A new distance-based estimator for forest regeneration assessment, the mean distance method, was developed by combining ideas and techniques from the wandering quarter method, T-square sampling and the random pairs method. The performance of the mean distance method was compared to conventional 4.05 square meter plot sampling through simulation analysis on 405 square meter blocks of a field sur...

This paper describes a Bayesian method for learning causal networks using samples that were selected in a non-random manner from a population of interest. Examples of data obtained by non-random sampling include convenience samples and case-control data in which a fixed number of samples with and without some condition is collected; such data are not uncommon. The paper describes a method for c...

This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-stage stochastic linear programming problems with complete recourse. The algorithm uses sampling when the random variables are represented by continuous distribution functions. Traditionally, this method has been applied...

a. Follow-ups to Session 13 • Comment on the codes from Session 13. The codes used in Session 13 use various " tricks " or shortcuts to make them more compact. In general, I don't recommend coding this way (so these codes should be rewritten!). For example, the choice of energy units is exploited so that energies are integers and therefore can be used as array indices. But if we added an extern...

Sampling representations and approximations of deterministic and random signals are important in communication and information theory. Finite sampling approximations are derived for certain functions and processes which are not bandlimited, as well as bounds on the approximation errors. Also derived are sampling representations for bounded linear operators acting on certain classes of band-limi...

SIMULATING DEPENDENT BINARY DATA WITH RANDOM EFFECTS. Aobo Wang, Roy T. Sabo, Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia 23298-0032. Dependent binary data can be simply simulated using the multinomial sampling method. We extend this method to simulate dependent binary data with clustered random effect structures. Several distributions are considered for co...

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Best linear unbiased predictors developed from mixed models are widely used, but their development requires distributional assumptions or an infinite population frame...

Complex situations of today's life have led to the complexity of urban life issues and not pay attention to the quality of life. Studies in this field are increasingly gaining the attention of urban planners and policymakers due to its usefulness in assessing and monitoring public policies, also they can use as effective tools in urban management and planning. This study investigated the QOL in...

Online Social Network has attracted lots of academies and industries to look into its characteristics, models and applications. There are many methods for crawling or sampling in networks, especially for the undirected networks. We focus on sampling the directed networks and intend to compare the efficiency, the accuracy and the stability between them. We consider the sampled nodes and links as...