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Featured researches published by Richard Fowles.


Stanford Law Review | 1998

Handcuffing the Cops? A Thirty-Year Perspective on Miranda's Harmful Effects on Law Enforcement

Paul G. Cassell; Richard Fowles

Critics charged that the Supreme Court’s 1966 decision in Miranda v. Arizona would “handcuff the cops.” Were critics’ concerns justified? This Article, using FBI data, finds that national crime clearance rates fell precipitously in the two years immediately after Miranda and have remained at lower levels in the decades since. Multiple regression analysis further reveals that rising crime rates and the aging baby-boom generation do not account for much of this decline in clearance rates. Rather, as this Article concludes, Miranda has in fact “handcuffed” the police, and society should begin to explore ways of loosening these shackles.


Archive | 2014

Avalanche Forecasting: Using Bayesian Additive Regression Trees (BART)

Gail Blattenberger; Richard Fowles

During the ski season, professional avalanche forecasters working for the Utah Department of Transportation (UDOT) monitor one of the most dangerous highways in the world. These forecasters continually evaluate the risk of avalanche activity and make road closure decisions.


Applied Economics | 2012

Understanding the cell phone effect on vehicle fatalities: a Bayesian view

Gail Blattenberger; Richard Fowles; Peter D. Loeb; Wm. A. Clarke

This article examines the potential effect of various factors on motor vehicle fatality rates using a rich set of panel data and classical regression analysis combined with Bayesian Extreme Bounds Analysis (EBA), Bayesian Model Averaging (BMA) and Stochastic Search Variable Selection (SSVS) procedures. The variables examined in the models include traditional motor vehicle and socioeconomic factors. In addition, the models address the effects of cell phone usage on such accidents. The use of both classical and Bayesian techniques diminish the model and parameter uncertainties which afflict more conventional modelling methods which rely on only one of the two methods.


Archive | 1995

Road Closure: Combining Data and Expert Opinion

Gail Blattenberger; Richard Fowles

Decisions to close the Little Cottonwood Canyon Highway to vehicular traffic are made by avalanche forecasters. These decisions are based on professional experience and on careful monitoring of the prevailing conditions. Considerable data on weather and snowpack conditions exist. These data are informally employed by the forecasters in the road closure decision but presently they do not use formal statistical methods. This paper attempts a more formal statistical analysis to determine to whether this might facilitate the road closure decision. The conclusion is that the statistical model provides information relevant to the road closure decision that is not identical to that of the experts. When the expert decision is augmented by the statistical information, better decisions are reached compared with decisions based on either the expert opinion alone or the statistical model.


Archive | 2018

Sturdy inference and the amelioration potential for driverless cars: The reduction of motor vehicle fatalities due to technology

Richard Fowles; Peter D. Loeb

Abstract Motor vehicle crashes continue to result in a large number of fatalities each year and represent the leading cause of death for young persons. This study is the first to examine specifically the effects of a set of focus variables thought to be major contributors to motor vehicle fatalities including distractions caused by, for example, cell phones, suicidal propensities among others using a newly developed Bayesian technique designed to measure the \sturdiness of the results. The analysis is conducted using a rich panel data set for the period 1980–2010 by the State and the District of Columbia which includes motor vehicle, economic, and driver-related variables. As mentioned, the analysis makes use of a new Bayesian statistic developed by Leamer, that is, S-values. This statistic summarizes both estimation uncertainty and model ambiguity by considering millions of potential models of motor vehicle fatalities. Once the major factors of motor vehicle fatalities are unambiguously determined and their influences measured, the study considers the ameliorating potential of driverless cars on such fatalities as well as their costs to society. In particular, the ability of driverless cars with, for example, their self-braking capacity, to reduce the number of crashes, and their associated fatalities and injuries in a significant manner is examined. In addition, they may offer individuals the ability to use cell phones for calls and texting while not enhancing risks to vehicle occupants and pedestrians. Obviously, they may also serve in place of a designated driver should alcohol use be an issue. However, the ability of driverless vehicles to provide safe transportation is not without costs. These include developing and maintaining reliable computer systems and sensors along with back-up systems while addressing legal and possible environmental issues. We conclude that driverless cars offer the potential to ameliorate motor vehicle fatalities due to distractions, such as with the use of cell phones, alcohol use, and suicidal propensities. In addition, modernization of the vehicle fleet is expected to reduce motor vehicle fatalities since newer vehicles are expected to incorporate technologies which may be life saving.


Eastern Economic Journal | 2018

Rational Econometric Man

Richard Fowles

No abstract available.


Advances in Econometrics | 2014

Variable Selection in Bayesian Models: Using Parameter Estimation and Non Parameter Estimation Methods

Gail Blattenberger; Richard Fowles; Peter D. Loeb

Abstract This paper examines variable selection among various factors related to motor vehicle fatality rates using a rich set of panel data. Four Bayesian methods are used. These include Extreme Bounds Analysis (EBA), Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), and Bayesian Additive Regression Trees (BART). The first three of these employ parameter estimation, the last, BART, involves no parameter estimation. Nonetheless, it also has implications for variable selection. The variables examined in the models include traditional motor vehicle and socioeconomic factors along with important policy-related variables. Policy recommendations are suggested with respect to cell phone use, modernization of the fleet, alcohol use, and diminishing suicidal behavior.


Criminology | 1996

WAGE INEQUALITY AND CRIMINAL ACTIVITY: AN EXTREME BOUNDS ANALYSIS FOR THE UNITED STATES, 1975–1990*

Richard Fowles; Mary Merva


The American Economic Review | 1989

Speeding, Coordination, and the 55-MPH Limit: Comment

Richard Fowles; Peter D. Loeb


Southern Economic Journal | 1995

Effects of Policy-Related Variables on Traffic Fatalities: An Extreme Bounds Analysis Using Time-Series Data*

Richard Fowles; Peter D. Loeb

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Peter D. Loeb

University of Medicine and Dentistry of New Jersey

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