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Journal articles on the topic 'Statistics - Applications'

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Published: 9 March 2023

Last updated: 10 March 2023

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1

Hoover, Donald R. "Basic Statistics and Pharmaceutical Statistical Applications." Journal of Biopharmaceutical Statistics 25, no. 1 (January 2, 2015): 226–29. http://dx.doi.org/10.1080/10543406.2015.985162.

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2

Li, Ning. "Basic Statistics and Pharmaceutical Statistical Applications." Controlled Clinical Trials 21, no. 6 (December 2000): 593–94. http://dx.doi.org/10.1016/s0197-2456(00)00090-8.

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3

Brooks, Marie A. "Applications: Statistical Process Control and Control Charts: An Application for Statistics Classes." Mathematics Teacher 80, no. 6 (September 1987): 474–86. http://dx.doi.org/10.5951/mt.80.6.0474.

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In teaching statistics at the high school level, we attempt to give students an appreciation of the usefulness of statistical measures and techniques. But too frequently we create a group of number-crunching robots who grind through formulas and see no connection to the real world. One way to avoid this dilemma is to use meaningful applications of mean, range, and standard deviation.

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4

Dialsingh, Isaac. "Basic Statistics and Pharmaceutical Statistical Applications, 2nd edn." Journal of the Royal Statistical Society: Series A (Statistics in Society) 170, no. 3 (July 2007): 854–55. http://dx.doi.org/10.1111/j.1467-985x.2007.00485_3.x.

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5

Kendrach, Mike. "Book Reviews: Basic Statistics and Pharmaceutical Statistical Applications." American Journal of Health-System Pharmacy 58, no. 10 (May 15, 2001): 923–24. http://dx.doi.org/10.1093/ajhp/58.10.923a.

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6

Balakrishnan, N., and C. R. Rao. "Handbook of Statistics 17: Order Statistics-Applications." Technometrics 42, no. 4 (November 2000): 445. http://dx.doi.org/10.1080/00401706.2000.10485751.

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7

Rees, D. G., and Edwin Mansfield. "Basic Statistics with Applications." Statistician 36, no. 1 (1987): 60. http://dx.doi.org/10.2307/2988277.

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8

Griffiths, David, and W. C. Schefler. "Statistics: Concepts and Applications." Mathematical Gazette 73, no. 465 (October 1989): 246. http://dx.doi.org/10.2307/3618466.

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9

Bertrand, Philip V., Harry Frank, and Steven C. Althoen. "Statistics: Concepts and Applications." Mathematical Gazette 80, no. 489 (November 1996): 637. http://dx.doi.org/10.2307/3618562.

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10

Borowiak, Dale S., Richard J. Larsen, and Morris L. Marx. "Statistics and Its Applications." Technometrics 28, no. 4 (November 1986): 403. http://dx.doi.org/10.2307/1268995.

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11

Loukas, S., W. Mendenhall, D. D. Wackerly, and R. L. Scheaffer. "Mathematical Statistics with Applications." Biometrics 48, no. 3 (September 1992): 977. http://dx.doi.org/10.2307/2532372.

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12

Jammalamadaka, Sreenivasa Rao, Joseph Glaz, and N. Balakrishnan. "Scan Statistics and Applications." Journal of the American Statistical Association 95, no. 452 (December 2000): 1376. http://dx.doi.org/10.2307/2669799.

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13

Williamson, Patricia Pepple. "Mathematical Statistics With Applications." Journal of the American Statistical Association 101, no. 473 (March 2006): 394–95. http://dx.doi.org/10.1198/jasa.2006.s73.

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14

Meek, Gary E. "Mathematical Statistics With Applications." Technometrics 29, no. 3 (August 1987): 384–85. http://dx.doi.org/10.1080/00401706.1987.10488256.

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15

Propst, Annabeth. "Statistics: Concepts and Applications." Technometrics 30, no. 4 (November 1988): 461–62. http://dx.doi.org/10.1080/00401706.1988.10488453.

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16

Young, Karen, W. Mendenhall, D. D. Wackerley, and R. L. Scheaffer. "Mathematical Statistics with Applications." Applied Statistics 41, no. 2 (1992): 433. http://dx.doi.org/10.2307/2347577.

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17

Wates, R. T., H. Frank, and S. Althoen. "Statistics: Concepts and Applications." Statistician 45, no. 1 (1996): 129. http://dx.doi.org/10.2307/2348422.

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18

Casanellas, Marta, Sonja Petrović, and Caroline Uhler. "Algebraic Statistics in Practice: Applications to Networks." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 227–50. http://dx.doi.org/10.1146/annurev-statistics-031017-100053.

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Algebraic statistics uses tools from algebra (especially from multilinear algebra, commutative algebra, and computational algebra), geometry, and combinatorics to provide insight into knotty problems in mathematical statistics. In this review, we illustrate this on three problems related to networks: network models for relational data, causal structure discovery, and phylogenetics. For each problem, we give an overview of recent results in algebraic statistics, with emphasis on the statistical achievements made possible by these tools and their practical relevance for applications to other scientific disciplines.

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19

Clifton, Jesse, and Eric Laber. "Q-Learning: Theory and Applications." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 279–301. http://dx.doi.org/10.1146/annurev-statistics-031219-041220.

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Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.

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20

Foster, Crawford. "Statistics at Square Two: Understanding Modern Statistical Applications in Medicim." Journal of The Royal Naval Medical Service 88, no. 2 (March 2002): 92. http://dx.doi.org/10.1136/jrnms-88-92.

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21

Roberts, Rebecca, and Andy Ness. "Statistics at Square Two: Understanding Modern Statistical Applications in Medicine." International Journal of Epidemiology 31, no. 4 (August 2002): 881. http://dx.doi.org/10.1093/ije/31.4.881.

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22

Kalina, Jan. "Robust Multivariate Statistics in Genetic Applications." Informační Bulletin České statistické společnosti 22, no. 1 (2011): 13–19. http://dx.doi.org/10.5300/ib/2011-1/13.

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23

Subbarao, Dr Uppu Venkata. "Order Statistics of Additive Uniform Exponential Distribution." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1084–87. http://dx.doi.org/10.22214/ijraset.2021.38573.

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Abstract: In this paper we investigated the order statistics by using Additive Uniform Exponential Distribution (AUED) proposed by Venkata Subbarao Uppu (2010).The probability density functions of rth order Statistics, lth moment of the rth order Statistic, minimum, maximum order statistics, mean of the maximum and minimum order statistics, the joint density function of two order statistics were calculated and discussed in detailed . Applications and several aspects were discussed Keywords: Additive Uniform Exponential Distribution, Moments, Minimum order statistic, Maximum order statistic, Joint density of the order Statistics, complete length of service.

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24

Doh, Nakju Lett, Keonyong Lee, and Changjoo Nam. "Novel Line Representation with Consistent Second Order Statistics for SLAM Applications." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 253–58. http://dx.doi.org/10.1299/jsmeicam.2010.5.253.

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25

Prokhorov, A. V., and A. V. Savitskii. "Characterizing Statistics and Their Applications." Theory of Probability & Its Applications 66, no. 3 (January 2021): 391–407. http://dx.doi.org/10.1137/s0040585x97t990472.

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26

Oppenheim, Alan J., Philip G. Enns, Heinz Kohler, and William J. Stevenson. "Business Statistics: Methods and Applications." Journal of Business & Economic Statistics 4, no. 4 (October 1986): 492. http://dx.doi.org/10.2307/1391507.

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27

Tripathy, Bibhuti Bhusan, and Sarbjit Singh. "Advances and Applications in Statistics." Advances and Applications in Statistics 81 (October 19, 2022): 81–108. http://dx.doi.org/10.17654/0972361722075.

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28

Espejo, Mariano Ruiz. "Environmental Statistics: Methods and Applications." Journal of the Royal Statistical Society: Series A (Statistics in Society) 168, no. 2 (March 2005): 458–59. http://dx.doi.org/10.1111/j.1467-985x.2005.358_3.x.

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29

Nolan, D., and T. P. Speed. "Teaching Statistics Theory Through Applications." American Statistician 53, no. 4 (November 1999): 370. http://dx.doi.org/10.2307/2686059.

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30

Kaigh, W. D. "O-statistics and their applications." Communications in Statistics - Theory and Methods 17, no. 7 (January 1988): 2191–210. http://dx.doi.org/10.1080/03610928808829741.

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31

Stamey, James. "Modern Mathematical Statistics with Applications." American Statistician 62, no. 4 (November 2008): 358. http://dx.doi.org/10.1198/tas.2008.s266.

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32

Ziegel, Eric R. "Environmental Statistics: Methods and Applications." Technometrics 46, no. 4 (November 2004): 498–99. http://dx.doi.org/10.1198/tech.2004.s249.

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33

Ridgway, Jim, James Nicholson, and Sean McCusker. "Teaching Statistics ? Despite Its Applications." Teaching Statistics 29, no. 2 (June 2007): 44–48. http://dx.doi.org/10.1111/j.1467-9639.2007.00276.x.

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34

Wikle, Christopher K. "Environmental Statistics: Methods and Applications." Journal of the American Statistical Association 101, no. 475 (September 2006): 1311–12. http://dx.doi.org/10.1198/jasa.2006.s124.

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35

Nolan, D., and T. P. Speed. "Teaching Statistics Theory through Applications." American Statistician 53, no. 4 (November 1999): 370–75. http://dx.doi.org/10.1080/00031305.1999.10474492.

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36

Bertie, Andrew. "Java Applications for Teaching Statistics." MSOR Connections 2, no. 3 (August 2002): 78–81. http://dx.doi.org/10.11120/msor.2002.02030078.

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37

Schmee, Josef. "Matrices With Applications in Statistics." Technometrics 27, no. 1 (February 1985): 88–89. http://dx.doi.org/10.1080/00401706.1985.10488021.

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38

Xiaoling, Peng, Pan Jianxin, Tian Guoliang, Li Gang, and Liang Jiajuan. "Multivariate statistics and its applications." SCIENTIA SINICA Mathematica 50, no. 5 (May 1, 2020): 571. http://dx.doi.org/10.1360/ssm-2020-0071.

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39

Luo, Shan, Ethan T. Vishniac, and Hugo Martel. "Three-dimensional Shape Statistics: Applications." Astrophysical Journal 468 (September 1996): 62. http://dx.doi.org/10.1086/177669.

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40

Morgan, David. "Bayesian applications in pharmaceutical statistics." Pharmaceutical Statistics 17, no. 4 (June 25, 2018): 298–300. http://dx.doi.org/10.1002/pst.1876.

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41

Chung, Jaewon, Eric Bridgeford, Jesús Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Liang Xiang, Carey E. Priebe, and Joshua T. Vogelstein. "Statistical Connectomics." Annual Review of Statistics and Its Application 8, no. 1 (March 7, 2021): 463–92. http://dx.doi.org/10.1146/annurev-statistics-042720-023234.

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The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

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42

Otárola-Castillo, Erik, and Melissa G. Torquato. "Bayesian Statistics in Archaeology." Annual Review of Anthropology 47, no. 1 (October 21, 2018): 435–53. http://dx.doi.org/10.1146/annurev-anthro-102317-045834.

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Null hypothesis significance testing (NHST) is the most common statistical framework used by scientists, including archaeologists. Owing to increasing dissatisfaction, however, Bayesian inference has become an alternative to these methods. In this article, we review the application of Bayesian statistics to archaeology. We begin with a simple example to demonstrate the differences in applying NHST and Bayesian inference to an archaeological problem. Next, we formally define NHST and Bayesian inference, provide a brief historical overview of their development, and discuss the advantages and limitations of each method. A review of Bayesian inference and archaeology follows, highlighting the applications of Bayesian methods to chronological, bioarchaeological, zooarchaeological, ceramic, lithic, and spatial analyses. We close by considering the future applications of Bayesian statistics to archaeological research.

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43

Frühwirth-Schnatter, Sylvia. "On statistical inference for fuzzy data with applications to descriptive statistics." Fuzzy Sets and Systems 50, no. 2 (September 1992): 143–65. http://dx.doi.org/10.1016/0165-0114(92)90213-n.

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44

Yun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun, and Yan-Qin Li Hong-Yan Xu. "Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications." 電腦學刊 33, no. 2 (April 2022): 169–76. http://dx.doi.org/10.53106/199115992022043302015.

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<p>The information integrity is needed to solving socio-economic statistical problems. However, the information integrity is destroyed by missing data which is caused by various subjective and objective reasons. So the missing data interpolation is used to supplement missing data. In this paper, missing data interpolation with variational Bayesian inference is proposed. This method is combined with Gaussian model to approximate the posterior distribution to obtain complete data. The experiments include two datasets (artificial dataset and actual dataset) based on three missing ratios separately. The missing data interpolation performance of variational Bayesian method is compared with that which is obtained by mean interpolation and K-nearest neighbor interpolation methods separately in MSE (Mean Square Error) and MAPE (Mean Absolute Percentage Error). The experimental results show that the proposed variational Bayesian method is better in MSE and MAPE.</p> <p> </p>

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45

Yun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun, and Yan-Qin Li Hong-Yan Xu. "Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications." 電腦學刊 33, no. 2 (April 2022): 169–76. http://dx.doi.org/10.53106/199115992022043302015.

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<p>The information integrity is needed to solving socio-economic statistical problems. However, the information integrity is destroyed by missing data which is caused by various subjective and objective reasons. So the missing data interpolation is used to supplement missing data. In this paper, missing data interpolation with variational Bayesian inference is proposed. This method is combined with Gaussian model to approximate the posterior distribution to obtain complete data. The experiments include two datasets (artificial dataset and actual dataset) based on three missing ratios separately. The missing data interpolation performance of variational Bayesian method is compared with that which is obtained by mean interpolation and K-nearest neighbor interpolation methods separately in MSE (Mean Square Error) and MAPE (Mean Absolute Percentage Error). The experimental results show that the proposed variational Bayesian method is better in MSE and MAPE.</p> <p> </p>

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46

Nolde, Natalia, and Chen Zhou. "Extreme Value Analysis for Financial Risk Management." Annual Review of Statistics and Its Application 8, no. 1 (March 7, 2021): 217–40. http://dx.doi.org/10.1146/annurev-statistics-042720-015705.

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This article reviews methods from extreme value analysis with applications to risk assessment in finance. It covers three main methodological paradigms: the classical framework for independent and identically distributed data with application to risk estimation for market and operational loss data, the multivariate framework for cross-sectional dependent data with application to systemic risk, and the methods for stationary serially dependent data applied to dynamic risk management. The article is addressed to statisticians with interest and possibly experience in financial risk management who are not familiar with extreme value analysis.

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47

Bertail, Patrice, Stéphan Clémençon, and Jessica Tressou. "Bootstrapping Robust Statistics for Markovian Data Applications to Regenerative R -Statistics and L -Statistics." Journal of Time Series Analysis 36, no. 3 (January 22, 2015): 462–80. http://dx.doi.org/10.1111/jtsa.12105.

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48

Bühlmann, Peter, Markus Kalisch, and Lukas Meier. "High-Dimensional Statistics with a View Toward Applications in Biology." Annual Review of Statistics and Its Application 1, no. 1 (January 3, 2014): 255–78. http://dx.doi.org/10.1146/annurev-statistics-022513-115545.

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49

Lipovetsky, Stan. "Advanced Statistics with Applications in R." Technometrics 63, no. 2 (April 3, 2021): 273–75. http://dx.doi.org/10.1080/00401706.2021.1904740.

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50

Kimber, Alan, and A. I. Khuri. "Advanced Calculus with Applications in Statistics." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 3 (1994): 506. http://dx.doi.org/10.2307/2983540.

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