Frequentist statistics as a theory of inductive inference

After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of $p$-values rather than as formal procedures for ``acceptance'' and ``rejection.'' A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of $p$-values in the specific circumstances of each application, as contrasted with a long-run interpretation. A variety of more complicated situations are discussed in which modification of the simple $p$-value may be essential.Comment: Published at http://dx.doi.org/10.1214/074921706000000400 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

A Hypothetical Cohort Model of Human Development

This research provides a model of growth of the human development index (HDI) by examining past changes and levels of HDI and creates four ÒcohortsÓ of countries. Using a hypothetical cohort approach reveals a model of HDI growth. Generalized Estimating Equations are used to determine the impact that country characteristics have on HDI. The analysis shows that conflict has a significant impact on HDI. Further, while in 1970, the countries whose HDI was most impacted by conflict were developing nations, currently, conflict is most detrimental to the least developed countries. The research also shows that the 1990s presented particular challenges to the least developed countries, perhaps attributable to ramifications of the AIDS crisis. The research then uses the model to predict HDI in the future and compares results from the prediction with projections that result when Ðrecalculating HDI using components that various agencies have separately projected.human development index, conflict, hypothetical cohorts

Framework for resource efficient profiling of spatial model performance, A

2022 Summer.Includes bibliographical references.We design models to understand phenomena, make predictions, and/or inform decision-making. This study targets models that encapsulate spatially evolving phenomena. Given a model M, our objective is to identify how well the model predicts across all geospatial extents. A modeler may expect these validations to occur at varying spatial resolutions (e.g., states, counties, towns, census tracts). Assessing a model with all available ground-truth data is infeasible due to the data volumes involved. We propose a framework to assess the performance of models at scale over diverse spatial data collections. Our methodology ensures orchestration of validation workloads while reducing memory strain, alleviating contention, enabling concurrency, and ensuring high throughput. We introduce the notion of a validation budget that represents an upper-bound on the total number of observations that are used to assess the performance of models across spatial extents. The validation budget attempts to capture the distribution characteristics of observations and is informed by multiple sampling strategies. Our design allows us to decouple the validation from the underlying model-fitting libraries to interoperate with models designed using different libraries and analytical engines; our advanced research prototype currently supports Scikit-learn, PyTorch, and TensorFlow. We have conducted extensive benchmarks that demonstrate the suitability of our methodology

Tax Compliance: Ethical Orientation, Risk Perception And The Role Of The Tax Preparer

In a voluntary tax system, taxpayers have an opportunity to avoid or evade paying taxes. The reasons for and the causes of noncompliance are expansive. One way to increase revenue to the government without increasing taxes is to focus on deterring tax evasion and tax underreporting. The purpose of this study was to examine the effects on tax compliance of the taxpayer’s ethical orientation and perceived financial risk, as well as the role of the tax preparer in the compliance decision. This research adds to the current tax compliance literature by investigating (in an experimental setting) the role of the tax preparer in situations where income is not reported to a third party and the tax law is clear, yet noncompliance still occurs. Additionally, this study improves upon previous studies by incorporating an income-earning task, rather than participants receiving an endowment or being given a hypothetical tax scenario. By having participants earn income, the study provides participants with the same sense of income ownership that real-world taxpayers would typically experience. Finally, this study improves upon current studies measuring risk by incorporating a domain specific risk perception measurement scale. For an individual, perceived risk may vary across different risk domains. Therefore, it is beneficial to use a financial risk perception measure, rather than a general measure of risk that includes nonfinancial items. I find a significant main effect regarding the enforcement message of the tax preparer. Individuals receiving a high enforcement message are significantly more compliant than individuals receiving a low enforcement message. Additionally, I find a significant interaction between taxpayer financial risk perception and ethical orientation, implying that the impact of ethical orientation on tax compliance depends on the level of the individual’s financial risk perception. Specifically, when financial risk perception is low, tax compliance does not differ based on the level of an individual’s ethical reasoning. However, when an individual perceives financial risk to be high, individuals with low ethical reasoning are significantly less compliant than individuals with high ethical reasoning. With regard to absolute compliance, the study demonstrates a significant positive relationship between 100% compliance and high ethical reasoning. Policymakers and regulators may be able to use this information in developing more effective means to increase individual tax compliance

The martyrdom effect : when pain and effort increase prosocial contributions

Most theories of motivation and behavior (and lay intuitions alike) consider pain and effort to be deterrents. In contrast to this widely held view, we provide evidence that the prospect of enduring pain and exerting effort for a prosocial cause can promote contributions to the cause. Specifically, we show that willingness to contribute to a charitable or collective cause increases when the contribution process is expected to be painful and effortful rather than easy and enjoyable. Across five experiments, we document this “martyrdom effect,” show that the observed patterns defy standard economic and psychological accounts, and identify a mediator and moderator of the effect. Experiment 1 showed that people are willing to donate more to charity when they anticipate having to suffer to raise money. Experiment 2 extended these findings to a non-charity laboratory context that involved real money and actual pain. Experiment 3 demonstrated that the martyrdom effect is not the result of an attribute substitution strategy (whereby people use the amount of pain and effort involved in fundraising to determine donation worthiness). Experiment 4 showed that perceptions of meaningfulness partially mediate the martyrdom effect. Finally, Experiment 5 demonstrated that the nature of the prosocial cause moderates the martyrdom effect: the effect is strongest for causes associated with human suffering. We propose that anticipated pain and effort lead people to ascribe greater meaning to their contributions and to the experience of contributing, thereby motivating higher prosocial contributions. We conclude by considering some implications of this puzzling phenomenon. Copyright © 2011 John Wiley & Sons, Ltd

Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method

We present a Python implementation for RS-HDMR-GPR (Random Sampling High Dimensional Model Representation Gaussian Process Regression). The method builds representations of multivariate functions with lower-dimensional terms, either as an expansion over orders of coupling or using terms of only a given dimensionality. This facilitates, in particular, recovering functional dependence from sparse data. The code also allows for imputation of missing values of the variables and for a significant pruning of the useful number of HDMR terms. The code can also be used for estimating relative importance of different combinations of input variables, thereby adding an element of insight to a general machine learning method. The capabilities of this regression tool are demonstrated on test cases involving synthetic analytic functions, the potential energy surface of the water molecule, kinetic energy densities of materials (crystalline magnesium, aluminum, and silicon), and financial market data.Comment: 47 pages, 13 figure