Academic literature on the topic 'Estimation par interval'

Forest inventories provide predictions of stand means on a routine basis from models with auxiliary variables from remote sensing as predictors and response variables from field data. Many forest inventory sampling designs do not afford a direct estimation of the among-stand variance. As consequence, the confidence interval for a model-based prediction of a stand mean is typically too narrow. We propose a new method to compute (from empirical regression residuals) an among-stand variance under sample designs that stratify sample selections by an auxiliary variable, but otherwise do not allow a direct estimation of this variance. We test the method in simulated sampling from a complex artificial population with an age class structure. Two sampling designs are used (one-per-stratum, and quasi systematic), neither recognize stands. Among-stand estimates of variance obtained with the proposed method underestimated the actual variance by 30-50%, yet 95% confidence intervals for a stand mean achieved a coverage that was either slightly better or at par with the coverage achieved with empirical linear best unbiased estimates obtained under less efficient two-stage designs.

Genetic evaluation in the Italian Mediterranean Buffalo (IMB) traditionally relied on the BLUP method (best linear unbiased predictor), a mixed model system incorporating both random and fixed effects simultaneously. However, recent advancements in genome sequencing technologies have opened up the opportunity to incorporate genomic information into genetic evaluations. The ssGBLUP (single-step best linear unbiased predictor) has become the method par excellence. It replaces the traditional relationship matrix with one that combines pedigree and genomic relationships, allowing for the estimation of genetic values for non-genotyped animals. The findings of this study highlight how genomic selection enhances the precision of breeding values, facilitates greater genetic advancement and reduces the generation interval, ultimately enabling a rapid return on investment.

The development of digital technologies contributes to the growth of the use of nonparametric methods. The presented study proposes a methodology for the forecast assessment of prices in the residential real estate market, taking into account the possible determination of the direction of dynamics in the anticipation period based on the application of nonparametric Nadaraya – Watson estimation. The forecast model construction in the work is considered on the basis of the historically established tendency of determination of the price level of the primary or secondary housing market of Krasnodar Krai and does not take into account other factors. Par-ticular attention is paid to the application of the Chow test to identify the point in time at which there was a struc-tural shift, which allows to determine the period devoid of structural breaks for modeling the trend in order to determine the confidence interval of the forecast. The existing housing problem reflects the relevance of the development of methods for forecasting price dynamics in the housing market, and the absence of additional factors reduces the error and increases the forecast quality.

In this article, estimation of stress-strength reliability $\delta=P\left(Y<X\right)$ based on progressively first failure censored data from two independent inverse Weibull distributions with different shape and scale parameters is studied. Maximum likelihood estimator and asymptotic confidence interval of $\delta$ are obtained. Bayes estimator of $\delta$ under generalized entropy loss function using non-informative and gamma informative priors is derived. Also, highest posterior density credible interval of $\delta$ is constructed. Markov Chain Monte Carlo (MCMC) technique is used for Bayes computation. The performance of various estimation methods are compared by a Monte Carlo simulation study. Finally, a pair of real life data is analyzed to illustrate the proposed methods of estimation.

Due to the impossibility of directly measuring of critical headway, numerous methods and procedures have been developed for its estimation. This paper uses the maximum likelihood method for estimating the same at five roundabouts, and based on the obtained results and pairs of accepted and maximum rejected headways, several predictive models based on machine learning techniques were trained and tested. Therefore, the main goal of the research is to create a model for the prediction (classification) of the critical headway, which as inputs, i.e. independent variables use pairs - accepted and maximum rejected headways. The basic task of the model is to associate one of the previously estimated values of the critical headway with a given input pair of headways. The final predictive model is chosen from several offered alternatives based on the accuracy of the prediction. The results of training and testing of various models based on machine learning techniques in IBM SPSS Modeler software indicate that the highest prediction accuracy is shown by the C5 decision tree model (73.266%), which was trained and tested on an extended data set obtained by augmentation or data set augmentation (Data Augmentation - DA).

BackgroundHaving a score to assess the occurrence and severity of flares of knee or hip osteoarthritis (OA) to guide interventions is essential.ObjectivesTo compare different methods of constructing a composite score for the Flare-OA-16 self-reported questionnaire for measuring knee and hip OA flare, defined as a cluster of symptoms of sufficient duration and intensity to require initiation, change or increase in therapy [1].MethodsParticipants with a physician diagnosis of knee and hip OA completed a validated 16-item questionnaire [2,3] assessing five dimensions of flare in OA: pain, swelling, stiffness, psychological aspects, and consequences of symptoms, endorsed by OMERACT. Three estimation methods were compared: the score obtained i) by second-order confirmatory factor analysis (CFA) weighting the factor loadings in a linear combination of the five dimensions; ii) by logistic regression, modeling the probability of having a flare according to the participant’s self-report (yes/no); and iii) by Rasch method, using the average of the weighted scores from a Rasch model in each dimension. For the scores obtained by the three methods, the disordered items were modified, and then the scores were standardized on a scale from 0 to 10. The distribution (floor effect without flare (FF) and ceiling effect with flare (CF)) of the scores in each model was compared. The similarity between the scores was analyzed by intraclass correlation coefficient (ICC) and their performance were compared by areas under the ROC curves (AUC) and 95% confidence interval. The intra-score test-retest reliability at 15 days was assessed by ICC.ResultsIn a sample of 381 participants with complete questionnaires, 247 reported having a flare. With CFA, good fit indices (CFI=0.94; RMSEA=.08) justified the estimation of an overall score mean=3.90 (SD=2.79), with FF effect 27.6% and CF 2.0%. For the logistic regression estimation, the overall score was mean=6.48 (SD=3.13), with FF 0% and CF 34.0% effect. With the Rasch model, the composite score was mean=4.15 (SD=2.45), with FF 18.7% and CF 0% effect. Similarity analysis indicated a greater concordance between the CFA and Rasch scores (ICC=.99) than between the logistic regression score and the two others (ICC=.87 for each). The ROC curve indicated similar performance of the overall scores estimated by logistic model (AUC=.88 [.85-.92]), by CFA (AUC=.86 [.82-.90]) and by Rasch model (AUC=.86 [.82-.90]). The performance in terms of reproducibility was ICC=.84[.95-.90] for Rasch and CFA scores and ICC=.78[.66-86] for logistic model.ConclusionThis comparison of methods for constructing a global score for knee and hip OA flare explored three satisfactory alternatives. The second-order CFA confirmed the uniqueness of the flare construct measure, the logistic model had a slight superiority explained by the anchor variable used (patient-reported flare), and the Rasch model ensured that an interval scale was obtained for each dimension. The distribution of scores with the lowest combination of floor and ceiling effects was in favor of the Rasch model. The next step will be to document their respective performance in terms of sensitivity to change. A score obtained from the patient’s point of view can help increase the adherence to the prescribed treatment and help physicians to optimize the scheduling and delivery of medical consultation.References[1]Guillemin F., et al. Developing a Preliminary Definition and Domains of Flare in Knee and Hip Osteoarthritis (OA): Consensus Building of the Flare-in-OA OMERACT Group. J Rheumatol. 2019 Sep;46(9):1188–91.[2]Traore Y., et al. Development and validation of the Flare-OA questionnaire for measuring flare in knee and hip osteoarthritis. Osteoarthritis Cartilage. 2022 May;30(5):689–96.[3]Queiroga F., et al. Validation et réduction d’une échelle de mesure des poussées dans l’arthrose de la hanche et du genou par un modèle de Rasch. Rev Épidémiol Santé Publique. 2022 May;70:S70.AcknowledgementsWe acknowledge the participants of the study samples that were used in the study, without whom this research could not been possible.Financial supportThis work was supported partly by the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE.Disclosure of InterestsNone Declared.

L'objectif de cette étude est de générer automatiquement des cartes de l'étendue des zones inondées dans le district d'Abidjan et d’évaluer les dommages causés. L’approche méthodologique a consisté à cartographier l'étendue des zones inondées en utilisant une méthode de détection des changements basée sur les données Sentinel-1 (SAR) avant et après une crue spécifique. Ensuite, les différentes classes d'enjeux (telles que les cultures, les zones habitées, les bâtiments, les routes et la densité de la population) ont été extraites à partir de diverses sources de données gratuites. Puis la superficie des enjeux affectés a été évaluée, en superposant les classes d’enjeux sur les zones inondées. De plus, une interface web a été conçue à l'aide des packages de Google Earth Engine. Cette interface web offre à l'utilisateur la possibilité de visualiser l'étendue des zones inondées et les cartes des enjeux affectés, avec une estimation statistique, pour une date donnée dans l'intervalle allant de 2015 à la date actuelle. La cartographie des zones inondées à la date du 25 juin 2020 a révélé une superficie totale de 25219,23 hectares de zones inondées soit 11,50% de la superficie totale du District d’Abidjan. Une estimation des dégâts causés par cette crue indique que 22 307,53 hectares d'enjeux ont été affectés en moyenne, ce qui représente 88,45 % des zones inondées. Cette répartition se décompose en 13 538,49 hectares (soit 53,68 %) de terres agricoles touchées et 8 769,04 hectares (soit 34,77 %) de zones urbaines touchées, impactant en moyenne 35 065 personnes. Les résultats de cette étude ont permis de constater que la partie centrale de la zone d'étude, au-dessus de la lagune, présente le plus grand potentiel de risque d'inondation en raison de la morphologie du terrain et de la vulnérabilité élevée des zones construites qui occupent la plaine inondable. 
 The objective of this study is to automatically generate maps of the extent of flooded areas in the Abidjan district and assess the resulting damages. The methodological approach involved mapping the extent of flooded areas using a change detection method based on Sentinel-1 (SAR) data before and after a specific flood event. Subsequently, various classes of assets, such as crops, residential areas, buildings, roads, and population density, were extracted from various free data sources. The affected asset areas were then evaluated by overlaying the asset classes on the flooded areas. Furthermore, a web interface was designed using Google Earth Engine packages. This web interface allows users to visualize the extent of flooded areas and maps of the affected assets, along with statistical estimates, for a specific date within the interval from 2015 to the current date. Mapping of the flooded areas as of June 25, 2020, revealed a total area of 25219.23 hectares of flooded areas, representing 11.50% of the total area of the Abidjan District. An estimation of the damages caused by this flood indicates that, on average, 22307.53 hectares of assets were affected, accounting for 88.45% of the flooded areas. This distribution breaks down into 13538.49 hectares (53.68%) of affected agricultural lands and 8769.04 hectares (34.77%) of affected urban areas, impacting an average of 35,065 people. The study results revealed that the central part of the study area, located above the lagoon, presents the highest flood risk potential due to the terrain's morphology and the high vulnerability of built-up areas occupying the floodplain.

This article provides a review of the approaches to the construction of prediction intervals. To increase the reliability of prediction, point prediction methods are replaced by intervals for many aims. The interval prediction generates a pair as future values, including the upper and lower bounds for each prediction point. That is, according to historical data, which include a graph of a continuous and discrete function, two functions will be obtained as a prediction, i.e., the upper and lower bounds of estimation. In this case, the prediction boundaries should provide guaranteed probability of the location of the true values inside the boundaries found. The task of building a model from a time series is, by its very nature, incorrect. This means that there is an infinite set of equations whose solution is close to the time series for machine learning. In the case of interval use, the inverse problem of dynamics allows us to choose from the entire range of modeling methods, using confidence intervals as solutions, or intervals of a given width, or those chosen as a solution to the problems of multi-criteria optimization of the criteria for evaluating interval solutions. This article considers a geometric view of the prediction intervals and a new approach is given.

The problem of increasing the accuracy of determining the orientation of a spacecraft (SC) using a system of star trackers (ST) is considered. Methods are proposed that make it possible to use a joint field of view and refine the relative position of ST to improve the accuracy of orientation determination. The use of several star trackers leads to an increase in the angle between the directions to the stars into the joint field of view, which makes it possible to reduce the condition number of the matrices used in calculating the orientation parameters. The paper develops a combinatorial method for interval estimation of the SC orientation with an arbitrary number of star trackers. To calculate the ST orientation, a linear problem of interval estimation of the orthogonal orientation matrix for a sufficiently large number of stars is solved. The orientation quaternion is determined under the condition that the corresponding orientation matrix belongs to the obtained interval estimates. The case is considered when the a priori estimate of the mutual binding of star trackers can have an error comparable to or greater than the error in measuring the angular coordinates of stars. With inaccurately specified matrices of the mutual orientation of the star trackers, the errors in the mutual orientations of the STs are added to the errors of measuring the directions to the stars, which leads to an expansion of the uncertainty intervals of the right-hand sides of the system of linear algebraic equations used to determine the orientation parameters. A method is proposed for solving the problem of refining the mutual reference of the internal coordinate systems of a pair of ST as an independent task, after which the main problem of increasing the accuracy of spacecraft orientation is solved. The developed method and algorithms for solving such a complex problem are based on interval estimates of orthogonal orientation matrices. For additional narrowing of the intervals, the property of orthogonality of orientation matrices is used. The numerical simulation carried out made it possible to evaluate the advantages and disadvantages of each of the proposed methods.