MbrlCatalogueTitleDetail

Do you wish to reserve the book?
Switching nonparametric regression models for multi-curve data
Switching nonparametric regression models for multi-curve data
Hey, we have placed the reservation for you!
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Switching nonparametric regression models for multi-curve data
Oops! Something went wrong.
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Title added to your shelf!
Title added to your shelf!
View what I already have on My Shelf.
Oops! Something went wrong.
Oops! Something went wrong.
While trying to add the title to your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Switching nonparametric regression models for multi-curve data
Switching nonparametric regression models for multi-curve data

Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
How would you like to get it?
We have requested the book for you! Sorry the robot delivery is not available at the moment
We have requested the book for you!
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Switching nonparametric regression models for multi-curve data
Switching nonparametric regression models for multi-curve data
Journal Article

Switching nonparametric regression models for multi-curve data

2017
Request Book From Autostore and Choose the Collection Method
Overview
We develop and apply an approach for analyzing multi-curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function, forcing the individual curve to “switch” from one function to another. Thus each curve follows what we call a switching nonparametric regression model. We develop an EM algorithm to estimate the model parameters. We also obtain standard errors for the parameter estimates of the state process. We consider three types of hidden states: those that are independent and identically distributed, those that follow a Markov structure, and those that are independent but with distribution depending on some covariate(s). A simulation study shows the frequentist properties of our estimates. We apply our methods to a building’s power usage data. Les auteures développent et mettent en application une approche d’analyse de données multicourbes où chaque courbe est générée par un processus latent. L’état d’un point particulier détermine une fonction lisse, forçnt les courbes individuelles à passer d’une fonction à l’autre. Chaque courbe suit ainsi ce que les auteures appellent un modèle de régression non paramétrique intermittent. Elles développent un algorithme EM pour estimer les paramètres et obtiennent les erreur-types pour les estimateurs des paramètres du modèle d’états. Les auteures considèrent trois types d’états cachés: ceux qui sont indépendants et identiquement distribués, ceux qui suivent une structure de Markov, et ceux qui sont indépendants mais dont la distribution dépend de covariables. Elles présentent une simulation afin de montrer les propriétés fréquentistes de leurs estimateurs et appliquent leur méthode à des données réelles de consommation d’énergie de bâtiments.