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Journal articles on the topic 'Statistical inference'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 April 2025

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1

Rennolls, Keith, P. H. Garthwaite, I. T. Jolliffe, and B. Jones. "Statistical Inference." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 3 (1996): 622. http://dx.doi.org/10.2307/2983341.

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2

Crowder, Martin, P. H. Garthwaite, I. T. Jolliffe, and B. Jones. "Statistical Inference." Statistician 45, no. 3 (1996): 386. http://dx.doi.org/10.2307/2988478.

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3

Brunson, Barry W., and Vijay K. Rohatgi. "Statistical Inference." American Mathematical Monthly 94, no. 2 (February 1987): 210. http://dx.doi.org/10.2307/2322441.

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4

Lindley, D. V., and Vijay K. Rohatgi. "Statistical Inference." Mathematical Gazette 69, no. 447 (March 1985): 63. http://dx.doi.org/10.2307/3616474.

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5

Rohatgi, V. K. "Statistical Inference." Biometrics 41, no. 4 (December 1985): 1102. http://dx.doi.org/10.2307/2530991.

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6

Ghosh, Malay, George Casella, and Roger L. Berger. "Statistical Inference." Journal of the American Statistical Association 89, no. 426 (June 1994): 712. http://dx.doi.org/10.2307/2290879.

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7

Casella, G., and R. L. Berger. "Statistical Inference." Biometrics 49, no. 1 (March 1993): 320. http://dx.doi.org/10.2307/2532634.

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8

Ziegel, Eric R. "Statistical Inference." Technometrics 44, no. 4 (November 2002): 407–8. http://dx.doi.org/10.1198/tech.2002.s94.

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9

Angus, John E. "Statistical Inference." Technometrics 33, no. 4 (November 1991): 493. http://dx.doi.org/10.1080/00401706.1991.10484898.

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10

Randles, Ronald H., and Vijay K. Rohatgi. "Statistical Inference." Journal of the American Statistical Association 81, no. 393 (March 1986): 258. http://dx.doi.org/10.2307/2288010.

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11

Roberts, Rosemary A., and J. G. Kalbfleisch. "Probability and Statistical Inference, Volume 2: Statistical Inference." Journal of the American Statistical Association 84, no. 407 (September 1989): 842. http://dx.doi.org/10.2307/2289686.

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12

Michalewicz, Zbigniew, and Anthony Yeo. "Multiranges and Multitrackers in Statistical Databases." Fundamenta Informaticae 11, no. 1 (January 1, 1988): 41–48. http://dx.doi.org/10.3233/fi-1988-11104.

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The goal of statistical databases is to provide statistics about groups of individuals while protecting their privacy. Sometimes. by correlating enough statistics, sensitive data about individual can be inferred. The problem of protecting against such indirect disclosures of confidential data is called the inference problem and a protecting mechanism – an inference control. A good inference control mechanism should be effective (it should provide security to a reasonable extent) and feasible (a practical way exists to enforce it). At the same time it should retain the richness of the information revealed to the users. During the last few years several techniques were developed for controlling inferences. One of the earliest inference controls for statistical databases restricts the responses computed over too small or too large query-sets. However, this technique is easily subverted. In this paper we propose a new query-set size inference control which is based on the idea of multiranges and has better performance then the original one.

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13

Heckman, Jonathan J. "Statistical inference and string theory." International Journal of Modern Physics A 30, no. 26 (September 18, 2015): 1550160. http://dx.doi.org/10.1142/s0217751x15501602.

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In this paper, we expose some surprising connections between string theory and statistical inference. We consider a large collective of agents sweeping out a family of nearby statistical models for an [Formula: see text]-dimensional manifold of statistical fitting parameters. When the agents making nearby inferences align along a [Formula: see text]-dimensional grid, we find that the pooled probability that the collective reaches a correct inference is the partition function of a nonlinear sigma model in [Formula: see text] dimensions. Stability under perturbations to the original inference scheme requires the agents of the collective to distribute along two dimensions. Conformal invariance of the sigma model corresponds to the condition of a stable inference scheme, directly leading to the Einstein field equations for classical gravity. By summing over all possible arrangements of the agents in the collective, we reach a string theory. We also use this perspective to quantify how much an observer can hope to learn about the internal geometry of a superstring compactification. Finally, we present some brief speculative remarks on applications to the AdS/CFT correspondence and Lorentzian signature space–times.

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14

Low, Mark, Axel Munk, and Alexandre Tsybakov. "Adaptive Statistical Inference." Oberwolfach Reports 11, no. 1 (2014): 721–79. http://dx.doi.org/10.4171/owr/2014/13.

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15

Randles, Ronald H., and Jean Dickinson Gibbons. "Nonparametric Statistical Inference." Technometrics 28, no. 3 (August 1986): 275. http://dx.doi.org/10.2307/1269084.

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16

Ziegel, Eric R., and Vic Barnett. "Comparative Statistical Inference." Technometrics 42, no. 4 (November 2000): 442. http://dx.doi.org/10.2307/1270977.

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17

Schabes, Yves. "Statistical grammar inference." Journal of the Acoustical Society of America 92, no. 4 (October 1992): 2368. http://dx.doi.org/10.1121/1.404865.

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18

Shalabh. "Nonparametric Statistical Inference." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 2 (March 14, 2011): 508–9. http://dx.doi.org/10.1111/j.1467-985x.2010.00681_6.x.

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19

Katsaounis, T. I. "Introductory Statistical Inference." Technometrics 50, no. 1 (February 2008): 89–90. http://dx.doi.org/10.1198/tech.2008.s529.

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20

Banerjee, Tathagata. "Introductory Statistical Inference." Journal of the American Statistical Association 102, no. 480 (December 2007): 1474. http://dx.doi.org/10.1198/jasa.2007.s231.

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21

Ziegel, Eric. "Nonparametric Statistical Inference." Technometrics 30, no. 4 (November 1988): 457. http://dx.doi.org/10.1080/00401706.1988.10488449.

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22

Ziegel, Eric R. "Nonparametric Statistical Inference." Technometrics 35, no. 2 (May 1993): 239–40. http://dx.doi.org/10.1080/00401706.1993.10485070.

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23

Shanmugam, Ram. "Parametric Statistical Inference." Technometrics 40, no. 2 (May 1998): 161. http://dx.doi.org/10.1080/00401706.1998.10485208.

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24

Barnett, Vic. "Comparative Statistical Inference." Technometrics 42, no. 4 (November 2000): 442. http://dx.doi.org/10.1080/00401706.2000.10485741.

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25

Periwal, Vipul. "Geometric statistical inference." Nuclear Physics B 554, no. 3 (August 1999): 719–30. http://dx.doi.org/10.1016/s0550-3213(99)00278-3.

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26

Malley, James D., and John Hornstein. "Quantum Statistical Inference." Statistical Science 8, no. 4 (November 1993): 433–57. http://dx.doi.org/10.1214/ss/1177010787.

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27

Anscombe, F. J., and Rupert G. Miller. "Simultaneous Statistical Inference." Journal of the American Statistical Association 80, no. 389 (March 1985): 250. http://dx.doi.org/10.2307/2288100.

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28

Sukhatme, Shashikala, and Jean Dickinson Gibbons. "Nonparametric Statistical Inference." Journal of the American Statistical Association 82, no. 399 (September 1987): 953. http://dx.doi.org/10.2307/2288823.

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29

Leslie, J., and J. D. Gibbons. "Nonparametric Statistical Inference." Journal of the Royal Statistical Society. Series A (General) 149, no. 3 (1986): 275. http://dx.doi.org/10.2307/2981565.

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30

Stoimenova, Eugenia. "Nonparametric statistical inference." Journal of Applied Statistics 39, no. 6 (June 2012): 1384–85. http://dx.doi.org/10.1080/02664763.2012.657415.

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31

Luo, Yu, and Jiaying Zhao. "Statistical Learning Creates Novel Object Associations via Transitive Relations." Psychological Science 29, no. 8 (May 22, 2018): 1207–20. http://dx.doi.org/10.1177/0956797618762400.

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A remarkable ability of the cognitive system is to make novel inferences on the basis of prior experiences. What mechanism supports such inferences? We propose that statistical learning is a process through which transitive inferences of new associations are made between objects that have never been directly associated. After viewing a continuous sequence containing two base pairs (e.g., A–B, B–C), participants automatically inferred a transitive pair (e.g., A–C) where the two objects had never co-occurred before (Experiment 1). This transitive inference occurred in the absence of explicit awareness of the base pairs. However, participants failed to infer the transitive pair from three base pairs (Experiment 2), showing the limits of the transitive inference (Experiment 3). We further demonstrated that this transitive inference can operate across the categorical hierarchy (Experiments 4–7). The findings revealed a novel consequence of statistical learning in which new transitive associations between objects are implicitly inferred.

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32

Zhang, Zhongzhan, Jiajia Dai, and Zhenhai Yang. "Randomized statistical inference: A unified statistical inference frame of frequentist, fiducial, and Bayesian inference." Science China Mathematics 63, no. 5 (March 10, 2020): 1007–28. http://dx.doi.org/10.1007/s11425-017-9325-9.

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33

Guo, Guangbao. "Parallel Statistical Computing for Statistical Inference." Journal of Statistical Theory and Practice 6, no. 3 (September 2012): 536–65. http://dx.doi.org/10.1080/15598608.2012.695705.

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34

Fraser, D. A. S. "Bayesian inference: an approach to statistical inference." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 4 (June 29, 2010): 487–96. http://dx.doi.org/10.1002/wics.102.

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35

Biddle, Jeff. "Statistical Inference in Economics in the 1920s and 1930s." History of Political Economy 53, no. 6 (August 26, 2021): 53–80. http://dx.doi.org/10.1215/00182702-9414775.

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Statistical inference is the process of drawing conclusions from samples of statistical data about things not fully described or recorded in those samples. During the 1920s, economists in the United States articulated a general approach to statistical inference that downplayed the value of the inferential measures derived from probability theory that later came to be central to the idea of statistical inference in economics. This approach is illustrated by the practices of economists of the Bureau of Economic Analysis of the US Department of Agriculture, who regularly analyzed statistical samples to forecast supplies of various agricultural products. Forecasting represents an interesting case for studying the development of inferential methods, as analysts receive regular feedback on the effectiveness of their inferences when forecasts are compared with actual events.

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36

Szász-Simon, Judit. "Statistical inference in school." Teaching Mathematics and Computer Science 2, no. 2 (November 9, 2015): 265–73. http://dx.doi.org/10.5485/tmcs.2004.0056.

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37

Thunder, Maurice, D. V. Huntsberger, and P. P. Billingsley. "Elements of Statistical Inference." Mathematical Gazette 72, no. 460 (June 1988): 149. http://dx.doi.org/10.2307/3618951.

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38

Leversha, Gerry, and Nitis Mukhopadhyay. "Probability and Statistical Inference." Mathematical Gazette 86, no. 506 (July 2002): 378. http://dx.doi.org/10.2307/3621919.

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39

Joseph, Lawrence, and Caroline Reinhold. "Statistical Inference for Proportions." American Journal of Roentgenology 184, no. 4 (April 2005): 1057–64. http://dx.doi.org/10.2214/ajr.184.4.01841057.

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40

Basu, Asit P., and A. H. Welsh. "Aspects of Statistical Inference." Technometrics 41, no. 3 (August 1999): 270. http://dx.doi.org/10.2307/1270585.

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41

Hand, David J. "Trustworthiness of statistical inference." Journal of the Royal Statistical Society: Series A (Statistics in Society) 185, no. 1 (October 12, 2021): 329–47. http://dx.doi.org/10.1111/rssa.12752.

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42

Santner, Thomas, Tim Robertson, F. T. Wright, and R. L. Dykstra. "Order Restricted Statistical Inference." Journal of the American Statistical Association 85, no. 410 (June 1990): 596. http://dx.doi.org/10.2307/2289813.

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43

Kelly, R. E., T. Robertson, F. T. Wright, and R. L. Dykstra. "Order Restricted Statistical Inference." Biometrics 46, no. 3 (September 1990): 878. http://dx.doi.org/10.2307/2532111.

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44

Kemp, A. W., R. Bartoszynski, and M. Niewiadomska-Bugaj. "Probability and Statistical Inference." Biometrics 54, no. 2 (June 1998): 796. http://dx.doi.org/10.2307/3109791.

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45

Kimber, Alan. "Principles of Statistical Inference." Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, no. 1 (January 10, 2008): 315–16. http://dx.doi.org/10.1111/j.1467-985x.2007.00521_8.x.

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46

Salant, Yuval, and Josh Cherry. "Statistical Inference in Games." Econometrica 88, no. 4 (2020): 1725–52. http://dx.doi.org/10.3982/ecta17105.

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We consider statistical inference in games. Each player obtains a small random sample of other players' actions, uses statistical inference to estimate their actions, and chooses an optimal action based on the estimate. In a sampling equilibrium with statistical inference (SESI), the sample is drawn from the distribution of players' actions based on this process. We characterize the set of SESIs in large two‐action games, and compare their predictions to those of Nash equilibrium, and for different sample sizes and statistical inference procedures. We then study applications to competitive markets, markets with network effects, monopoly pricing, and search and matching markets.

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47

Hahn, Gerald J., and William Q. Meeker. "Assumptions for Statistical Inference." American Statistician 47, no. 1 (February 1993): 1. http://dx.doi.org/10.2307/2684774.

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48

Barndorff-Nielsen, Ole E., Richard D. Gill, and Peter E. Jupp. "On quantum statistical inference." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 4 (October 28, 2003): 775–804. http://dx.doi.org/10.1111/1467-9868.00415.

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49

Mulekar, Madhuri. "Statistical Inference in Science." Technometrics 43, no. 3 (August 2001): 376–77. http://dx.doi.org/10.1198/tech.2001.s632.

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50

Harvill, Jane L. "Essentials of Statistical Inference." Journal of the American Statistical Association 103, no. 481 (March 1, 2008): 425–26. http://dx.doi.org/10.1198/jasa.2008.s213.

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