Estimation of Crop Yields and Insurance Premiums Using a Shrinkage Estimator

We explore the estimation of crop yields and insurance premiums using a hierarchical Bayes small area estimator. The estimator is evaluated for Area Yield Production (AYP) policy using quasi-simulated corn yields in the United States. Its performance in producing reliable mean county yield and premium estimates is compared to that of a naive estimator. We also investigate the impact of these efficiency improvements on the residual losses between a farm-level policy and AYP. The proposed estimator is found to be substantially more efficient and less biased than the naive estimator.     

Finding all Nash equilibria of a finite game using polynomial algebra

The set of Nash equilibria of a finite game is the set of nonnegative solutions to a system of polynomial equations. In this survey article, we describe how to construct certain special games and explain how to find all the complex roots of the corresponding polynomial systems, including all the Nash equilibria. We then explain how to find all the complex roots of the polynomial systems for arbitrary generic games, by polyhedral homotopy continuation starting from the solutions to the specially constructed games. We describe the use of Gröbner bases to solve these polynomial systems and to learn geometric information about how the solution set varies with the payoff functions. Finally, we review the use of the Gambit software package to find all Nash equilibria of a finite game.     

Some Evidence on the Uniqueness of Initial Public Debt Offerings

Debt initial public offerings (IPOs) represent a major shift in a firm's financing policy by both extending debt maturity and altering the public-private debt mix. In contrast to findings for seasoned debt offerings, we document a significantly negative stock price response to debt IPO announcements. This result is consistent with debt maturity and debt ownership structure theories. The equity wealth effect is negatively related to the offer's maturity, and positively related to the degree of bank monitoring. We find that firms with less information asymmetry and firms with higher growth opportunities experience a less adverse stock price response.     

Impact of perceived value on the online purchase intention of base of the pyramid consumers

Despite the voluminous purchasing power of the consumers at the base of the pyramid (BOP), substantial research has not been carried out on their online buying behaviour. This study investigates the factors that trigger the online shopping intention of BOP consumers. We conducted semi-structured in-depth interviews with 52 BOP respondents from India. Our thematic analysis indicates four perceived benefits and five sacrifices influencing BOP consumers' perceived value for online purchases, subsequently determining their adoption behaviour. As a novel contribution to the literature, this study unfolds several unknown factors that motivate/demotivate BOP consumers to buy online using the Value-based Adoption Model (VAM). The findings of this study will help e-commerce marketers enhance BOP consumers' usage intention toward online purchases.     

Adoption of Ubiquitous Customer Relationship Management (uCRM) in Enterprise: Leadership Support and Technological Competence as Moderators

Abstract

Ubiquitous Customer Relationship Management (uCRM) signifies the realization of special characteristics, including proactiveness, context awareness, and mobility. This uCRM helps to enhance promptness and timeliness of the traditional system, thereby creating an environment associated with a state of being everywhere in no time and covering any device and any network. uCRM provides high personalization of service. The uCRM system has opened a new area of satisfaction for customers. Context awareness computing, a uCRM characteristic, is presumed to have enhanced benefits for a firm adopting uCRM in India. In this context, this study has taken a holistic attempt to analyze how technological competence and leadership support can enhance the business performance of a firm adopting uCRM. With input from literature studies, a hypothesis and a conceptual model have been formulated. Statistically, those have been validated with survey work considering 165 usable respondents from 22 different firms situated in three metropolitan cities in India. Results show that the effect of technological competence and leadership support positively help a uCRM-dependent firm towards achieving better business benefits. The study ends with a comprehensive conclusion.     

Horseshoe Regularisation for Machine Learning in Complex and Deep Models1

Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; for which systematic surveys are available. The purpose of the current article is to demonstrate that the horseshoe regularisation is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularisation in non-linear and non-Gaussian models, multivariate models and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.     

On Ethical Violations in Microfinance Backed Small Businesses: Family and Household Welfare

Journal of Business Ethics - The microfinance business model focuses largely on lending to the woman in the household, rather than the man. The belief is that women are more trustworthy borrowers...     

Non‐parametric regression in clustered multistate current status data with informative cluster size

Datasets examining periodontal disease records current (disease) status information of tooth‐sites, whose stochastic behavior can be attributed to a multistate system with state occupation determined at a single inspection time. In addition, the tooth‐sites remain clustered within a subject, and the number of available tooth‐sites may be representative of the true periodontal disease status of that subject, leading to an ‘informative cluster size’ scenario. To provide insulation against incorrect model assumptions, we propose a non‐parametric regression framework to estimate state occupation probabilities at a given time and state exit/entry distributions, utilizing weighted monotonic regression and smoothing techniques. We demonstrate the superior performance of our proposed weighted estimators over the unweighted counterparts via a simulation study and illustrate the methodology using a dataset on periodontal disease.     

A risk-based decision support framework for selection of appropriate safety measure system for underground coal mines

In the context of underground coal mining industry, the increased economic issues regarding implementation of additional safety measure systems, along with growing public awareness to ensure high level of workers safety, have put great pressure on the managers towards finding the best solution to ensure safe as well as economically viable alternative selection. Risk-based decision support system plays an important role in finding such solutions amongst candidate alternatives with respect to multiple decision criteria. Therefore, in this paper, a unified risk-based decision-making methodology has been proposed for selecting an appropriate safety measure system in relation to an underground coal mining industry with respect to multiple risk criteria such as financial risk, operating risk, and maintenance risk. The proposed methodology uses interval-valued fuzzy set theory for modelling vagueness and subjectivity in the estimates of fuzzy risk ratings for making appropriate decision. The methodology is based on the aggregative fuzzy risk analysis and multi-criteria decision making. The selection decisions are made within the context of understanding the total integrated risk that is likely to incur while adapting the particular safety system alternative. Effectiveness of the proposed methodology has been validated through a real-time case study. The result in the context of final priority ranking is seemed fairly consistent.     

California’s Three Strikes Law Revisited: Assessing the Long-Term Effects of the Law

Previous studies on the three strikes legislation in California draw conclusions based on data immediately following the passage of the law, which cannot account for the full deterrent effect of the law. Moreover, they do not account for the incapacitation effect. Distinguishing between deterrence and incapacitation is important for determining the efficacy of the law. Using data from 1991 to 2009, this paper is the first to examine the long-run effects. Our results show that the law had a significant deterrent effect on all crimes, including non-triggering offenses. Overall, the incapacitation effect is less significant. Further, enhanced penalties cause some felons to substitute between violent crimes and property crimes which carry a lower sentence. However, there is little evidence of substitution within crime categories. Surprisingly however, the added deterrent effect of the third strike is significantly smaller than for the second strike. The small marginal deterrence from the third strike also raises questions about the justification for such extreme severity of punishment.