Existing tests for factorial designs in the non-parametric case are based on hypotheses formulated in terms of distribution functions. Typical null hypotheses, however, are formulated in terms of ...some parameters or effect measures, particularly in heteroscedastic settings. Here this idea is extended to non-parametric models by introducing a novel non-parametric analysis-of-variance type of statistic based on ranks or pseudoranks which is suitable for testing hypotheses formulated in meaningful non-parametric treatment effects in general factorial designs. This is achieved by a careful detailed study of the common distribution of rank-based estimators for the treatment effects. Since the statistic is asymptotically not a pivotal quantity we propose three different approximation techniques, discuss their theoretic properties and compare them in extensive simulations together with two additional Wald-type tests. An extension of the presented idea to general repeated measures designs is briefly outlined. The rankand pseudorank-based procedures proposed maintain the preassigned type I error rate quite accurately, also in unbalanced and heteroscedastic models.
Control functionals for Monte Carlo integration Oates, Chris J.; Girolami, Mark; Chopin, Nicolas
Journal of the Royal Statistical Society. Series B, Statistical methodology,
June 2017, Letnik:
79, Številka:
3
Journal Article
Recenzirano
Odprti dostop
A non-parametric extension of control variâtes is presented.These leverage gradient information on the sampling density to achieve substantial variance reduction. It is not required that the sampling ...density be normalized. The novel contribution of this work is based on two important insights: a trade-off between random sampling and deterministic approximation and a new gradient-based function space derived from Stein's identity. Unlike classical control variâtes, our estimators improve rates of convergence, often requiring orders of magnitude fewer simulations to achieve a fixed level of precision. Theoretical and empirical results are presented, the latter focusing on integration problems arising in hierarchical models and models based on non-linear ordinary differential equations.
On April 1st, 2019, the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO), joined by the Advanced Virgo detector, began the third observing run, a year-long dedicated search for ...gravitational radiation. The LIGO detectors have achieved a higher duty cycle and greater sensitivity to gravitational waves than ever before, with LIGO Hanford achieving angle-averaged sensitivity to binary neutron star coalescences to a distance of 111 Mpc, and LIGO Livingston to 134 Mpc with duty factors of 74.6% and 77.0% respectively. The improvement in sensitivity and stability is a result of several upgrades to the detectors, including doubled intracavity power, the addition of an in-vacuum optical parametric oscillator for squeezed-light injection, replacement of core optics and end reaction masses, and installation of acoustic mode dampers. This paper explores the purposes behind these upgrades, and explains to the best of our knowledge the noise currently limiting the sensitivity of each detector.
We study generalized additive models, with shape restrictions (e.g. monotonicity, convexity and concavity) imposed on each component of the additive prediction function. We show that this framework ...facilitates a non-parametric estimator of each additive component, obtained by maximizing the likelihood. The procedure is free of tuning parameters and under mild conditions is proved to be uniformly consistent on compact intervals. More generally, our methodology can be applied to generalized additive index models. Here again, the procedure can be justified on theoretical grounds and, like the original algorithm, has highly competitive finite sample performance. Practical utility is illustrated through the use of these methods in the analysis of two real data sets. Our algorithms are publicly available in the R package scar, short for shape-constrained additive regression.
This paper examines the relationship between bribery and firm survival when facing different levels of market competition, credit constraints, and other institutional limitations. Using panel data ...from surveys of small‐ and medium‐sized enterprises in Vietnam over a 10‐year period and a semi‐parametric Cox proportional hazards model approach, we provide empirical support for the “greasing‐the‐wheels” hypothesis of firm survival. Effects are found to be more pronounced for formally registered and larger firms, explained by their greater bargaining power vis‐à‐vis public officials. Moreover, bribery as a “risk‐of‐exit” reducing strategy is found only for firms not institutionally or financially constrained and for firms operating in sectors with low levels of competition.
We developed a mid-infrared ZnGeP 2 (ZGP) master oscillator power amplifier (MOPA) system with an output energy of 52 mJ, operating at a 2 kHz repetition rate. The system utilizes a 2097-nm ...electro-optical Q-switched two-stage holmium:yttrium-aluminum-garnet MOPA laser source with a total maximum output power of 222 W. Due to transmission and depolarization losses, the incident pump power delivered to the ZGP optical parametric oscillator and optical parametric amplifier is 65.8 W and 132.9 W, respectively. We achieved a maximum average output power of 103.9 W in the 3-5 μm range with an optical conversion efficiency of 52.3%. This corresponds to beam quality factors of 10.3 and 8.6 in the horizontal and vertical directions, respectively.
Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic ...interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest
in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.
Statistical issues arising in modelling univariate extremes of a random sample have been successfully used in the most diverse fields, such as biometrics, finance, insurance and risk theory. ...Statistics of univariate extremes (SUE), the subject to be dealt with in this review paper, has recently faced a huge development, partially because rare events can have catastrophic consequences for human activities, through their impact on the natural and constructed environments. In the last decades, there has been a shift from the area of parametric SUE, based on probabilistic asymptotic results in extreme value theory, towards semi-parametric approaches. After a brief reference to Gumbel's block methodology and more recent improvements in the parametric framework, we present an overview of the developments on the estimation of parameters of extreme events and on the testing of extreme value conditions under a semi-parametric framework. We further discuss a few challenging topics in the area of SUE.
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