15,949 research outputs found
A bi-dimensional finite mixture model for longitudinal data subject to dropout
In longitudinal studies, subjects may be lost to follow-up, or miss some of
the planned visits, leading to incomplete response sequences. When the
probability of non-response, conditional on the available covariates and the
observed responses, still depends on unobserved outcomes, the dropout mechanism
is said to be non ignorable. A common objective is to build a reliable
association structure to account for dependence between the longitudinal and
the dropout processes. Starting from the existing literature, we introduce a
random coefficient based dropout model where the association between outcomes
is modeled through discrete latent effects. These effects are outcome-specific
and account for heterogeneity in the univariate profiles. Dependence between
profiles is introduced by using a bi-dimensional representation for the
corresponding distribution. In this way, we define a flexible latent class
structure which allows to efficiently describe both dependence within the two
margins of interest and dependence between them. By using this representation
we show that, unlike standard (unidimensional) finite mixture models, the non
ignorable dropout model properly nests its ignorable counterpart. We detail the
proposed modeling approach by analyzing data from a longitudinal study on the
dynamics of cognitive functioning in the elderly. Further, the effects of
assumptions about non ignorability of the dropout process on model parameter
estimates are (locally) investigated using the index of (local) sensitivity to
non-ignorability
Lagrangian Time Series Models for Ocean Surface Drifter Trajectories
This paper proposes stochastic models for the analysis of ocean surface
trajectories obtained from freely-drifting satellite-tracked instruments. The
proposed time series models are used to summarise large multivariate datasets
and infer important physical parameters of inertial oscillations and other
ocean processes. Nonstationary time series methods are employed to account for
the spatiotemporal variability of each trajectory. Because the datasets are
large, we construct computationally efficient methods through the use of
frequency-domain modelling and estimation, with the data expressed as
complex-valued time series. We detail how practical issues related to sampling
and model misspecification may be addressed using semi-parametric techniques
for time series, and we demonstrate the effectiveness of our stochastic models
through application to both real-world data and to numerical model output.Comment: 21 pages, 10 figure
Estimating healthcare demand for an aging population: a flexible and robust bayesian joint model
In this paper, we analyse two frequently used measures of the demand for health care, namely hospital visits and out-of-pocket health care expenditure, which have been analysed separately in the existing literature. Given that these two measures of healthcare demand are highly likely to be closely correlated, we propose a framework to jointly model hospital visits and out-of-pocket medical expenditure. Furthermore, the joint framework allows for the presence of non-linear effects of covariates using splines to capture the effects of aging on healthcare demand. Sample heterogeneity is modelled robustly with the random effects following Dirichlet process priors with explicit cross-part correlation. The findings of our empirical analysis of the U.S. Health and Retirement Survey indicate that the demand for healthcare varies with age and gender and exhibits significant cross-part correlation that provides a rich understanding of how aging affects health care demand, which is of particular policy relevance in the context of an aging population
Semi-Parametric Empirical Best Prediction for small area estimation of unemployment indicators
The Italian National Institute for Statistics regularly provides estimates of
unemployment indicators using data from the Labor Force Survey. However, direct
estimates of unemployment incidence cannot be released for Local Labor Market
Areas. These are unplanned domains defined as clusters of municipalities; many
are out-of-sample areas and the majority is characterized by a small sample
size, which render direct estimates inadequate. The Empirical Best Predictor
represents an appropriate, model-based, alternative. However, for non-Gaussian
responses, its computation and the computation of the analytic approximation to
its Mean Squared Error require the solution of (possibly) multiple integrals
that, generally, have not a closed form. To solve the issue, Monte Carlo
methods and parametric bootstrap are common choices, even though the
computational burden is a non trivial task. In this paper, we propose a
Semi-Parametric Empirical Best Predictor for a (possibly) non-linear mixed
effect model by leaving the distribution of the area-specific random effects
unspecified and estimating it from the observed data. This approach is known to
lead to a discrete mixing distribution which helps avoid unverifiable
parametric assumptions and heavy integral approximations. We also derive a
second-order, bias-corrected, analytic approximation to the corresponding Mean
Squared Error. Finite sample properties of the proposed approach are tested via
a large scale simulation study. Furthermore, the proposal is applied to
unit-level data from the 2012 Italian Labor Force Survey to estimate
unemployment incidence for 611 Local Labor Market Areas using auxiliary
information from administrative registers and the 2011 Census
Technical Efficiency of Australian Wool Production: Point and Confidence Interval Estimates
A balanced panel of data is used to estimate technical efficiency, employing a fixed-effects stochastic frontier specification for wool producers in Australia. Both point estimates and confidence intervals for technical efficiency are reported. The confidence intervals are constructed using the Multiple Comparisons with the Best (MCB) procedure of Horrace and Schmidt (2000). The confidence intervals make explicit the precision of the technical efficiency estimates and underscore the dangers of drawing inferences based solely on point estimates. Additionally, they allow identification of wool producers that are statistically efficient and those that are statistically inefficient. The data reveal at the 95% confidence level that twenty-one of the twenty-six wool farms analyzed may be efficient.Wool, Technical Efficiency, MCB, MCC
Food Superstores, Food Deserts and Traffic Generation in the UK: A Semi-Parametric Regression Approach
This study contributes another route towards explaining and tackling ‘food desert’ effects. It features the estimation of a (semi-parametric) trip attraction model for food superstores in the UK using a composite dataset. The data comprises information from the UK Census of Population, the NOMIS (National Online Manpower Information System) archive and traffic and site-specific data from the TRICS (Trip Rate Information Computer System) databases. The results indicate that traffic to a given food superstore, ceteris paribus, increases with household car ownership, store parking provision, site size (floor space), and distance to the nearest competitor. Furthermore, increases in public transport provision are shown to be associated with increasing car trips. This latter effect is discussed in the light of planning policy for development control purposes and a role linked to the reinforcement of ‘food deserts’. The results also reveal activity-specific household economies of scope and scale. It is suggested how these may also further perpetuate unsustainable development and ‘food desert’ characteristics.Traffic Generation, Food Superstores, Food Deserts, Activity Based Travel, Sustainable Development, Modelling
MIGRATION AND LOCAL OFF-FARM WORKING IN RURAL CHINA
The paper analyzes the decision-making of rural Chinese households with three alternatives: stay exclusively on farm, take local off-farm jobs, and migrate. Based on a survey of rural Chinese households, we extend the dynamic discrete choice model of Wooldridge (2002a,b) to a trichotomous setting and apply it to a five-year panel study. We observe statistically significant state dependence between the current period response and decisions of the previous time period. We also conclude that education, household size, and social networks play important roles in job-location decision-making of rural Chinese households.Labor and Human Capital,
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