Techniques of quality of adjustment of statistical models with evaluation of probability distributions using production data of laying quails

The goal of our study was to evaluate the quality of fit from different types of probability distributions for continuous data. For this, performance traits and quality of quail egg in the production of nutraceutical eggs were used as a continuous data source. The data were collected over 42 days, the experimental design was completely randomized with 7 treatments, 6 repetitions, with 252 animals allocated in 36 cages. The distributions for continuous data used were the exponential, gamma, gaussian, and lognormal. The R Open Source and SAS® University Edition software was used to perform the analysis. The graphical analysis of the traits was performed from the predicted versus observed values, Cumulative Distribution Function (CDF), and skewness-kurtosis. The fits were also evaluated by the Akaike information criterion (AIC), Bayesian information criterion (BIC), Conditional model of adjusted R-Square (), Conditional model of adjusted concordance correlation (), Kolmogorov-Smirnov test (KS), Cramer-von Mises test (CvM), Anderson-Darling test (AD), Watanabe-Akaike Information Criterion (WAIC) and Leave-one-out cross-validation (LOO). All the tests indicated the Gaussian distribution as the most suitable and they excluded the exponential distribution for all the evaluated characteristics.

Modification of Random Vibration Theory for the Near-fault Region

Standard random vibration theory (RVT) methods for estimating the horizontal spectral acceleration (PSA) amplitudes given the Fourier spectral amplitude (FAS) and ground-motion duration underestimate the PSA values for near-fault ground motions from large magnitudes. An empirical model that is unbiased for near-fault ground motions is developed using the statistical moments of the FAS and the duration as model input parameters, similar to standard RVT, but with a modification to also include magnitude and distance as input parameters. The data set used for the regression includes strong-motion recordings from active crustal regions in the NGA-West2 Pacific Earthquake Engineering Research (PEER) database from California earthquakes (M3.0-7.25) and global earthquakes (M5.0 – 7.9). The zeroth, first, and second spectral moments of the FAS of the 5%-damped oscillator response are used in the empirical model. The scaling with the moments of the FAS replaces the peak factor terms used in RVT. A parametric model for the 5-80% duration that depends on magnitude and distance is also developed to simplify the application of the model. Compared to the standard RVT approach, the conditional model significantly improves the estimation of near-fault PSA values estimated from the FAS. The conditional model is appropriate for use in converting non-ergodic FAS GMMs to non-ergodic PSA GMMs.

Cooperative verifier-based testing with CoVeriTest

Testing is a widely applied technique to evaluate software quality, and coverage criteria are often used to assess the adequacy of a generated test suite. However, manually constructing an adequate test suite is typically too expensive, and numerous techniques for automatic test-suite generation were proposed. All of them come with different strengths. To build stronger test-generation tools, different techniques should be combined. In this paper, we study cooperative combinations of verification approaches for test generation, which exchange high-level information. We present CoVeriTest, a hybrid technique for test-suite generation. CoVeriTest iteratively applies different conditional model checkers and allows users to adjust the level of cooperation and to configure individual time limits for each conditional model checker. In our experiments, we systematically study different CoVeriTest cooperation setups, which either use combinations of explicit-state model checking and predicate abstraction, or bounded model checking and symbolic execution. A comparison with state-of-the-art test-generation tools reveals that CoVeriTest achieves higher coverage for many programs (about 15%).

Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market

This paper aims to develop some static and conditional (dynamic) models to predict portfolio returns in the Borsa Istanbul (BIST) that are calibrated to combine the capital asset-pricing model (CAPM) and corporate governance quality. In our conditional model proposals, both the traditional CAPM (beta) coefficient and model constant are allowed to vary on a binary basis with any degradation or improvement in the country’s international trade competitiveness, and meanwhile a new variable is added to the models to represent the portfolio’s sensitivity to excess returns on the governance portfolio (BIST Governance) over the market. Some robust and Bayesian linear models have been derived using the monthly capital gains between December 2009 and December 2019 of four leading index portfolios. A crude measure is then introduced that we think can be used in assessing governance quality of portfolios. This is called governance quality score (GQS). Our robust regression findings suggest both superiority of conditional models assuming varying beta coefficients over static model proposals and significant impact of corporate governance quality on portfolio returns. The Bayesian model proposals, however, exhibited robust findings that favor the static model with fixed beta estimates and were lacking in supporting significance of corporate governance quality.