Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R
We present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains. This data structure arises from combining periodically published government survey statistics, such as are reported in the Current...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2016-08-01
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| Series: | Journal of Statistical Software |
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| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/2800 |
| _version_ | 1828436010818600960 |
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| author | Terrance D. Savitsky |
| author_facet | Terrance D. Savitsky |
| author_sort | Terrance D. Savitsky |
| collection | DOAJ |
| description | We present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains. This data structure arises from combining periodically published government survey statistics, such as are reported in the Current Population Study (CPS). The CPS publishes monthly, by-state estimates of employment levels, where each state expresses a noisy time series. Published state-level estimates from the CPS are composed from household survey responses in a model-free manner and express high levels of volatility due to insufficient sample sizes. Existing software solutions borrow information over a modeled time-based dependence to extract a de-noised time series for each domain. These solutions, however, ignore the dependence among the domains that may be additionally leveraged to improve estimation efficiency. The growfunctions package offers two fully nonparametric mixture models that simultaneously estimate both a time and domain-indexed dependence structure for a collection of time series: (1) A Gaussian process (GP) construction, which is parameterized through the covariance matrix, estimates a latent function for each domain. The covariance parameters of the latent functions are indexed by domain under a Dirichlet process prior that permits estimation of the dependence among functions across the domains: (2) An intrinsic Gaussian Markov random field prior construction provides an alternative to the GP that expresses different computation and estimation properties. In addition to performing denoised estimation of latent functions from published domain estimates, growfunctions allows estimation of collections of functions for observation units (e.g., households), rather than aggregated domains, by accounting for an informative sampling design under which the probabilities for inclusion of observation units are related to the response variable. growfunctions includes plot functions that allow visual assessments of the fit performance and dependence structure of the estimated functions. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++. |
| first_indexed | 2024-12-10T19:19:15Z |
| format | Article |
| id | doaj.art-150209287e934b1f86df0077751f51e9 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-10T19:19:15Z |
| publishDate | 2016-08-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-150209287e934b1f86df0077751f51e92022-12-22T01:36:32ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602016-08-0172113410.18637/jss.v072.i021029Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in RTerrance D. SavitskyWe present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains. This data structure arises from combining periodically published government survey statistics, such as are reported in the Current Population Study (CPS). The CPS publishes monthly, by-state estimates of employment levels, where each state expresses a noisy time series. Published state-level estimates from the CPS are composed from household survey responses in a model-free manner and express high levels of volatility due to insufficient sample sizes. Existing software solutions borrow information over a modeled time-based dependence to extract a de-noised time series for each domain. These solutions, however, ignore the dependence among the domains that may be additionally leveraged to improve estimation efficiency. The growfunctions package offers two fully nonparametric mixture models that simultaneously estimate both a time and domain-indexed dependence structure for a collection of time series: (1) A Gaussian process (GP) construction, which is parameterized through the covariance matrix, estimates a latent function for each domain. The covariance parameters of the latent functions are indexed by domain under a Dirichlet process prior that permits estimation of the dependence among functions across the domains: (2) An intrinsic Gaussian Markov random field prior construction provides an alternative to the GP that expresses different computation and estimation properties. In addition to performing denoised estimation of latent functions from published domain estimates, growfunctions allows estimation of collections of functions for observation units (e.g., households), rather than aggregated domains, by accounting for an informative sampling design under which the probabilities for inclusion of observation units are related to the response variable. growfunctions includes plot functions that allow visual assessments of the fit performance and dependence structure of the estimated functions. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++.https://www.jstatsoft.org/index.php/jss/article/view/2800Gaussian processGaussian Markov random fieldDirichlet processBayesian hierarchical modelstime seriesfunctional dataRC++ |
| spellingShingle | Terrance D. Savitsky Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R Journal of Statistical Software Gaussian process Gaussian Markov random field Dirichlet process Bayesian hierarchical models time series functional data R C++ |
| title | Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R |
| title_full | Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R |
| title_fullStr | Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R |
| title_full_unstemmed | Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R |
| title_short | Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R |
| title_sort | bayesian nonparametric mixture estimation for time indexed functional data in r |
| topic | Gaussian process Gaussian Markov random field Dirichlet process Bayesian hierarchical models time series functional data R C++ |
| url | https://www.jstatsoft.org/index.php/jss/article/view/2800 |
| work_keys_str_mv | AT terrancedsavitsky bayesiannonparametricmixtureestimationfortimeindexedfunctionaldatainr |