Articoli di riviste: "Proportion inference"

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Xu, XingZhong, e Fang Liu. "Statistical inference on mixing proportion". Science in China Series A: Mathematics 51, n. 9 (13 giugno 2008): 1593–608. http://dx.doi.org/10.1007/s11425-008-0016-0.

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Subbiah, M., e V. Rajeswaran. "proportion: A comprehensive R package for inference on single Binomial proportion and Bayesian computations". SoftwareX 6 (2017): 36–41. http://dx.doi.org/10.1016/j.softx.2017.01.001.

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Mielke, Paul W., e Kenneth J. Berry. "Nonasymptotic Inferences Based on Cochran's Q Test". Perceptual and Motor Skills 81, n. 1 (agosto 1995): 319–22. http://dx.doi.org/10.2466/pms.1995.81.1.319.

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Abstract (sommario):

A nonasymptotic inference procedure for Cochran's Q test for the equality of matched proportions is presented. The nonasymptotic method provides improvement over the asymptotic method when there is a small number of subjects and/or a relatively small proportion of successes for subjects.

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Garthwaite, Paul H., e John R. Crawford. "Inference for a binomial proportion in the presence of ties". Journal of Applied Statistics 38, n. 9 (settembre 2011): 1915–34. http://dx.doi.org/10.1080/02664763.2010.537649.

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Mohamed, Nuri Eltabit. "Approximate confidence intervals for the Population Proportion based on linear model". Al-Mukhtar Journal of Basic Sciences 21, n. 2 (5 maggio 2024): 58–62. http://dx.doi.org/10.54172/rtn9cg93.

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When the linear model errors are non-normal, one might be interested in making inference concerning proportion. The goal of this article is to construct approximate confidence intervals for the proportion founded on the supposed linear model which cover a true value of a proportion that close to a specific nominal value of the level of significant.

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Frey, Jesse. "Bayesian Inference on a Proportion Believed to be a Simple Fraction". American Statistician 61, n. 3 (agosto 2007): 201–6. http://dx.doi.org/10.1198/000313007x222866.

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Nandram, Balgobin, Dilli Bhatta, Dhiman Bhadra e Gang Shen. "Bayesian predictive inference of a finite population proportion under selection bias". Statistical Methodology 11 (marzo 2013): 1–21. http://dx.doi.org/10.1016/j.stamet.2012.08.003.

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Khan, K. Daniel, e J. A. A. Vargas-Guzmán. "Facies Proportions From the Inference of Nonlinear Conditional Beta-Field Parameters". SPE Journal 18, n. 06 (6 maggio 2013): 1033–42. http://dx.doi.org/10.2118/163147-pa.

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Summary Conditional beta distributions are proposed with examples to evaluate the probability of intercepting specific proportions of target rocks in well planning. Geological facies or rock-type proportions are random variables pk(x) at each location, x. This paper recalls and further demonstrates that facies proportions can be modeled by local beta distributions. However, the highly variable shapes of the conditional probability-density functions (PDFs) for the random variables in the field lead to complex nonstationarity and nonlinearity issues. A practical and robust approach is to transform the proportion random variables to Gaussian variables, thus enabling the use of classical geostatistics. Although a direct relationship between Gaussian and beta random variables appears intractable, a suitable transformation that involves second-order expectations of proportions is proposed. The conditional parameters of the beta variables are recovered from kriging estimates after back transformation to proportions through Riemann sums.

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Hilbig, Benjamin E., e Rüdiger F. Pohl. "Recognizing Users of the Recognition Heuristic". Experimental Psychology 55, n. 6 (gennaio 2008): 394–401. http://dx.doi.org/10.1027/1618-3169.55.6.394.

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The recognition heuristic is hypothesized to be a frugal inference strategy assuming that inferences are based on the recognition cue alone. This assumption, however, has been questioned by existing research. At the same time most studies rely on the proportion of choices consistent with the heuristic as a measure of its use which may not be fully appropriate. In this study, we propose an index to identify true users of the heuristic contrasting them to decision makers who incorporate further knowledge beyond recognition. The properties and the applicability of the proposed index are investigated in the reanalyses of four published experiments and corroborated by a new study drawn up to rectify the shortcomings of the reanalyzed experiments. Applying the proposed index to explore the influence of knowledge we found that participants who were more knowledgeable made use of the information available to them and achieved the highest proportion of correct inferences.

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Rahardja, Dewi, e Yan D. Zhao. "Bayesian inference of a binomial proportion using one-sample misclassified binary data". Model Assisted Statistics and Applications 7, n. 1 (16 febbraio 2012): 17–22. http://dx.doi.org/10.3233/mas-2011-0197.

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Heller, Glenn, e Jing Qin. "Pairwise Rank-Based Likelihood for Estimation and Inference on the Mixture Proportion". Biometrics 57, n. 3 (settembre 2001): 813–17. http://dx.doi.org/10.1111/j.0006-341x.2001.00813.x.

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Miles, Caleb H., Maya Petersen e Mark J. van der Laan. "Causal inference when counterfactuals depend on the proportion of all subjects exposed". Biometrics 75, n. 3 (3 aprile 2019): 768–77. http://dx.doi.org/10.1111/biom.13034.

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Nandram, Balgobin. "Bayesian Predictive Inference of a Proportion Under a Twofold Small-Area Model". Journal of Official Statistics 32, n. 1 (1 marzo 2016): 187–208. http://dx.doi.org/10.1515/jos-2016-0009.

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Abstract We extend the twofold small-area model of Stukel and Rao (1997; 1999) to accommodate binary data. An example is the Third International Mathematics and Science Study (TIMSS), in which pass-fail data for mathematics of students from US schools (clusters) are available at the third grade by regions and communities (small areas). We compare the finite population proportions of these small areas. We present a hierarchical Bayesian model in which the firststage binary responses have independent Bernoulli distributions, and each subsequent stage is modeled using a beta distribution, which is parameterized by its mean and a correlation coefficient. This twofold small-area model has an intracluster correlation at the first stage and an intercluster correlation at the second stage. The final-stage mean and all correlations are assumed to be noninformative independent random variables. We show how to infer the finite population proportion of each area. We have applied our models to synthetic TIMSS data to show that the twofold model is preferred over a onefold small-area model that ignores the clustering within areas. We further compare these models using a simulation study, which shows that the intracluster correlation is particularly important.

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Terpstra, Jeff T., e Zachary A. Miller. "Exact Inference for a Population Proportion Based on a Ranked Set Sample". Communications in Statistics - Simulation and Computation 35, n. 1 (gennaio 2006): 19–26. http://dx.doi.org/10.1080/03610910500416124.

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Nandram, B., e J. Sedransk. "Bayesian Predictive Inference for a Finite Population Proportion: Two-Stage Cluster Sampling". Journal of the Royal Statistical Society: Series B (Methodological) 55, n. 2 (gennaio 1993): 399–408. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01910.x.

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Miller, T. J., e J. R. Skalski. "Integrating design- and model-based inference to estimate length and age composition in North Pacific longline catches". Canadian Journal of Fisheries and Aquatic Sciences 63, n. 5 (1 maggio 2006): 1092–114. http://dx.doi.org/10.1139/f06-022.

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Age and size structure are attributes of fishery stocks important for predicting future productivity. As such, estimating age and length composition of catches has long been an important fisheries management activity. Many observer programs sample catches to obtain length measurements and otoliths (or other structures for ageing) from targeted species. In North Pacific groundfish fisheries, observers collect these data through a stratified multiphase sampling design. Sampling variance and covariance estimates for catch- or proportions-at-length or -age that reflect the randomization inherent in the sampling design provide important measures of uncertainty that correspond to measurement error components in length- or age-structured stock assessment models. We compare sampling variances and covariances of Pacific cod (Gadus macrocephalus) proportions-at-length and sablefish (Anoplopoma fimbria) proportions-at-age with those provided by the overdispersed multinomial model sometimes used in these assessment models. For example, the sampling variance estimates for 2002 Pacific cod proportion-at-length estimates in the Bering Sea Aleutian Islands are at most 13% of the variances provided by multinomial and square-root sample size assumptions. Furthermore, some proportion estimates are positively correlated, whereas only negative correlation occurs with the multinomial distribution.

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Kang, Moonsu, e Jaewon Lee. "Bayesian inference for the proportion of true null hypotheses using minimum Hellinger distance". Journal of Statistical Planning and Inference 142, n. 4 (aprile 2012): 820–25. http://dx.doi.org/10.1016/j.jspi.2011.10.001.

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Zhang, Shiwen, Yingying Xing, Jian Lu e H. Michael Zhang. "Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System". Journal of Advanced Transportation 2019 (29 dicembre 2019): 1–13. http://dx.doi.org/10.1155/2019/3879385.

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The truck operation of freeway has an impact on traffic safety. In particular, the gradually increasing in truck proportion will inevitably affect the freeway traffic operation of different traffic volume. In this paper, VISSIM simulation is used to supply the field data and orthogonal experimental is designed for calibrate the simulation data. Then, SSAM modeling is combined to analyze the impact of truck proportion on traffic flow parameters and traffic conflicts. The serious and general conflict prediction model based on the Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to determine the impact of the truck proportion on freeway traffic safety. The results show that when the truck proportion is around 0.4 under 3200 veh/h and 0.6 under 2600 veh/h, there are more traffic conflicts and the number of serious conflicts is more than the number of general conflicts, which also reflect the relationship between truck proportion and traffic safety. Under 3000 veh/h, travel time and average delay increasing while mean speed and mean speed of small car decreases with truck proportion increases. The mean time headway rises largely with the truck proportion increasing above 3000 veh/h. The speed standard deviation increases initially and then fall with truck proportion increasing. The lane-changing decreases while truck proportion increasing. In addition, ANFIS can accurately determine the impact of truck proportion on traffic conflicts under different traffic volume, and also validate the learning ability of ANFIS.

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Tataru, Paula, Maéva Mollion, Sylvain Glémin e Thomas Bataillon. "Inference of Distribution of Fitness Effects and Proportion of Adaptive Substitutions from Polymorphism Data". Genetics 207, n. 3 (25 settembre 2017): 1103–19. http://dx.doi.org/10.1534/genetics.117.300323.

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Choi, Sangbum, Xuelin Huang, Janice N. Cormier e Kjell A. Doksum. "A semiparametric inverse-Gaussian model and inference for survival data with a cured proportion". Canadian Journal of Statistics 42, n. 4 (19 settembre 2014): 635–49. http://dx.doi.org/10.1002/cjs.11226.

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Messam, Locksley L. McV, Hsin-Yi Weng, Nicole W. Y. Rosenberger, Zhi Hao Tan, Stephanie D. M. Payet e Mahishi Santbakshsing. "The reporting of p values, confidence intervals and statistical significance in Preventive Veterinary Medicine (1997–2017)". PeerJ 9 (24 novembre 2021): e12453. http://dx.doi.org/10.7717/peerj.12453.

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Background Despite much discussion in the epidemiologic literature surrounding the use of null hypothesis significance testing (NHST) for inferences, the reporting practices of veterinary researchers have not been examined. We conducted a survey of articles published in Preventive Veterinary Medicine, a leading veterinary epidemiology journal, aimed at (a) estimating the frequency of reporting p values, confidence intervals and statistical significance between 1997 and 2017, (b) determining whether this varies by article section and (c) determining whether this varies over time. Methods We used systematic cluster sampling to select 985 original research articles from issues published in March, June, September and December of each year of the study period. Using the survey data analysis menu in Stata, we estimated overall and yearly proportions of article sections (abstracts, results-texts, results-tables and discussions) reporting p values, confidence intervals and statistical significance. Additionally, we estimated the proportion of p values less than 0.05 reported in each section, the proportion of article sections in which p values were reported as inequalities, and the proportion of article sections in which confidence intervals were interpreted as if they were significance tests. Finally, we used Generalised Estimating Equations to estimate prevalence odds ratios and 95% confidence intervals, comparing the occurrence of each of the above-mentioned reporting elements in one article section relative to another. Results Over the 20-year period, for every 100 published manuscripts, 31 abstracts (95% CI [28–35]), 65 results-texts (95% CI [61–68]), 23 sets of results-tables (95% CI [20–27]) and 59 discussion sections (95% CI [56–63]) reported statistical significance at least once. Only in the case of results-tables, were the numbers reporting p values (48; 95% CI [44–51]), and confidence intervals (44; 95% CI [41–48]) higher than those reporting statistical significance. We also found that a substantial proportion of p values were reported as inequalities and most were less than 0.05. The odds of a p value being less than 0.05 (OR = 4.5; 95% CI [2.3–9.0]) or being reported as an inequality (OR = 3.2; 95% CI [1.3–7.6]) was higher in the abstracts than in the results-texts. Additionally, when confidence intervals were interpreted, on most occasions they were used as surrogates for significance tests. Overall, no time trends in reporting were observed for any of the three reporting elements over the study period. Conclusions Despite the availability of superior approaches to statistical inference and abundant criticism of its use in the epidemiologic literature, NHST is substantially the most common means of inference in articles published in Preventive Veterinary Medicine. This pattern has not changed substantially between 1997 and 2017.

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Isomura, Takuya, Thomas Parr e Karl Friston. "Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence". Neural Computation 31, n. 12 (dicembre 2019): 2390–431. http://dx.doi.org/10.1162/neco_a_01239.

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To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an interesting problem: When receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning of the processes that generate sensory inputs (e.g., listening and understanding) and reproduction of those inputs (e.g., talking or singing), under multiple generative models. This is based on recent advances in theoretical neurobiology—namely, active inference and post hoc (online) Bayesian model selection. In brief, this scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability that each model could have generated the input (i.e., model evidence). The proposed scheme is demonstrated using a series of (real zebra finch) birdsongs, where each song is generated by several different birds. The scheme is implemented using physiologically plausible models of birdsong production. We show that generalized Bayesian filtering, combined with model selection, leads to successful learning across generative models, each possessing different parameters. These results highlight the utility of having multiple internal models when making inferences in social environments with multiple sources of sensory information.

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Masten, Matthew A., e Alexandre Poirier. "Inference on breakdown frontiers". Quantitative Economics 11, n. 1 (2020): 41–111. http://dx.doi.org/10.3982/qe1288.

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Given a set of baseline assumptions, a breakdown frontier is the boundary between the set of assumptions which lead to a specific conclusion and those which do not. In a potential outcomes model with a binary treatment, we consider two conclusions: First, that ATE is at least a specific value (e.g., nonnegative) and second that the proportion of units who benefit from treatment is at least a specific value (e.g., at least 50%). For these conclusions, we derive the breakdown frontier for two kinds of assumptions: one which indexes relaxations of the baseline random assignment of treatment assumption, and one which indexes relaxations of the baseline rank invariance assumption. These classes of assumptions nest both the point identifying assumptions of random assignment and rank invariance and the opposite end of no constraints on treatment selection or the dependence structure between potential outcomes. This frontier provides a quantitative measure of the robustness of conclusions to relaxations of the baseline point identifying assumptions. We derive N ‐consistent sample analog estimators for these frontiers. We then provide two asymptotically valid bootstrap procedures for constructing lower uniform confidence bands for the breakdown frontier. As a measure of robustness, estimated breakdown frontiers and their corresponding confidence bands can be presented alongside traditional point estimates and confidence intervals obtained under point identifying assumptions. We illustrate this approach in an empirical application to the effect of child soldiering on wages. We find that sufficiently weak conclusions are robust to simultaneous failures of rank invariance and random assignment, while some stronger conclusions are fairly robust to failures of rank invariance but not necessarily to relaxations of random assignment.

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Dokka, Kalpana, Hyeshin Park, Michael Jansen, Gregory C. DeAngelis e Dora E. Angelaki. "Causal inference accounts for heading perception in the presence of object motion". Proceedings of the National Academy of Sciences 116, n. 18 (17 aprile 2019): 9060–65. http://dx.doi.org/10.1073/pnas.1820373116.

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The brain infers our spatial orientation and properties of the world from ambiguous and noisy sensory cues. Judging self-motion (heading) in the presence of independently moving objects poses a challenging inference problem because the image motion of an object could be attributed to movement of the object, self-motion, or some combination of the two. We test whether perception of heading and object motion follows predictions of a normative causal inference framework. In a dual-report task, subjects indicated whether an object appeared stationary or moving in the virtual world, while simultaneously judging their heading. Consistent with causal inference predictions, the proportion of object stationarity reports, as well as the accuracy and precision of heading judgments, depended on the speed of object motion. Critically, biases in perceived heading declined when the object was perceived to be moving in the world. Our findings suggest that the brain interprets object motion and self-motion using a causal inference framework.

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Wu, Xiaoshu, Jun Cui, Dandan Niu, Zhipeng Ren e Yong Wei. "Compositional Variation of the Dayside Martian Ionosphere: Inference from Photochemical Equilibrium Computations". Astrophysical Journal 923, n. 1 (1 dicembre 2021): 29. http://dx.doi.org/10.3847/1538-4357/ac24fe.

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Abstract The Martian ionosphere plays a crucial role in driving the interactions of the planet with solar photons and solar wind particles. The variations of the dayside Martian ionosphere with several controlling factors, including the solar extreme ultraviolet radiation, the background atmosphere, and the underlying thermal structure, have been the topic of extensive research in terms of electron distribution. In contrast, how the ionospheric composition varies has not been systematically investigated, a topic that is attempted in this study based on photochemical equilibrium computations performed at 100–200 km altitude, including a large number of reactions. Our calculations reveal the following compositional variations as natural outcomes of the ionospheric chemistry on Mars. (1) With increasing solar irradiance, the proportions of the majority of nonterminal ions are enhanced at the expense of reduced proportions of terminal ions, including O 2 + , HCO+, NO+, and H3O+. (2) At high electron temperatures, the proportion of NO+ is modestly reduced, whereas the proportions of the other species are nearly unaffected. (3) The response of the ionospheric composition to the upper atmospheric composition is complicated, showing the strong negative response of many trace ions to ambient CO2, O, and CO, as well as the strong positive response of protonated ions to H2, nitrogen-bearing ions to N and N2, water-group ions to H2O, and HO 2 + to O2. As an application of the model results, the recent ion measurements made on board the Mars Atmosphere and Volatile Evolution are used to provide hints about the realistic composition of the Martian upper atmosphere.

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Beyhum, Jad. "Inference robust to outliers with ℓ1-norm penalization". ESAIM: Probability and Statistics 24 (2020): 688–702. http://dx.doi.org/10.1051/ps/2020014.

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This paper considers inference in a linear regression model with outliers in which the number of outliers can grow with sample size while their proportion goes to 0. We propose a square-root lasso ℓ1-norm penalized estimator. We derive rates of convergence and establish asymptotic normality. Our estimator has the same asymptotic variance as the OLS estimator in the standard linear model. This enables us to build tests and confidence sets in the usual and simple manner. The proposed procedure is also computationally advantageous, it amounts to solving a convex optimization program. Overall, the suggested approach offers a practical robust alternative to the ordinary least squares estimator.

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Inoue, T., R. Manabe, A. Murayama e H. Koizumi. "THE EFFECT OF CULTURE-SPECIFIC DIFFERENCES IN URBAN STREETSCAPES ON THE INFERENCE ACCURACY OF DEEP LEARNING MODELS". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W3-2022 (14 ottobre 2022): 73–80. http://dx.doi.org/10.5194/isprs-annals-x-4-w3-2022-73-2022.

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Abstract. Owing to the increasing focus on places in urban planning and design, methods to evaluate the quality and value of urban places is crucially needed. Many studies use deep learning models to identify the proportion and composition of landscape elements in images for evaluation. The accuracy of semantic segmentation achieved with such models is often validated using Cityscapes, a street-level image dataset taken from German cities. However, few studies have quantitatively revealed the inference accuracy decrease caused by culture-specific characteristics of streetscapes.In this study, we calculated by-class intersection over union (IoU) and newly-defined indices of false inferences to demonstrate how and to what extent deep learning models can infer each landscape element falsely when applied to Japanese street-level images. Our analysis revealed that certain landscape elements are more difficult to infer correctly based on specific causes, such as their appearances in images and unique characteristics of the fixed physical configuration of Japanese streets. By applying the false inference categorization framework presented in this study, researchers can adjust their approaches considering two aspects: a decrease in inference accuracies of deep learning models and the impact of culture-specific characteristics of streetscapes on people's perception and valuation of urban places. Based on the results and analyses, a future research direction is to develop and implement more accurate image recognition models considering culture-specific characteristics to understand people's perceptions of urban spaces and assess the value of urban places by using the big data including street-level images.

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Piao, Jin, Jing Ning, Christina D. Chambers e Ronghui Xu. "Semiparametric model and inference for spontaneous abortion data with a cured proportion and biased sampling". Biostatistics 19, n. 1 (18 maggio 2017): 54–70. http://dx.doi.org/10.1093/biostatistics/kxx024.

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Romero-Leiton, Jhoana P., Kernel Prieto, Daniela Reyes-Gonzalez e Ayari Fuentes-Hernandez. "Optimal control and Bayes inference applied to complex microbial communities". Mathematical Biosciences and Engineering 19, n. 7 (2022): 6860–82. http://dx.doi.org/10.3934/mbe.2022323.

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<abstract><p>Interactions between species are essential in ecosystems, but sometimes competition dominates over mutualism. The transition between mutualism-competition can have several implications and consequences, and it has hardly been studied in experimental settings. This work studies the mutualism between cross-feeding bacteria in strains that supply an essential amino acid for their mutualistic partner when both strains are exposed to antimicrobials. When the strains are free of antimicrobials, we found that, depending on the amount of amino acids freely available in the environment, the strains can exhibit extinction, mutualism, or competition. The availability of resources modulates the behavior of both species. When the strains are exposed to antimicrobials, the population dynamics depend on the proportion of bacteria resistant to the antimicrobial, finding that the extinction of both strains is eminent for low levels of the resource. In contrast, competition between both strains continues for high levels of the resource. An optimal control problem was then formulated to reduce the proportion of resistant bacteria, which showed that under cooperation, both strains (sensitive and resistant) are immediately controlled, while under competition, only the density of one of the strains is decreased. In contrast, its mutualist partner with control is increased. Finally, using our experimental data, we did parameters estimation in order to fit our mathematical model to the experimental data.</p></abstract>

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Zhao, Xuebin, Hong Chen, Yingjie Wang, Weifu Li, Tieliang Gong, Yulong Wang e Feng Zheng. "Error-Based Knockoffs Inference for Controlled Feature Selection". Proceedings of the AAAI Conference on Artificial Intelligence 36, n. 8 (28 giugno 2022): 9190–98. http://dx.doi.org/10.1609/aaai.v36i8.20905.

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Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the procedure of model-X knockoffs depends heavily on the coefficient-based feature importance and only concerns the control of false discovery rate (FDR). To further improve its adaptivity and flexibility, in this paper, we propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees on controlling false discovery proportion (FDP), FDR, or k-familywise error rate (k-FWER). Empirical evaluations demonstrate the competitive performance of our approach on both simulated and real data.

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Ge, Yongchao, e Xiaochun Li. "Control of the False Discovery Proportion for Independently Tested Null Hypotheses". Journal of Probability and Statistics 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/320425.

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Consider the multiple testing problem of testingmnull hypothesesH1,…,Hm, among whichm0hypotheses are truly null. Given theP-values for each hypothesis, the question of interest is how to combine theP-values to find out which hypotheses are false nulls and possibly to make a statistical inference onm0. Benjamini and Hochberg proposed a classical procedure that can control the false discovery rate (FDR). The FDR control is a little bit unsatisfactory in that it only concerns the expectation of the false discovery proportion (FDP). The control of the actual random variable FDP has recently drawn much attention. For any level1−α, this paper proposes a procedure to construct an upper prediction bound (UPB) for the FDP for a fixed rejection region. When1−α=50%, our procedure is very close to the classical Benjamini and Hochberg procedure. Simultaneous UPBs for all rejection regions' FDPs and the upper confidence bound for the unknownm0are presented consequently. This new proposed procedure works for finite samples and hence avoids the slow convergence problem of the asymptotic theory.

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Rahardja, Dewi. "Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample". Stats 2, n. 1 (11 febbraio 2019): 111–20. http://dx.doi.org/10.3390/stats2010009.

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We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

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Zhang, Xiangpo, Jianzhong Shang, Xun Chen, Chunhua Zhang e Yashun Wang. "Lifetime assessment method for products with dependent competing failures based on copula theory". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, n. 9 (28 ottobre 2013): 1622–33. http://dx.doi.org/10.1177/0954406213508756.

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Based on copula theory and methods, we construct the dependent relationship between the margin distribution functions of the competing failure modes and their joint distribution function through copula function. With the dependent relationship, we study statistical inference method of the life testing with dependent competing failure modes, and found the maximum likelihood estimation (MLE) model for the parameter estimations to evaluate the lifetime of the products. The results and analysis of case studies prove that sample size, proportion of censored samples, proportion of failure samples with masked failure mode, and copula model types have great impact on the accuracy of the lifetime assessment of the products with dependent competing failure modes. And with appropriate test data and right copula modes, method developed in this paper has very good accuracy for the lifetime assessment with dependent competing failure modes. It provides an effective and accurate way to solve the problems of statistical inference of life testing with dependent competing failure modes, and also an accurate way of lifetime assessment for products.

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Zhang, B. T., e Y. Pi. "Robust fractional order proportion-plus-differential controller based on fuzzy inference for permanent magnet synchronous motor". IET Control Theory & Applications 6, n. 6 (2012): 829. http://dx.doi.org/10.1049/iet-cta.2011.0412.

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McRoberts, Ronald E. "Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data". Remote Sensing of Environment 114, n. 5 (maggio 2010): 1017–25. http://dx.doi.org/10.1016/j.rse.2009.12.013.

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36

Dewi, Oktriviani, e Kristina Wijayanti. "Mathematical Critical Thinking Ability in Terms of Students Learning Motivation in Probing Prompting Learning". Unnes Journal of Mathematics Education 11, n. 2 (31 agosto 2022): 183–90. http://dx.doi.org/10.15294/ujme.v11i2.59937.

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Abstract (sommario):

This research aimed (1) to analyze the effectiveness of Probing Prompting learning on students' mathematical critical thinking skills and (2) to describe mathematical critical thinking skills in terms of students' learning motivation in learning with the Probing Prompting model. This research method was a combination research method (mixed method) with the type of Explanatory Sequential Design. The results showed that: (1) the mathematical critical thinking ability of the Probing Prompting class achieved classical completeness; (2) the proportion of mathematical critical thinking skills in the Probing Prompting class is more than the proportion of mathematical critical thinking skills in the Problem Based Learning class; (3) the average mathematical critical thinking ability of the Probing Prompting class is more than the average mathematical critical thinking ability of the Problem Based Learning class. Subjects with high learning motivation are able to meet all indicators of mathematical critical thinking skills, namely indicators of clarification, assessment, inference, and strategies. Subjects with moderate learning motivation tend to be able to fulfill the clarification indicators; less able to meet the assessment indicators; tend to be able to meet the inference indicators; and able to meet the indicators of strategies. Subjects with low learning motivation tend to be able to fulfill the indicators of clarification, less able to meet the assessment indicators, less able to meet the inference indicators, and tend to be able to meet the indicators of strategies.

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Maddison, Wayne P. "PhyIN: trimming alignments by phylogenetic incompatibilities among neighbouring sites". PeerJ 12 (5 dicembre 2024): e18504. https://doi.org/10.7717/peerj.18504.

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In phylogenomics, regions of low alignment reliability and high noise are typically trimmed from multiple sequence alignments before they are used in phylogenetic inference. I introduce a new trimming tool, PhyIN, which deletes regions in which a large proportion of sites (characters) have conflicting phylogenetic signal. It does not require inference of a phylogenetic tree, as it finds neighbouring characters that cannot agree on any possible tree. In phylogenomic data of ultraconserved elements (UCE), PhyIN effectively finds the boundaries between chaotic (conflicted) and orderly regions of alignments with data for only a single locus. Its ability to work on individual loci allows it to preserve discord between gene trees and species trees.

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Goeman, Jelle J., Rosa J. Meijer, Thijmen J. P. Krebs e Aldo Solari. "Simultaneous control of all false discovery proportions in large-scale multiple hypothesis testing". Biometrika 106, n. 4 (23 settembre 2019): 841–56. http://dx.doi.org/10.1093/biomet/asz041.

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Summary Closed testing procedures are classically used for familywise error rate control, but they can also be used to obtain simultaneous confidence bounds for the false discovery proportion in all subsets of the hypotheses, allowing for inference robust to post hoc selection of subsets. In this paper we investigate the special case of closed testing with Simes local tests. We construct a novel fast and exact shortcut and use it to investigate the power of this approach when the number of hypotheses goes to infinity. We show that if a minimal level of signal is present, the average power to detect false hypotheses at any desired false discovery proportion does not vanish. Additionally, we show that the confidence bounds for false discovery proportion are consistent estimators for the true false discovery proportion for every nonvanishing subset. We also show close connections between Simes-based closed testing and the procedure of Benjamini and Hochberg.

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Audemard, Julien. "Understanding vote transfers in two-round elections without resorting to declared data. The contribution of ecological inference, consolidated with factual information from a case study of the 2014 municipal elections in Montpellier". Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 162, n. 1 (aprile 2024): 13–34. http://dx.doi.org/10.1177/07591063241236062.

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In electoral sociology, the analysis of vote transfers has traditionally depended on individual data obtained from surveys. Because such data suffer from a significant amount of declaration and memory bias, replacing them with the electoral statistics available down to polling station level may be advantageous. Recent developments in models of ecological inference allow us to use these aggregated data to establish estimates of vote transfers while minimising the risk of ecological error. Nonetheless, the reliability of ecological inference models for estimating vote transfers has thus far received little attention in the form of empirical evaluations. The purpose of the present article is to cast light on this blind spot by analysing a model for predicting electoral volatility in a two-round election, namely the municipal election held in Montpellier in 2014. What makes this approach original is its use of observed information – the proportion of non-voters in both rounds – first to compare this data with the estimates produced by the model and then to integrate it as a modelling parameter to measure its impact on estimated vote allocation. This analysis reveals that the initial model's results are relatively reliable regarding the known parameter, although they slightly overestimate its amplitude and underestimate its variability. The model that integrates information regarding the proportion of consistent non-voters yields estimates close to those obtained using the “raw” model. In terms of interpretative capacity, the value added by integrating this additional information is, therefore, slight. However, integrating the information does make it possible to establish narrower density intervals, reducing the uncertainty associated with the interpretation of the other parameters, particularly proportions associated with candidates who received few votes in the first round.

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Bugni, Federico A., Ivan A. Canay e Azeem M. Shaikh. "Inference under covariate‐adaptive randomization with multiple treatments". Quantitative Economics 10, n. 4 (2019): 1747–85. http://dx.doi.org/10.3982/qe1150.

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This paper studies inference in randomized controlled trials with covariate‐adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control. As in Bugni, Canay, and Shaikh (2018), covariate‐adaptive randomization refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. Importantly, in contrast to Bugni, Canay, and Shaikh (2018), we not only allow for multiple treatments, but further allow for the proportion of units being assigned to each of the treatments to vary across strata. We first study the properties of estimators derived from a “fully saturated” linear regression, that is, a linear regression of the outcome on all interactions between indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity‐consistent estimator of the asymptotic variance are invalid in the sense that they may have limiting rejection probability under the null hypothesis strictly greater than the nominal level; on the other hand, tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact in the sense that they have limiting rejection probability under the null hypothesis equal to the nominal level. For the special case in which the target proportion of units being assigned to each of the treatments does not vary across strata, we additionally consider tests based on estimators derived from a linear regression with “strata fixed effects,” that is, a linear regression of the outcome on indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity‐consistent estimator of the asymptotic variance are conservative in the sense that they have limiting rejection probability under the null hypothesis no greater than and typically strictly less than the nominal level, but tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact, thereby generalizing results in Bugni, Canay, and Shaikh (2018) for the case of a single treatment to multiple treatments. A simulation study and an empirical application illustrate the practical relevance of our theoretical results.

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Jing, Guo Lin, Wen Ting Du, Xiang Chen e Huan Yi. "Prediction Model in Electrodialysis Process Based on ANFIS". Advanced Materials Research 268-270 (luglio 2011): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.332.

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Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to predict separation percent(SP) of NaCl solution as a function of concentration, temperature, flow rate and voltage. Besides, in the MATLAB, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically. We obtained fitted values of SP by ANFIS. Then, we studied these influencing factors on fitted values of SP. Finally, we draw a conclusion that SP is in direct proportion to temperature and voltage, but in inverse proportion to concentration and flow rate.

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Hong, Han, Michael P. Leung e Jessie Li. "Inference on finite-population treatment effects under limited overlap". Econometrics Journal 23, n. 1 (2 settembre 2019): 32–47. http://dx.doi.org/10.1093/ectj/utz017.

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Summary This paper studies inference on finite-population average and local average treatment effects under limited overlap, meaning that some strata have a small proportion of treated or untreated units. We model limited overlap in an asymptotic framework, sending the propensity score to zero (or one) with the sample size. We derive the asymptotic distribution of analogue estimators of the treatment effects under two common randomization schemes: conditionally independent and stratified block randomization. Under either scheme, the limit distribution is the same and conventional standard error formulas remain asymptotically valid, but the rate of convergence is slower the faster the propensity score degenerates. The practical import of these results is two-fold. When overlap is limited, standard methods can perform poorly in smaller samples, as asymptotic approximations are inadequate owing to the slower rate of convergence. However, in larger samples, standard methods can work quite well even when the propensity score is small.

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Hackman, Jada, Carmen Sheppard, Jody Phelan, William Jones-Warner, Ben Sobkowiak, Sonal Shah, David Litt et al. "Phylogenetic inference of pneumococcal transmission from cross-sectional data, a pilot study". Wellcome Open Research 8 (6 ottobre 2023): 427. http://dx.doi.org/10.12688/wellcomeopenres.19219.1.

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Background: Inference on pneumococcal transmission has mostly relied on longitudinal studies which are costly and resource intensive. Therefore, we conducted a pilot study to test the ability to infer who infected whom from cross-sectional pneumococcal sequences using phylogenetic inference. Methods: Five suspected transmission pairs, for which there was epidemiological evidence of who infected whom, were selected from a household study. For each pair, Streptococcus pneumoniae full genomes were sequenced from nasopharyngeal swabs collected on the same day. The within-host genetic diversity of the pneumococcal population was used to infer the transmission direction and then cross-validated with the direction suggested by the epidemiological records. Results: The pneumococcal genomes clustered into the five households from which the samples were taken. The proportion of concordantly inferred transmission direction generally increased with increasing minimum genome fragment size and single nucleotide polymorphisms. We observed a larger proportion of unique polymorphic sites in the source bacterial population compared to that of the recipient in four of the five pairs, as expected in the case of a transmission bottleneck. The only pair that did not exhibit this effect was also the pair that had consistent discordant transmission direction compared to the epidemiological records suggesting potential misdirection as a result of false-negative sampling. Conclusions: This pilot provided support for further studies to test if the direction of pneumococcal transmission can be reliably inferred from cross-sectional samples if sequenced with sufficient depth and fragment length.

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Yuan, Ye, Carlos Cosme, Taylor Sterling Adams, Jonas Schupp, Koji Sakamoto, Nikos Xylourgidis, Matthew Ruffalo, Jiachen Li, Naftali Kaminski e Ziv Bar-Joseph. "CINS: Cell Interaction Network inference from Single cell expression data". PLOS Computational Biology 18, n. 9 (12 settembre 2022): e1010468. http://dx.doi.org/10.1371/journal.pcbi.1010468.

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Abstract (sommario):

Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.

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Hoidn, Oliver, Aashwin Ananda Mishra e Apurva Mehta. "Probabilistic Mixture Model-Based Spectral Unmixing". Applied Sciences 14, n. 11 (3 giugno 2024): 4836. http://dx.doi.org/10.3390/app14114836.

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Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in environment monitoring, geological exploration, etc. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most extant approaches assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, current non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference algorithm that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and the presence of non-isotropic Gaussian noise. As a direct impact of our methodology, access to reliable uncertainties on the unmixing solutions would enable robust solutions to noise, as well as informed decision-making for HSI applications and other unmixing problems.

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Moore, Hunter D., Andrew Stephens e William Scherer. "An Understanding of the Vulnerability of Datasets to Disparate Membership Inference Attacks". Journal of Cybersecurity and Privacy 2, n. 4 (14 dicembre 2022): 882–906. http://dx.doi.org/10.3390/jcp2040045.

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Recent efforts have shown that training data is not secured through the generalization and abstraction of algorithms. This vulnerability to the training data has been expressed through membership inference attacks that seek to discover the use of specific records within the training dataset of a model. Additionally, disparate membership inference attacks have been shown to achieve better accuracy compared with their macro attack counterparts. These disparate membership inference attacks use a pragmatic approach to attack individual, more vulnerable sub-sets of the data, such as underrepresented classes. While previous work in this field has explored model vulnerability to these attacks, this effort explores the vulnerability of datasets themselves to disparate membership inference attacks. This is accomplished through the development of a vulnerability-classification model that classifies datasets as vulnerable or secure to these attacks. To develop this model, a vulnerability-classification dataset is developed from over 100 datasets—including frequently cited datasets within the field. These datasets are described using a feature set of over 100 features and assigned labels developed from a combination of various modeling and attack strategies. By averaging the attack accuracy over 13 different modeling and attack strategies, the authors explore the vulnerabilities of the datasets themselves as opposed to a particular modeling or attack effort. The in-class observational distance, width ratio, and the proportion of discrete features are found to dominate the attributes defining dataset vulnerability to disparate membership inference attacks. These features are explored in deeper detail and used to develop exploratory methods for hardening these class-based sub-datasets against attacks showing preliminary mitigation success with combinations of feature reduction and class-balancing strategies.

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Bowman, Howard, Anna Bonkhoff, Tom Hope, Christian Grefkes e Cathy Price. "Inflated Estimates of Proportional Recovery From Stroke". Stroke 52, n. 5 (maggio 2021): 1915–20. http://dx.doi.org/10.1161/strokeaha.120.033031.

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Abstract (sommario):

The proportional recovery rule states that most survivors recover a fixed proportion (≈70%) of lost function after stroke. A strong (negative) correlation between the initial score and subsequent change (outcome minus initial; ie, recovery) is interpreted as empirical support for the proportional recovery rule. However, this rule has recently been critiqued, with a central observation being that the correlation of initial scores with change over time is confounded in the situations in which it is typically assessed. This critique has prompted reassessments of patients’ behavioral trajectory following stroke in 2 prominent papers. The first of these, by van der Vliet et al presented an impressive modeling of upper limb deficits following stroke, which avoided the confounded correlation of initial scores with change. The second by Kundert et al reassessed the value of the proportional recovery rule, as classically formulated as the correlation between initial scores and change. They argued that while effective prediction of recovery trajectories of individual patients is not supported by the available evidence, group-level inferences about the existence of proportional recovery are reliable. In this article, we respond to the van der Vliet and Kundert papers by distilling the essence of the argument for why the classic assessment of proportional recovery is confounded. In this respect, we reemphasize the role of mathematical coupling and compression to ceiling in the confounded nature of the correlation of initial scores with change. We further argue that this confound will be present for both individual-level and group-level inference. We then focus on the difficulties that can arise from ceiling effects, even when initial scores are not being correlated with change/recovery. We conclude by emphasizing the need for new techniques to analyze recovery after stroke that are not confounded in the ways highlighted here.

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Thorson, James T., Trevor A. Branch e Olaf P. Jensen. "Using model-based inference to evaluate global fisheries status from landings, location, and life history data". Canadian Journal of Fisheries and Aquatic Sciences 69, n. 4 (aprile 2012): 645–55. http://dx.doi.org/10.1139/f2012-016.

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Assessing fishery collapses worldwide is hindered by the lack of biomass data for most stocks, leading to the use of landings-based proxies or the assumption that existing stock assessments are globally representative. We argue that the use of sparse assessments to evaluate fishery status requires model-based inference because assessment availability varies spatially and temporally, and we derive a model that extrapolates from assessment results to available landings, life history, and location data. This model uses logistic regression to classify stocks into different prediction bins and estimates the probability of collapse in each using cross-validation. Results show that landings, life history, and location are informative to discriminate among different probabilities of collapse. We find little evidence that regions with fewer assessments have a greater proportion of collapsed stocks, while acknowledging weak inferential support regarding regions with one or fewer assessments. Our extrapolation suggests that 4.5%–6.5% of stocks defined by landings data are collapsed, but that this proportion is increasing. Finally, we propose a research agenda that combines stock assessment and landings databases while overcoming limitations in each.

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Nguyen, Dao Thi Anh, Insu Won, Kyusung Kim e Jangwoo Kwon. "A Development of Clinical Decision Support System for Video Head Impulse Test Based on Fuzzy Inference System". Journal of Sensors 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/7168524.

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Abstract (sommario):

This paper represents the clinical decision support system for video head impulse test (vHIT) based on fuzzy inference system. It examines the eye and head movement recorded by the eye movement tracking device, calculates the vestibulo-ocular reflex (VOR) gain, and applies fuzzy inference system to output the normality and artifact index of the test result. The position VOR gain and the proportion of covert and overt catch-up saccades (CUS) within the dataset are used as the input of the inference system. In addition, this system yields one more factor, the artifact index, which represents the current interference in the dataset. Data of fifteen vestibular neuritis patients and two of normal subjects were evaluated. The artifact index appears to be very high in the lesion side of vestibular neuritis (VN) patients, indicating highly theoretical contradictions, which are low gain but without CUS, or normal gain with the appearance of CUS. Both intact side and normal subject show high normality and low artifact index, even though the intact side has slightly lower normality and higher artifact index. In conclusion, this is a robust system, which is the first one that takes gain and CUS into account, to output not only the normality of the vHIT dataset, but also the artifacts.

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Williams, Thomas, James M. McCaw e James M. Osborne. "Spatial information allows inference of the prevalence of direct cell–to–cell viral infection". PLOS Computational Biology 20, n. 7 (23 luglio 2024): e1012264. http://dx.doi.org/10.1371/journal.pcbi.1012264.

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Abstract (sommario):

The role of direct cell–to–cell spread in viral infections—where virions spread between host and susceptible cells without needing to be secreted into the extracellular environment—has come to be understood as essential to the dynamics of medically significant viruses like hepatitis C and influenza. Recent work in both the experimental and mathematical modelling literature has attempted to quantify the prevalence of cell–to–cell infection compared to the conventional free virus route using a variety of methods and experimental data. However, estimates are subject to significant uncertainty and moreover rely on data collected by inhibiting one mode of infection by either chemical or physical factors, which may influence the other mode of infection to an extent which is difficult to quantify. In this work, we conduct a simulation–estimation study to probe the practical identifiability of the proportion of cell–to–cell infection, using two standard mathematical models and synthetic data that would likely be realistic to obtain in the laboratory. We show that this quantity cannot be estimated using non–spatial data alone, and that the collection of a data which describes the spatial structure of the infection is necessary to infer the proportion of cell–to–cell infection. Our results provide guidance for the design of relevant experiments and mathematical tools for accurately inferring the prevalence of cell–to–cell infection in in vitro and in vivo contexts.

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