To assess the accuracy of our sampling, we calculated the average of four commonly used abundance based estimators of species richness (ACE, CHAO1, JACK1 and Bootstrap) using ESTIMATES v. 8.2 (University of Connecticut, Storrs, CT, USA).
Using RNA-seq data from massively parallel pyrosequencing we examine patterns of autosomal and Z-linked gene expression in brain and address how RNA-seq based estimators of dosage compensation compare to values obtained from microarray or qPCR experiments.
Markov Chain Monte Carlo (MCMC) approach taking into account phylogenetic relationships among haplotypes as implemented in FLUCTUATE v. 1.4 [ 18], was further used to assess the goodness of fit of the exponential growth model characterised by equation (θt = θinitial-gt) to our data and to compute Maximum Likelihood (ML) based estimators of exponential growth rate (g).
Fig. 6 MSE performance comparison of BSC based estimator for different values of the number of the pilot 100, 50, and 10 versus SNR. Figure 7 compares the (MSE) performance versus SNR among BCS, threshold-BSC, MT-BCS, LS, OMP and the Bilinear Approximate Message Passing (Bi-AMP) [26].
Since 'strong' rather than 'weak' observability of the system ensures better performance of Kalman based estimators to develop a reliable soil moisture estimation algorithm, the main objective of this study is to discuss observability analysis of this nonlinear agro-hydrological system.
A multi-model observer based estimator (OBE), which consists of a set of simple sub-models describing typical process states and a knowledge-based system, was developed for monitoring and long-term prediction of the anaerobic digestion process.
Another important conclusion is that the first order difference based estimator that achieves minimax rate of convergence in the one-dimensional case does not do the same in the high dimensional case.
The temporal based estimator has another expected source of imprecision: the temporal distance between samples.
In contrast with the DL estimator, which is derived as a 1st order method of moments estimator, the HM estimator is a 2nd order method of moments based estimator and has the following expression (7) σ H M 2 = Q (2 k − 1 + Q S 1 − S 2 / S 1 The HM estimator is consistently positive and has been shown to yield accurate and precise estimates of the between-trial variance [ 7, 9, 23].
Its Moran Coefficient based estimators exhibit statistical properties of unbiasedness, efficiency, and consistency.
