Matches in SemOpenAlex for { <https://semopenalex.org/work/W2246114310> ?p ?o ?g. }
Showing items 1 to 57 of
57
with 100 items per page.
- W2246114310 abstract "Sometimes we are faced with data that could reasonably be represented either as a single line, or as two or more line segments. How do we identify the best breakpoint(s), and decide how many segments are ''really'' present? Most of us are taught to distrust piecewise regression, because it can be easily abused. The best method for identifying the breakpoint varies according to specifics of the data; for example, the minimum sum of squares method excels for ''well-behaved'' data. In some cases, hidden Markov methods are more likely to succeed than are more ''obvious'' methods. Likewise, the most appropriate method for deciding between one or two lines depends on your expectations and understanding of the data: an unexpected break requires more justification than an expected one, and some decision criteria (e.g., the Akaike Information Criterion) are less strict than others (e.g., the Bayesian Information Criterion). This presentation will review some options and make specific, practical recommendations." @default.
- W2246114310 created "2016-06-24" @default.
- W2246114310 creator A5030857734 @default.
- W2246114310 creator A5048071171 @default.
- W2246114310 date "2006-10-12" @default.
- W2246114310 modified "2023-09-27" @default.
- W2246114310 title "One Line or Two? Perspectives on Piecewise Regression" @default.
- W2246114310 doi "https://doi.org/10.2172/899336" @default.
- W2246114310 hasPublicationYear "2006" @default.
- W2246114310 type Work @default.
- W2246114310 sameAs 2246114310 @default.
- W2246114310 citedByCount "0" @default.
- W2246114310 crossrefType "report" @default.
- W2246114310 hasAuthorship W2246114310A5030857734 @default.
- W2246114310 hasAuthorship W2246114310A5048071171 @default.
- W2246114310 hasBestOaLocation W22461143103 @default.
- W2246114310 hasConcept C105795698 @default.
- W2246114310 hasConcept C120068334 @default.
- W2246114310 hasConcept C134306372 @default.
- W2246114310 hasConcept C152877465 @default.
- W2246114310 hasConcept C164660894 @default.
- W2246114310 hasConcept C198352243 @default.
- W2246114310 hasConcept C2524010 @default.
- W2246114310 hasConcept C33923547 @default.
- W2246114310 hasConcept C35519122 @default.
- W2246114310 hasConcept C41008148 @default.
- W2246114310 hasConcept C83546350 @default.
- W2246114310 hasConceptScore W2246114310C105795698 @default.
- W2246114310 hasConceptScore W2246114310C120068334 @default.
- W2246114310 hasConceptScore W2246114310C134306372 @default.
- W2246114310 hasConceptScore W2246114310C152877465 @default.
- W2246114310 hasConceptScore W2246114310C164660894 @default.
- W2246114310 hasConceptScore W2246114310C198352243 @default.
- W2246114310 hasConceptScore W2246114310C2524010 @default.
- W2246114310 hasConceptScore W2246114310C33923547 @default.
- W2246114310 hasConceptScore W2246114310C35519122 @default.
- W2246114310 hasConceptScore W2246114310C41008148 @default.
- W2246114310 hasConceptScore W2246114310C83546350 @default.
- W2246114310 hasLocation W22461143101 @default.
- W2246114310 hasLocation W22461143102 @default.
- W2246114310 hasLocation W22461143103 @default.
- W2246114310 hasOpenAccess W2246114310 @default.
- W2246114310 hasPrimaryLocation W22461143101 @default.
- W2246114310 hasRelatedWork W1505859985 @default.
- W2246114310 hasRelatedWork W1621861229 @default.
- W2246114310 hasRelatedWork W2016216486 @default.
- W2246114310 hasRelatedWork W2100261036 @default.
- W2246114310 hasRelatedWork W2127072394 @default.
- W2246114310 hasRelatedWork W2362208065 @default.
- W2246114310 hasRelatedWork W2375721435 @default.
- W2246114310 hasRelatedWork W247449116 @default.
- W2246114310 hasRelatedWork W3121557470 @default.
- W2246114310 hasRelatedWork W4245623645 @default.
- W2246114310 isParatext "false" @default.
- W2246114310 isRetracted "false" @default.
- W2246114310 magId "2246114310" @default.
- W2246114310 workType "report" @default.