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331.
How do individuals come to hold certain beliefs about meritocracy, individualism, and redistribution? Several sociodemographic factors are regularly used to understand variation in these beliefs, but other predictors remain untested. Meanwhile, scholars studying family dynamics are increasingly turning their attention toward the long-term social effects of distinct family features. One facet of this work focuses on the social role of siblings. We investigate the relationship at the intersection of these literatures. How does growing up with (more) siblings influence one's inequality beliefs? Do siblings promote collectivism or might these same relationships foster competition and individualism? Could trust be the mechanism connecting sibship size with inequality beliefs? Or, do sibling effects reflect a broader promotion of conservative thinking in larger families? We investigate these questions using data from the General Social Survey from 1984 to 2018. Our results indicate that siblings are associated with more conservative meritocracy beliefs. On the other hand, we find limited evidence of siblings influencing attitudes toward redistribution. 相似文献
332.
Roberto Di Mari Antonio Punzo Zsuzsa Bakk 《Australian & New Zealand Journal of Statistics》2023,65(3):213-233
Usually in latent class (LC) analysis, external predictors are taken to be cluster conditional probability predictors (LC models with external predictors), and/or score conditional probability predictors (LC regression models). In such cases, their distribution is not of interest. Class-specific distribution is of interest in the distal outcome model, when the distribution of the external variables is assumed to depend on LC membership. In this paper, we consider a more general formulation, that embeds both the LC regression and the distal outcome models, as is typically done in cluster-weighted modelling. This allows us to investigate (1) whether the distribution of the external variables differs across classes, (2) whether there are significant direct effects of the external variables on the indicators, by modelling jointly the relationship between the external and the latent variables. We show the advantages of the proposed modelling approach through a set of artificial examples, an extensive simulation study and an empirical application about psychological contracts among employees and employers in Belgium and the Netherlands. 相似文献
333.
Joseph S. Fulda 《Business and Society Review》1999,104(2):179-189
334.
Ni WangJye-Chi Lu Di ChenPaul H. Kvam 《Journal of statistical planning and inference》2011,141(1):140-155
This article proposes an adjusted empirical likelihood estimation (AMELE) method to model and analyze accelerated life testing data. This approach flexibly and rigorously incorporates distribution assumptions and regression structures by estimating equations within a semiparametric estimation framework. An efficient method is provided to compute the empirical likelihood estimates, and asymptotic properties are studied. Real-life examples and numerical studies demonstrate the advantage of the proposed methodology. 相似文献
335.
The aim of this paper is to develop a Bayesian local influence method (Zhu et al. 2009, submitted) for assessing minor perturbations
to the prior, the sampling distribution, and individual observations in survival analysis. We introduce a perturbation model
to characterize simultaneous (or individual) perturbations to the data, the prior distribution, and the sampling distribution.
We construct a Bayesian perturbation manifold to the perturbation model and calculate its associated geometric quantities
including the metric tensor to characterize the intrinsic structure of the perturbation model (or perturbation scheme). We
develop local influence measures based on several objective functions to quantify the degree of various perturbations to statistical
models. We carry out several simulation studies and analyze two real data sets to illustrate our Bayesian local influence
method in detecting influential observations, and for characterizing the sensitivity to the prior distribution and hazard
function. 相似文献
336.
Large databases of routinely collected data are a valuable source of information for detecting potential associations between drugs and adverse events (AE). A pharmacovigilance system starts with a scan of these databases for potential signals of drug-AE associations that will subsequently be examined by experts to aid in regulatory decision-making. The signal generation process faces some key challenges: (1) an enormous volume of drug-AE combinations need to be tested (i.e. the problem of multiple testing); (2) the results are not in a format that allows the incorporation of accumulated experience and knowledge for future signal generation; and (3) the signal generation process ignores information captured from other processes in the pharmacovigilance system and does not allow feedback. Bayesian methods have been developed for signal generation in pharmacovigilance, although the full potential of these methods has not been realised. For instance, Bayesian hierarchical models will allow the incorporation of established medical and epidemiological knowledge into the priors for each drug-AE combination. Moreover, the outputs from this analysis can be incorporated into decision-making tools to help in signal validation and posterior actions to be taken by the regulators and companies. We discuss in this paper the apparent advantage of the Bayesian methods used in safety signal generation and the similarities and differences between the two widely used Bayesian methods. We will also propose the use of Bayesian hierarchical models to address the three key challenges and discuss the reasons why Bayesian methodology still have not been fully utilised in pharmacovigilance activities. 相似文献
337.
Jones B Roger J Lane PW Lawton A Fletcher C Cappelleri JC Tate H Moneuse P;PSI Health Technology Special Interest Group Evidence Synthesis sub-team 《Pharmaceutical statistics》2011,10(6):523-531
We introduce health technology assessment and evidence synthesis briefly, and then concentrate on the statistical approaches used for conducting network meta-analysis (NMA) in the development and approval of new health technologies. NMA is an extension of standard meta-analysis where indirect as well as direct information is combined and can be seen as similar to the analysis of incomplete-block designs. We illustrate it with an example involving three treatments, using fixed-effects and random-effects models, and using frequentist and Bayesian approaches. As most statisticians in the pharmaceutical industry are familiar with SAS? software for analyzing clinical trials, we provide example code for each of the methods we illustrate. One issue that has been overlooked in the literature is the choice of constraints applied to random effects, and we show how this affects the estimates and standard errors and propose a symmetric set of constraints that is equivalent to most current practice. Finally, we discuss the role of statisticians in planning and carrying out NMAs and the strategy for dealing with important issues such as heterogeneity. 相似文献
338.
The adaptive cluster sampling (ACS) is a suitable sampling design for rare and clustered populations. In environmental and
ecological applications, biological populations are generally animals or plants with highly patchy spatial distribution. However,
ACS would be a less efficient design when the study population is not rare with low aggregation since the final sample size
could be easily out of control. In this paper, a new variant of ACS is proposed in order to improve the performance (in term
of precision and cost) of ACS versus simple random sampling (SRS). The idea is to detect the optimal sample size by means
of a data-driven stopping rule in order to determine when to stop the adaptive procedure. By introducing a stopping rule the
theoretical basis of ACS are not respected and the behaviour of the ordinary estimators used in ACS is explored by using Monte
Carlo simulations. Results show that the proposed variant of ACS allows to control the effective sample size and to prevent
from excessive efficiency loss typical of ACS when the population is less clustered than anticipated. The proposed strategy
may be recommended especially when no prior information about the population structure is available as it does not require
a prior knowledge of the degree of rarity and clustering of the population of interest. 相似文献
339.
In a recent article by Rosenthal, Zydiak, and Chaudhry (1995), a mixed integer linear programming model was introduced to solve the vendor selection problem for the case in which the vendor can sell items individually or as part of a bundle. Each vendor offered only one type of bundle, and the buyer could purchase at most one bundle per vendor. The model employed n(m+ 1) binary variables, where n is the number of vendors and m is the number of products they sell. The existing model can lead to a purchasing paradox: it may force the buyer to pay more to receive less. We suggest a reformulation of the same problem that (i) eliminates this paradox and reveals a more cost-effective purchasing strategy; (ii) uses only n integer variables and significantly reduces the computational workload; and (iii) permits the buyer to purchase more than one bundle per vendor. 相似文献
340.