Asymptotic properties of approximate maximum quasi-likelihood estimator in quasi-likelihood nonlinear models with random effects

Quasi-likelihood nonlinear models with random effects (QLNMWRE) include generalized linear models with random effects and quasi-likelihood nonlinear models as special cases. In this paper, some regularity conditions analogous to those given by Breslow and Clatyton (1993) are proposed. On the basis of the proposed regularity conditions and Laplace approximation, the existence, the strong consistency and asymptotic normality of the approximate maximum quasi-likelihood estimation of the fixed effects are proved in QLNMWRE.     

Using jackknife to correct bias of MLE for the truncated Pareto distribution

The Pareto distribution is a well-known probability distribution in statistics, which has been widely used in many fields, such as finance, physics, hydrology, geology and astronomy. However, the parameter estimation for the truncated Pareto distribution is much more complicated than that for the Pareto distribution. In this paper, we demonstrate that the bias of the maximum likelihood estimation for the truncated Pareto distribution can be significantly reduced by its jackknife estimation, which has a very simple form.     

A unified empirical likelihood approach for testing MCAR and subsequent estimation

For an estimation with missing data, a crucial step is to determine if the data are missing completely at random (MCAR), in which case a complete‐case analysis would suffice. Most existing tests for MCAR do not provide a method for a subsequent estimation once the MCAR is rejected. In the setting of estimating means, we propose a unified approach for testing MCAR and the subsequent estimation. Upon rejecting MCAR, the same set of weights used for testing can then be used for estimation. The resulting estimators are consistent if the missingness of each response variable depends only on a set of fully observed auxiliary variables and the true outcome regression model is among the user‐specified functions for deriving the weights. The proposed method is based on the calibration idea from survey sampling literature and the empirical likelihood theory.     

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.     

Joint models for a GLM-type longitudinal response and a time-to-event with smooth random effects

ABSTRACT

Longitudinal studies often entail non-Gaussian primary responses. When dropout occurs, potential non-ignorability of the missingness process may occur, and a joint model for the primary response and a time-to-event may represent an appealing tool to account for dependence between the two processes. As an extension to the GLMJM, recently proposed, and based on Gaussian latent effects, we assume that the random effects follow a smooth, P-spline based density. To estimate model parameters, we adopt a two-step conditional Newton–Raphson algorithm. Since the maximization of the penalized log-likelihood requires numerical integration over the random effect, which is often cumbersome, we opt for a pseudo-adaptive Gaussian quadrature rule to approximate the model likelihood. We discuss the proposed model by analyzing an original dataset on dilated cardiomyopathies and through a simulation study.     

Adaptively transformed mixed‐model prediction of general finite‐population parameters

For estimating area‐specific parameters (quantities) in a finite population, a mixed‐model prediction approach is attractive. However, this approach strongly depends on the normality assumption of the response values, although we often encounter a non‐normal case in practice. In such a case, transforming observations to make them suitable for normality assumption is a useful tool, but the problem of selecting a suitable transformation still remains open. To overcome the difficulty, we here propose a new empirical best predicting method by using a parametric family of transformations to estimate a suitable transformation based on the data. We suggest a simple estimating method for transformation parameters based on the profile likelihood function, which achieves consistency under some conditions on transformation functions. For measuring the variability of point prediction, we construct an empirical Bayes confidence interval of the population parameter of interest. Through simulation studies, we investigate the numerical performance of the proposed methods. Finally, we apply the proposed method to synthetic income data in Spanish provinces in which the resulting estimates indicate that the commonly used log transformation would not be appropriate.     

A family of efficient estimators of the finite population mean in simple random sampling

In this paper an estimator of the finite population mean using auxiliary information in sample surveys has been proposed. The bias and mean squared error are obtained under large sample approximation. It has been shown that the proposed estimator performs better than some recently published estimators.     

Using clustering of rankings to explain brand preferences with personality and socio-demographic variables

The primary aim of market segmentation is to identify relevant groups of consumers that can be addressed efficiently by marketing or advertising campaigns. This paper addresses the issue whether consumer groups can be identified from background variables that are not brand-related, and how much personality vs. socio-demographic variables contribute to the identification of consumer clusters. This is done by clustering aggregated preferences for 25 brands across 5 different product categories, and by relating socio-demographic and personality variables to the clusters using logistic regression and random forests over a range of different numbers of clusters. Results indicate that some personality variables contribute significantly to the identification of consumer groups in one sample. However, these results were not replicated on a second sample that was more heterogeneous in terms of socio-demographic characteristics and not representative of the brands target audience.     

The embeddedness of social relations in inter-firm competitive structures

Collaboration with direct competitors is a valuable, yet risky, strategy that might pave the way for competitive advantages. Thus far, we have lacked a detailed understanding of how competitive relations between firms shape collaborative relations between their managers, and consequently the structural interdependencies at both levels. The primary objective of this study is to understand the multi-level structure underlying the ambiguous relationship of coopetition, a state of simultaneous collaboration and competition. Hence, we investigate the influence of perceived inter-firm competition on interpersonal knowledge flows among managers within three high-tech clusters by applying multi-level exponential random graph models to network data. Our results reveal that the interpersonal exchange of advice and information depends strongly on the inter-firm competitive structure and that competitive structures shape these interpersonal exchanges in different ways. Thereby, our research demonstrates that managers utilize asymmetries by means of perceived competitive relations to seek work-related advice and information from their competitors and that stronger forms of competition are significant, but not insuperable, obstacles to the exchange of knowledge.     

Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models

Likelihood-based marginalized models using random effects have become popular for analyzing longitudinal categorical data. These models permit direct interpretation of marginal mean parameters and characterize the serial dependence of longitudinal outcomes using random effects [12,22]. In this paper, we propose model that expands the use of previous models to accommodate longitudinal nominal data. Random effects using a new covariance matrix with a Kronecker product composition are used to explain serial and categorical dependence. The Quasi-Newton algorithm is developed for estimation. These proposed methods are illustrated with a real data set and compared with other standard methods.