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## ‣ Wavelet regression with correlated errors on a piecewise Holder class

Fonte: ELSEVIER SCIENCE BV
Publicador: ELSEVIER SCIENCE BV

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.999688%

This paper generalizes the methodology of Cat and Brown [Cai, T., Brown, L.D., 1998. Wavelet shrinkage for nonequispaced samples. The Annals of Statistics 26, 1783-1799] for wavelet shrinkage for nonequispaced samples, but in the presence of correlated stationary Gaussian errors. If the true function is a member of a piecewise Holder class, it is shown that, even for long memory errors, the rate of convergence of the procedure is almost-minimax relative to the independent and identically distributed errors case. (c) 2008 Elsevier B.V. All rights reserved.; CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); FAPESP[03/10105-2]; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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## ‣ Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data

Fonte: TAYLOR & FRANCIS LTD; ABINGDON
Publicador: TAYLOR & FRANCIS LTD; ABINGDON

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.6215%

#AUTOREGRESSIVE STRUCTURE#CORRELATED DATA#ELLIPTICAL DISTRIBUTIONS#HETEROSCEDASTIC MODELS#NONLINEAR MODELS#LINEAR-MODELS#LOCAL INFLUENCE#LIKELIHOOD#ERRORS#STATISTICS & PROBABILITY

In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. [22] by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping [1] under normality.; FAPESP; FAPESP; FACEPE; FACEPE; CNPq (Brazil); CNPq, Brazil

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## ‣ O uso de ondaletas em modelos FANOVA; Wavelets FANOVA models

Fonte: Biblioteca Digital da Unicamp
Publicador: Biblioteca Digital da Unicamp

Tipo: Tese de Doutorado
Formato: application/pdf

Publicado em 20/10/2011
Português

Relevância na Pesquisa

48.06189%

#Wavelets (Matemática)#Análise de variância funcional#Teste de hipótese não paramétrico#Erros correlacionados (Estatística)#Estatística matemática#Wavelets (Mathematics)#Functional analysis of variance#Nonparametric hypothesis testing#Correlated errors (Statistics)#Mathematical statistics

O problema de estimação funcional vem sendo estudado de formas variadas na literatura. Uma possibilidade bastante promissora se dá pela utilização de bases ortonormais de wavelets (ondaletas). Essa solução _e interessante por sua: frugalidade; otimalidade assintótica; e velocidade computacional. O objetivo principal do trabalho é estender os testes do modelo FANOVA de efeitos fixos, com erros i.i.d., baseados em ondaletas propostos em Abramovich et al. (2004), para modelos FANOVA de efeitos fixos com erros dependentes. Propomos um procedimento iterativo tipo Cocharane-Orcutt para estimar os parâmetros e a função. A função é estimada de forma não paramétrica via estimador ondaleta que limiariza termo a termo ou estimador linear núcleo ondaleta. Mostramos que, com erros i.i.d., a convergência individual do estimador núcleo ondaleta em pontos diádicos para uma variável aleatória com distribuição normal implica na convergência conjunta deste vetor para uma variável aleatória com distribuição normal multivariada. Além disso, mostramos a convergência em erro quadrático do estimador nos pontos diádicos. Sob uma restrição é possível mostrar que este estimador converge nos pontos diádicos para uma variável com distribuição normal mesmo quando os erros são correlacionados. O vetor das convergências individuais também converge para uma variável normal multivariada.; The functional estimation problem has been studied variously in the literature. A promising possibility is by use of orthonormal bases of wavelets. This solution is appealing because of its: frugality...

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## ‣ Different Dreams, Same Bed : Collecting, Using, and Interpreting Employment Statistics in Sub-Saharan Africa--The Case of Uganda

Fonte: World Bank, Washington, DC
Publicador: World Bank, Washington, DC

Português

Relevância na Pesquisa

37.808735%

#ACCOUNTING#ADJUSTMENT#AGGREGATE EMPLOYMENT#ATTENTION#ATTRITION#CHILD LABOR#DEVELOPMENT PLANNING#DEVELOPMENT STRATEGIES#ECONOMIC ANALYSIS#ECONOMIC GROWTH#ECONOMIC THEORY

Employment and earnings statistics are
the key link between the size and structure of economic
growth and the welfare of households, which is the ultimate
goal of development policy, so it is important to monitor
employment outcomes consistently. A cursory review of
employment data for low-income Sub-Saharan African countries
shows both large gaps and improbable variation within
countries over time and among countries, suggesting that low
quality data are routinely reported by national statistics
offices. Unfortunately, policies are formed and projects
developed and implemented on the basis of these statistics.
Therefore, errors of measurement could be having profound
implications on the strategic priorities and policies of a
country. This paper explains the improbable results observed
by using data from Uganda, where the labor module contains
variation both within and across surveys, to show the
sensitivity of employment outcomes to survey methodology. It
finds that estimates of employment outcomes are unreliable
if the questionnaire did not use screening questions...

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## ‣ Modeling Compositional Regression with uncorrelated and correlated errors: a Bayesian approach

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/07/2015
Português

Relevância na Pesquisa

57.50794%

Compositional data consist of known compositions vectors whose components are
positive and defined in the interval (0,1) representing proportions or
fractions of a "whole". The sum of these components must be equal to one.
Compositional data is present in different knowledge areas, as in geology,
economy, medicine among many others. In this paper, we introduce a Bayesian
analysis for compositional regression applying additive log-ratio (ALR)
transformation and assuming uncorrelated and correlated errors. The Bayesian
inference procedure based on Markov Chain Monte Carlo Methods (MCMC). The
methodology is illustrated on an artificial and a real data set of volleyball.

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## ‣ Moving Taylor Bayesian Regression for nonparametric multidimensional function estimation with possibly correlated errors

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 12/04/2012
Português

Relevância na Pesquisa

47.69107%

#Physics - Data Analysis, Statistics and Probability#Statistics - Methodology#Primary 62G08, Secondary 62F15, 65D05, 65D10, 41A10, 65D25#G.3#G.1.1#G.1.4

We present a nonparametric method for estimating the value and several
derivatives of an unknown, sufficiently smooth real-valued function of
real-valued arguments from a finite sample of points, where both the function
arguments and the corresponding values are known only up to measurement errors
having some assumed distribution and correlation structure. The method, Moving
Taylor Bayesian Regression (MOTABAR), uses Bayesian updating to find the
posterior mean of the coefficients of a Taylor polynomial of the function at a
moving position of interest. When measurement errors are neglected, MOTABAR
becomes a multivariate interpolation method. It contains several well-known
regression and interpolation methods as special or limit cases. We demonstrate
the performance of MOTABAR using the reconstruction of the Lorenz attractor
from noisy observations as an example.; Comment: 22 pages, 4 figures

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## ‣ Optimal designs for random effect models with correlated errors with applications in population pharmacokinetics

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 15/11/2010
Português

Relevância na Pesquisa

47.673174%

We consider the problem of constructing optimal designs for population
pharmacokinetics which use random effect models. It is common practice in the
design of experiments in such studies to assume uncorrelated errors for each
subject. In the present paper a new approach is introduced to determine
efficient designs for nonlinear least squares estimation which addresses the
problem of correlation between observations corresponding to the same subject.
We use asymptotic arguments to derive optimal design densities, and the designs
for finite sample sizes are constructed from the quantiles of the corresponding
optimal distribution function. It is demonstrated that compared to the optimal
exact designs, whose determination is a hard numerical problem, these designs
are very efficient. Alternatively, the designs derived from asymptotic theory
could be used as starting designs for the numerical computation of exact
optimal designs. Several examples of linear and nonlinear models are presented
in order to illustrate the methodology. In particular, it is demonstrated that
naively chosen equally spaced designs may lead to less accurate estimation.; Comment: Published in at http://dx.doi.org/10.1214/09-AOAS324 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org)

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## ‣ Assessing Colour-dependent Occupation Statistics Inferred from Galaxy Group Catalogues

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 18/05/2015
Português

Relevância na Pesquisa

38.089312%

We investigate the ability of current implementations of galaxy group finders
to recover colour-dependent halo occupation statistics. To test the fidelity of
group catalogue inferred statistics, we run three different group finders used
in the literature over a mock that includes galaxy colours in a realistic
manner. Overall, the resulting mock group catalogues are remarkably similar,
and most colour-dependent statistics are recovered with reasonable accuracy.
However, it is also clear that certain systematic errors arise as a consequence
of correlated errors in group membership determination, central/satellite
designation, and halo mass assignment. We introduce a new statistic, the halo
transition probability (HTP), which captures the combined impact of all these
errors. As a rule of thumb, errors tend to equalize the properties of distinct
galaxy populations (i.e. red vs. blue galaxies or centrals vs. satellites), and
to result in inferred occupation statistics that are more accurate for red
galaxies than for blue galaxies. A statistic that is particularly poorly
recovered from the group catalogues is the red fraction of central galaxies as
function of halo mass. Group finders do a good job in recovering galactic
conformity, but also have a tendency to introduce weak conformity when none is
present. We conclude that proper inference of colour-dependent statistics from
group catalogues is best achieved using forward modelling (i.e....

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## ‣ On the Combination Procedure of Correlated Errors

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 29/07/2015
Português

Relevância na Pesquisa

47.53219%

#Physics - Data Analysis, Statistics and Probability#High Energy Physics - Experiment#High Energy Physics - Phenomenology#Nuclear Experiment

When averages of different experimental determinations of the same quantity
are computed, each with statistical and systematic error components, then
frequently the statistical and systematic components of the combined error are
quoted explicitly. These are important pieces of information since statistical
errors scale differently and often more favorably with the sample size than
most systematical or theoretical errors. In this communication we describe a
transparent procedure by which the statistical and systematic error components
of the combination uncertainty can be obtained. We develop a general method and
derive a general formula for the case of Gaussian errors with or without
correlations. The method can easily be applied to other error distributions, as
well. For the case of two measurements, we also define disparity and
misalignment angles, and discuss their relation to the combination weight
factors.; Comment: 11 pages

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## ‣ Regularized estimation in sparse high-dimensional time series models

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

37.808735%

Many scientific and economic problems involve the analysis of
high-dimensional time series datasets. However, theoretical studies in
high-dimensional statistics to date rely primarily on the assumption of
independent and identically distributed (i.i.d.) samples. In this work, we
focus on stable Gaussian processes and investigate the theoretical properties
of $\ell _1$-regularized estimates in two important statistical problems in the
context of high-dimensional time series: (a) stochastic regression with
serially correlated errors and (b) transition matrix estimation in vector
autoregressive (VAR) models. We derive nonasymptotic upper bounds on the
estimation errors of the regularized estimates and establish that consistent
estimation under high-dimensional scaling is possible via
$\ell_1$-regularization for a large class of stable processes under sparsity
constraints. A key technical contribution of the work is to introduce a measure
of stability for stationary processes using their spectral properties that
provides insight into the effect of dependence on the accuracy of the
regularized estimates. With this proposed stability measure, we establish some
useful deviation bounds for dependent data, which can be used to study several
important regularized estimates in a time series setting.; Comment: Published at http://dx.doi.org/10.1214/15-AOS1315 in the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org)

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## ‣ Computing maximum likelihood estimates in recursive linear models with correlated errors

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.29115%

In recursive linear models, the multivariate normal joint distribution of all
variables exhibits a dependence structure induced by a recursive (or acyclic)
system of linear structural equations. These linear models have a long
tradition and appear in seemingly unrelated regressions, structural equation
modelling, and approaches to causal inference. They are also related to
Gaussian graphical models via a classical representation known as a path
diagram. Despite the models' long history, a number of problems remain open. In
this paper, we address the problem of computing maximum likelihood estimates in
the subclass of `bow-free' recursive linear models. The term `bow-free' refers
to the condition that the errors for variables $i$ and $j$ be uncorrelated if
variable $i$ occurs in the structural equation for variable $j$. We introduce a
new algorithm, termed Residual Iterative Conditional Fitting (RICF), that can
be implemented using only least squares computations. In contrast to existing
algorithms, RICF has clear convergence properties and finds parameter estimates
in closed form whenever possible.; Comment: 22 pages; removed an incorrect identifiability claim

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## ‣ Autocovariance estimation in change-point regression with $m$-dependent errors: A difference-based approach

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.89008%

We propose a class of difference-based estimators for the autocovariance in
nonparametric regression when the signal is discontinuous (change-point model),
possibly highly fluctuating, and the errors form a stationary $m$-dependent
Gaussian process. These estimators circumvent the explicit pre-estimation of
the unknown regression function, a task that is particularly challenging in
change-point regression with correlated errors. We provide finite sample
expressions for their mean squared errors when the signal function is piecewise
constant. Based on this, we distinguish the signal as the major source of the
mean squared errors and derive biased-optimized estimates. These optimal
estimates do not depend on the particular (unknown) autocovariance structure,
and notably, for positive correlated errors, in addition, they minimize that
part of the variance which is influenced by the unknown regression function.
Further, we provide some asymptotic analysis of our estimators. We show their
$\sqrt{n}$-consistency in the context of a piecewise H\"older signal with
non-Gaussian stationary $m$-dependent errors and when the number of
change-points tends to infinity.
Finally, we combine our biased-optimized autocovariance estimates with a
projection-based approach and derive covariance matrix estimates for
change-point regression...

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## ‣ Correlated samples with fixed and nonnormal latent variables

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

37.895918%

A general structural equation model is fitted on a panel data set that
consists of $I$ correlated samples. The correlated samples could be data from
correlated populations or correlated observations from occasions of panel data.
We consider cases in which the full pseudo-normal likelihood cannot be used,
for example, in highly unbalanced data where the participating individuals do
not appear in consecutive years. The model is estimated by a partial likelihood
that would be the full and correct likelihood for independent and normal
samples. It is proved that the asymptotic standard errors (a.s.e.'s) for the
most important parameters and an overall-fit measure are the same as the
corresponding ones derived under the standard assumptions of normality and
independence for all the observations. These results are very important since
they allow us to apply classical statistical methods for inference, which use
only first- and second-order moments, to correlated and nonnormal data. Via a
simulation study we show that the a.s.e.'s based on the first two moments have
negligible bias and provide less variability than the a.s.e.'s computed by an
alternative robust estimator that utilizes up to fourth moments. Our
methodology and results are applied to real panel data...

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## ‣ Estimating Mediation Effects under Correlated Errors with an Application to fMRI

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 27/10/2014
Português

Relevância na Pesquisa

47.770713%

Mediation analysis assesses the effect passing through a intermediate
variable (mediator) in a causal pathway from the treatment variable to the
outcome variable. Structure equation model (SEM) is a popular approach to
estimate the mediation effect. However, causal interpretation usually requires
strong assumptions, such as ignorability of the mediator, which may not hold in
many social and scientific studies. In this paper, we use mediation analysis in
an fMRI experiment to assess the effect of randomized binary stimuli passing
through a brain pathway of two brain regions. We propose a two-layer SEM
framework for mediation analysis that provides valid inference even if
correlated additive errors are present. In the first layer, we use a liner SEM
to model the subject level fMRI data, where the continuous mediator and outcome
variables may contain correlated additive errors. We propose a constrained
optimization approach to estimate the model coefficients, analyze its
asymptotic properties, and characterize the nonidentifiability issue due to the
correlation parameter. To address the identifiability issue, we introduce a
linear mixed effects SEM with an innovation to estimate the unknown correlation
parameter in the first layer, instead of sensitivity analysis. Using extensive
simulated data and a real fMRI dataset...

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## ‣ Parameter Estimation from Time-Series Data with Correlated Errors: A Wavelet-Based Method and its Application to Transit Light Curves

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 03/09/2009
Português

Relevância na Pesquisa

47.223076%

#Astrophysics - Earth and Planetary Astrophysics#Physics - Data Analysis, Statistics and Probability

We consider the problem of fitting a parametric model to time-series data
that are afflicted by correlated noise. The noise is represented by a sum of
two stationary Gaussian processes: one that is uncorrelated in time, and
another that has a power spectral density varying as $1/f^\gamma$. We present
an accurate and fast [O(N)] algorithm for parameter estimation based on
computing the likelihood in a wavelet basis. The method is illustrated and
tested using simulated time-series photometry of exoplanetary transits, with
particular attention to estimating the midtransit time. We compare our method
to two other methods that have been used in the literature, the time-averaging
method and the residual-permutation method. For noise processes that obey our
assumptions, the algorithm presented here gives more accurate results for
midtransit times and truer estimates of their uncertainties.; Comment: Accepted in ApJ. Illustrative code may be found at
http://www.mit.edu/~carterja/code/ . 17 pages

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## ‣ Optimal design for linear models with correlated observations

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 12/03/2013
Português

Relevância na Pesquisa

37.718657%

In the common linear regression model the problem of determining optimal
designs for least squares estimation is considered in the case where the
observations are correlated. A necessary condition for the optimality of a
given design is provided, which extends the classical equivalence theory for
optimal designs in models with uncorrelated errors to the case of dependent
data. If the regression functions are eigenfunctions of an integral operator
defined by the covariance kernel, it is shown that the corresponding measure
defines a universally optimal design. For several models universally optimal
designs can be identified explicitly. In particular, it is proved that the
uniform distribution is universally optimal for a class of trigonometric
regression models with a broad class of covariance kernels and that the arcsine
distribution is universally optimal for the polynomial regression model with
correlation structure defined by the logarithmic potential. To the best
knowledge of the authors these findings provide the first explicit results on
optimal designs for regression models with correlated observations, which are
not restricted to the location scale model.; Comment: Published in at http://dx.doi.org/10.1214/12-AOS1079 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org)

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## ‣ How to combine correlated data sets -- A Bayesian hyperparameter matrix method

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

37.872202%

#Astrophysics - Instrumentation and Methods for Astrophysics#Astrophysics - Cosmology and Nongalactic Astrophysics#Statistics - Applications#Statistics - Computation

We construct a "hyperparameter matrix" statistical method for performing the
joint analyses of multiple correlated astronomical data sets, in which the
weights of data sets are determined by their own statistical properties. This
method is a generalization of the hyperparameter method constructed by Lahav et
al. (2000) and Hobson, Bridle, & Lahav (2002) which was designed to combine
independent data sets. The advantage of our method is to treat correlations
between multiple data sets and gives appropriate relevant weights of multiple
data sets with mutual correlations. We define a new "element-wise" product,
which greatly simplifies the likelihood function with hyperparameter matrix. We
rigorously prove the simplified formula of the joint likelihood and show that
it recovers the original hyperparameter method in the limit of no covariance
between data sets. We then illustrate the method by applying it to a
demonstrative toy model of fitting a straight line to two sets of data. We show
that the hyperparameter matrix method can detect unaccounted systematic errors
or underestimated errors in the data sets. Additionally, the ratio of Bayes'
factors provides a distinct indicator of the necessity of including
hyperparameters. Our example shows that the likelihood we construct for joint
analyses of correlated data sets can be widely applied to many astrophysical
systems.; Comment: 13 pages...

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## ‣ Observation of correlated-photon statistics using a single detector

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.47378%

We report experimental observations of correlated-photon statistics in the
single-photon detection rate. The usual quantum interference in a two-photon
polarization interferometer always accompanies a dip in the single detector
counting rate, regardless of whether a dip or peak is seen in the coincidence
rate. This effect is explained by taking into account all possible photon
number states that reach the detector, rather than considering just the state
post-selected by the coincidence measurement. We also report an
interferometeric scheme in which the interference peak or dip in coincidence
corresponds directly to a peak or dip in the single-photon detection rate.; Comment: 4 pages, two-column (minor errors corrected.)

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## ‣ An extension to GUM methodology: degrees-of-freedom calculations for correlated multidimensional estimates

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 02/11/2013
Português

Relevância na Pesquisa

48.03918%

#Physics - Data Analysis, Statistics and Probability#Statistics - Applications#Statistics - Methodology#62H12 (Primary) 62E17, 62P30, 62P35 (Secondary)

The Guide to the Expression of Uncertainty in Measurement advocates the use
of an 'effective number of degrees of freedom' for the calculation of an
interval of measurement uncertainty. However, it does not describe how this
number is to be calculated when (i) the measurand is a vector quantity or (ii)
when the errors in the estimates of the quantities defining the measurand (the
'input quantities') are not incurred independently. An appropriate analysis for
a vector-valued measurand has been described (Metrologia 39 (2002) 361-9), and
a method for a one-dimensional measurand with dependent errors has also been
given (Metrologia 44 (2007) 340-9). This paper builds on those analyses to
present a method for the situation where the problem is multidimensional and
involves correlated errors. The result is an explicit general procedure that
reduces to simpler procedures where appropriate. The example studied is from
the field of radio-frequency metrology, where measured quantities are often
complex-valued and can be regarded as vectors of two elements.; Comment: 30 pages with 2 embedded figures

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## ‣ Evaluation of the occurrence and type of antiretroviral and opportunistic infection medication errors within the inpatient setting

Fonte: Pharmacy Practice (Granada)
Publicador: Pharmacy Practice (Granada)

Tipo: info:eu-repo/semantics/article; journal article; info:eu-repo/semantics/publishedVersion
Formato: text/html; application/pdf

Publicado em 01/03/2015
Português

Relevância na Pesquisa

37.80126%

Background: Previous data reports inpatient antiretroviral (ARV) and opportunistic infection (OI) medication errors in as many as 86% of patients, with averages ranging from 1.16-2.7 errors/patient. Objective: To determine the occurrence and type of inpatient ARV and OI medication errors at our institution. Methods: A retrospective, observational, electronic medical chart review of patients with HIV/AIDS admitted between February 15, 2011- May 22, 2012 was conducted to assess the occurrence and type of ARV and OI medication errors. Secondary outcomes included assessing each medication with an error and evaluating its potential for a medication error, calculating a medication error rate per patient, evaluating whether a non-formulary (NF) medication impacted the error potential, determining whether a clinical pharmacist on service decreased the medication error rate, and assessing whether patients who experienced an error were more likely to have a longer length of stay (LOS). Analysis included descriptive statistics, averages, and Spearmen rank correlation. Results: There were 344 patients included in this analysis, 132 (38%) experienced 190 medication errors (1.44 errors/patient). An omitted order was the most frequent ARV error and accounted for 30% (n=57) of total errors. There were 166 patients requiring OI medications...

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