Model occurrence and model selection in panel data sets

In this study we focus attention on model selection in the presence of panel data. Our approach is eclectic in that it combines both classical and Bayesian techniques. It is also novel in that we address not only model selection, but also model occurrence, i.e., the process by which ‘nature’ chooses a statistical framework in which to generate the data of interest. For a given data subset, there exist competing models each of which have an ex ante positive probability of being the correct model, but for any one generated sample, ex post exactly one such model is the basis for the observed data set. Attention focuses on how the underlying model occurrence probabilities of the competing models depend on characteristics of the environments in which the data subsets are generated. Classical, Bayesian, and mixed estimation approaches are developed. Bayesian approaches are shown to be especially attractive whenever the models are nested.     

Risk assessment for enterprise resource planning (ERP) system implementations: a fault tree analysis approach

Enterprise resource planning (ERP) system implementations are often characterised with large capital outlay, long implementation duration, and high risk of failure. In order to avoid ERP implementation failure and realise the benefits of the system, sound risk management is the key. This paper proposes a probabilistic risk assessment approach for ERP system implementation projects based on fault tree analysis, which models the relationship between ERP system components and specific risk factors. Unlike traditional risk management approaches that have been mostly focused on meeting project budget and schedule objectives, the proposed approach intends to address the risks that may cause ERP system usage failure. The approach can be used to identify the root causes of ERP system implementation usage failure and quantify the impact of critical component failures or critical risk events in the implementation process.     

On nonparametric latent trait models

Some nonparametric latent trait models for dichotomous data are considered. We deal with n subjects, each answering to the same set of of k items, each item being scored dichotomously. We are interested in ordering item difficulties α₁,...α_k . In Sec. 2 it is shown that in the considered nonparametric models the ordering is identifiable. Then an order estimator is defined and its quality is described by the probabilities of correct, wrong and deferred decision. Asymptotic behaviour of these probabilities are considered for n→∞ and any k≥2. The hypothesis that the probability of wrong decision diminishes when the model is “more distant” from so called random response model, is proved for n≤3 and verified numerically for n≥3. In Sec. 4 we discuss critically some parameters of nonparametric models known in the literature as “coefficients of scalability”. In particular, for k=2 their connections with the evaluation of positive dependence are considered.     

Incompatibility Between Hazard- and Logistic-Regression in Modeling Survival Data with Multiple Causes of Failure

This paper examines the relationship between outcomes from separate hazard modeling of two competing risks and those based on hazard-modeling of the overall risk combined with logistic-regression on the conditional probability of the risk of interest. The formulation presented in this paper generializes for continuous time models and allows for non-categorical covariates. Our analytical results show that the two approaches are incompatible because they address different issues. Such a straightforward explanation of the incompatibility between the two approaches is expected to prompt investigators to focus on identifying situations of when the issue addressed by each approach may be of substantive interest.     

MULTIPLE FAILURE CAUSES AND OBSERVATIONS OF TIME DEPENDENT COVARIABLES

In this paper firstly a summary of the theory of competing risks is presented; it shows how cause specific failure time distributions, failure probabilities and failure rate functions relate to the joint survivor function in the presence of various competing failure causes. This first part of the paper gives the insight useful for the second part, which concerns the way in which the dependency of the failure process on given time–dependent observations of covariables is specified and estimated. An application is presented briefly.     

Fully Bayesian analysis of ARMA time series models

Statistical analysis of autoregressive-moving average (ARMA) models is an important non-standard problem. No classical approach is widely accepted; legitimacy for most classical approaches is based solely on asymptotic grounds, while small sample sizes are common. The only obstacle to the Bayesian approach are designing a structure through which prior information can be incorporated and designing a practical computational method. The objective of this work is to overcome these two obstacles. In addition to the standard results, the Bayesian approach gives a different method of determining the order of the ARMA model, that is (p, q).     

On generalized progressive hybrid censoring in presence of competing risks

The progressive Type-II hybrid censoring scheme introduced by Kundu and Joarder (Comput Stat Data Anal 50:2509–2528, 2006), has received some attention in the last few years. One major drawback of this censoring scheme is that very few observations (even no observation at all) may be observed at the end of the experiment. To overcome this problem, Cho et al. (Stat Methodol 23:18–34, 2015) recently introduced generalized progressive censoring which ensures to get a pre specified number of failures. In this paper we analyze generalized progressive censored data in presence of competing risks. For brevity we have considered only two competing causes of failures, and it is assumed that the lifetime of the competing causes follow one parameter exponential distributions with different scale parameters. We obtain the maximum likelihood estimators of the unknown parameters and also provide their exact distributions. Based on the exact distributions of the maximum likelihood estimators exact confidence intervals can be obtained. Asymptotic and bootstrap confidence intervals are also provided for comparison purposes. We further consider the Bayesian analysis of the unknown parameters under a very flexible beta–gamma prior. We provide the Bayes estimates and the associated credible intervals of the unknown parameters based on the above priors. We present extensive simulation results to see the effectiveness of the proposed method and finally one real data set is analyzed for illustrative purpose.     

“When Should Manufacturers Want Fair Trade?”: New Insights from Asymmetric Information when Supply Chains Compete

We study a model of competing manufacturer/retailer pairs where adverse selection and moral hazard are coupled with promotional externalities at the downstream level. In contrast to earlier models mainly focusing on a bilateral monopoly setting, we show that with competing brands a ‘laissez‐faire’ approach towards vertical price control might not always promote productive efficiency. Giving manufacturers freedom to control retail prices is more likely to harm consumers when retailers impose positive promotional externalities on each other, and the converse is true otherwise. Our simple model also suggests that, with competing supply chains, consumers and manufacturers might prefer different contractual modes if promotional externalities have substantial effects on demands.     

Estimation in a disequilibrium model and the value of information

Suits' model of the watermelon market is reformulated as a disequilibrium model and the likelihood function is derived under various assumptions concerning the amount of available information. Such models are characterized by a min condition as in y_t = min(D_t, S_t) for an ordinary demand-supply model, where the observed quantity y_t is the smaller demand and supply. Varying amounts of information may be available depending on which variables are observed and on whether prior information is available for separating the sample into subsets for which D_t < S_t and conversely. The likelihood function corresponding to the least amount of prior information is shown to be unbounded without a certain variance ratio restriction. Computations are successfully carried out both with the original model and Monte Carlo experiments and the effect of different amounts of information on MSE's is analyzed.     

On the maximum number of balancing subsets

Let a₁,…,a_n be a finite sequence of n real numbers. A subset {i₁,…,i_r} of the set {1,…,n } is called a balancing subset for a₁,…,a_n if a_i1 + … + a_ir = 0. We discuss in this paper the maximum number of balancing subsets for given n.     

Animal spirits and unemployment: a disequilibrium analysis

The past decade has seen a number of advances in modelling disequilibrium dynamics. This paper draws on separate approaches to disequilibrium dynamics to demonstrate a Keynesian result concerning the formal relevance of “animal spirits” in production economies. Specifically, it is shown that a parameter that can be associated with the “animal spirits” of firms is crucial to the stability of full employment equilibrium in a production economy. This approach to “animal spirits” is different to that taken by recent New Keynesian DSGE-type models, but similar in spirit to “Old Keynesian” approaches, including that of the General Theory. The corollary of the main conclusion is that price flexibility is not a sufficient condition for convergence on full employment equilibrium.     

Analysis of Survival Data with Multiple Causes of Failure

The purpose of the paper is to compare results of estimation and inference concerning covariate effects as obtained from two approaches to the analysis of survival data with multiple causes of failure. The first approach involves a dynamic model for the cause-specific hazard rate. The second is based on a static logistic regression model for the conditional probability of having had an event of interest. The influence of sociodemographic characteristics on the rate of family initiation and, more importantly, on the choice between marriage and cohabitation as a first union, is examined. We found that results, generally, are similar across the methods considered. Some issues in relation to censoring mechanisms and independence among causes of failure are discussed.     

Identifying root causes of inefficiencies in road haulage: case studies from Sweden,Switzerland and Germany

ABSTRACT

Road haulage operations in general, and distribution in particular, are inefficient. Given the societal importance of road haulage and the low efficiency of the sector, road hauliers need to improve their operations and systematically tackle inefficiencies. However, the real causes of these inefficiencies, the ‘root causes’, have not been sufficiently examined in previous studies. This paper expands on prior research by adapting existing models that can be used to identify the root causes of inefficiencies, and to enable systematic improvements in road transport operations. Our research is based on a lean approach and an adapted version of Ishikawa’s model. It is shown as a matrix based on transport processes and the Ishikawa categories to identify the root causes of inefficiencies in road haulage that influence performance. The adapted model was tested in three road haulage case studies. Our findings suggest that most efficiency problems appear in the actual transport execution.     

AFT regression-adjusted monitoring of reliability data in cascade processes

Today’s competitive market has witnessed a growing interest in improving the reliability of products in both service and industrial operations. A large number of monitoring schemes have been introduced to effectively control the reliability-related quality characteristics. These methods have focused on single-stage processes or considered quality variables which are independent. However, the main feature of multistage processes is the cascade property which needs to be justified for the sake of optimal process monitoring. The problem becomes complicated when the presence of censored observations is pronounced. Therefore, both the effects of influential covariates and censored data must be taken into account while presenting a monitoring scheme. In this paper, the accelerated failure time models are used and two regression-adjusted control schemes based on Cox-Snell residuals are devised. Two different scenarios with censored and non-censored data are considered respectively. The competing control charts are compared in terms of zero-state and steady-state average run length criteria using Markov chain approach. The comparison study reveals that the cumulative sum based monitoring procedure is superior and more effective. It should be noted that the application of the proposed monitoring schemes are not restricted to manufacturing processes and thus service operations such as healthcare systems can benefit from them.     

Investigating design for survival models

The aim of this paper is to derive methodology for designing ‘time to event’ type experiments. In comparison to estimation, design aspects of ‘time to event’ experiments have received relatively little attention. We show that gains in efficiency of estimators of parameters and use of experimental material can be made using optimal design theory. The types of models considered include classical failure data and accelerated testing situations, and frailty models, each involving covariates which influence the outcome. The objective is to construct an optimal design based of the values of the covariates and associated model or indeed a candidate set of models. We consider D-optimality and create compound optimality criteria to derive optimal designs for multi-objective situations which, for example, focus on the number of failures as well as the estimation of parameters. The approach is motivated and demonstrated using common failure/survival models, for example, the Weibull distribution, product assessment and frailty models.     

Selection correction and sensitivity analysis for ordered treatment effect on count response

In estimating the effect of an ordered treatment τ on a count response y with an observational data where τ is self‐selected (not randomized), observed variables x and unobserved variables ε can be unbalanced across the control group (τ = 0) and the treatment groups (τ = 1, …, J). While the imbalance in x causes ‘overt bias’ which can be removed by controlling for x, the imbalance in ε causes ‘covert (hidden or selection) bias’ which cannot be easily removed. This paper makes three contributions. First, a proper counter‐factual causal framework for ordered treatment effect on count response is set up. Second, with no plausible instrument available for τ, a selection correction approach is proposed for the hidden bias. Third, a nonparametric sensitivity analysis is proposed where the treatment effect is nonparametrically estimated under no hidden bias first, and then a sensitivity analysis is conducted to see how sensitive the nonparametric estimate is to the assumption of no hidden bias. The analytic framework is applied to data from the Health and Retirement Study: the treatment is ordered exercise levels in five categories and the response is doctor office visits per year. The selection correction approach yields very large effects, which are however ruled out by the nonparametric sensitivity analysis. This finding suggests a good deal of caution in using selection correction approaches. Copyright © 2004 John Wiley & Sons, Ltd.     

A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time‐varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying latent exit decisions in continuous time while only a time interval containing the exit time is observed, as is common in economic data. We do not make the simplifying assumption of discretizing exit decisions—our competing risk model setup allows for latent exit times of different risk types to be realized within the same time period. In this setting, we derive a tractable likelihood based on scaled GIG Laplace transforms and their higher‐order derivatives. We apply our approach to analyzing the determinants of unemployment duration with exits to jobs in the same industry or a different industry among unemployment insurance recipients on nationally representative individual‐level survey data from the US Department of Labor. Our approach allows us to conduct a counterfactual policy experiment by changing the replacement rate: we find that the impact of its change on the probability of exit from unemployment is inelastic. Copyright © 2014 John Wiley & Sons, Ltd.     

Accounting for length of hospital stay in regression models in clinical epidemiology

In hospital epidemiology, logistic regression is a popular model to study risk factors of hospital-acquired infections. One key issue in this analysis is how to incorporate the time dependency of acquiring an infection during the hospital stay. In the applied literature, researchers often simply adjust for the entire length of hospital stay, which also includes the time after infection. A further issue is that discharge and death are competing events for hospital-acquired infections. After discussing the limitations of logistic regression adjusted for length of stay in this setting, we compare this approach with appropriate analyses incorporating competing risks and with an illness–death model with hospital-acquired infection as an intermediate event. The cumulative incidence function, cause-specific hazard ratios, and subdistribution hazard ratios are considered as reference measures. Real-life and simulated data are used to demonstrate biases and limitations associated with logistic regression adjusted for length of stay. We conclude that logistic regression adjusted for length of stay should not be used when investigating hospital-acquired infections and that appropriate methods involving the use of multistate models should be used to capture the time dependency in time-to-event settings, especially in the presence of competing events.     

A game theoretic approach to optimize multi-stakeholder utilities for land acquisition negotiations with informality

To combat the critical stresses of rising urbanization, the government acquires land from private owners using the power of eminent domain. This land assembly causes negative externalities such as increasing social tension and injustice that may impose a long-term threat to stability and sustainable development. Therefore, there is a need to make the land acquisition process more transparent and just. Considering the unique Indian context where informality has a strong presence, we propose an approach based on game theory that models the bargain through a three-stage Nash equilibrium game. Four agents – the government, the private developer, the landowner, and the free rider – are considered. We provide conditional solutions for the generalizable Case and proceed to model different stakeholder behavior patterns through two utility functional forms – linear and exponential. In the linear case, we find that the free rider obtains half of the revenue of the project, whereas the landowner gains between one-fourth and one-half of the revenue. Thus, we highlight the undeniably crucial role free riders play in land acquisition negotiations. However, closed form solutions cannot be obtained for the exponential form, due to which we use simulations to demonstrate a solution procedure. We conclude by stating that the proposed model can be useful in formulating future land policies in a sustainable and inclusive manner, with optimal utility derivations for all concerned stakeholders. Our model can also be extended to other spatial contexts where informality features heavily in the land market, especially in the Global South.     

Opportunism or strategic opportunity seeking? Three approaches to emerging country sourcing

The current interest in low-cost or emerging country sourcing (ECS) has inspired a multitude of empirical studies. These studies are based on varying methodological approaches and have come to somewhat different conclusions regarding the motivations for and outcomes of ECS. This paper argues that the overall approach to the new markets, especially the strategic network context surrounding each buyer-supplier relationship in the ECS setting, impacts on the prerequisites for success.The aim of the paper is to identify and discuss approaches to ECS. It begins with a review of recent empirical studies, scrutinising research interests and the approaches applied by the companies studied. It suggests and illustrates three broad categories of approaches: (1) the transactional approach focusing on individual sourcing in the emerging country, (2) the supply base approach focusing on establishing a set of supplier relationships in the emerging country, and (3) the network positioning approach in which both supplier and customer relationships are developed in the emerging country. The paper also discusses the three approaches as stages in an internationalisation process.The paper concludes that of the three strategic approaches the viability of two – the transactional approach and the supply base approach – can be questioned on several grounds, but that when seen as a process directed towards developing a network position in a new (emerging) market all three approaches make sense as different stages in a process moving towards achieving that goal. An explanation for the process of increasing commitment in emerging markets, relying on learning in interaction and on relational investments beginning with suppliers and continuing with customers, is also discussed.