Influence diagnostics in the tobit censored response model

In this article, we develop influence diagnostic tools for the tobit model. Specifically, we discuss global influence methods based on the Cook distance and residuals with envelopes, and total and conformal local influence techniques. In order to analyze the sensitivity of the maximum likelihood estimators of the parameters of the model to small perturbations on the assumptions of the model and/or data, we consider several perturbation schemes, such as case-weight and response perturbations. Finally, we illustrate the developed methodology by means of a real data set.     

Estimating expected sojourn times for a markov renewal process

We consider the estimation of the expected sojourn time in a Markov renewal process under the data condition that only the counts of the exits from the states are available for fixed intervals of time. For analytical and illustrative purposes we concentrate on the two-state process case. We present least squares and method of moments estimators and compare their statistical properties both analytically and empirically. We also present modified estimators with improved properties based upon an overlapping interval sampling strategy. The major results indicate that the least squares estimator is biased in general with the bias depending on the size of the sampling interval and the first two moments of the sojourn time distribution function. The bias becomes negligible as the size of the sampling interval increases. Analytical and empirical results indicate that the method of moments estimator is less sensitive to the size of the sampling interval and has slightly better mean squared error properties than the least squares estimator.     

Measures of radial asymmetry for bivariate random vectors

This article considers the problem of measuring radial asymmetry of a bivariate vector of continuous random variables whose components are symmetric about the center of symmetry. We introduce a set of axioms for measures of radial asymmetry, define new measures satisfying the axiomatic, study some properties, and present corresponding results for sample statistics. Several examples illustrate our results.     

Exact combinatorial algorithms and experiments for?finding maximum k-plexes

We propose new practical algorithms to find maximum-cardinality k-plexes in graphs. A k-plex denotes a vertex subset in a graph inducing a subgraph where every vertex has edges to all but at most k vertices in the k-plex. Cliques are 1-plexes. In analogy to the special case of finding maximum-cardinality cliques, finding maximum-cardinality k-plexes is NP-hard. Complementing previous work, we develop exact combinatorial algorithms, which are strongly based on methods from parameterized algorithmics. The experiments with our freely available implementation indicate the competitiveness of our approach, for many real-world graphs outperforming the previously used methods.     

The value of audit quality in public and private companies: evidence from Spain

This paper compares the value of audit quality, proxied by the selection of a big N auditor, to the external claimholders of private and public companies. Although the combination of a lower ownership concentration of public companies, the greater demand for financial information quality about these companies and their higher litigation risk can result in the expectation that audit quality should be more valuable for public than for private companies, the greater information asymmetry between the managers and the external stakeholders and the unavailability of alternative mechanisms for monitoring the managers can make external audit more valuable for the external claimholders of private companies. In this paper, we test these two competing views by analysing if banks and lenders take into account auditor selection in the formation of the cost of debt. Our results support the second view: we find that only private companies obtain a lower cost of debt when they are audited by a high-quality auditor. These results are robust to both endogeneity and unobserved firm-specific heterogeneity.     

Hypothesis Testing in Functional Comparative Calibration Models

The purpose of this article is to investigate hypothesis testing in functional comparative calibration models. Wald type statistics are considered which are asymptotically distributed according to the chi-square distribution. The statistics are based on maximum likelihood, corrected score approach, and method of moment estimators of the model parameters, which are shown to be consistent and asymptotically normally distributed. Results of analytical and simulation studies seem to indicate that the Wald statistics based on the method of moment estimators and the corrected score estimators are, as expected, less efficient than the Wald type statistic based on the maximum likelihood estimators for small n. Wald statistic based on moment estimators are simpler to compute than the other Wald statistics tests and their performance improves significantly as n increases. Comparisons with an alternative F statistics proposed in the literature are also reported.     

Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling

Fuzzy rule–based models, a key element in soft computing (SC), have arisen as an alternative for time series analysis and modeling. One difference with preexisting models is their interpretability in terms of human language. Their interactions with other components have also contributed to a huge development in their identification and estimation procedures. In this article, we present fuzzy rule–based models, their links with some regime-switching autoregressive models, and how the use of soft computing concepts can help the practitioner to solve and gain a deeper insight into a given problem. An example on a realized volatility series is presented to show the forecasting abilities of a fuzzy rule–based model.     

On limiting behavior for arrays of rowwise negatively orthant dependent random variables

In this paper, the authors study limiting behavior for arrays of rowwise negatively orthant dependent random variables and obtain some new results which extend and improve the corresponding theorems by Hu, Móricz, and Taylor (1989), Taylor, Patterson, and Bozorgnia (2002) and Wu and Zhu (2010).     

On the Run Length of a State-Space Control Chart for Multivariate Autocorrelated Data

The literature on statistical process control (SPC) describes the negative effects of autocorrelation in terms of the increase in false alarms. This has been treated by the individual modeling of each series or the application of VAR models. In the former case, the analysis of the cross correlation structure between the variables is altered. In the latter, if the cross correlation is not strong, the filtering process may modify the weakest relations. In order to improve these aspects, state-space models have been introduced in multivariate statistical process control (MSPC). This article presents a proposal for building a control chart for innovations, estimating its average run length to highlight its advantages over the VAR approach mentioned above.     

Detection of a change-point in student-t linear regression models

In this work the Schwarz Inforamtion Criterion (SIC) is used in order to locate a change-point in linear regression models with independent errors distributed according to the Student-t distribution. The methodology is applied to data sets from the financial area.