Arbeitspapier

A confidence corridor for sparse longitudinal data curves

to connected objects

Longitudinal data analysis is a central piece of statistics. The data are curves and they are observed at random locations. This makes the construction of a simultaneous confidence corridor (SCC) (confidence band) for the mean function a challenging task on both the theoretical and the practical side. Here we propose a method based on local linear smoothing that is implemented in the sparse (i.e., low number of nonzero coefficients) modelling situation. An SCC is constructed based on recent results obtained in applied probability theory. The precision and performance is demonstrated in a spectrum of simulations and applied to growth curve data. Technically speaking, our paper intensively uses recent insights into extreme value theory that are also employed to construct a shoal of confidence intervals (SCI).

Language: Englisch

Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2011-002

Classification: Wirtschaft
Semiparametric and Nonparametric Methods: General
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models

Subject: longitudinal data
confidence band
Karhunen-Loève L2 representation
local linear estimator
extreme value
double sum
strong approximation
Langzeitstudie
Schätztheorie
Theorie

Event: Geistige Schöpfung

(who): Zheng, Shuzhuan
Yang, Lijian
Härdle, Wolfgang Karl

Event: Veröffentlichung

(who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk

(where): Berlin

(when): 2010

Handle: http://hdl.handle.net/10419/56725

Last update: 10.03.2025, 11:43 AM CET

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Object type

Arbeitspapier

Associated

Zheng, Shuzhuan
Yang, Lijian
Härdle, Wolfgang Karl
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk

Time of origin

2010

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