Books on the topic 'Empirical Bayes methods'

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1

Maritz, J. S. Empirical Bayes methods. 2nd ed. London: Chapman and Hall, 1989.

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2

Carlin, Bradley P. Bayes and empirical Bayes methods for data analysis. Boca Raton: Chapman & Hall/CRC, 1998.

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Carlin, Bradley P. Bayes and Empirical Bayes methods for data analysis. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2000.

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4

1944-, Louis Thomas A., ed. Bayes and empirical Bayes methods for data analysis. London: Chapman & Hall, 1996.

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5

Large-scale inference: Empirical Bayes methods for estimation, testing, and prediction. Cambridge: Cambridge University Press, 2010.

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6

Phillips, Peter C. B. Bayes methods for trending multiple time eries with an empirical application to the U. S. economy. New Haven, CN: Yale University, Cowles Foundation, 1992.

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7

Maritz, J. S., and T. Lwin. Empirical Bayes Methods. Routledge, 2018. http://dx.doi.org/10.4324/9781351140645.

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8

Maritz, J. S. Empirical Bayes Methods with Applications. Taylor & Francis Group, 2018.

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9

Maritz, J. S. Empirical Bayes Methods with Applications. Taylor & Francis Group, 2018.

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10

Maritz, J. S. Empirical Bayes Methods with Applications. Taylor & Francis Group, 2018.

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11

Empirical Bayes Methods with Applications. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781351071666.

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12

Maritz, J. S. Empirical Bayes Methods with Applications. Taylor & Francis Group, 2018.

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13

Empirical Bayes Methods with Applications. Taylor & Francis Group, 2017.

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14

Maritz, J. S. Empirical Bayes Methods with Applications. Taylor & Francis Group, 2018.

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15

Carlin, Bradley P., and Thomas A. Louis. Bayes and Empirical Bayes Methods for Data Analysis, Second Edition. Taylor & Francis Group, 2000.

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16

Carlin, Bradley P., Thomas A. Louis, and Bradley Carlin. Bayes and Empirical Bayes Methods for Data Analysis, Second Edition. 2nd ed. Chapman & Hall/CRC, 2000.

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17

Carlin, Bradley, and Thomas Louis. Bayes and Empirical Bayes Methods for Data Analysis, Second Edition. Chapman and Hall/CRC, 2000. http://dx.doi.org/10.1201/9781420057669.

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18

Largescale Inference Empirical Bayes Methods For Estimation Testing And Prediction. Cambridge University Press, 2013.

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19

Bickel, David R. Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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20

Bickel, David R. Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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21

Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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22

Bickel, David. Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods. Taylor & Francis Group, 2019.

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23

Efron, Bradley. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press, 2010.

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24

Efron, Bradley. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press, 2013.

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25

Efron, Bradley. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press, 2010.

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26

Efron, Bradley. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press, 2010.

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27

Katsoulacos, Yannis, and Eleni Metsiou. The Economics of Antitrust Sanctioning. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198810674.003.0008.

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In this chapter, we first discuss the empirical evidence on cartel formation that is particularly worrying in recent years for the case of BRICS and developing countries and the traditional economic theory on deterrence as applied to sanctioning policies. We evaluate the available empirical evidence and discuss the reasons why cartels are still very active, despite the significant increase in the fines imposed, the application of leniency programmes, and the tougher antitrust enforcement. We point out three reasons why current sanctioning methods are not as effective as they could be, specifically currently: (1) penalties are misdirected to corporations and not to responsible individuals; (2) penalties on corporations are inefficiently designed by using wrong penalty bases; and (3) there is no exploitation of complementary policies, specifically for policies preventing recidivism in cartel formation. We conclude by pointing to a number of proposals for enhancing the effectiveness of sanctioning regimes.
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28

Frey, Bruno S., and Jana Gallus. Awards and Academic Performance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198798507.003.0004.

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Awards may honour and induce performance. There are many different academic awards, ranging from the Nobel Prizes to best paper awards, and to awards for young scholars and rising stars. The Synthetic Control Method allows us to show empirically that the performance of recipients of the well-known John Bates Clark Medal (given by the American Economic Association to a scholar under the age of 40 ‘who is judged to have made the most significant contribution to economic thought and knowledge’) subsequently increases. Clark Medallists raise their publication activity and the work they had previously published is cited considerably more often (in line with a status effect). The same effects can be observed when researchers are elected as Fellows of the Econometric Society, also a prestigious honour.
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29

Ślusarski, Marek. Metody i modele oceny jakości danych przestrzennych. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-30-4.

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The quality of data collected in official spatial databases is crucial in making strategic decisions as well as in the implementation of planning and design works. Awareness of the level of the quality of these data is also important for individual users of official spatial data. The author presents methods and models of description and evaluation of the quality of spatial data collected in public registers. Data describing the space in the highest degree of detail, which are collected in three databases: land and buildings registry (EGiB), geodetic registry of the land infrastructure network (GESUT) and in database of topographic objects (BDOT500) were analyzed. The results of the research concerned selected aspects of activities in terms of the spatial data quality. These activities include: the assessment of the accuracy of data collected in official spatial databases; determination of the uncertainty of the area of registry parcels, analysis of the risk of damage to the underground infrastructure network due to the quality of spatial data, construction of the quality model of data collected in official databases and visualization of the phenomenon of uncertainty in spatial data. The evaluation of the accuracy of data collected in official, large-scale spatial databases was based on a representative sample of data. The test sample was a set of deviations of coordinates with three variables dX, dY and Dl – deviations from the X and Y coordinates and the length of the point offset vector of the test sample in relation to its position recognized as a faultless. The compatibility of empirical data accuracy distributions with models (theoretical distributions of random variables) was investigated and also the accuracy of the spatial data has been assessed by means of the methods resistant to the outliers. In the process of determination of the accuracy of spatial data collected in public registers, the author’s solution was used – resistant method of the relative frequency. Weight functions, which modify (to varying degree) the sizes of the vectors Dl – the lengths of the points offset vector of the test sample in relation to their position recognized as a faultless were proposed. From the scope of the uncertainty of estimation of the area of registry parcels the impact of the errors of the geodetic network points was determined (points of reference and of the higher class networks) and the effect of the correlation between the coordinates of the same point on the accuracy of the determined plot area. The scope of the correction was determined (in EGiB database) of the plots area, calculated on the basis of re-measurements, performed using equivalent techniques (in terms of accuracy). The analysis of the risk of damage to the underground infrastructure network due to the low quality of spatial data is another research topic presented in the paper. Three main factors have been identified that influence the value of this risk: incompleteness of spatial data sets and insufficient accuracy of determination of the horizontal and vertical position of underground infrastructure. A method for estimation of the project risk has been developed (quantitative and qualitative) and the author’s risk estimation technique, based on the idea of fuzzy logic was proposed. Maps (2D and 3D) of the risk of damage to the underground infrastructure network were developed in the form of large-scale thematic maps, presenting the design risk in qualitative and quantitative form. The data quality model is a set of rules used to describe the quality of these data sets. The model that has been proposed defines a standardized approach for assessing and reporting the quality of EGiB, GESUT and BDOT500 spatial data bases. Quantitative and qualitative rules (automatic, office and field) of data sets control were defined. The minimum sample size and the number of eligible nonconformities in random samples were determined. The data quality elements were described using the following descriptors: range, measure, result, and type and unit of value. Data quality studies were performed according to the users needs. The values of impact weights were determined by the hierarchical analytical process method (AHP). The harmonization of conceptual models of EGiB, GESUT and BDOT500 databases with BDOT10k database was analysed too. It was found that the downloading and supplying of the information in BDOT10k creation and update processes from the analyzed registers are limited. An effective approach to providing spatial data sets users with information concerning data uncertainty are cartographic visualization techniques. Based on the author’s own experience and research works on the quality of official spatial database data examination, the set of methods for visualization of the uncertainty of data bases EGiB, GESUT and BDOT500 was defined. This set includes visualization techniques designed to present three types of uncertainty: location, attribute values and time. Uncertainty of the position was defined (for surface, line, and point objects) using several (three to five) visual variables. Uncertainty of attribute values and time uncertainty, describing (for example) completeness or timeliness of sets, are presented by means of three graphical variables. The research problems presented in the paper are of cognitive and application importance. They indicate on the possibility of effective evaluation of the quality of spatial data collected in public registers and may be an important element of the expert system.
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