{"id":"W1987489278","doi":"10.1016/j.insmatheco.2008.04.004","title":"The Markovian regime-switching risk model with a threshold dividend strategy","year":2008,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dividend; Poisson distribution; Exponential function; Markov process; Applied mathematics; Risk model; Mathematics; Compound Poisson process; Exponential distribution; Markov chain; Econometrics; Mathematical economics; Mathematical optimization; Economics; Poisson process; Mathematical analysis; Statistics; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002882785,0.0009336025,0.002412095,0.001119622,0.000770559,0.003762819,0.002674124,0.00451356,0.007731407],"category_scores_gemma":[0.007370279,0.0008529872,0.001395587,0.001025485,0.002608462,0.004208298,0.001475831,0.002791779,0.0007253357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001677344,"about_ca_system_score_gemma":0.001631496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005393293,"about_ca_topic_score_gemma":0.002284182,"domain_scores_codex":[0.9987993,0.0003918341,0.00005070287,0.0002367127,0.0002187985,0.0003025339],"domain_scores_gemma":[0.9952302,0.003086692,0.0006697605,0.0002511448,0.0003142902,0.0004479601],"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":[0.0002246451,0.0001322301,0.00139821,0.0001049446,0.0001137927,0.0004973985,0.0001661834,0.2676006,0.001522866,0.7205923,0.001973287,0.005673602],"study_design_scores_gemma":[0.00007383016,0.00004556876,0.0004478929,0.00001577703,0.00003776039,0.0001080673,0.00002877449,0.8047006,0.0001438662,0.1938522,0.0005086923,0.00003694834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4122699,0.002422384,0.5287614,0.009377959,0.0004482688,0.0001446175,0.001024586,0.0004443231,0.04510659],"genre_scores_gemma":[0.9727878,0.0008838789,0.006716096,0.0002138098,0.0001930598,0.00006338972,0.0001232814,0.00003266931,0.01898599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007731407,"threshold_uncertainty_score":0.02586412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1001013698613302,"score_gpt":0.2911726623687015,"score_spread":0.1910712925073713,"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."}}