A new dimension to content analysis: Exploring relationships among thematic categories

Conventional content analysis uses “hard-edged” categories for coding qualitative data (e.g., content themes), and this practice not only loses valuable information but also restricts the ways in which such data may be analyzed. This paper presents a procedure based on fuzzy set theory which extends content analysis by permitting the researcher to use fuzzy, or “blurred” categories for coding. These categories are allowed to overlap one another, thereby enabling the researcher to investigate overlap and inclusion relationships among thematic categories. The technique is briefly explained, and the bulk of the paper is devoted to a demonstration of its use in an applied research context. The final section discusses some extensions of this technique and its applications in exploratory data analysis.     

Predicting binary outcomes

We address the issue of using a set of covariates to categorize or predict a binary outcome. This is a common problem in many disciplines including economics. In the context of a prespecified utility (or cost) function we examine the construction of forecasts suggesting an extension of the  and  maximum score approach. We provide analytical properties of the method and compare it to more common approaches such as forecasts or classifications based on conditional probability models. Large gains over existing methods can be attained when models are misspecified.     

异因同果关联神经网络的研究

提出了一种异因同果关联神经网络模型,可以从不同角度分别建立不同的模型,并由其得到互不相同的模型预测值。异因同果关联神经网络模型将不同角度建立的模型有机结合起来,进而能够将多个神经网络模型进行综合考虑,得到一个综合的统一的模型预测结果。研究了新型模型的机理,结合实例进行仿真并与传统的神经网络模型的预测仿真结果比较,结果表明新型模型具有更高的预测精度。     

Statistical methods for some simple disclosure limitation rules

To guard the confidentiality of information provided by respondents, statistical offices apply disclosure limitation techniques. An often applied technique is to ensure that there are no categories for which the population frequency is presumed to be small (‘rare’ categories). This is attained by recoding, top-coding or setting values to ‘unknown’. Since population frequencies are usually not available, the decision that a category is rare is often based on intuitive considerations. This is a time consuming process, involving many decisions of the disclosure limitation practitioners. In this paper it will be explored to what extent the sample frequencies can be used to make such decisions. This leads to a procedure which enables to automatically scan a data set for rare category combinations, whereby ‘rare’ is defined by the disclosure limitation policy of the statistical office.     

Estimated Precision for Predictions from Generalized Linear Models in Sociological Research

In this paper I present a general method forconstructing confidence intervals for predictionsfrom the generalized linear model in sociologicalresearch. I demonstrate that the method used forconstructing confidence intervals for predictions inclassical linear models is indeed a special case ofthe method for generalized linear models. I examinefour such models – the binary logit, the binaryprobit, the ordinal logit, and the Poissonregression model – to construct confidence intervalsfor predicted values in the form of probability,odds, Z score, or event count. The estimatedconfidence interval for an event prediction, whenapplied judiciously, can give the researcher usefulinformation and an estimated measure of precisionfor the prediction so that interpretation ofestimates from the generalized linear model becomeseasier.     

A framework for analysing social sequences

Longitudinal categorical data arise in many diverse areas of the social sciences and methods for its analysis have taken two broad directions. Heuristically, one can attempt to model the state space (i.e., the categories) or the sequence space (i.e., the subjects), typically with event history models or optimal matching respectively. This study proposes a more general framework for inference from such data which acknowledges not only the analytic approach (split into stochastic models and algorithmic differencing) but also hypothesis, sequences, categorisation and representation. The individual sequence can be thought of as a map from time to the state space. The hypothesis relates to how these maps are similar and how they deviate from this structure. The analytical frameworks define what is assumed, what is uncertain, and how this is modelled. The categories of the state variable define what is considered pivotal as an event. Representations create explorative tools and describe structure, as well as communicating high dimensional inferences. It is the interaction between these ideas which is fundamental to making inferences, as well as their relationship to time, which is essential to the social science treatment of sequences. Thus, the analysis should not prefer one approach to analysis over another but appreciate the origin of the data and the theory under examination.     

Selecting stochastic mortality models for the Italian population

The future revision of capital requirements and a market-consistent valuation of non-hedgeable liabilities lead to an increasing attention on forecasting longevity trends. In this field, many methodologies focus on either modeling mortality or pricing mortality-linked securities (as longevity bonds). Following Lee–Carter method (proposed in 1992), actuarial literature has provided several extensions in order to consider different trends observed in European data set (e.g., the cohort effect). The purpose of the paper is to compare the features of main mortality models proposed over the years. Model selection became indeed a primary task with the aim to identify the “best” model. What is meant by best is controversial, but good selection techniques are usually based on a good balance between goodness of fit and simplicity. In this regard, different criteria, mainly based on residual and projected rates analysis, are here used. For the sake of comparison, main forecasting methods have been applied to deaths and exposures to risk of male Italian population. Weaknesses and strengths have been emphasized, by underlying how various models provide a different goodness of fit according to different data sets. At the same time, the quality and the variability of forecasted rates have been compared by evaluating the effect on annuity values. Results confirm that some models perform better than others, but no single model can be defined as the best method.     

The use of dummy variables to compute predictions,prediction errors,and confidence intervals

This paper presents a method for computing predictions, prediction error variances, and confidence intervals, which can be implemented with any regression program. It demonstrates that a regression estimated for an augmented data set, obtained by (1) combining n sample points with r forecast points, and (2) including r dummy variables (each equalling one only for the corresponding forecast point), will yield r dummy variable coefficients and variances which equal the corresponding prediction errors and prediction error variances. Since most programs lack special routines to calculate these magnitudes, while manual computation is cumbersome, the proposed method is of considerable practical value.     

Calling recessions in real time

This paper surveys efforts to automate the dating of business cycle turning points. Doing this on a real time, out-of-sample basis is a bigger challenge than many academics might assume, due to factors such as data revisions and changes in economic relationships over time. The paper stresses the value of both simulated real-time analysis — looking at what the inference of a proposed model would have been using data as they were actually released at the time — and actual real-time analysis, in which a researcher stakes his or her reputation on publicly using the model to generate out-of-sample, real-time predictions. The immediate publication capabilities of the internet make the latter a realistic option for researchers today, and many are taking advantage of it. The paper reviews a number of approaches to dating business cycle turning points and emphasizes the fundamental trade-off between parsimony — trying to keep the model as simple and robust as possible — and making full use of the available information. Different approaches have different advantages, and the paper concludes that there may be gains from combining the best features of several different approaches.     

CLASSIFYING MANAGERIAL PROBLEMS: AN EMPIRICAL STUDY OF DEFINITIONAL CONTENT*

Problem categories play an important role in the thinking activities of many professionals. Organizational researchers have proposed that managers employ such categories as ‘threat’, ‘opportunity’ and ‘marketing problem’ in their thinking. This paper reports the results of a study of managerial problem categories, based on an analysis of managers’ verbal definitions of a variety of organizational problems. Problem categories previously proposed in the literature were rarely evidenced in these data. However, an iterative, inductive analysis led to the identification of a rich set of managerial problem categories satisfying appropriate criteria.     

Combination schemes for turning point predictions

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.     

Characterization of optima in smooth Pareto economic systems

Simple techniques of calculus and geometry are used to study and characterize the optima of pure exchange economies in which the utility functions are smooth but not necessarily convex. It is also shown how one can reduce the problem of optimizing p functions on the manifold of states to that of maximizing a single function on a submanifold of this space. Two models are described: one in which a person cannot trade to an optimum unless he starts at one; and one in which a person cannot even get near a local Pareto optimum along continuous ‘trade curves’ from most initial distributions. Finally, the set of optima is described for a generic set of utility mappings.     

Fatigue in Dynamic Tournaments

Employee overwork and fatigue are a concern of managers in many organizations, as they may increase health and safety risks and decrease productivity. The problem is especially severe in competitive environments, where compensation and promotions are awarded, explicitly or implicitly, on the basis of relative performance. We propose a theory for, and study experimentally, the phenomenon of fatigue in a dynamic competitive environment. We find that subjects react strongly to changes in the environment related to fatigue and follow the comparative statics of equilibrium predictions. At the same time, within a given environment, subjects behave as if they are unaware of the deteriorating effect of fatigue on their competitiveness.     

Application of Shrinkage Techniques in Logistic Regression Analysis: A Case Study

Logistic regression analysis may well be used to develop a predictive model for a dichotomous medical outcome, such as short-term mortality. When the data set is small compared to the number of covariables studied, shrinkage techniques may improve predictions. We compared the performance of three variants of shrinkage techniques: 1) a linear shrinkage factor, which shrinks all coefficients with the same factor; 2) penalized maximum likelihood (or ridge regression), where a penalty factor is added to the likelihood function such that coefficients are shrunk individually according to the variance of each covariable; 3) the Lasso, which shrinks some coefficients to zero by setting a constraint on the sum of the absolute values of the coefficients of standardized covariables.
Logistic regression models were constructed to predict 30-day mortality after acute myocardial infarction. Small data sets were created from a large randomized controlled trial, half of which provided independent validation data. We found that all three shrinkage techniques improved the calibration of predictions compared to the standard maximum likelihood estimates. This study illustrates that shrinkage is a valuable tool to overcome some of the problems of overfitting in medical data.     

Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model

Assessing regional population compositions is an important task in many research fields. Small area estimation with generalized linear mixed models marks a powerful tool for this purpose. However, the method has limitations in practice. When the data are subject to measurement errors, small area models produce inefficient or biased results since they cannot account for data uncertainty. This is particularly problematic for composition prediction, since generalized linear mixed models often rely on approximate likelihood inference. Obtained predictions are not reliable. We propose a robust multivariate Fay–Herriot model to solve these issues. It combines compositional data analysis with robust optimization theory. The nonlinear estimation of compositions is restated as a linear problem through isometric logratio transformations. Robust model parameter estimation is performed via penalized maximum likelihood. A robust best predictor is derived. Simulations are conducted to demonstrate the effectiveness of the approach. An application to alcohol consumption in Germany is provided.     

Combining forecasts: Performance and coherence

There is general agreement in many forecasting contexts that combining individual predictions leads to better final forecasts. However, the relative error reduction in a combined forecast depends upon the extent to which the component forecasts contain unique/independent information. Unfortunately, obtaining independent predictions is difficult in many situations, as these forecasts may be based on similar statistical models and/or overlapping information. The current study addresses this problem by incorporating a measure of coherence into an analytic evaluation framework so that the degree of independence between sets of forecasts can be identified easily. The framework also decomposes the performance and coherence measures in order to illustrate the underlying aspects that are responsible for error reduction. The framework is demonstrated using UK retail prices index inflation forecasts for the period 1998–2014, and implications for forecast users are discussed.     

LoMEF: A framework to produce local explanations for global model time series forecasts

Global forecasting models (GFMs) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches. One aspect of the popularity of statistical forecasting models such as ETS and ARIMA is their relative simplicity and interpretability (in terms of relevant lags, trend, seasonality, and other attributes), while GFMs typically lack interpretability, especially relating to particular time series. This reduces the trust and confidence of stakeholders when making decisions based on the forecasts without being able to understand the predictions. To mitigate this problem, we propose a novel local model-agnostic interpretability approach to explain the forecasts from GFMs. We train simpler univariate surrogate models that are considered interpretable (e.g., ETS) on the predictions of the GFM on samples within a neighbourhood that we obtain through bootstrapping, or straightforwardly as the one-step-ahead global black-box model forecasts of the time series which needs to be explained. After, we evaluate the explanations for the forecasts of the global models in both qualitative and quantitative aspects such as accuracy, fidelity, stability, and comprehensibility, and are able to show the benefits of our approach.     

Time-varying combinations of predictive densities using nonlinear filtering

We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to approximate the filtering and predictive densities. The combination approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of simulated data, US macroeconomic time series and surveys of stock market prices. Simulation results indicate that, for a set of linear autoregressive models, the combination strategy is successful in selecting, with probability close to one, the true model when the model set is complete and it is able to detect parameter instability when the model set includes the true model that has generated subsamples of data. Also, substantial uncertainty appears in the weights when predictors are similar; residual uncertainty reduces when the model set is complete; and learning reduces this uncertainty. For the macro series we find that incompleteness of the models is relatively large in the 1970’s, the beginning of the 1980’s and during the recent financial crisis, and lower during the Great Moderation; the predicted probabilities of recession accurately compare with the NBER business cycle dating; model weights have substantial uncertainty attached. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 1990’s and switches to giving more weight to the professional forecasts over time. Information on the complete predictive distribution and not just on some moments turns out to be very important, above all during turbulent times such as the recent financial crisis. More generally, the proposed distributional state space representation offers great flexibility in combining densities.     

The dimensional assessment of personality in drug addicts: a mixed-effects Rasch model approach

Drug abuse results from a series of different factors, such as social and family issues. Subjects more vulnerable to develop an addiction are, for instance, people living in high-stress environments who may resort to addiction in order to cope with their circumstances, such as demanding jobs, family crisis or other situations or people living in low-income households where violence occurs, who may be triggered into addiction as a way to escape negative emotions or ignore any underlying problems or issues. Psychometric research in the field of drug dependence has focused on identifying certain personality characteristics. It is now generally agreed that personality may influence, precipitate or perpetuate substance abuse. The aim of this paper is to perform a dimensional assessment of personality in a sample of drug addicts. To better understand the complexity of addictive behaviours of substance-using individuals, the Cloninger’s temperament and character inventory test is employed while the item response data analysis is performed by mixed-effects Rasch models. These models combine the advantages both of Rasch measurement framework for latent variables and of models with hierarchical data. To evaluate the differences in dimensions of temperament and character inventory test in subjects with drug addiction, we fit and compare a sequence of mixed-effects Rasch models. Results from models fitting are compared and discussed for a data set of 84 participants.