On robust estimation of the location parameter

The aim of the present study was to examine further the role that self-identity plays in the theory of planned behaviour and, more specifically, to: (1) examine the combined effects of self-identity and social identity constructs on intention and behaviour, and (2) examine the effects of self-identity as a function of past experience of performing the behaviour. The study was concerned with the prediction of intention to engage in household recycling and reported recycling behaviour. A sample of 143 community residents participated in the study. It was prospective in design: measures of the predictors and intention were obtained at the first wave of data collection, whereas behaviour was assessed two weeks later. Selfidentity significantly predicted behavioural intention, a relationship that was not dependent on the extent to which the behaviour had been performed in the past. As expected, there was also evidence that the perceived norm of a behaviourally relevant reference group was related to behavioural intention, but only for participants who identified strongly with the group, whereas the relationship between perceived behavioural control (a personal factor) and intention was strongest for low identifiers.

The theory of planned behaviour: Self identity, social identity, and group norms

High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference. In this article, we present a brief account of the recent developments of theory, methods, and implementations for high dimensional variable selection. What limits of the dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high dimensional variable selection, with emphasis on independence screening and two-scale methods.

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A Selective Overview of Variable Selection in High Dimensional Feature Space.

Research in closed-loop supply chains (CLSCs) has grown rapidly over the last 10 years. The authors offer a critical review of influential CLSC research that takes a business economics perspective. Much of the research was inspired by practice-driven thoughtpieces, and this helped to keep research focused on relevant issues. However, CLSC research has several assumptions, such as perfect substitution between new and remanufactured products that risk becoming institutionalized. There is a strong need to carefully examine current industrial practice so that research remains focused on relevant problems. Deeper understanding of consumer perceptions of remanufactured products, product diffusion, and valuation of returned products are needed for the field to continue to add insights into developing sustainable economies.

Production and Operations Management

Probably because of their interpretability and transparent nature, synthetic controls have become widely applied in empirical research in economics and the social sciences. This article aims to provide practical guidance to researchers employing synthetic control methods. The article starts with an overview and an introduction to synthetic control estimation. The main sections discuss the advantages of the synthetic control framework as a research design, and describe the settings where synthetic controls provide reliable estimates and those where they may fail. The article closes with a discussion of recent extensions, related methods, and avenues for future research.

https://pubs.aeaweb.org/doi/pdf/10.1257/jel.20191450

Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects

In high-dimensional model selection problems, penalized least-square approaches have been extensively used. This paper addresses the question of both robustness and efficiency of penalized model selection methods, and proposes a data-driven weighted linear combination of convex loss functions, together with weighted L1-penalty. It is completely data-adaptive and does not require prior knowledge of the error distribution. The weighted L1-penalty is used both to ensure the convexity of the penalty term and to ameliorate the bias caused by the L1-penalty. In the setting with dimensionality much larger than the sample size, we establish a strong oracle property of the proposed method that possesses both the model selection consistency and estimation efficiency for the true non-zero coefficients. As specific examples, we introduce a robust method of composite L1-L2, and optimal composite quantile method and evaluate their performance in both simulated and real data examples.

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Penalized Composite Quasi-Likelihood for Ultrahigh-Dimensional Variable Selection

High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection. In this paper we establish strong oracle properties of nonconcave penalized methods for nonpolynomial (NP) dimensional data with censoring in the framework of Cox's proportional hazards model. A class of folded-concave penalties are employed and both LASSO and SCAD are discussed specifically. We unveil the question under which dimensionality and correlation restrictions can an oracle estimator be constructed and grasped. It is demonstrated that nonconcave penalties lead to significant reduction of the "irrepresentable condition" needed for LASSO model selection consistency. The large deviation result for martingales, bearing interests of its own, is developed for characterizing the strong oracle property. Moreover, the nonconcave regularized estimator, is shown to achieve asymptotically the information bound of the oracle estimator. A coordinate-wise algorithm is developed for finding the grid of solution paths for penalized hazard regression problems, and its performance is evaluated on simulated and gene association study examples.

Regularization for Cox's proportional hazards model with NP-dimensionality

High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection. In this paper we establish strong oracle properties of non-concave penalized methods for non-polynomial (NP) dimensional data with censoring in the framework of Cox’s proportional hazards model. A class of folded-concave penalties are employed and both LASSO and SCAD are discussed specifically. We unveil the question under which dimensionality and correlation restrictions can an oracle estimator be constructed and grasped. It is demonstrated that non-concave penalties lead to significant reduction of the “irrepresentable condition” needed for LASSO model selection consistency. The large deviation result for martingales, bearing interests of its own, is developed for characterizing the strong oracle property. Moreover, the non-concave regularized estimator, is shown to achieve asymptotically the information bound of the oracle estimator. A coordinate-wise algorithm is developed for finding the grid of solution paths for penalized hazard regression problems, and its performance is evaluated on simulated and gene association study examples.

/pdf/regularization-for-cox-s-proportional-hazards-model-with-np-1j1i2g5xhs.pdf

Regularization for cox's proportional hazards model with np-dimensionality.

We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model sparsity or the loading vector representing the hypothesis. Providing asymptotically valid methods for testing general linear functions of the regression parameters in high-dimensions is extremely challenging - especially without making restrictive or unverifiable assumptions on the number of non-zero elements. We propose to test the moment conditions related to the newly designed restructured regression, where the inputs are transformed and augmented features. These new features incorporate the structure of the null hypothesis directly. The test statistics are constructed in such a way that lack of sparsity in the original model parameter does not present a problem for the theoretical justification of our procedures. We establish asymptotically exact control on Type I error without imposing any sparsity assumptions on mode...

/pdf/linear-hypothesis-testing-in-dense-high-dimensional-linear-5a1kxjnx98.pdf

Linear Hypothesis Testing in Dense High-Dimensional Linear Models

Nonprofit organizations use direct-mail marketing to cultivate one-time donors and convert them into recurring contributors. Cultivated donors generate much more revenue than new donors, but also lapse with time, making it important to steadily draw in new cultivations. The direct-mail budget is limited, but better-designed mailings can improve success rates without increasing costs. We propose an empirical model to analyze the effectiveness of several design approaches used in practice, based on a massive data set covering 8.6 million direct-mail communications with donors to the American Red Cross during 2009–2011. We find evidence that mailed appeals are more effective when they emphasize disaster preparedness and training efforts over post-disaster cleanup. Including small cards that affirm donors’ identity as Red Cross supporters is an effective strategy, whereas including gift items such as address labels is not. Finally, very recent acquisitions are more likely to respond to appeals that ask them t...

Jelena Bradic

Papers

Penalized Composite Quasi-Likelihood for Ultrahigh-Dimensional Variable Selection

Regularization for Cox's proportional hazards model with NP-dimensionality

Regularization for cox's proportional hazards model with np-dimensionality.

Linear Hypothesis Testing in Dense High-Dimensional Linear Models

Cultivating Disaster Donors Using Data Analytics