{"id":97,"date":"2016-09-04T21:43:17","date_gmt":"2016-09-04T21:43:17","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=97"},"modified":"2016-09-06T02:17:02","modified_gmt":"2016-09-06T02:17:02","slug":"signed-likelihood-root-with-a-simple-skewness-correction-regular-models-second-order","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/signed-likelihood-root-with-a-simple-skewness-correction-regular-models-second-order\/","title":{"rendered":"Signed likelihood root with a simple skewness correction: regular models, second order"},"content":{"rendered":"
A standardized maximum likelihood departure, a standardized score departure, the signed\u00a0likelihood root: these are familiar inference outputs from statistical packages, with the\u00a0signed likelihood root often viewed as the most reliable. A third-order adjusted signed\u00a0likelihood root called r is available from likelihood theory, but the formulas and development\u00a0methods are not always easily implemented. We use a log-model Taylor expansion\u00a0to develop a simple second order adjustment to the signed likelihood root, an adjustment
\nthat is easy to calculate and easy to explain, and easy to motivate. The theory is developed,\u00a0simulations are recorded to indicate repetition accuracy, real data are analyzed, and\u00a0connections to alternatives are discussed.<\/p>\n