodels/labels/comp-genmod","name":"comp-genmod","color":"ededed","default":false},{"id":5001600,"node_id":"MDU6TGFiZWw1MDAxNjAw","url":"https://api.github.com/repos/statsmodels/statsmodels/labels/comp-tools","name":"comp-tools","color":"DDDDDD","default":false},{"id":142961,"node_id":"MDU6TGFiZWwxNDI5NjE=","url":"https://api.github.com/repos/statsmodels/statsmodels/labels/type-enh","name":"type-enh","color":"99ffff","default":false}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-12-06T15:47:10Z","updated_at":"2018-12-06T17:21:02Z","closed_at":null,"author_association":"MEMBER","body":"(just a quick idea)\r\n\r\nWe orthogonalize in several cases one set from another set of variables. \r\nIf we have weights, then we need to orthogonalize with respect to the measure defined by the weights.\r\n\r\nI think in the WLS case we can just use the weighted variables, e.g. `wexog`.\r\n\r\nusecase:\r\nhessian/EIM in GLM when \"absorbing\" some exog like categorical fixed effects.\r\nOnce we have the `hessian_factor`, the hessian is similar to the one in WLS. We should be able to make it block-diagonal by reparameterization and compute the inverse by partitioned inverse or inverting just the block of interest.\r\n\r\n(I don't have time right now to think about how and whether that actually works.)\r\n\r\nThe setup is similar to score_test for added variables in GLM when we use the partitioned inverse for the distribution of the score."},"comment":{"url":"https://api.github.com/repos/statsmodels/statsmodels/issues/comments/444954816","html_url":"https://github.com/statsmodels/statsmodels/issues/5410#issuecomment-444954816","issue_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/5410","id":444954816,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0NDk1NDgxNg==","user":{"login":"josef-pkt","id":440735,"node_id":"MDQ6VXNlcjQ0MDczNQ==","avatar_url":"https://avatars1.githubusercontent.com/u/440735?v=4","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"created_at":"2018-12-06T17:21:02Z","updated_at":"2018-12-06T17:21:02Z","author_association":"MEMBER","body":"It sounds kind of obvious, only I never thought about it in the more general context of GLM outside of WLS/IRLS version, i.e. this should apply locally to wherever we have `hessian_factor` structure of the hessian with respect to the exog params. (Other params, like dispersion in NB and GP, might need separate treatment, and orthogonalizing exog subsets won't be enough if we cannot separate out or orthogonalize the extra params as in two-part models, e.g. ZIGP?, I guess.)\r\nhttp://scs.math.yorku.ca/index.php/Statistics:_Frisch-Waugh-Lovell_and_GLS\r\n"}}
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