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The effects of utilization review on hospital use and expenditures: a covariance analysis.

Wickizer, T M
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /04/1992 EN
Relevância na Pesquisa
55.43%
Hospital utilization review (UR) has expanded rapidly in recent years and is now widely used by private payers as an approach to cost containment. This article reports estimates of the effects of UR on hospital utilization and medical expenditures based on a covariance estimation procedure. Claims data on 223 privately insured groups were analyzed covering a three-year period, 1984 through 1986. UR was associated with an approximate 12 percent decrease in admissions, a 14 percent decrease in hospital routine expenditures, and a 6 percent decrease in total medical expenditures. UR appears to reduce expenditures mainly by reducing admissions; hospital inpatient expenditures per admission were unaffected by the review activity. Analysis showed the effect of UR to have been greatest during the quarters immediately following implementation of the review activity. This finding underscores the need to analyze longitudinal data having sufficient time-series observations to obtain reliable estimates of long-term program impact. The analysis described here offers a computationally efficient alternative specification to the standard fixed-effects approach for analyzing pooled data, and is especially useful when the number of cross-section units is large.

Risk assessment of environmentally influenced airway diseases based on time-series analysis.

Herbarth, O
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /09/1995 EN
Relevância na Pesquisa
45.5%
Threshold values are of prime importance in providing a sound basis for public health decisions. A key issue is determining threshold or maximum exposure values for pollutants and assessing their potential health risks. Environmental epidemiology could be instrumental in assessing these levels, especially since the assessment of ambient exposures involves relatively low concentrations of pollutants. This paper presents a statistical method that allows the determination of threshold values as well as the assessment of the associated risk using a retrospective, longitudinal study design with a prospective follow-up. Morbidity data were analyzed using the Fourier method, a time-series analysis that is based on the assumption of a high temporal resolution of the data. This method eliminates time-dependent responses like temporal inhomogeneity and pseudocorrelation. The frequency of calls for respiratory distress conditions to the regional Mobile Medical Emergency Service (MMES) in the city of Leipzig were investigated. The entire population of Leipzig served as a pool for data collection. In addition to the collection of morbidity data, air pollution measurements were taken every 30 min for the entire study period using sulfur dioxide as the regional indicator variable. This approach allowed the calculation of a dose-response curve for respiratory diseases and air pollution indices in children and adults. Significantly higher morbidities were observed above a 24-hr mean value of 0.6 mg SO2/m3 air for children and 0.8 mg SO2/m3 for adults.(ABSTRACT TRUNCATED AT 250 WORDS)

Procedures for reliable estimation of viral fitness from time-series data.

Bonhoeffer, Sebastian; Barbour, Andrew D; De Boer, Rob J
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 22/09/2002 EN
Relevância na Pesquisa
45.5%
In order to develop a better understanding of the evolutionary dynamics of HIV drug resistance, it is necessary to quantify accurately the in vivo fitness costs of resistance mutations. However, the reliable estimation of such fitness costs is riddled with both theoretical and experimental difficulties. Experimental fitness assays typically suffer from the shortcoming that they are based on in vitro data. Fitness estimates based on the mathematical analysis of in vivo data, however, are often questionable because the underlying assumptions are not fulfilled. In particular, the assumption that the replication rate of the virus population is constant in time is frequently grossly violated. By extending recent work of Marée and colleagues, we present here a new approach that corrects for time-dependent viral replication in time-series data for growth competition of mutants. This approach allows a reliable estimation of the relative replicative capacity (with confidence intervals) of two competing virus variants growing within the same patient, using longitudinal data for the total plasma virus load, the relative frequency of the two variants and the death rate of infected cells. We assess the accuracy of our method using computer-generated data. An implementation of the developed method is freely accessible on the Web (http://www.eco.ethz.ch/fitness.html).

Comparing meta-analysis and ecological-longitudinal analysis in time-series studies. A case study of the effects of air pollution on mortality in three Spanish cities

Saez, M; Figueiras, A; Ballester, F; Perez-Hoyos, S; Ocana, R; Tobias, A
Fonte: BMJ Group Publicador: BMJ Group
Tipo: Artigo de Revista Científica
Publicado em /06/2001 EN
Relevância na Pesquisa
75.57%
STUDY OBJECTIVE—The objective of this paper is to introduce a different approach, called the ecological-longitudinal, to carrying out pooled analysis in time series ecological studies. Because it gives a larger number of data points and, hence, increases the statistical power of the analysis, this approach, unlike conventional ones, allows the complementation of aspects such as accommodation of random effect models, of lags, of interaction between pollutants and between pollutants and meteorological variables, that are hardly implemented in conventional approaches.
DESIGN—The approach is illustrated by providing quantitative estimates of the short-term effects of air pollution on mortality in three Spanish cities, Barcelona, Valencia and Vigo, for the period 1992-1994. Because the dependent variable was a count, a Poisson generalised linear model was first specified. Several modelling issues are worth mentioning. Firstly, because the relations between mortality and explanatory variables were non-linear, cubic splines were used for covariate control, leading to a generalised additive model, GAM. Secondly, the effects of the predictors on the response were allowed to occur with some lag. Thirdly, the residual autocorrelation, because of imperfect control...

Modeling Individual Damped Linear Oscillator Processes with Differential Equations: Using Surrogate Data Analysis to Estimate the Smoothing Parameter

Deboeck, Pascal R.; Boker, Steven M.; Bergeman, C. S.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/10/2008 EN
Relevância na Pesquisa
45.5%
Among the many methods available for modeling intraindividual time series, differential equation modeling has several advantages that make it promising for applications to psychological data. One interesting differential equation model is that of the damped linear oscillator (DLO), which can be used to model variables that have a tendency to fluctuate around some typical, or equilibrium, value. Methods available for fitting the damped linear oscillator model using differential equation modeling can yield biased parameter estimates when applied to univariate time series. The degree of this bias depends on a smoothing-like parameter, which balances the need for increasing smoothing to minimize error variance but not smoothing so much as to obscure change of interest. This article explores a technique that uses surrogate data analysis to select such a parameter, thereby producing approximately unbiased parameter estimates. Furthermore the smoothing parameter, which is usually researcher-selected, is produced in an automated manner so as to reduce the experience required by researchers to apply these methods. Focus is placed on the damped linear model; however, similar issues are expected with other differential equation models and other techniques in which parameter estimates depend on a smoothing parameter. An example using affect data from the Notre Dame Longitudinal Study of Aging (2004) is presented...

A dynamic approach for reconstructing missing longitudinal data using the linear increments model

Aalen, Odd O.; Gunnes, Nina
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Publicado em /07/2010 EN
Relevância na Pesquisa
45.54%
Missing observations are commonplace in longitudinal data. We discuss how to model and analyze such data in a dynamic framework, that is, taking into consideration the time structure of the process and the influence of the past on the present and future responses. An autoregressive model is used as a special case of the linear increments model defined by Farewell (2006. Linear models for censored data, [PhD Thesis]. Lancaster University) and Diggle and others (2007. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal. Journal of the Royal Statistical Society, Series C (Applied Statistics, 56, 499–550). We wish to reconstruct responses for missing data and discuss the required assumptions needed for both monotone and nonmonotone missingness. The computational procedures suggested are very simple and easily applicable. They can also be used to estimate causal effects in the presence of time-dependent confounding. There are also connections to methods from survival analysis: The Aalen–Johansen estimator for the transition matrix of a Markov chain turns out to be a special case. Analysis of quality of life data from a cancer clinical trial is analyzed and presented. Some simulations are given in the supplementary material available at Biostatistics online.

A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data

Bringmann, Laura F.; Vissers, Nathalie; Wichers, Marieke; Geschwind, Nicole; Kuppens, Peter; Peeters, Frenk; Borsboom, Denny; Tuerlinckx, Francis
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 04/04/2013 EN
Relevância na Pesquisa
45.45%
In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed.

Estimation of Smooth Growth Trajectories with Controlled Acceleration from Time Series Shape Data

Fishbaugh, James; Durrleman, Stanley; Gerig, Guido
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em //2011 EN
Relevância na Pesquisa
45.42%
Longitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model parameterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth.

On Varying-coefficient Independence Screening for High-dimensional Varying-coefficient Models

Song, Rui; Yi, Feng; Zou, Hui
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em //2014 EN
Relevância na Pesquisa
55.43%
Varying coefficient models have been widely used in longitudinal data analysis, nonlinear time series, survival analysis, and so on. They are natural non-parametric extensions of the classical linear models in many contexts, keeping good interpretability and allowing us to explore the dynamic nature of the model. Recently, penalized estimators have been used for fitting varying-coefficient models for high-dimensional data. In this paper, we propose a new computationally attractive algorithm called IVIS for fitting varying-coefficient models in ultra-high dimensions. The algorithm first fits a gSCAD penalized varying-coefficient model using a subset of covariates selected by a new varying-coefficient independence screening (VIS) technique. The sure screening property is established for VIS. The proposed algorithm then iterates between a greedy conditional VIS step and a gSCAD penalized fitting step. Simulation and a real data analysis demonstrate that IVIS has very competitive performance for moderate sample size and high dimension.

Facility-level intervention to improve attendance and adherence among patients on anti-retroviral treatment in Kenya—a quasi-experimental study using time series analysis

Boruett, Patrick; Kagai, Dorine; Njogo, Susan; Nguhiu, Peter; Awuor, Christine; Gitau, Lillian; Chalker, John; Ross-Degnan, Dennis; Wahlström, Rolf; Tomson, Göran
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
EN_US
Relevância na Pesquisa
45.47%
Background: Achieving high rates of adherence to antiretroviral therapy (ART) in resource-poor settings comprises serious, but different, challenges in both the first months of treatment and during the life-long maintenance phase. We measured the impact of a health system-oriented, facility-based intervention to improve clinic attendance and patient adherence. Methods: This was a quasi-experimental, longitudinal, controlled intervention study using interrupted time series analysis. The intervention consisted of (1) using a clinic appointment diary to track patient attendance and monitor monthly performance; (2) changing the mode of asking for self-reported adherence; (3) training staff on adherence concepts, intervention methods, and use of monitoring data; (4) conducting visits to support facility teams with the implementation. We conducted the study in 12 rural district hospitals (6 intervention, 6 control) in Kenya and randomly selected 1894 adult patients over 18 years of age in two cohorts: experienced patients on treatment for at least one year, and newly treated patients initiating ART during the study. Outcome measures were: attending the clinic on or before the date of a scheduled appointment, attending within 3 days of a scheduled appointment...

A Retrospective-Longitudinal Examination of the Relationship between Apportionment of Seat Time in Community-College Algebra Courses and Student Academic Performance

Roig-Watnik, Steven M
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Relevância na Pesquisa
45.56%
During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and...

Time Trends and Predictors of Initiation for Cigarette and Waterpipe Smoking Among Jordanian School Children: Irbid, 2008-2011

McKelvey, Karma L, PhD
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Relevância na Pesquisa
85.46%
Smoking prevalence among adolescents in the Middle East remains high while rates of smoking have been declining among adolescents elsewhere. The aims of this research were to (1) describe patterns of cigarette and waterpipe (WP) smoking, (2) identify determinants of WP smoking initiation, and (3) identify determinants of cigarette smoking initiation in a cohort of Jordanian school children. Among this cohort of school children in Irbid, Jordan, (age ≈ 12.6 at baseline) the first aim (N=1,781) described time trends in smoking behavior, age at initiation, and changes in frequency of smoking from 2008-2011 (grades 7 – 10). The second aim (N=1,243) identified determinants of WP initiation among WP-naïve students; and the third aim (N=1,454) identified determinants of cigarette smoking initiation among cigarette naïve participants. Determinants of initiation were assessed with generalized mixed models. All analyses were stratified by gender. Baseline prevalence of current smoking (cigarettes or WP) for boys and girls was 22.9% and 8.7% respectively. Prevalence of ever- and current- any smoking, cigarette smoking, WP smoking, and dual cigarette/WP smoking was higher in boys than girls each year (p These studies reveal intensive smoking patterns at early ages among Jordanian youth in Irbid...

Evaluating pharmaceutical policy impacts using interrupted time series analysis: An Australian case study

Kemp, A.; Preen, D.; Sanfilippo, F.; Glover, J.; Semmens, J.; Roughead, E.
Fonte: Nova Science Publishers, Inc Publicador: Nova Science Publishers, Inc
Tipo: Artigo de Revista Científica
Publicado em //2011 EN
Relevância na Pesquisa
55.56%
Determining the impacts of policy on health outcomes is important for policy makers, clinicians and consumers. Interrupted time series analysis is a powerful quasi-experimental method for quantifying change in an outcome after policy implementation. We illustrate the use of interrupted time series analysis for policy evaluation with an Australian case study. Use of prescription medicines in Australia were examined before and after the implementation of pharmaceutical-subsidy changes. METHODS: Interrupted time series analysis compares longitudinal data, aggregated into time-units, before and after a change-point. A line of best fit is calculated for the period before and after the change-point and the differences in the level (i.e. height) and trend (i.e. slope) of these lines are quantified. In our case study, dispensings of specified medicines in Australia were compared for 60 months before and 33 months after a substantial increase in prescription costs in January 2005. RESULTS: Interrupted time series analysis quantifies level and trend changes occurring after a change-point and indicates when, and for how long, changes occur. Significant change in the level of a series indicates an immediate policy impact while a significant trend change indicates an on-going impact on an outcome. We found significant decreases in the level or trend of dispensings for 12 medicine classes indicating both immediate and on-going declines in use. Declines were largest for low income patients and for medicines used preventatively to treat asymptomatic conditions. CONCLUSIONS: Interrupted time series analysis provides a simple and feasible method of evaluating the impact of already-implemented policies on health outcomes. Findings from the case study...

Citizen Science and Smart Cities

CRAGLIA Massimo; GRANELL CANUT CARLOS
Fonte: Publications Office of the European Union Publicador: Publications Office of the European Union
Tipo: EUR - Scientific and Technical Research Reports Formato: Online
ENG
Relevância na Pesquisa
45.44%
The report summarizes the presentations, discussions, and conclusions of the Citizen Science and Smart Cities Summit organised by the European Commission Joint Research Centre on 5-7th February 2014. In the context of the Summit, the label Citizen Science was used to include both citizen science projects, and others that are about user-generated content, not necessarily addressing a scientific process or issues. The evidence presented by 27 different projects shows the vitality and diversity of the field but also a number of critical points: • Citizen science project are more than collecting data: they are about raising awareness, building capacity, and strengthening communities. • Likewise, smart cities are not only about ICT, energy and transport infrastructures: Smart cities are about smart citizens, who participate in their city’s daily governance, are concerned about increasing the quality of life of their fellow-citizens, and about protecting their environment. Technology may facilitate, but is no solution per se. • Unfortunately to date there seems to be little synergy between citizen science and smart cities initiatives, and there is little interoperability and reusability of the data, apps, and services developed in each project. • It is difficult to compare the results among citizen science...

Bayesian analysis of autoregressive panel data model: application in genetic evaluation of beef cattle; Análise Bayesiana do modelo auto-regressivo para dados em painel: aplicação na avaliação genética de bovinos de corte

SILVA, Fabyano Fonseca e; SÁFADI, Thelma; MUNIZ, Joel Augusto; ROSA, Guilherme Jordão Magalhães; AQUINO, Luiz Henrique de; MOURÃO, Gerson Barreto; SILVA, Carlos Henrique Osório
Fonte: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz" Publicador: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Tipo: Relatório
ENG
Relevância na Pesquisa
65.53%
The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p), panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1), independent Multivariate Student's t Inverse Gamma (model 2) and Jeffrey's (model 3). Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD) of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95%) in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.; A previsão dos valores genéticos de animais em tempos futuros constitui importante inovação tecnológica para a área de Zootecnia...

Bayesian analysis of autoregressive panel data model: application in genetic evaluation of beef cattle

Silva,Fabyano Fonseca e; Sáfadi,Thelma; Muniz,Joel Augusto; Rosa,Guilherme Jordão Magalhães; Aquino,Luiz Henrique de; Mourão,Gerson Barreto; Silva,Carlos Henrique Osório
Fonte: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz" Publicador: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2011 EN
Relevância na Pesquisa
65.51%
The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p), panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1), independent Multivariate Student's t Inverse Gamma (model 2) and Jeffrey's (model 3). Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD) of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95%) in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.

Arbitrary-order Hilbert spectral analysis for time series possessing scaling statistics: a comparison study with detrended fluctuation analysis and wavelet leaders

Huang, Y. X.; Schmitt, F. G.; Hermand, J. -P.; Gagne, Y.; Lu, Z. M.; Liu, Y. L.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 18/07/2011
Relevância na Pesquisa
75.52%
In this paper we present an extended version of Hilbert-Huang transform, namely arbitrary-order Hilbert spectral analysis, to characterize the scale-invariant properties of a time series directly in an amplitude-frequency space. We first show numerically that due to a nonlinear distortion, traditional methods require high-order harmonic components to represent nonlinear processes, except for the Hilbert-based method. This will lead to an artificial energy flux from the low-frequency (large scale) to the high-frequency (small scale) part. Thus the power law, if it exists, is contaminated. We then compare the Hilbert method with structure functions (SF), detrended fluctuation analysis (DFA), and wavelet leader (WL) by analyzing fractional Brownian motion and synthesized multifractal time series. For the former simulation, we find that all methods provide comparable results. For the latter simulation, we perform simulations with an intermittent parameter {\mu} = 0.15. We find that the SF underestimates scaling exponent when q > 3. The Hilbert method provides a slight underestimation when q > 5. However, both DFA and WL overestimate the scaling exponents when q > 5. It seems that Hilbert and DFA methods provide better singularity spectra than SF and WL. We finally apply all methods to a passive scalar (temperature) data obtained from a jet experiment with a Taylor's microscale Reynolds number Relambda \simeq 250. Due to the presence of strong ramp-cliff structures...

Critical evaluation of magnetic field detections reported for pulsating B-type stars in the light of ESPaDOnS, Narval and reanalyzed FORS1/2 observations

Shultz, M.; Wade, G. A.; Grunhut, J.; Bagnulo, S.; Landstreet, J. D.; Neiner, C.; Alecian, E.; Hanes, D.; Collaboration, the MiMeS
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
55.47%
Recent spectropolarimetric studies of 7 SPB and $\beta$ Cep stars have suggested that photospheric magnetic fields are more common in B-type pulsators than in the general population of B stars, suggesting a significant connection between magnetic and pulsational phenomena. We present an analysis of new and previously published spectropolarimetric observations of these stars. New Stokes $V$ observations obtained with the high-resolution ESPaDOnS and Narval instruments confirm the presence of a magnetic field in one of the stars ($\epsilon$ Lup), but find no evidence of magnetism in 5 others. A re-analysis of the published longitudinal field measurements obtained with the low-resolution FORS1/2 spectropolarimeters finds that the measurements of all stars show more scatter from zero than can be attributed to Gaussian noise, suggesting the presence of a signal and/or systematic under-estimation of error bars. Re-reduction and re-measurement of the FORS1/2 spectra from the ESO archive demonstrates that small changes in reduction procedure lead to substantial changes in the inferred longitudinal field, and substantially reduces the number of field detections at the 3$\sigma$ level. Furthermore, we find that the published periods are not unique solutions to the time series of either the original or the revised FORS1/2 data. We conclude that the reported field detections...

Análise Bayesiana do modelo auto-regressivo para dados em painel: aplicação na avaliação genética de bovinos de corte; Bayesian analysis of autoregressive panel data model: application in genetic evaluation of beef cattle

Silva, Fabyano Fonseca e; Sáfadi, Thelma; Muniz, Joel Augusto; Rosa, Guilherme Jordão Magalhães; Aquino, Luiz Henrique de; Mourão, Gerson Barreto; Silva, Carlos Henrique Osório
Fonte: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz Publicador: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; ; ; ; Formato: application/pdf
Publicado em 01/04/2011 ENG
Relevância na Pesquisa
65.53%
The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p), panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1), independent Multivariate Student's t Inverse Gamma (model 2) and Jeffrey's (model 3). Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD) of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95%) in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.; A previsão dos valores genéticos de animais em tempos futuros constitui importante inovação tecnológica para a área de Zootecnia...

Adaptive varying-coefficient linear models

Fan, Jianqing; Yao, Qiwei; Cai, Zongwu
Fonte: Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science Publicador: Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science
Tipo: Monograph; NonPeerReviewed Formato: application/pdf
Publicado em /04/2000 EN; EN
Relevância na Pesquisa
55.42%
Varying-coefficient linear models arise from multivariate nonparametric regression, nonlinear time series modelling and forecasting, functional data analysis, longitudinal data analysis, and others. It has been a common practice to assume that the vary-coefficients are functions of a given variable which is often called an index. A frequently asked question is which variable should be used as the index. In this paper, we explore the class of the varying-coefficient linear models in which the index is unknown and is estimated as a linear combination of regression and/or other variables. This will enlarge the modelling capacity substantially. We search for the index such that the derived varying-coefficient model provides the best approximation to the underlying unknown multi-dimensional regression function in the least square sense. The search is implemented through the newly proposed hybrid backfitting algorithm. The core of the algorithm is the alternative iteration between estimating the index through a one-step scheme and estimating coefficient functions through a one-dimensional local linear smoothing. The generalised cross-validation method for choosing bandwidth is efficiently incorporated into the algorithm. The locally significant variables are selected in terms of the combined use of t-statistic and Akaike information criterion. We further extend the algorithm for the models with two indices. Simulation shows that the proposed methodology has appreciable flexibility to model complex multivariate nonlinear structure and is practically feasible with average modern computers. The methods are further illustrated through the Canadian mink-muskrat data in 1925-1994 and the pound/dollar exchange rates in 1974-1983.