Background. At a group level, cancer results in reduced labor earnings. Public benefits common to welfare states may, however, compensate for all or parts of the decline in earnings. Norwegian cancer survivors' in...
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Background. At a group level, cancer results in reduced labor earnings. Public benefits common to welfare states may, however, compensate for all or parts of the decline in earnings. Norwegian cancer survivors' incomes, including both labor earnings and compensatory welfare benefits, were compared to those of the cancer-free population to assess potential welfare consequences of cancer. Possible modifying effects of parental and marital status, education, prior earnings and age were assessed in depth. Material and methods. log-linear regression models were used to estimate incomes across different sociodemographic variables using register data covering the entire Norwegian population 40-59 years old with any income in 2008, 536 600 men and 502 500 women, of whom more than 17 000 were diagnosed with cancer in 2000-2007. Results. Compared to the cancer-free general population, cancer was associated with a modest 7% decline in incomes overall. The decline was, however, significantly associated with sociodemographic factors, marital status exempted. Childless men with low education and low prior earnings were most adversely affected. Lymphomas and lung cancer accounted largely for these unfavorable effects. Conclusions. Declines in earnings after cancer are to a large degree compensated by the Norwegian welfare state, and incomes overall are only modestly decreased among cancer survivors compared to the general population. Persons with multiple unfavorable sociodemographic characteristics experience particularly low incomes after cancer. This is of concern in a supposedly egalitarian society with public health care and antidiscrimination acts in place. Welfare state interventions, i.e. work reintegration efforts and/or compensations for labor earning drops, directed specifically towards these subgroups might be warranted.
We define the group-lasso estimator for the natural parameters of the exponential families of distributions representing hierarchical log-linear models under multinomial sampling scheme. Such estimator arises as the s...
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We define the group-lasso estimator for the natural parameters of the exponential families of distributions representing hierarchical log-linear models under multinomial sampling scheme. Such estimator arises as the solution of a convex penalized likelihood optimization problem based on the group-lasso penalty. We illustrate how it is possible to construct an estimator of the underlying log-linear model using the blocks of nonzero coefficients recovered by the group-lasso procedure. We investigate the asymptotic properties of the group-lasso estimator as a model selection method in a double-asymptotic framework, in which both the sample size and the model complexity grow simultaneously. We provide conditions guaranteeing that the group-lasso estimator is model selection consistent, in the sense that, with overwhelming probability as the sample size increases, it correctly identifies all the sets of nonzero interactions among the variables. Provided the sequences of true underlying models is sparse enough, recovery is possible even if the number of cells grows larger than the sample size. Finally, we derive some central limit type of results for the log-linear group-lasso estimator.
. In this paper, we provide a definition of pattern of outliers in contingency tables within a model-based framework. In particular, we make use of log-linear models and exact goodness-of-fit tests to specify the noti...
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. In this paper, we provide a definition of pattern of outliers in contingency tables within a model-based framework. In particular, we make use of log-linear models and exact goodness-of-fit tests to specify the notions of outlier and pattern of outliers. The language and some techniques from Algebraic Statistics are essential tools to make the definition clear and easily applicable. We also analyse several numerical examples to show how to use our definitions.
Explicit length modelling has been previously explored in statistical pattern recognition with successful results. In this paper, two length models along with two parameter estimation methods and two alternative param...
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Explicit length modelling has been previously explored in statistical pattern recognition with successful results. In this paper, two length models along with two parameter estimation methods and two alternative parametrisations for statistical machine translation (SMT) are presented. More precisely, we incorporate explicit bilingual length modelling in a state-of-the-art log-linear SMT system as an additional feature function in order to prove the contribution of length information. Finally, a systematic evaluation on reference SMT tasks considering different language pairs proves the benefits of explicit length modelling. (C) 2012 Elsevier Ltd. All rights reserved.
Objective: In health research, ordinal scales are extensively used. Reproducibility of ratings using these scales is important to assess their quality. This study aimed to compare two methods analyzing reproducibility...
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Objective: In health research, ordinal scales are extensively used. Reproducibility of ratings using these scales is important to assess their quality. This study aimed to compare two methods analyzing reproducibility: weighted Kappa statistic and log-linear models. Study Design and Setting: Contributions of each method to the reproducibility assessment of ratings using ordinal scales were compared using intra- and interobserver data chosen in three different fields: Crow's feet scale in dermatology, dysplasia scale in oncology, updated Sydney scale in gastroenterology. Results: Both methods provided an agreement level. In addition, log-linear models allowed evaluation of the structure of agreement. For the Crow's feet scale, both methods gave equivalent high agreement levels. For the dysplasia scale, log-linear models highlighted scale defects and Kappa statistic showed a moderate agreement. For the updated Sydney scale, log-linear models underlined a null distinguishability between two adjacent categories, whereas Kappa statistic gave a high global agreement level. Conclusion: Methods that can investigate level and structure of agreement between ordinal ratings are valuable tools, since they may highlight heterogeneities within the scales structure and suggest modifications to improve their reproducibility. (C) 2008 Elsevier Inc. All rights reserved.
In this paper, we discuss the MLEs for log-linear models with partially classified data. We propose to apply the Aitken delta(2) method of Aitken [Aitken, A.C., 1926. On Bernoulli's numerical solution of algebraic...
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In this paper, we discuss the MLEs for log-linear models with partially classified data. We propose to apply the Aitken delta(2) method of Aitken [Aitken, A.C., 1926. On Bernoulli's numerical solution of algebraic equations. Proc. R. Soc. Edinburgh 46, 289-305] to the EM and ECM algorithms to accelerate their convergence. The Aitken 2 accelerated algorithm shares desirable properties of the EM algorithm, such as numerical stability, computational simplicity and flexibility in interpreting the incompleteness of data. We show the convergence of the Aitken delta(2) accelerated algorithm and compare its speed of convergence with that of the EM algorithm, and we also illustrate their performance by means of a simulation. (C) 2008 Elsevier B.V. All rights reserved.
We study in detail the log-linear return approximation introduced by Campbell and Shiller (1988a). First, we derive an upper bound for the mean approximation error, given stationarity of the log dividend-price ratio. ...
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We study in detail the log-linear return approximation introduced by Campbell and Shiller (1988a). First, we derive an upper bound for the mean approximation error, given stationarity of the log dividend-price ratio. Next, we simulate various rational bubbles that have explosive conditional expectation, and we investigate the magnitude of the approximation error in those cases. We find that, surprisingly, the Campbell-Shiller approximation is very accurate even in the presence of large explosive bubbles. Only in very large samples do we find evidence that bubbles generate large approximation errors. Finally, we show that a bubble model in which expected returns are constant can explain the predictability of stock returns from the dividend-price ratio that many previous studies have documented.
When confronted with violations of justice, people may be motivated not only to punish the violator, but also to compensate the victim. Whereas prior research has primarily concentrated on the question of when people ...
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When confronted with violations of justice, people may be motivated not only to punish the violator, but also to compensate the victim. Whereas prior research has primarily concentrated on the question of when people are willing to punish, we provide a more comprehensive picture by also studying the willingness to compensate and by assessing the moderating role of empathic concern. Study 1 introduces the altruistic compensation game and shows that especially high empathic (compared to low empathic) people are willing to give up parts of their own resources to financially compensate the victims of distributive injustice. Study 2 completes the picture by directly comparing altruistic compensation with altruistic punishment. The study showed that high empathic people decided to compensate the victim, but low empathic people decided to punish the offender. Copyright (c) 2012 John Wiley & Sons, Ltd.
This article deals with the relationship between educational homogamy and educational mobility in 29 European countries. It answers three interrelated questions: (1) Is there any relationship between educational mobil...
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This article deals with the relationship between educational homogamy and educational mobility in 29 European countries. It answers three interrelated questions: (1) Is there any relationship between educational mobility and educational homogamy? (2) Does educational homogamy diverge from educational mobility (negative relationship) or does educational heterogamy strengthen educational mobility (positive relationship)? (3) If there is any positive relationship, do educational mobility and educational heterogamy indicate the level of educational inequality to the same degree? To answer these questions the authors use data from three waves of the European Social Survey (2002, 2004 and 2006). The answers are given in absolute (percentages) as well as in relative measures (log-multiplicative effects). The results show that there is a positive relationship between educational mobility and educational heterogamy. For all countries analysed, relative educational heterogamy is a stronger indicator of educational inequalities than relative educational mobility. The systematic deviation of educational heterogamy from educational mobility is explained by a number of factors that the authors discuss in the last part of the article.
The general diagnostic model (GDM) utilizes located latent classes for modeling a multidimensional proficiency variable. In this paper, the GDM is extended by employing a log-linear model for multiple populations that...
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