63 resultados para statistical methods


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Power law distributions, a well-known model in the theory of real random variables, characterize a wide variety of natural and man made phenomena. The intensity of earthquakes, the word frequencies, the solar ares and the sizes of power outages are distributed according to a power law distribution. Recently, given the usage of power laws in the scientific community, several articles have been published criticizing the statistical methods used to estimate the power law behaviour and establishing new techniques to their estimation with proven reliability. The main object of the present study is to go in deep understanding of this kind of distribution and its analysis, and introduce the half-lives of the radioactive isotopes as a new candidate in the nature following a power law distribution, as well as a \canonical laboratory" to test statistical methods appropriate for long-tailed distributions.

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Our essay aims at studying suitable statistical methods for the clustering ofcompositional data in situations where observations are constituted by trajectories ofcompositional data, that is, by sequences of composition measurements along a domain.Observed trajectories are known as “functional data” and several methods have beenproposed for their analysis.In particular, methods for clustering functional data, known as Functional ClusterAnalysis (FCA), have been applied by practitioners and scientists in many fields. To ourknowledge, FCA techniques have not been extended to cope with the problem ofclustering compositional data trajectories. In order to extend FCA techniques to theanalysis of compositional data, FCA clustering techniques have to be adapted by using asuitable compositional algebra.The present work centres on the following question: given a sample of compositionaldata trajectories, how can we formulate a segmentation procedure giving homogeneousclasses? To address this problem we follow the steps described below.First of all we adapt the well-known spline smoothing techniques in order to cope withthe smoothing of compositional data trajectories. In fact, an observed curve can bethought of as the sum of a smooth part plus some noise due to measurement errors.Spline smoothing techniques are used to isolate the smooth part of the trajectory:clustering algorithms are then applied to these smooth curves.The second step consists in building suitable metrics for measuring the dissimilaritybetween trajectories: we propose a metric that accounts for difference in both shape andlevel, and a metric accounting for differences in shape only.A simulation study is performed in order to evaluate the proposed methodologies, usingboth hierarchical and partitional clustering algorithm. The quality of the obtained resultsis assessed by means of several indices

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In this article we compare regression models obtained to predict PhD students’ academic performance in the universities of Girona (Spain) and Slovenia. Explanatory variables are characteristics of PhD student’s research group understood as an egocentered social network, background and attitudinal characteristics of the PhD students and some characteristics of the supervisors. Academic performance was measured by the weighted number of publications. Two web questionnaires were designed, one for PhD students and one for their supervisors and other research group members. Most of the variables were easily comparable across universities due to the careful translation procedure and pre-tests. When direct comparison was notpossible we created comparable indicators. We used a regression model in which the country was introduced as a dummy coded variable including all possible interaction effects. The optimal transformations of the main and interaction variables are discussed. Some differences between Slovenian and Girona universities emerge. Some variables like supervisor’s performance and motivation for autonomy prior to starting the PhD have the same positive effect on the PhD student’s performance in both countries. On the other hand, variables like too close supervision by the supervisor and having children have a negative influence in both countries. However, we find differences between countries when we observe the motivation for research prior to starting the PhD which increases performance in Slovenia but not in Girona. As regards network variables, frequency of supervisor advice increases performance in Slovenia and decreases it in Girona. The negative effect in Girona could be explained by the fact that additional contacts of the PhD student with his/her supervisor might indicate a higher workload in addition to or instead of a better advice about the dissertation. The number of external student’s advice relationships and social support mean contact intensity are not significant in Girona, but they have a negative effect in Slovenia. We might explain the negative effect of external advice relationships in Slovenia by saying that a lot of external advice may actually result from a lack of the more relevant internal advice

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Background: The COSMIN checklist (COnsensus-based Standards for the selection of health status Measurement INstruments) was developed in an international Delphi study to evaluate the methodological quality of studies on measurement properties of health-related patient reported outcomes (HR-PROs). In this paper, we explain our choices for the design requirements and preferred statistical methods for which no evidence is available in the literature or on which the Delphi panel members had substantial discussion. Methods: The issues described in this paper are a reflection of the Delphi process in which 43 panel members participated. Results: The topics discussed are internal consistency (relevance for reflective and formative models, and distinction with unidimensionality), content validity (judging relevance and comprehensiveness), hypotheses testing as an aspect of construct validity (specificity of hypotheses), criterion validity (relevance for PROs), and responsiveness (concept and relation to validity, and (in) appropriate measures).Conclusions: We expect that this paper will contribute to a better understanding of the rationale behind the items, thereby enhancing the acceptance and use of the COSMIN checklist.

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A new statistical parallax method using the Maximum Likelihood principle is presented, allowing the simultaneous determination of a luminosity calibration, kinematic characteristics and spatial distribution of a given sample. This method has been developed for the exploitation of the Hipparcos data and presents several improvements with respect to the previous ones: the effects of the selection of the sample, the observational errors, the galactic rotation and the interstellar absorption are taken into account as an intrinsic part of the formulation (as opposed to external corrections). Furthermore, the method is able to identify and characterize physically distinct groups in inhomogeneous samples, thus avoiding biases due to unidentified components. Moreover, the implementation used by the authors is based on the extensive use of numerical methods, so avoiding the need for simplification of the equations and thus the bias they could introduce. Several examples of application using simulated samples are presented, to be followed by applications to real samples in forthcoming articles.

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Every year, flash floods cause economic losses and major problems for undertaking daily activity in the Catalonia region (NE Spain). Sometimes catastrophic damage and casualties occur. When a long term analysis of floods is undertaken, a question arises regarding the changing role of the vulnerability and the hazard in risk evolution. This paper sets out to give some information to deal with this question, on the basis of analysis of all the floods that have occurred in Barcelona county (Catalonia) since the 14th century, as well as the flooded area, urban evolution, impacts and the weather conditions for any of most severe events. With this objective, the identification and classification of historical floods, and characterisation of flash-floods among these, have been undertaken. Besides this, the main meteorological factors associated with recent flash floods in this city and neighbouring regions are well-known. On the other hand, the identification of rainfall trends that could explain the historical evolution of flood hazard occurrence in this city has been analysed. Finally, identification of the influence of urban development on the vulnerability to floods has been carried out. Barcelona city has been selected thanks to its long continuous data series (daily rainfall data series, since 1854; one of the longest rainfall rate series of Europe, since 1921) and for the accurate historical archive information that is available (since the Roman Empire for the urban evolution). The evolution of flood occurrence shows the existence of oscillations in the earlier and later modern-age periods that can be attributed to climatic variability, evolution of the perception threshold and changes in vulnerability. A great increase of vulnerability can be assumed for the period 1850¿1900. The analysis of the time evolution for the Barcelona rainfall series (1854¿2000) shows that no trend exists, although, due to changes in urban planning, flash-floods impact has altered over this time. The number of catastrophic flash floods has diminished, although the extraordinary ones have increased.

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Una de las herramientas estadísticas más importantes para el seguimiento y análisis de la evolución de la actividad económica a corto plazo es la disponibilidad de estimaciones de la evolución trimestral de los componentes del PIB, en lo que afecta tanto a la oferta como a la demanda. La necesidad de disponer de esta información con un retraso temporal reducido hace imprescindible la utilización de métodos de trimestralización que permitan desagregar la información anual a trimestral. El método más aplicado, puesto que permite resolver este problema de manera muy elegante bajo un enfoque estadístico de estimador óptimo, es el método de Chow-Lin. Pero este método no garantiza que las estimaciones trimestrales del PIB en lo que respecta a la oferta y a la demanda coincidan, haciendo necesaria la aplicación posterior de algún método de conciliación. En este trabajo se desarrolla una ampliación multivariante del método de Chow-Lin que permite resolver el problema de la estimación de los valores trimestrales de manera óptima, sujeta a un conjunto de restricciones. Una de las aplicaciones potenciales de este método, que hemos denominado método de Chow-Lin restringido, es precisamente la estimación conjunta de valores trimestrales para cada uno de los componentes del PIB en lo que afecta tanto a la demanda como a la oferta condicionada a que ambas estimaciones trimestrales del PIB sean iguales, evitando así la necesidad de aplicar posteriormente métodos de conciliación

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Una de las herramientas estadísticas más importantes para el seguimiento y análisis de la evolución de la actividad económica a corto plazo es la disponibilidad de estimaciones de la evolución trimestral de los componentes del PIB, en lo que afecta tanto a la oferta como a la demanda. La necesidad de disponer de esta información con un retraso temporal reducido hace imprescindible la utilización de métodos de trimestralización que permitan desagregar la información anual a trimestral. El método más aplicado, puesto que permite resolver este problema de manera muy elegante bajo un enfoque estadístico de estimador óptimo, es el método de Chow-Lin. Pero este método no garantiza que las estimaciones trimestrales del PIB en lo que respecta a la oferta y a la demanda coincidan, haciendo necesaria la aplicación posterior de algún método de conciliación. En este trabajo se desarrolla una ampliación multivariante del método de Chow-Lin que permite resolver el problema de la estimación de los valores trimestrales de manera óptima, sujeta a un conjunto de restricciones. Una de las aplicaciones potenciales de este método, que hemos denominado método de Chow-Lin restringido, es precisamente la estimación conjunta de valores trimestrales para cada uno de los componentes del PIB en lo que afecta tanto a la demanda como a la oferta condicionada a que ambas estimaciones trimestrales del PIB sean iguales, evitando así la necesidad de aplicar posteriormente métodos de conciliación

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Objective: Health status measures usually have an asymmetric distribution and present a highpercentage of respondents with the best possible score (ceiling effect), specially when they areassessed in the overall population. Different methods to model this type of variables have beenproposed that take into account the ceiling effect: the tobit models, the Censored Least AbsoluteDeviations (CLAD) models or the two-part models, among others. The objective of this workwas to describe the tobit model, and compare it with the Ordinary Least Squares (OLS) model,that ignores the ceiling effect.Methods: Two different data sets have been used in order to compare both models: a) real datacomming from the European Study of Mental Disorders (ESEMeD), in order to model theEQ5D index, one of the measures of utilities most commonly used for the evaluation of healthstatus; and b) data obtained from simulation. Cross-validation was used to compare thepredicted values of the tobit model and the OLS models. The following estimators werecompared: the percentage of absolute error (R1), the percentage of squared error (R2), the MeanSquared Error (MSE) and the Mean Absolute Prediction Error (MAPE). Different datasets werecreated for different values of the error variance and different percentages of individuals withceiling effect. The estimations of the coefficients, the percentage of explained variance and theplots of residuals versus predicted values obtained under each model were compared.Results: With regard to the results of the ESEMeD study, the predicted values obtained with theOLS model and those obtained with the tobit models were very similar. The regressioncoefficients of the linear model were consistently smaller than those from the tobit model. In thesimulation study, we observed that when the error variance was small (s=1), the tobit modelpresented unbiased estimations of the coefficients and accurate predicted values, specially whenthe percentage of individuals wiht the highest possible score was small. However, when theerrror variance was greater (s=10 or s=20), the percentage of explained variance for the tobitmodel and the predicted values were more similar to those obtained with an OLS model.Conclusions: The proportion of variability accounted for the models and the percentage ofindividuals with the highest possible score have an important effect in the performance of thetobit model in comparison with the linear model.

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Objective: To describe the methodology of Confirmatory Factor Analyis for categorical items and to apply this methodology to evaluate the factor structure and invariance of the WHO-Disability Assessment Schedule (WHODAS-II) questionnaire, developed by the World HealthOrganization.Methods: Data used for the analysis come from the European Study of Mental Disorders(ESEMeD), a cross-sectional interview to a representative sample of the general population of 6 european countries (n=8796). Respondents were administered a modified version of theWHODAS-II, that measures functional disability in the previous 30 days in 6 differentdimensions: Understanding and Communicating; Self-Care, Getting Around, Getting Along withOthers, Life Activities and Participation. The questionnaire includes two types of items: 22severity items (5 points likert) and 8 frequency items (continuous). An Exploratory factoranalysis (EFA) with promax rotation was conducted on a random 50% of the sample. Theremaining half of the sample was used to perform a Confirmatory Factor Analysis (CFA) inorder to compare three different models: (a) the model suggested by the results obtained in theEFA; (b) the theoretical model suggested by the WHO with 6 dimensions; (c) a reduced modelequivalent to model b where 4 of the frequency items are excluded. Moreover, a second orderfactor was also evaluated. Finally, a CFA with covariates was estimated in order to evaluatemeasurement invariance of the items between Mediterranean and non-mediterranean countries.Results: The solution that provided better results in the EFA was that containing 7 factors. Twoof the frequency items presented high factor loadings in the same factor, and one of thempresented factor loadings smaller than 0.3 with all the factors. With regard to the CFA, thereduced model (model c) presented the best goodness of fit results (CFI=0.992,TLI=0.996,RMSEA=0.024). The second order factor structure presented adequate goodness of fit (CFI=0.987,TLI=0.991, RMSEA=0.036). Measurement non-invariance was detected for one of the items of thequestionnaire (FD20 ¿ Embarrassment due to health problems).Conclusions: AFC confirmed the initial hypothesis about the factorial structure of the WHODAS-II in 6factors. The second order factor supports the existence of a global dimension of disability. The use of 4of the frequency items is not recommended in the scoring of the corresponding dimensions.

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Generally, medicine books are concentrated almost exclusively in explaining methodology that analyzes fixed measures, measures done in a certain moment, nevertheless the evolution of the measurement and correct interpretation of the missed values are very important and sometimes can give the key information of the results obtained. Thus, the analysis of the temporary series and spectral analysis or analysis of the time series in the dominion of frequencies can be regarded as an appropriate tool for this kind of studies.In this work the frequency of the pulsating secretion of luteinizing hormone LH (thatregulates the fertile life of women) were analyzed in order to determine the existence of the significant frequencies obtained by analysis of Fourier. Detection of the frequencies, with which the pulsating secretion of the LH takes place, is a quite difficult question due topresence of the random errors in measures and samplings, i.e. that pulsating secretions of small amplitude are not detected and disregarded. In physiology it is accepted that cyclical patterns in the secretion of the LH exist and in the results of this research confirm this pattern and determine its frequency presented in the corresponded periodograms to each of studied cycle. The obtained results can be used as key pattern for future sampling frequencies in order to ¿catch¿ the significant picks of the luteinizing hormone and reflect on time forproductivity treatment of women.

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En este artículo abordamos el uso y la importancia de las herramientas estadísticas que se utilizan principalmente en los estudios médicos del ámbito de la oncología y la hematología, pero aplicables a muchos otros campos tanto médicos como experimentales o industriales. El objetivo del presente trabajo es presentar de una manera clara y precisa la metodología estadística necesaria para analizar los datos obtenidos en los estudios rigurosa y concisamente en cuanto a las hipótesis de trabajo planteadas por los investigadores. La medida de la respuesta al tratamiento elegidas en al tipo de estudio elegido determinarán los métodos estadísticos que se utilizarán durante el análisis de los datos del estudio y también el tamaño de muestra. Mediante la correcta aplicación del análisis estadístico y de una adecuada planificación se puede determinar si la relación encontrada entre la exposición a un tratamiento y un resultado es casual o por el contrario, está sujeto a una relación no aleatoria que podría establecer una relación de causalidad. Hemos estudiado los principales tipos de diseño de los estudios médicos más utilizados, tales como ensayos clínicos y estudios observacionales (cohortes, casos y controles, estudios de prevalencia y estudios ecológicos). También se presenta una sección sobre el cálculo del tamaño muestral de los estudios y cómo calcularlo, ¿Qué prueba estadística debe utilizarse?, los aspectos sobre fuerza del efecto ¿odds ratio¿ (OR) y riesgo relativo (RR), el análisis de supervivencia. Se presentan ejemplos en la mayoría de secciones del artículo y bibliografía más relevante.

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En la actualidad es difícil hablar de procesos estadísticos de análisis cuantitativo de datos sin hacer referencia a la informática aplicada a la investigación. Estos recursos informáticos se basan a menudo en paquetes de programas informáticos que tienen por objetivo ayudar al/la investigador/a en la fase de análisis de datos. En estos momentos uno de los paquetes más perfeccionados y completos es el SPSS (Statistical Package for the Social Sciences). El SPSS es un paquete de programas para llevar a cabo el análisis estadístico de los datos. Constituye una aplicación estadística muy potente, de la que se han ido desarrollando diversas versiones desde sus inicios, en los años setenta. En esta ficha las salidas de ordenador que se presentan corresponden a la versión 11.0.1. No obstante, aunque la forma ha ido variando desde sus inicios, su funcionamiento sigue siendo muy similar entre las diferentes versiones. Antes de iniciarnos en la utilización de las aplicaciones del SPSS es importante familiarizarse con algunas de las ventanas que más usaremos. Al entrar al SPSS lo primero que nos encontramos es el editor de datos. Esta ventana visualiza, básicamente, los datos que iremos introduciendo. El editor de datos incluye dos opciones: la vista de los datos y la de las variables. Estas opciones pueden seleccionarse a partir de las dos pestañas que se presentan en la parte inferior. La vista de datos contiene el menú general y la matriz de datos. Esta matriz está estructurada ubicando los casos en las filas y las variables en las columnas.

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The present study explores the statistical properties of a randomization test based on the random assignment of the intervention point in a two-phase (AB) single-case design. The focus is on randomization distributions constructed with the values of the test statistic for all possible random assignments and used to obtain p-values. The shape of those distributions is investigated for each specific data division defined by the moment in which the intervention is introduced. Another aim of the study consisted in testing the detection of inexistent effects (i.e., production of false alarms) in autocorrelated data series, in which the assumption of exchangeability between observations may be untenable. In this way, it was possible to compare nominal and empirical Type I error rates in order to obtain evidence on the statistical validity of the randomization test for each individual data division. The results suggest that when either of the two phases has considerably less measurement times, Type I errors may be too probable and, hence, the decision making process to be carried out by applied researchers may be jeopardized.

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Interdependence is the main feature of dyadic relationships and, in recent years, various statistical procedures have been proposed for quantifying and testing this social attribute in different dyadic designs. The purpose of this paper is to develop several functions for this kind of statistical tests in an R package, known as nonindependence, for use by applied social researchers. A Graphical User Interface (GUI) is also developed to facilitate the use of the functions included in this package. Examples drawn from psychological research and simulated data are used to illustrate how the software works.