Evaluating Maximum Likelihood Estimation Methods to Determine the Hurst Coefficient

A maximum likelihood estimation method implemented in S-PLUS (S-MLE) to estimate the Hurst coefficient (H) is evaluated. The Hurst coefficient, with 0.5\u3cHS-MLE was developed to estimate H for fractionally differenced (fd) processes. However, in practice it is difficult to distinguish between fd processes and fractional Gaussian noise (fGn) processes. Thus, the method is evaluated for estimating H for both fd and fGn processes. S-MLE gave biased results of H for fGn processes of any length and for fd processes of lengths less than 210. A modified method is proposed to correct for this bias. It gives reliable estimates of H for both fd and fGn processes of length greater than or equal to 211

Illustrating the Benefits of Openness: A Large-Scale Spatial Economic Dispatch Model Using the Julia Language

In this paper we introduce a five-fold approach to open science comprised of open data, open-source software (that is, programming and modeling tools, model code, and numerical solvers), as well as open-access dissemination. The advantages of open energy models are being discussed. A fully open-source bottom-up electricity sector model with high spatial resolution using the Julia programming environment is then being developed, describing source code and a data set for Germany. This large-scale model of the electricity market includes both generation dispatch from thermal and renewable sources in the spot market as well as the physical transmission network, minimizing total system costs in a linear approach. It calculates the economic dispatch on an hourly basis for a full year, taking into account demand, infeed from renewables, storage, and exchanges with neighboring countries. Following the open approach, the model code and used data set are fully publicly accessible and we use open-source solvers like ECOS and CLP. The model is then being benchmarked regarding runtime of building and solving against a representation in GAMS as a commercial algebraic modeling language and against Gurobi, CPLEX, and Mosek as commercial solvers. With this paper we demonstrate in a proof-of-concept the power and abilities, as well as the beauty of open-source modeling systems. This openness has the potential to increase the transparency of policy advice and to empower stakeholders with fewer financial possibilities.BMWi, 03ET4028A, Verbundvorhaben: Langfristige Planung und kurzfristige Optimierung des Elektrizitätssystems in Deutschland im europäischen Kontext, Teilvorhaben: Langfristige Planung des Übertragungsnetzes in Deutschland unter Berücksichtigung der Interpendenzen zur Erzeugungsplanung, sowie zum Wärme- und Gassektor

Effect of pooling samples on the efficiency of comparative studies using microarrays

Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample-pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and exact results can be large. In this study, we have characterized quantitatively the effect of pooling samples on the efficiency of microarray experiments for the detection of differential gene expression between two classes. We present exact formulas for calculating the power of microarray experimental designs involving sample pooling and technical replications. The formulas can be used to determine the total numbers of arrays and biological subjects required in an experiment to achieve the desired power at a given significance level. The conditions under which pooled design becomes preferable to non-pooled design can then be derived given the unit cost associated with a microarray and that with a biological subject. This paper thus serves to provide guidance on sample pooling and cost effectiveness. The formulation in this paper is outlined in the context of performing microarray comparative studies, but its applicability is not limited to microarray experiments. It is also applicable to a wide range of biomedical comparative studies where sample pooling may be involved.Comment: 8 pages, 1 figure, 2 tables; to appear in Bioinformatic

Reconsidering Revolutions: The Impact of Breakthrough Elections on Democratization in Croatia, Serbia, Moldova, and Georgia

Recent research highlights the democratizing impact of breakthrough elections in postcommunist Eurasia, some of which have been accompanied by the so-called color revolutions. Because elections expand opportunities for civil society organization and contentious politics, scholars have noted improvements in democracy procedures and accountability in those countries where breakthrough elections produced government turnover. Drawing on evidence from Croatia, Serbia, Moldova, and Georgia, this paper investigates the extent to which individual breakthrough elections contributed to democratic development. While these countries have experienced overall democratic progress, improvements in some areas, such as civil society development, the autonomy of media outlets, and electoral processes, have been less robust than one would expect. Contrary to the conclusions of previous studies that the uneven democratization process in these countries is the result of longer-term structural conditions, this analysis shows how elite decisions can be critical in shaping structural conditions and governance trajectories

Effects of whole life exposure to Bisphenol A or 17α-ethinyl estradiol in uterus of nulligravida CD1 mice

AbstractBisphenol A (BPA) is an endocrine disrupting chemical (EDC) with known estrogenic activity. Exposure to BPA in adult mice was shown previously to increase uterine pathology with associated alterations in the immune response and fibrosis. Reported here are uterine histopathology findings from CD1 mice exposed to BPA or 17α-ethinyl estradiol at multiple doses from conception through postnatal day 90. Along with uterine pathology, impacts of exposure on collagen accumulation and F4/80 positive macrophage numbers, as an indicator of immune response in the endometrium and myometrium, are presented. These companion data are from offspring (F1) of the dams analyzed for effects of adult exposures published in the Reproductive Toxicology manuscript titled “Strain-Specific Induction of Endometrial Periglandular Fibrosis in Mice Exposed during Adulthood to the Endocrine Disrupting Chemical Bisphenol A” (doi: 10.1016/j.reprotox.2015.08.001)

On correcting the overestimation of the permutation-based false discovery rate estimator

Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR), which is defined as the expected percentage of the number of false positive genes among the claimed significant genes. As a consequence, the accuracy of the FDR estimators will be important for correctly controlling FDR. Xie et al. found that the standard permutation method of estimating FDR is biased and proposed to delete the predicted differentially expressed (DE) genes in the estimation of FDR for one-sample comparison. However, we notice that the formula of the FDR used in their paper is incorrect. This makes the comparison results reported in their paper unconvincing. Other problems with their method include the biased estimation of FDR caused by over- or under-deletion of DE genes in the estimation of FDR and by the implicit use of an unreasonable estimator of the true proportion of equivalently expressed (EE) genes. Due to the great importance of accurate FDR estimation in microarray data analysis, it is necessary to point out such problems and propose improved methods