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Journal articles on the topic 'Statistics - Applications'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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1

Hoover, Donald R. "Basic Statistics and Pharmaceutical Statistical Applications." Journal of Biopharmaceutical Statistics 25, no. 1 (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 (2000): 593–94. http://dx.doi.org/10.1016/s0197-2456(00)00090-8.

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3

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

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4

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

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5

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

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6

张, 东. "The Probability Properties and Statistical Applications of Order Statistics." Pure Mathematics 14, no. 01 (2024): 53–64. http://dx.doi.org/10.12677/pm.2024.141007.

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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 (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 (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 (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 (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 (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 (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 (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 (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 (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

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

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20

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

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21

Clifton, Jesse, and Eric Laber. "Q-Learning: Theory and Applications." Annual Review of Statistics and Its Application 7, no. 1 (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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22

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

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23

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 (1986): 492. http://dx.doi.org/10.2307/1391507.

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24

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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25

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

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26

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

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27

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

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28

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

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29

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

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30

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

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31

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

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32

Alaasam, Ameer B. A. "Quantum Computing Applications in Statistics." Babylonian Journal of Mathematics 2023 (December 29, 2023): 105–7. http://dx.doi.org/10.58496/bjm/2023/020.

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Quantum computing promises to disrupt statistical analysis and data modeling through specialized algorithms and methodologies designed to take advantage of quantum resources. This paper explores imminent applications in areas ranging from sampling and simulation to dimensionality reduction and density estimation. Quantum approaches such as amplitude estimation, quantum principal component analysis, and quantum generative training provide demonstrable speedups and modeling improvements compared to classical techniques. However, despite great future potential, practical implementation today faces restrictions around limited qubit counts, error correction, and developing optimized hybrid quantum-classical workflows. Key near-term applications likely to deliver on quantum advantage include accelerated sampling for uncertainty quantification, quantum-enabled principal component analysis for dimensionality reduction, and quantum machine learning models for fitting complex, multimodal distributions. Longer-term, much more transformational analysis of immense, intricate datasets can be unlocked as quantum hardware progresses towards fault tolerance. Organizations should proactively update statistical pipelines to best leverage coming quantum-centered advancements in data analysis and predictive modeling.

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33

Jazouli, Soumaya Idrissi, Mohamed Ben Ali, Oumaima Ollada, Ahmed Adri, and Said Rifai. "Advances and Applications in Statistics." Advances and Applications in Statistics 91, no. 11 (2024): 1421–43. http://dx.doi.org/10.17654/0972361724073.

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34

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

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35

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

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36

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

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37

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

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38

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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39

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

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40

Dong, Yihang. "Descriptive Statistics and Its Applications." Highlights in Science, Engineering and Technology 47 (May 11, 2023): 16–23. http://dx.doi.org/10.54097/hset.v47i.8159.

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This review paper examines the topic of descriptive statistics, which is a study of statistics, deriving from basic mathematics. The paper discusses common descriptive statistics using a combination of numerical examples and graphical demonstrations. The paper introduces the definitions of some basic descriptive statistics and their corresponding formulas. The paper then reviews several research articles published in recent years in which researchers have used descriptive statistics to help analyze housing prices and the relationship between the stock market and trust. This review paper consults tables from other research articles with descriptive statistics and uses specific data to demonstrate how these statistics help researchers draw meaningful conclusions about the subjects of the analysis. The final conclusion is that although descriptive statistics is a simple method of summarizing data, it is fundamental and meaningful in statistical analysis and that can be used as a building block on which further analysis can be based.

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41

Al-Marshadi, Ali Hussein, Abdullah Hamoud Alharby, and Muhammad Qaiser Shahbaz. "Advances and Applications in Statistics." Advances and Applications in Statistics 87, no. 1 (2023): 1–11. http://dx.doi.org/10.17654/0972361723025.

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42

MUKHERJEE, S. P. "WHITHER STATISTICS?" IAPQR Transactions 47, no. 1-2 (2023): 1–15. http://dx.doi.org/10.32381/iapqrt.2023.47.01-02.1.

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The present article reflects on a few issues regarding the on-going trend in theory and applications of Statistics. The aspect of applications and the problems with sample surveys in several domains have been emphasized.

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43

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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44

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

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45

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

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46

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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47

Haigh, John, and Gunnar Blom. "Probability and Statistics: Theory and Applications." Mathematical Gazette 74, no. 468 (1990): 193. http://dx.doi.org/10.2307/3619401.

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48

Ziegel, Eric R., and Andre Khuri. "Advanced Calculus with Applications in Statistics." Technometrics 36, no. 3 (1994): 333. http://dx.doi.org/10.2307/1269402.

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49

Hodgson, Vincent, Galen R. Shorack, and Jon A. Wellner. "Empirical Processes with Applications to Statistics." Technometrics 29, no. 4 (1987): 499. http://dx.doi.org/10.2307/1269480.

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50

Fearn, T., and S. J. Press. "Bayesian Statistics: Principles, Models, and Applications." Biometrics 46, no. 2 (1990): 540. http://dx.doi.org/10.2307/2531460.

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