| Title:
|
$M$-estimation in nonlinear regression for longitudinal data (English) |
| Author:
|
Orsáková, Martina |
| Language:
|
English |
| Journal:
|
Kybernetika |
| ISSN:
|
0023-5954 |
| Volume:
|
43 |
| Issue:
|
1 |
| Year:
|
2007 |
| Pages:
|
61-74 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
The longitudinal regression model $Z_i^j=m(\theta _0,{\mathbb{X}}_i(T_i^j))+ \varepsilon _i^j,$ where $Z_i^j$ is the $j$th measurement of the $i$th subject at random time $T_i^j$, $m$ is the regression function, ${\mathbb{X}}_i(T_i^j)$ is a predictable covariate process observed at time $T_i^j$ and $\varepsilon _i^j$ is a noise, is studied in marked point process framework. In this paper we introduce the assumptions which guarantee the consistency and asymptotic normality of smooth $M$-estimator of unknown parameter $\theta _0$. (English) |
| Keyword:
|
$M$-estimation |
| Keyword:
|
nonlinear regression |
| Keyword:
|
longitudinal data |
| MSC:
|
60G55 |
| MSC:
|
62F10 |
| MSC:
|
62F12 |
| MSC:
|
62M10 |
| idZBL:
|
Zbl 1252.62069 |
| idMR:
|
MR2343331 |
| . |
| Date available:
|
2009-09-24T20:21:11Z |
| Last updated:
|
2013-09-21 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/135754 |
| . |
| Reference:
|
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| . |
