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Journal articles on the topic 'Inferenza statistica'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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1

Ludbrook, John, and Hugh Dudley. "ISSUES IN BIOMEDICAL STATISTICS: STATISTICAL INFERENCE." ANZ Journal of Surgery 64, no. 9 (1994): 630–36. http://dx.doi.org/10.1111/j.1445-2197.1994.tb02308.x.

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2

Loosmore, N. Bert, and E. David Ford. "STATISTICAL INFERENCE USING THEGORKPOINT PATTERN SPATIAL STATISTICS." Ecology 87, no. 8 (2006): 1925–31. http://dx.doi.org/10.1890/0012-9658(2006)87[1925:siutgo]2.0.co;2.

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3

Barber, Stuart. "All of Statistics: a Concise Course in Statistical Inference." Journal of the Royal Statistical Society: Series A (Statistics in Society) 168, no. 1 (2005): 261. http://dx.doi.org/10.1111/j.1467-985x.2004.00347_18.x.

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4

Craigmile, Peter F. "All of Statistics: A Concise Course in Statistical Inference." American Statistician 59, no. 2 (2005): 203–4. http://dx.doi.org/10.1198/tas.2005.s30.

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5

Zhang, Jingwen, Joseph Ibrahim, Tengfei Li, and Hongtu Zhu. "A Powerful Global Test Statistic for Functional Statistical Inference." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5765–72. http://dx.doi.org/10.1609/aaai.v33i01.33015765.

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We consider the problem of performing an association test between functional data and scalar variables in a varying coefficient model setting. We propose a functional projection regression model and an associated global test statistic to aggregate relatively weak signals across the domain of functional data, while reducing the dimension. An optimal functional projection direction is selected to maximize signal-to-noise ratio with ridge penalty. Theoretically, we systematically study the asymptotic distribution of the global test statistic and provide a strategy to adaptively select the optimal

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6

Curran-Everett, Douglas. "Explorations in statistics: the bootstrap." Advances in Physiology Education 33, no. 4 (2009): 286–92. http://dx.doi.org/10.1152/advan.00062.2009.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the sample mean. The appeal of the bootstrap is that we can use it to make an inference about some experimental result when the statistical theory is uncertain or even unknown. We can also use the bootstrap to assess how well the statistical theory holds: that

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7

Xu, Jinfeng, Lincheng Zhao, and Chenlei Leng. "Statistical inference for induced L-statistics: a random perturbation approach." Journal of Nonparametric Statistics 21, no. 7 (2009): 863–76. http://dx.doi.org/10.1080/10485250902980584.

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8

Subba Rao, Suhasini. "Statistical inference for spatial statistics defined in the Fourier domain." Annals of Statistics 46, no. 2 (2018): 469–99. http://dx.doi.org/10.1214/17-aos1556.

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9

Rohana, Rohana, and Yunika Lestaria Ningsih. "STUDENTS’ STATISTICAL REASONING IN STATISTICS METHOD COURSE." Jurnal Pendidikan Matematika 14, no. 1 (2019): 81–90. http://dx.doi.org/10.22342/jpm.14.1.6732.81-90.

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The role of statistics is wide and crucial in daily life, making statistics important. Many students have difficulty understanding statistics. This study aims to determine students' statistical reasoning about inference statistics, which is limited to the subject matter of the testing hypotheses about two-sample hypotheses testing. This study used descriptive research method. The subjects were 25 students of third-year Mathematics Education Departement at Universitas PGRI Palembang in the academic year 2018/2019. Data were collected through tests and interviews. Data were analyzed through desc

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10

SARASSANTI, YUMI, ALI HASMY, and YULIA RAHMAWATI Z. "PERSEPSI MAHASISWA TERHADAP KUIS PADA MATERI STATISTIKA INFERENSIA." AL KHAWARIZMI: Jurnal Pendidikan Matematika 3, no. 1 (2023): 19–22. https://doi.org/10.46368/kjpm.v3i1.1354.

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Abstrak: Tujuan Penelitian ini adalah untuk mengetahui persepsi mahasiswa terhadap kuis pada materi statistika inferensia. Penelitian ini menggunakan penelitian kualitatif deskriptif dengan metode penelitian survey, sampel penelitian ini diambil dari mahasiswa semester IV Program Studi Manajemen Dakwah IAIN Pontianak dengan Kelas A sebanyak 16 org dan kelas B sebanyak 20 orang. Teknik pengambilan data yang digunakan adalah angket. Berdasarkan hasil penelitian didapatkan hasil persepsi mahasiswa pada materi statistika inferensia secara garis besar termasuk berkategori baik dengan persentase 63,

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11

Kuchibhotla, Arun K., John E. Kolassa, and Todd A. Kuffner. "Post-Selection Inference." Annual Review of Statistics and Its Application 9, no. 1 (2022): 505–27. http://dx.doi.org/10.1146/annurev-statistics-100421-044639.

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We discuss inference after data exploration, with a particular focus on inference after model or variable selection. We review three popular approaches to this problem: sample splitting, simultaneous inference, and conditional selective inference. We explain how each approach works and highlight its advantages and disadvantages. We also provide an illustration of these post-selection inference approaches.

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12

Nordhausen, Klaus, and Hannu Oja. "Robust Nonparametric Inference." Annual Review of Statistics and Its Application 5, no. 1 (2018): 473–500. http://dx.doi.org/10.1146/annurev-statistics-031017-100247.

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13

Alhihi, Suad, Maalee Almheidat, Ghassan Abufoudeh, Raed Abu Awwad, Samer Alokaily, and Ayat Almomani. "Statistical Inference of Normal Distribution Based on Several Divergence Measures: A Comparative Study." Symmetry 16, no. 2 (2024): 212. http://dx.doi.org/10.3390/sym16020212.

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Statistical predictive analysis is a very useful tool for predicting future observations. Previous literature has addressed both Bayesian and non-Bayesian predictive distributions of future statistics based on past sufficient statistics. This study focused on evaluating Bayesian and Wald predictive-density functions of a future statistic V based on a past sufficient statistic W obtained from a normal distribution. Several divergence measures were used to assess the closeness of the predictive densities to the future density. The difference between these divergence measures was investigated, us

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14

Mayo-Wilson, Conor. "Statistical Inference as Severe Testing: How to Get beyond the Statistics." Philosophical Review 130, no. 1 (2021): 185–89. http://dx.doi.org/10.1215/00318108-8699656.

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15

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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16

Baggio, Hugo C., Alexandra Abos, Barbara Segura, et al. "Statistical inference in brain graphs using threshold‐free network‐based statistics." Human Brain Mapping 39, no. 6 (2018): 2289–302. http://dx.doi.org/10.1002/hbm.24007.

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17

Valenzuela-Ruiz, Silvia M., Carmen Batanero, Nuria Begué, and José A. Garzón-Guerrero. "Conocimientos didáctico-matemáticos de profesores de educación secundaria en formación sobre inferencia estadística." Bolema: Boletim de Educação Matemática 37, no. 76 (2023): 602–24. http://dx.doi.org/10.1590/1980-4415v37n76a11.

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Resumen Actualmente, la inferencia estadística es un tema enseñado en el Bachillerato español y evaluado en los exámenes de acceso a la universidad, pero la preparación didáctica específica no es suficiente para los profesores encargados de la enseñanza del tema. El objetivo del trabajo es evaluar el conocimiento matemático común de este contenido y las facetas epistémica y cognitiva del conocimiento didáctico en una muestra de futuros profesores españoles. Con esta finalidad se les plantea la resolución de una tarea tomada de las pruebas de acceso a la universidad para estudiantes de Bachille

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18

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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19

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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20

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

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21

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

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22

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

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23

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

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24

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

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25

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

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26

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

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27

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

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28

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

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29

Kokoszka, Gregory. "STATISTICAL INFERENCE FOR AUTOCOVARIANCE OF FUNCTIONAL TIME SERIES UNDER CONDITIONAL HETEROSCEDASTICITY." Global Multidisciplinary Journal 01, no. 01 (2022): 01–06. http://dx.doi.org/10.55640/gmj-abc111.

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This paper investigates statistical inference methods for autocovariance estimation in functional time series under the presence of conditional heteroscedasticity. Functional time series data, which are characterized by observations evolving over continuous time or space, often exhibit complex dependencies and time-varying volatility patterns. In the presence of conditional heteroscedasticity, traditional autocovariance estimators may be biased or inefficient, necessitating the development of robust inference techniques. We propose a novel approach based on robust covariance estimation and boo

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30

Shaw, Dr Eleanor, Prof Jacob M. Lin, and Dr Priya Natarajan. "Navigating Statistical Inference: Addressing Enduring Misconceptions in Social Science Hypothesis Testing." International Journal of Social Sciences, Language and Linguistics 05, no. 04 (2025): 06–09. https://doi.org/10.55640/ijssll-05-04-02.

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Despite decades of statistical education, fundamental misconceptions about hypothesis testing persist in the social sciences. This article explores common errors in the interpretation and application of statistical inference—such as misunderstanding p-values, conflating statistical with practical significance, and over-reliance on null hypothesis significance testing (NHST). Drawing on recent methodological critiques and pedagogical studies, the paper analyzes how these misconceptions shape research outcomes, influence publication decisions, and perpetuate flawed scientific reasoning. The arti

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31

El-Din, M. M. Mohie, Nahed S. A. Ali, M. M. Amein, and M. S. Mohamed. "Statistical Inference of Concomitants Based on Morgenstern Family under Generalized Order Statistics." Mathematical Sciences Letters 5, no. 3 (2016): 243–54. http://dx.doi.org/10.18576/msl/050305.

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32

El-Deen, M. "Statistical Inference for Kumaraswamy Distribution Based on Generalized Order Statistics with Applications." British Journal of Mathematics & Computer Science 4, no. 12 (2014): 1710–43. http://dx.doi.org/10.9734/bjmcs/2014/9193.

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33

Beutner, E., and U. Kamps. "Order restricted statistical inference for scale parameters based on sequential order statistics." Journal of Statistical Planning and Inference 139, no. 9 (2009): 2963–69. http://dx.doi.org/10.1016/j.jspi.2009.01.017.

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34

Bibaut, Aurélien, and Nathan Kallus. "Demystifying Inference After Adaptive Experiments." Annual Review of Statistics and Its Application 12, no. 1 (2025): 407–23. https://doi.org/10.1146/annurev-statistics-040522-015431.

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Adaptive experiments such as multi-armed bandits adapt the treatment-allocation policy and/or the decision to stop the experiment to the data observed so far. This has the potential to improve outcomes for study participants within the experiment, to improve the chance of identifying the best treatments after the experiment, and to avoid wasting data. As an experiment (and not just a continually optimizing system), it is still desirable to draw statistical inferences with frequentist guarantees. The concentration inequalities and union bounds that generally underlie adaptive experimentation al

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35

Blomberg, Per. "Preparing prospective primary school teachers in teaching informal statistical inference." NOMAD Nordic Studies in Mathematics Education 30, no. 1 (2025): 59–82. https://doi.org/10.7146/nomad.v30i1.152931.

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In recent decades, the statistics education community has focused extensively on research aimed at modernising mathematics curricula by integrating powerful statistical concepts relevant to the 21st century. As a result, the professional development needs of statistics teachers are evolving. There is an increasing demand for teachers to be well-equipped to teach foundational statistical concepts, including inferential statistics, and gaining these insights has become essential. This paper presents findings from an educational design research study conducted with participants in primary teacher

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36

Wasserman, Larry, Aaditya Ramdas, and Sivaraman Balakrishnan. "Universal inference." Proceedings of the National Academy of Sciences 117, no. 29 (2020): 16880–90. http://dx.doi.org/10.1073/pnas.1922664117.

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We propose a general method for constructing confidence sets and hypothesis tests that have finite-sample guarantees without regularity conditions. We refer to such procedures as “universal.” The method is very simple and is based on a modified version of the usual likelihood-ratio statistic that we call “the split likelihood-ratio test” (split LRT) statistic. The (limiting) null distribution of the classical likelihood-ratio statistic is often intractable when used to test composite null hypotheses in irregular statistical models. Our method is especially appealing for statistical inference i

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37

KYBURG, HENRY E., and CHOH MAN TENG. "STATISTICAL INFERENCE AS DEFAULT REASONING." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (1999): 267–83. http://dx.doi.org/10.1142/s021800149900015x.

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Classical statistical inference is nonmonotonic in nature. We show how it can be formalized in the default logic framework. The structure of statistical inference is the same as that represented by default rules. In particular, the prerequisite corresponds to the sample statistics, the justifications require that we do not have any reason to believe that the sample is misleading, and the consequence corresponds to the conclusion sanctioned by the statistical test.

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38

Bowers, Jake, Mark M. Fredrickson, and Peter M. Aronow. "Research Note: A More Powerful Test Statistic for Reasoning about Interference between Units." Political Analysis 24, no. 3 (2016): 395–403. http://dx.doi.org/10.1093/pan/mpw018.

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Bowers, Fredrickson, and Panagopoulos (2013, Reasoning about interference between units: A general framework, Political Analysis 21(1):97–124; henceforth BFP) showed that one could use Fisher's randomization-based hypothesis testing framework to assess counterfactual causal models of treatment propagation and spillover across social networks. This research note improves the statistical inference presented in BFP (2013) by substituting a test statistic based on a sum of squared residuals and incorporating information about the fixed network for the simple Kolmogorov–Smirnov test statistic (Holl

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39

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 (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 sep

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40

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 (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 sep

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41

ESTRELLA, SOLEDAD, MARITZA MÉNDEZ-REINA, and PEDRO VIDAL-SZABÓ. "EXPLORING INFORMAL STATISTICAL INFERENCE IN EARLY STATISTICS: A LEARNING TRAJECTORY FOR THIRD-GRADE STUDENTS." STATISTICS EDUCATION RESEARCH JOURNAL 22, no. 2 (2023): 10. http://dx.doi.org/10.52041/serj.v22i2.426.

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Recent research suggests the benefits of supporting a progressive understanding of concepts of inference prior to the teaching of procedures and formal calculations through the study of informal statistical inference (ISI). To contribute to the growing knowledge about the early learning and teaching of statistics, particularly regarding the development of informal inferential reasoning (IIR), we designed a learning trajectory (LT) that addresses ISI in K–4 students (ages 5 to 9 years). This article describes part of the LT in detail, in which third-grade students (n = 12) were introduced to sa

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42

Joe, H. "Statistical inference for general-order-statistics and nonhomogeneous-Poisson-process software reliability models." IEEE Transactions on Software Engineering 15, no. 11 (1989): 1485–90. http://dx.doi.org/10.1109/32.41340.

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43

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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44

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

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45

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

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46

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

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47

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

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48

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

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49

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

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50

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

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