Conditional feature screening for mean and variance functions in models with multiple-index structure

The existing methods for feature screening focus mainly on the mean function of regression models. The variance function, however, plays an important role in statistical theory and application. We thus investigate feature screening for mean and variance functions with multiple-index framework in high dimensional regression models. Notice that some information about predictors can be known in advance from previous investigations and experience, for example, a certain set of predictors is related to the response. Based on the conditional information, together with empirical likelihood, we propose conditional feature screening procedures. Our methods can consistently estimate the sets of active predictors in the mean and variance functions. It is interesting that the proposed screening procedures can avoid estimating the unknown link functions in the mean and variance functions, and moreover, can work well in the case of high correlation among the predictors without iterative algorithm. Therefore, our proposal is of computational simplicity. Furthermore, as a conditional method, our method is robust to the choice of the conditional set. The theoretical results reveal that the proposed procedures have sure screening properties. The attractive finite sample performance of our method is illustrated in simulations and a real data application.     

The Evolution of Production Structures,Analyzed by a Multi-layer Procedure

In this paper, a new method of so-called qualitative input–output analysis is outlined, which is called minimal flow analysis (MFA). It extracts the characteristic production structure given in an input–output table, on the basis of anendogenized threshold value. Formally, this is achieved by the binarization of the entries of different table layers which are reformulated according to the Eulerian sequence. The condensed characteristic structure of the economy is then obtained by means of graph theoretical methods. The new method is able to uncover production structures, even in highly aggregated tables. If applied to a chronological sequence of tables, as shown for German tables 1978–88 in detail, the MFA method can disclose the evoluton of sectoral structures.     

The use of network analysis to handle semantic differential data

The aim of this paper is to propose a method to transform semantic differential data into a network whose graph representation is interpreted as an empirical network of adjectives. The graph is constituted by the adjectives of the semantic differential task. Two adjectives are linked depending on the scoring assigned by a set of respondents. The proposed approach aims at using concepts and methods of Social Network Analysis to explore the network structure and study roles and positions of dominant adjectives. A simulation design has been realized to assess the stability of results under different conditions, i.e. in order to set the optimal threshold in presence of different data generator processes. A case study carried out on real data shows how the emerging network of adjectives can be effectively used to define the concept arising from a semantic differential task.     

Optimal Design Approach to GMM Estimation of Parameters Based on Empirical Transforms

Parameter estimation based on the generalized method of moments (GMM) is proposed. The proposed method employs a distance between an empirical and the corresponding theoretical transform. Estimation by the empirical characteristic function (CF) is a typical example, but alternative empirical transforms are also employed, such as the empirical Laplace transform when dealing with non‐negative random variables. D‐optimal designs are discussed, whereby the arguments of the empirical transform are chosen by maximizing the determinant of the asymptotic Fisher information matrix for the resulting estimators. The methods are applied to some parametric models for which classical inference is complicated.     

Shrinkage estimation for the mean of the inverse Gaussian population

We consider improved estimation strategies for a two-parameter inverse Gaussian distribution and use a shrinkage technique for the estimation of the mean parameter. In this context, two new shrinkage estimators are suggested and demonstrated to dominate the classical estimator under the quadratic risk with realistic conditions. Furthermore, based on our shrinkage strategy, a new estimator is proposed for the common mean of several inverse Gaussian distributions, which uniformly dominates the Graybill–Deal type unbiased estimator. The performance of the suggested estimators is examined by using simulated data and our shrinkage strategies are shown to work well. The estimation methods and results are illustrated by two empirical examples.     

Are firms ready to use big data analytics to create value? The role of structural and psychological readiness

In this study, we leverage Information Technology (IT) readiness literature and resource-based view (RBV) to investigate the impact of firm structural and psychological readiness on firm value creation, as mediated by big data analytics usage. The proposed research model is empirically validated using survey data from 179 senior IT managers. The findings demonstrate the importance of both structural (i.e. IT infrastructure capability, tools functionality, employee analytical capability, and bigness of data) and psychological readiness (i.e. IT proactive climate) in enhancing firm value creation through big data analytics usage. These results provide interesting theoretical and practical insights.     

Continuous-time modelling of irregularly spaced panel data using a cubic spline model

Continuous-time modelling remains a somewhat 'idealized' representation tool. Even though conceptualizing a dynamic process as a continuous process has clear appeal from a theoretical standpoint, practical tools that allow researchers to effectively map an idealized continuous model onto a set of discrete-time observed data are still lacking observed data. Irregularly spaced longitudinal data frequently arise in empirical settings because of the prevalence of longitudinal studies with partially randomized measurement intervals and other related designs. We present a practical approach that capitalizes on a nonparametric spline interpolation approach to impute the gaps in irregularly spaced panel data. Simulated and empirical examples are provided to demonstrate the applicability of the proposed approach to studies of group-based dynamics using panel data.     

Extension of the Elo rating system to margin of victory

The Elo rating system is one of the most popular methods for estimating the ability of competitors over time in sport. The standard Elo system focuses on predicting wins and losses, but there is often also interest in the margin of victory (MOV) because it reflects the magnitude of a result. There have been few theoretical investigations and comparisons of Elo-based models. In the present study, we propose four model options for an MOV Elo system: linear, joint additive, multiplicative, and logistic. Notations and guidance for tuning each model are provided. The models were applied to men’s tennis for several MOV choices. The results showed that all MOV approaches using within-set statistics improved the predictive performance compared with the standard Elo system, but only the joint additive model yielded unbiased ratings with stable variance in the simulation study. This general framework for MOV Elo ratings provide sports modelers with a new set of tools for building systems to rate competitors and forecast outcomes in sport.     

Policy consequences of direct legislation theory, empirical models and evidence

Most theoretical models predict that institutions allowing for direct legislation should lead, on average, to policies more closely reflecting the wishes of the voters. While some agreement exists at the theoretical level about the expected policy consequences of direct legislation, empirical evidence has been scant so far. In this paper I discuss the reasons for this scantiness of empirical evidence, namely the intricacies of the adequate empirical model to test the theoretical proposition, and suggest possible solutions to this problem. Re-analyzing a dataset with which some authors have found no evidence in support of the theoretical claim, I show that with a better adapted empirical model we find results in synch with our theoretical expectations. Thus, policies in states that allow for direct legislation reflect on average more closely the voters?? wishes. Using Monte-Carlo simulations I also demonstrate the properties of the proposed estimator and suggest that it could be used in other contexts, like when assessing the responsiveness of legislators.     

Connectedness and risk spillovers in China’s stock market: A sectoral analysis

This paper shows how sectors in the Chinese stock market are connected and investigates risk spillovers across these sectors. Using graph theory and a recently developed time series technique, we are able to identify the systemically important sector in the market and the patterns of risk spillovers across sectors over time. Unlike standard econometric modeling, graph theory enables us to approach this question in a more reader-friendly way. The empirical results show that Industrial sector plays a central role and should thus be considered the systemically most important sector in the Chinese stock market. The spillover structure is found to be time-varying. While Industrial sector dominates the system for most of the time, other sectors such as Consumer Discretionary sector also occasionally appear as the central sector. Our empirical results also indicate that the simple correlation-based approach can produce equally useful information as more advanced econometric models.     

爆炸荷载及爆炸对RC结构影响的研究

过去几十在爆炸荷载的结构分析和结构设计方面已经有着显著的发展.本文以大量系统的文献为背景,综合分析爆炸荷载的计算,爆炸对混凝土结构的影响和结构对爆炸响应分析.本文还介绍了几部设计手册作为分析爆炸荷载和混凝土结构爆炸响应的参考书,推荐几套计算程序可以作为分析和研究的工具.     

Forecasting value at risk with intra-day return curves

Methods for incorporating high resolution intra-day asset price data into risk forecasts are being developed at an increasing pace. Existing methods such as those based on realized volatility depend primarily on reducing the observed intra-day price fluctuations to simple scalar summaries. In this study, we propose several methods that incorporate full intra-day price information as functional data objects in order to forecast value at risk (VaR). Our methods are based on the recently proposed functional generalized autoregressive conditionally heteroscedastic (GARCH) models and a new functional linear quantile regression model. In addition to providing daily VaR forecasts, these methods can be used to forecast intra-day VaR curves, which we considered and studied with companion backtests to evaluate the quality of these intra-day risk measures. Using high-frequency trading data from equity and foreign exchange markets, we forecast the one-day-ahead daily and intra-day VaR with the proposed methods and various benchmark models. The empirical results suggested that the functional GARCH models estimated based on the overnight cumulative intra-day return curves exhibited competitive performance with benchmark models for daily risk management, and they produced valid intra-day VaR curves.     

Social preferences for health states: An empirical evaluation of three measurement techniques

Health state preferences measured on the general public provide useful information in their own right as well as being necessary data for the application of many health status index models. But, how should the preferences be measured? This paper reports the results of an empirical investigation in which three measurement techniques are applied to several samples of the general public to measure the social preferences for ten different health states. The standard gamble technique by von Neumann-Morgenstern, a time trade-off technique by the author, and a category scaling method are analyzed with respect to their feasibility, reliability, validity and comparability.     

An approximate long-memory range-based approach for value at risk estimation

This paper proposes new approximate long-memory VaR models that incorporate intra-day price ranges. These models use lagged intra-day range with the feature of considering different range components calculated over different time horizons. We also investigate the impact of the market overnight return on the VaR forecasts, which has not yet been considered with the range in VaR estimation. Model estimation is performed using linear quantile regression. An empirical analysis is conducted on 18 market indices. In spite of the simplicity of the proposed methods, the empirical results show that they successfully capture the main features of the financial returns and are competitive with established benchmark methods. The empirical results also show that several of the proposed range-based VaR models, utilizing both the intra-day range and the overnight returns, are able to outperform GARCH-based methods and CAViaR models.     

Empirical evidence on individual, group and shrinkage seasonal indices

This paper provides empirical evidence on forecasting seasonal demand using both individual and group seasonal indices methods. The findings show that the group seasonal indices methods outperform the individual seasonal indices method. This paper also offers empirical results from comparing two shrinkage methods with the group seasonal indices methods. The theoretical rules developed by the authors for choosing between group seasonal indices and individual seasonal indices produce more accurate forecasts than do published rules for choosing between shrinkage methods, when measured by the MSE, and are competitive when measured by the symmetric MAPE.     

Fisher information under Gaussian quadrature models

This paper develops formulae to compute the Fisher information matrix for the regression parameters of generalized linear models with Gaussian random effects. The Fisher information matrix relies on the estimation of the response variance under the model assumptions. We propose two approaches to estimate the response variance: the first is based on an analytic formula (or a Taylor expansion for cases where we cannot obtain the closed form), and the second is an empirical approximation using the model estimates via the expectation–maximization process. Further, simulations under several response distributions and a real data application involving a factorial experiment are presented and discussed. In terms of standard errors and coverage probabilities for model parameters, the proposed methods turn out to behave more reliably than does the ‘disparity rule’ or direct extraction of results from the generalized linear model fitted in the last expectation–maximization iteration.     

Algebra for Enterprise Ontology: towards analysis and synthesis of enterprise models

Enterprise modeling methodologies have made enterprises more likely to be the object of systems engineering rather than craftsmanship. However, the current state of research in enterprise modeling methodologies lacks investigations of the mathematical background embedded in these methodologies. Abstract algebra, a broad subfield of mathematics, and the study of algebraic structures may provide interesting implications in both theory and practice. Therefore, this research gives an empirical challenge to establish an algebraic structure for one aspect model proposed in Design & Engineering Methodology for Organizations (DEMO), which is a major enterprise modeling methodology in the spotlight as a modeling principle to capture the skeleton of enterprises for developing enterprise information systems. The results show that the aspect model behaves well in the sense of algebraic operations and indeed constructs a Boolean algebra. This article also discusses comparisons with other modeling languages and suggests future work.     

Breeding places for ethnic entrepreneurs: a comparative marketing approach

This paper aims to examine the performance conditions of ethnic (migrant) entrepreneurs in a modern economy. After a broad overview of key issues, an analytical tool from marketing theory is proposed, based on the five Ps (Product, Price, Place, Personnel and Promotion). Next, an empirical application is presented, in which results from an in-depth interview study on Moroccan entrepreneurs in Amsterdam are discussed. Given the linguistic and qualitative information in our data base, two recently developed pattern recognition methods for categorized information, namely Apriori and rough set methods, are deployed in order to derive meaningful association and classification rules that are helpful to identify conditional success or performance rules.     

A method for agent-based models validation

This paper proposes a new method to empirically validate simulation models that generate artificial time series data comparable with real-world data. The approach is based on comparing structures of vector autoregression models which are estimated from both artificial and real-world data by means of causal search algorithms. This relatively simple procedure is able to tackle both the problem of confronting theoretical simulation models with the data and the problem of comparing different models in terms of their empirical reliability. Moreover the paper provides an application of the validation procedure to the agent-based macroeconomic model proposed by Dosi et al. (2015).