Consider the partial linear models of the form Y=X { ;+ g(T)+e, where the p-variate explanatory X is erroneously measured, and both T and the response Y are measured exactly. Let X be the surrogate variable for X with measurement error. Let the primary data set be that containing independent observa
Estimation of partial linear error-in-variables models for ρ−-mixing dependence data
✍ Scribed by Guang-hui Cai
- Publisher
- Springer
- Year
- 2007
- Tongue
- English
- Weight
- 129 KB
- Volume
- 43
- Category
- Article
- ISSN
- 0259-9791
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
We present an estimation algorithm for dynamic shock-error models (DSEM) given l-bit quantized noisy measurements of the input and output. The algorithm is called the binary series estimation algorithm (BSEA). BSEA is computationally inexpensive, since it involves counting the number of occurrences
This paper studies a semi-linear errors-in-variables model of the form Y i = x$ i ;+ g(T i )+e i , X i =x i +u i (1 i n). The estimators of parameters ;, \_ 2 and of the smooth function g are derived by using the nearest neighbor-generalized least square method. Under some weak conditions, it is sho
new estimator for linear errors-in-variables models is considered that is baaed on the Fourier transform of a weight function. The consistency of the estimator is verified. Examples and simulation results are aleo presented.