4,439 research outputs found
Age-period-cohort analysis of U.S. fertility: a realistic approach
No full textA key question for the explanation of fertility trends in advanced societies is whether, in addition to age, period- rather than cohort-related factors matter. In this paper, we analyze a standard set of age-specific fertility rates – from the Human Fertility Database – on the United States between 1933 and 2015. More specifically, we describe and apply an Age-Period-Cohort (APC) modeling approach that relies on second differences as identifiable parameters. Results of our APC analyses tend to be consistent with an interpretation that gives a greater weight to period effects over shorter time horizons, with a significant pres-ence of smooth cohort effects over the longer term
How to Compute a Mean? The Chisini Approach and Its Applications
No full textScholars often consider the arithmetic mean as the only mean available. This gives rise to several mistakes. Thus, in a first course in statistics, it is necessary to introduce them to a more general concept of mean. In this work we present the notion of mean suggested by Oscar Chisini in 1929 which has a double advantage. It focuses students' minds on the substance of the problem for which a mean is required, thus discouraging any automatic procedure, and it does not require a preliminary list of the different mean formulas.
Advantages and limits of the Chisini mean are discussed by means of
examples
Stochastic Population Forecasting: A Bayesian Approach Based on Evaluation by Experts
No full textWe suggest a procedure for deriving expert based stochastic population forecasts within the Bayesian approach. According to the traditional and commonly used cohort-component model, the inputs of the forecasting procedures are the fertility and mortality age schedules along with the distribution of migrants by age. Age schedules and distributions are derived from summary indicators, such as total fertility rates, male and female life expectancy at birth, and male and female number of immigrants and emigrants. The joint distributions of all summary indicators are obtained based on evaluations by experts, elicited according to a conditional procedure that makes it possible to derive information on the centers of the indicators, their variability, their across-time correlations, and the correlations between the indicators. The forecasting method is based on a mixture model within the Supra-Bayesian approach that treats the evaluations by experts as data and the summary indicators as parameters. The derived posterior distributions are used as forecast distributions of the summary indicators of interest. A Markov Chain Monte Carlo algorithm is designed to approximate such posterior distributions
The sensitivity of the Scaled Model of Error with respect to the choice of the correlation parameters: A simulation study
No full textThe Scaled Model of Error has gained considerable popularity during the past ten years as a device for computing probabilistic population forecasts of the cohort-component type. In this report we investigate how sensitive probabilistic population forecasts produced by means of the Scaled Model of Error are for small changes in the correlation parameters. We consider changes in the correlation of the age-specific fertility forecast error increments across time and age, and changes in the correlation of the age-specific mortality forecast error increments across time, age and sex. Next we analyse the impact of such changes on the forecasts of the Total Fertility Rate and of the Male and Female Life Expectancies respectively. For age specific fertility we find that the correlation across ages has only limited impact on the uncertainty in the Total Fertility Rate. As a consequence, annual numbers of births will be little affected. The autocorrelation in error increments is an important parameter, in particular in the long run. Also, the autocorrelation in error increments for age specific mortality is important. It has a large effect on long run uncertainty in life expectancy values, and hence on the uncertainty around the elderly population in the future. In empirical applications of the Scaled Model of Error, one should give due attention to a correct estimation of these two parameters
A Bayesian approach to discrete multiple outcome network meta-analysis
No full textIn this paper we suggest a new Bayesian approach to network meta-analysis for the case of discrete multiple outcomes. The joint distribution of the discrete outcomes is modeled through a Gaussian copula with binomial marginals. The remaining elements of the hierarchial random effects model are specified in a standard way, with the logit of the success probabilities given by the sum of a baseline log-odds and random effects comparing the log-odds of each treatment against the reference and having a Gaussian distribution centered at the vector of pooled effects. An adaptive Markov Chain Monte Carlo algorithm is devised for running posterior inference. The model is applied to two datasets from Cochrane reviews, already analysed in two papers so to assess and compare its performance. We implemented the model in a freely available R package called netcopula
Understanding personal mobile technologies: decomposing and de-averaging the value of a smartphone
No full textThe study focuses on the multifaceted motives for adopting personal technologies. Specifically, it uses earlier models of technology adoption to develop a model of smartphone acceptance. The model is unique in that it decomposes attitudinal beliefs into three components: functional value, hedonic value, and symbolic value. Latent class analysis facilitates the identification of three user types. The analysis shows that value drivers, control beliefs, and normative beliefs play different roles for determining smartphone acceptance, depending on three different individual characteristics (i.e., playfulness, public self-consciousness, and innovativeness). The paper makes a contribution to the information systems literature by providing an analysis of the drivers of overall value perceptions for multipurpose information appliances and of the role of individual differences among potential users in forming these attitudes
Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm
No full textThe paper suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in a previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution, i.e. fertility, mortality and migration. Here two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but it is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called Supra-Bayesian approach according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution and a Markov Chain Monte Carlo algorithm is designed to approximate this posterior. The paper provides the questionnaire which was designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian Population from 2010 up to 2065 is proposed
Bayesian Inference for Lancaster Probabilities
No full textInference for bivariate distributions with fixed marginals is very important in applications. When a bayesian approach is followed, the problem of defining a (prior) distribution on a class of probabilities having given marginals arises. We consider the class
of Lancaster distributions. It is a convex and compact set, so that any element may be represented as a mixture of extreme points.
Therefore a prior distribution can be assigned to the Lancaster class by assuming the mixing measure as a random probability. We analyse in detail the Lancaster class with Gamma marginals. Choosing as mixing measure a Dirichlet process, the model turns out to be a
Dirichlet process mixture model. Many quantities relevant for statistical purposes are
linear functionals of the Dirichlet process.
Posterior laws are determined; in order to approximate these laws a MCMC algorithm is suggested. Results of an example with simulated data are discussed
Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome
No full textThe effect of a targeted agent on a cancer patients clinical outcome putatively is mediated through the agents effect on one or more early biological events. This is motivated by pre-clinical experiments with cells or animals that identify such events, represented by binary or quantitative biomarkers. When evaluating targeted agents in humans, central questions are whether the distribution of a targeted biomarker changes following treatment, the nature and magnitude of this change, and whether it is associated with clinical outcome. Major difficulties in estimating these effects are that a biomarkers distribution may be complex, vary substantially between patients, and have complicated relationships with clinical outcomes. We present a probabilistically coherent framework for modeling and estimation in this setting, including a hierarchical Bayesian nonparametric mixture model for biomarkers that we use to define a functional profile of pre-versus-post-treatment biomarker distribution change. The functional is similar to the receiver operating characteristic used in diagnostic testing. The hierarchical
model yields clusters of individual patient biomarker profile functionals, and we use the profile as a covariate in a regression model for clinical outcome. The methodology is illustrated by analysis of a dataset from a clinical trial in prostate cancer using imatinib to target platelet-derived growth factor, with the clinical aim to improve progression-free survival time
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