A Small-Sample Estimator for the Sample-Selection Model
نویسندگان
چکیده
منابع مشابه
Model Selection for Mixture Models Using Perfect Sample
We have considered a perfect sample method for model selection of finite mixture models with either known (fixed) or unknown number of components which can be applied in the most general setting with assumptions on the relation between the rival models and the true distribution. It is, both, one or neither to be well-specified or mis-specified, they may be nested or non-nested. We consider mixt...
متن کاملFeature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...
متن کاملA covariance estimator for GEE with improved small-sample properties.
In this paper, we propose an alternative covariance estimator to the robust covariance estimator of generalized estimating equations (GEE). Hypothesis tests using the robust covariance estimator can have inflated size when the number of independent clusters is small. Resampling methods, such as the jackknife and bootstrap, have been suggested for covariance estimation when the number of cluster...
متن کاملAutoregressive model order selection by a finite sample estimator for the Kullback-Leibler discrepancy
In other words, when (u; u(0N); 11 1;u(01)) belongs to U , asymptotically periodic inputs produce asymptotically periodic outputs with the same period. The proof of this theorem makes use of a contraction-mapping fixed-point argument. The techniques used in our omitted proofs are also useful in connection with related problems that are " more nonlinear. " In particular, related results are give...
متن کاملA Concentrated, Nonlinear Information-Theoretic Estimator for the Sample Selection Model
This paper develops a semi-parametric, Information-Theoretic method for estimating parameters for nonlinear data generated under a sample selection process. Considering the sample selection as a set of inequalities makes this model inherently nonlinear. This estimator (i) allows for a whole class of different priors, and (ii) is constructed as an unconstrained, concentrated model. This estimato...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Econometric Reviews
سال: 2004
ISSN: 0747-4938,1532-4168
DOI: 10.1081/etc-120028837