2,754 research outputs found
Promoting Social Participation for Healthy Ageing - A Counterfactual Analysis from the Survey of Health, Ageing, and Retirement in Europe (SHARE)
Promoting social participation of the older population (e.g. membership in voluntary associations) is often seen as a promising strategy for 'healthy ageing' in Europe. Although a growing body of academic literature challenges the idea that the link between social participation and health is well established, some statistical evidence suggest a robust positive relationship may exist for older people. One reason could be that aged people have more time to take part in social activities (due to retirement, fewer familial constraints, etc.); so that such involvement in voluntary associations contributes to maintain network size for social and emotional support; and preserves individuals' cognitive capacities. Using SHARE data for respondents aged fifty and over in 2004, this study proposes to test these hypotheses by evaluating the contribution of social participation to self-reported health (SRH) in eleven European countries. The probability to report good or very good health is calculated for the whole sample (after controlling for age, education, income and household composition) using regression coefficients estimated for individuals who do and for those who do not take part in social activities (with correction for selection bias in these two cases). Counterfactual national levels of SRH are derived from integral computation of cumulative distribution functions of the predicted probability thus obtained. The analysis reveals that social participation contributes by three percentage points to the increase in the share of individuals reporting good or very good health on average. Higher rates of social participation could improve health status and reduce health inequalities within the whole sample and within every country. Our results thus suggest that 'healthy ageing' policies based on social participation promotion may be beneficial for the aged population in Europe.Healthy ageing, Self-reported health, Social participation, Social capital, SHARE data, Counterfactual analysis, Stochastic dominance
Continuous Influence-based Community Partition for Social Networks
Community partition is of great importance in social networks because of the
rapid increasing network scale, data and applications. We consider the
community partition problem under LT model in social networks, which is a
combinatorial optimization problem that divides the social network to disjoint
communities. Our goal is to maximize the sum of influence propagation
through maximizing it within each community. As the influence propagation
function of community partition problem is supermodular under LT model, we use
the method of Lov{}sz Extension to relax the target influence
function and transfer our goal to maximize the relaxed function over a matroid
polytope. Next, we propose a continuous greedy algorithm using the properties
of the relaxed function to solve our problem, which needs to be discretized in
concrete implementation. Then, random rounding technique is used to convert the
fractional solution to integer solution. We present a theoretical analysis with
approximation ratio for the proposed algorithms. Extensive experiments
are conducted to evaluate the performance of the proposed continuous greedy
algorithms on real-world online social networks datasets and the results
demonstrate that continuous community partition method can improve influence
spread and accuracy of the community partition effectively.Comment: arXiv admin note: text overlap with arXiv:2003.1043
A k-hop Collaborate Game Model: Extended to Community Budgets and Adaptive Non-Submodularity
Revenue maximization (RM) is one of the most important problems on online
social networks (OSNs), which attempts to find a small subset of users in OSNs
that makes the expected revenue maximized. It has been researched intensively
before. However, most of exsiting literatures were based on non-adaptive
seeding strategy and on simple information diffusion model, such as
IC/LT-model. It considered the single influenced user as a measurement unit to
quantify the revenue. Until Collaborate Game model appeared, it considered
activity as a basic object to compute the revenue. An activity initiated by a
user can only influence those users whose distance are within k-hop from the
initiator. Based on that, we adopt adaptive seed strategy and formulate the
Revenue Maximization under the Size Budget (RMSB) problem. If taking into
account the product's promotion, we extend RMSB to the Revenue Maximization
under the Community Budget (RMCB) problem, where the influence can be
distributed over the whole network. The objective function of RMSB and RMCB is
adatpive monotone and not adaptive submodular, but in some special cases, it is
adaptive submodular. We study the RMSB and RMCB problem under both the speical
submodular cases and general non-submodular cases, and propose RMSBSolver and
RMCBSolver to solve them with strong theoretical guarantees, respectively.
Especially, we give a data-dependent approximation ratio for RMSB problem under
the general non-submodular cases. Finally, we evaluate our proposed algorithms
by conducting experiments on real datasets, and show the effectiveness and
accuracy of our solutions
Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes
In a randomized study, leveraging covariates related to the outcome (e.g.
disease status) may produce less variable estimates of the effect of exposure.
For contagion processes operating on a contact network, transmission can only
occur through ties that connect affected and unaffected individuals; the
outcome of such a process is known to depend intimately on the structure of the
network. In this paper, we investigate the use of contact network features as
efficiency covariates in exposure effect estimation. Using augmented
generalized estimating equations (GEE), we estimate how gains in efficiency
depend on the network structure and spread of the contagious agent or behavior.
We apply this approach to simulated randomized trials using a stochastic
compartmental contagion model on a collection of model-based contact networks
and compare the bias, power, and variance of the estimated exposure effects
using an assortment of network covariate adjustment strategies. We also
demonstrate the use of network-augmented GEEs on a clustered randomized trial
evaluating the effects of wastewater monitoring on COVID-19 cases in
residential buildings at the the University of California San Diego.Comment: Substantial revisio
Regression adjustments for estimating the global treatment effect in experiments with interference
Standard estimators of the global average treatment effect can be biased in
the presence of interference. This paper proposes regression adjustment
estimators for removing bias due to interference in Bernoulli randomized
experiments. We use a fitted model to predict the counterfactual outcomes of
global control and global treatment. Our work differs from standard regression
adjustments in that the adjustment variables are constructed from functions of
the treatment assignment vector, and that we allow the researcher to use a
collection of any functions correlated with the response, turning the problem
of detecting interference into a feature engineering problem. We characterize
the distribution of the proposed estimator in a linear model setting and
connect the results to the standard theory of regression adjustments under
SUTVA. We then propose an estimator that allows for flexible machine learning
estimators to be used for fitting a nonlinear interference functional form. We
propose conducting statistical inference via bootstrap and resampling methods,
which allow us to sidestep the complicated dependences implied by interference
and instead rely on empirical covariance structures. Such variance estimation
relies on an exogeneity assumption akin to the standard unconfoundedness
assumption invoked in observational studies. In simulation experiments, our
methods are better at debiasing estimates than existing inverse propensity
weighted estimators based on neighborhood exposure modeling. We use our method
to reanalyze an experiment concerning weather insurance adoption conducted on a
collection of villages in rural China.Comment: 38 pages, 7 figure
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