| 1. |
- Thorburn, Daniel, 1945-, et al.
(författare)
-
An optimal calibration distance leading to the optimal regression estimator
- 2005
-
Ingår i: Survey Methodology. - Ottawa : Statistics Canada. - 0714-0045 .- 1492-0921. ; 31:1, s. 95-99
-
Tidskriftsartikel (refereegranskat)abstract
- When there is auxiliary information in survey sampling, the design based "optimal (regression) estimator" of a finite population total/mean is known to be (at least asymptotically) more efficient than the corresponding GREG estimator. We will illustrate this by some simulations with stratified sampling from skewed populations. The GREG estimator was originally constructed using an assisting linear superpopulation model. It may also be seen as a calibration estimator; i.e., as a weighted linear estimator, where the weights obey the calibration equation and, with that restriction, are as close as possible to the original "Horvitz-Thompson weights" (according to a suitable distance). We show that the optimal estimator can also be seen as a calibration estimator in this respect, with a quadratic distance measure closely related to the one generating the GREG estimator. Simple examples will also be given, revealing that this new measure is not always easily obtained.
|
|
| 2. |
|
|
| 3. |
- Thorburn, Daniel, 1945-
(författare)
-
Significance testing, interval estimation or Bayesian inference : comments to "Extracting a maximum of useful information from statistical research data" by S. Sohlberg and G. Andersson
- 2005
-
Ingår i: Scandinavian Journal of Psychology. - Oxford : Blackwell. - 0036-5564 .- 1467-9450. ; 46:1, s. 79-82
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Statistical inference plays an important part in the formation of scientific knowledge in psychology. Starting from a paper by Sohlberg and Andersson (2005; Scandinavian Journal of Psychology, 46, 69-77) these issues are discussed. It is argued that interval estimates are easy to understand and that they are more suitable than significance testing for most problems. Bayesian inference is a coherent description of the information building process. With some examples it is shown that null hypothesis significance testing is full of contradictions. Finally, some other important issues like convenience sampling and model selection are shortly mentioned.
|
|
