{"id":"W4232947516","doi":"10.1016/j.jspi.2007.03.011","title":"Exact inference for a simple step-stress model with Type-I hybrid censored data from the exponential distribution","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Exponential distribution; Estimator; Accelerated life testing; Applied mathematics; Statistics; Monte Carlo method; Moment (physics); Parametric statistics; Parametric model; Delta method; Exponential function; Inference; Weibull distribution; Mathematical analysis; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004934187,0.0001298173,0.0002309688,0.00002061082,0.0001977913,0.000103386,0.0002709083,0.00004278984,0.00004911014],"category_scores_gemma":[0.004888681,0.00008268584,0.00001828696,0.00008574363,0.0001714107,0.0001770909,0.00006142762,0.0002327291,0.000002342904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002791486,"about_ca_system_score_gemma":0.0001117416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001932821,"about_ca_topic_score_gemma":0.000008682238,"domain_scores_codex":[0.9987721,0.00003559067,0.0005073213,0.0001789027,0.0002949681,0.0002110512],"domain_scores_gemma":[0.9927053,0.006181003,0.0003193012,0.0002465509,0.0003878945,0.0001599859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001097539,0.0003446694,0.004236903,0.00009545057,0.0001358749,0.00003538994,0.0002706834,0.0008484194,0.0003241114,0.9220097,0.05888635,0.0117149],"study_design_scores_gemma":[0.00148806,0.0003684376,0.03916567,0.0003007175,0.0002979527,0.00003936216,0.0004631982,0.7252194,0.0002829882,0.2303789,0.001690098,0.0003052553],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03316837,0.00003620214,0.9536689,0.0001509177,0.00003505339,0.0001458924,0.01273063,0.00001511534,0.00004894114],"genre_scores_gemma":[0.8786587,0.00001059491,0.119362,0.00005194803,0.00007244903,0.000003338257,0.001824948,0.000007736759,0.000008236764],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8454903,"threshold_uncertainty_score":0.5852561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1344027248027278,"score_gpt":0.4180899922856365,"score_spread":0.2836872674829087,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}