Analysis of multiple change-points in normally distributed series
Paper 1: Change-Point Estimation for a Sequence of Normal Observations and Integration with Q-Charts. This is the first research regarding to change-point analysis for independent observations normally distributed. It considers the case when a single step change has occurred and distribution’s p...
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| Formato: | Tesis |
| Lenguaje: | Spanish / Castilian |
| Publicado: |
2013
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| Acceso en línea: | http://eprints.uanl.mx/3469/1/1080256657.pdf |
| _version_ | 1824368598529867776 |
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| author | Garza Venegas, Jorge Arturo |
| author_facet | Garza Venegas, Jorge Arturo |
| author_sort | Garza Venegas, Jorge Arturo |
| collection | Repositorio Institucional |
| description | Paper 1: Change-Point Estimation for a Sequence of Normal Observations and Integration
with Q-Charts.
This is the first research regarding to change-point analysis for independent observations
normally distributed. It considers the case when a single step change has occurred and
distribution’s parameters (before and after change) are unknown. Development of
maximum likelihood estimators (MLEs) for the change-point and parameters is the main
concern as well as an integration with control charts in order to show its application in
practice, with which retrospective and on-line analysis are both covered. A change is
considered as one of the three different cases: (1) change only in mean parameter, (2)
change only in variance parameter or (3) change in both parameters. Due to there are
change-point estimators for change in mean and change in variance, comparison is done to
show what estimator is recommended to use in each situation.
Paper 2: Estimation of multiple change-points in time series normally distributed using a
construction Heuristic and a Genetic Algorithm.
This is the second research related to change-point analysis for independent observations
normally distributed. It considers case when multiple step changes have occurred assuming
that distribution’s parameters as well as change-point positions are unknown. Maximum
Likelihood estimators for change-points as well as for parameters were developed
considering three cases based on single step change problem: (1) multiple changes only in
the mean, (2) multiple changes only in variance and (3) multiple changes in both
parameters at the same time. Obtaining these change-points MLEs could be considered as
an optimization problem, so a Construction Heuristic and a Genetic Algorithm
(Evolutionary) were developed based on them. Comparison between these estimators was
done in order to show their performance. |
| format | Tesis |
| id | eprints-3469 |
| institution | UANL |
| language | Spanish / Castilian |
| publishDate | 2013 |
| record_format | eprints |
| spelling | eprints-34692017-02-15T14:49:04Z http://eprints.uanl.mx/3469/ Analysis of multiple change-points in normally distributed series Garza Venegas, Jorge Arturo Paper 1: Change-Point Estimation for a Sequence of Normal Observations and Integration with Q-Charts. This is the first research regarding to change-point analysis for independent observations normally distributed. It considers the case when a single step change has occurred and distribution’s parameters (before and after change) are unknown. Development of maximum likelihood estimators (MLEs) for the change-point and parameters is the main concern as well as an integration with control charts in order to show its application in practice, with which retrospective and on-line analysis are both covered. A change is considered as one of the three different cases: (1) change only in mean parameter, (2) change only in variance parameter or (3) change in both parameters. Due to there are change-point estimators for change in mean and change in variance, comparison is done to show what estimator is recommended to use in each situation. Paper 2: Estimation of multiple change-points in time series normally distributed using a construction Heuristic and a Genetic Algorithm. This is the second research related to change-point analysis for independent observations normally distributed. It considers case when multiple step changes have occurred assuming that distribution’s parameters as well as change-point positions are unknown. Maximum Likelihood estimators for change-points as well as for parameters were developed considering three cases based on single step change problem: (1) multiple changes only in the mean, (2) multiple changes only in variance and (3) multiple changes in both parameters at the same time. Obtaining these change-points MLEs could be considered as an optimization problem, so a Construction Heuristic and a Genetic Algorithm (Evolutionary) were developed based on them. Comparison between these estimators was done in order to show their performance. 2013 Tesis NonPeerReviewed text es cc_by_nc_nd http://eprints.uanl.mx/3469/1/1080256657.pdf http://eprints.uanl.mx/3469/1.haspreviewThumbnailVersion/1080256657.pdf Garza Venegas, Jorge Arturo (2013) Analysis of multiple change-points in normally distributed series. Maestría thesis, Universidad Autónoma de Nuevo León. |
| spellingShingle | Garza Venegas, Jorge Arturo Analysis of multiple change-points in normally distributed series |
| thumbnail | https://rediab.uanl.mx/themes/sandal5/images/online.png |
| title | Analysis of multiple change-points in normally distributed series |
| title_full | Analysis of multiple change-points in normally distributed series |
| title_fullStr | Analysis of multiple change-points in normally distributed series |
| title_full_unstemmed | Analysis of multiple change-points in normally distributed series |
| title_short | Analysis of multiple change-points in normally distributed series |
| title_sort | analysis of multiple change points in normally distributed series |
| url | http://eprints.uanl.mx/3469/1/1080256657.pdf |
| work_keys_str_mv | AT garzavenegasjorgearturo analysisofmultiplechangepointsinnormallydistributedseries |