I.B. MacNeill  V.K. Jandhyala  A. Kaul  S.B. Fotopoulos 《Environmetrics》2020,31(1)

The “Bayes‐type” method of deriving change‐point test statistics was introduced by Chernoff and Zacks (1964). Other authors subsequently adapted this approach and derived Bayes‐type statistics for at most one change (AMOC), and for multiple change points, under a variety of model formulations. Asymptotic distribution theory has always been limited to the AMOC statistics because of the perceived complexity of multiple change‐point statistics. In this article, it is shown that the Bayes‐type statistic derived to test for multiple change points is directly proportional to the AMOC statistic. This result immediately provides distributional results for Bayes‐type multiple change‐point statistics. In addition, it fundamentally alters the current understanding of the AMOC statistic. It follows from this result that the Bayes‐type statistic derived under AMOC conditions in fact tests for at least one change (ALOC), even though the statistic is derived under AMOC formulation. Under asymptotic consideration, the result also extends to the case of model errors following a stationary process. As an example, the classical Nile River data are revisited and analyzed for the presence of multiple change points.  相似文献   

Contrasting Pattern of Methane Flux in Rice Agriculture     

Smita Singh  J. S. Singh  A. K. Kashyap 《Die Naturwissenschaften》1998,85(10):494-497

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Die Mitte-Rand-Variation der Balmerlinien Hα−Hδ auf der Sonnenscheibe     

P. ten Bruggencate  H. Gollnow  S. Günther  W. Strohmeier 《Die Naturwissenschaften》1948,35(10):312-313

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Über den Einfluß des Sauerstoffes auf die endogene Atmung gramnegativer Darmbakterien     

S. Hollmann  E. Thofern 《Die Naturwissenschaften》1955,42(21):586-586

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Besprechungen     

W. Gurski  S. Koritnig  K. Marguerre 《Die Naturwissenschaften》1960,47(14):336-336

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E. Porcu  S. Castruccio  A. Alegría  P. Crippa 《Environmetrics》2019,30(1)

Decades of research in spatial statistics have prompted the development of a wide variety of models and methods whose primary goal is optimal linear interpolation (kriging), as well as sound assessment of the associated uncertainty (kriging variance). While kriging is of paramount importance for scientific investigations requiring high‐resolution maps, spatial statistics can be used for other classes of applications as well. Indeed, new areas are emerging where the main goal is to simulate from a statistical model whose parameters have been estimated from the data. This paper focuses on two different ways to model global data with axially symmetric Gaussian processes, for which the covariance function is nonstationary over latitudes and stationary over longitudes. Both strategies are illustrated through a global data set on surface temperatures generated by the National Center for Atmospheric Research (NCAR). On the one hand, we downscale surface temperatures through a classical geostatistical approach. We exploit Gaussianity assumption to focus on the second‐order structure, and we develop a novel class of axially symmetric models inspired from currently available isotropic models. We also propose a new covariance model that is axially symmetric. Covariance‐based approaches are notorious for their computational burden, and a considerable amount of recent literature has been devoted to overcome this problem. We propose a simulation‐based approach that works for processes defined on a lattice only. For such an approach, kriging cannot be performed as there is an underlying continuous process. At the same time, inference can be performed exactly on extremely large data sets.  相似文献   

Chromsomal polymorphism in annual chrysanthemum     

R. S. Rana  H. K. Jain 《Die Naturwissenschaften》1964,51(2):44-45

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B. Ezquieta  M. Beneyto  R. Muñoz-Pacheco  R. Barrio  M. Oyarzabal  J. L. Lechuga  C. Luzuriaga  F. Hermoso  S. Quinteiro  S. Martinez 《黑龙江环境通报》2006,26(12):1172-1178

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Edda S.P. Aradttir  Hlmfrídur Sigurdardttir  Bergur Sigfússon  Einar Gunnlaugsson 《Greenhouse Gases: Science and Technology》2011,1(2):105-118

CarbFix, a combined industrial‐academic pilot program, was developed in order to assess the feasibility of in situ CO₂ mineral sequestration in basaltic rocks. Unique to CarbFix is its connection to the Hellisheidi geothermal power plant, allowing for capture of otherwise emitted CO₂ in addition to CO₂ transport and mineral sequestration. Extensive research has been conducted in order to characterize physical properties of the pilot injection site in Hellisheidi. Tracer tests have been carried out and continuous well‐logging confirmed separation of the target formation from shallower groundwater systems. Alteration mineralogy in natural analogs has been mapped out in order to predict which minerals are likely to precipitate upon CO₂ injection. In addition to carbonates, these include clays, zeolites, and poorly crystalline hydroxides. Some of the secondary minerals will compete with carbonates for cations dissolved from the rock matrix. Numerical modeling plays an important role in the CarbFix project as it provides tools to predict and optimize long‐term management of the injection site as well as to quantify the amount of CO₂ that can be mineralized. A reactive transport model has been developed and numerical simulations of the pilot CO₂ injection are ongoing. Extensive monitoring provides the basis for testing, validating, and calibrating reactive transport models. It is anticipated that the results of CarbFix will be used to optimize the in situ carbon mineralization process, enabling it in basalt and ultramafic rock formations throughout the world. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd  相似文献   

    

N. Desai  D. Krantz  A. Roman  A. Fleischer  S. Boulis  B. Rochelson 《黑龙江环境通报》2014,34(2):159-162

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