This dissertation investigates the prosecution and punishment of corporate crime in the U.S., focusing on the evolution of corporate criminal justice toward a compliance model. The underenforcement and lenient punishment of corporate crime have intensified in recent decades. Drawing on a sociolegal framework, the dissertation explores how various actors socially and politically construct corporate criminality and operationalize corporate penality around compliance within a neoliberal context. It posits that state actors defer to compliance structures in prosecutorial and sentencing decision-making and rely on business organizations and legal professionals to construct the meaning of compliance in corporate criminal justice. This dissertation develops a neoliberal corporate penality theory to elucidate the interplay among the state, the business, and the populace. According to this theory, the U.S. corporate criminal justice system is imbued with neoliberal ideologies and policies characterized by self-policing and self-regulation through the mechanism of legal endogeneity. Building upon this theory, the study examines how various stakeholders develop and propagate compliance rhetoric during the promulgation of the 1991 U.S. Sentencing Commission (USSC) Organizational Sentencing Guidelines. Additionally, utilizing USSC sentencing data, government documents, and court records, the study engages in quantitative and qualitative analyses to explore how legal actors defer to compliance structures in corporate prosecution and sentencing and how such deference influences legal decisions and outcomes. The findings reveal the significant role of the compliance model in shaping the prosecution and penalization of corporate offending. During the lawmaking of the Organizational Sentencing Guidelines, various actors reframed the legal ideals, devised compliance rhetoric, and mobilized symbolic structures to codify the compliance model into the Sentencing Guidelines. While compliance programs may not always lead to favorable decisions and mitigated penalties for business organizations, they exert a defensive function by facilitating large companies in securing non-criminal dispositions such as Non-Prosecution Agreements (NPAs) and Deferred Prosecution Agreements (DPAs). Prosecutors and judges often defer to symbols of existing compliance structures and ongoing and future compliance efforts when determining corporate liability, resolution, and punishment without performing substantive assessments or exercising sufficient oversight. These findings corroborate and add nuances to the theory of legal endogeneity. Building upon the idea of “Too Big to Jail,” this thesis posits that legal deference to compliance structures contributes to the institutionalization of the self-regulation paradigm and the perpetuation of institutional favoritism toward large companies.

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric fits is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates of contiguous alternatives that can be detected consistently by the adaptive Neyman test. Applications of the procedures to the partially linear models are thoroughly discussed. Our simulation studies show that the new testing procedures are indeed powerful and omnibus. The power of the proposed tests is comparable to the F-test statistic even in the situations where F-test is known to be suitable and can be far more powerful than the F-test statistic in other situations. An application to testing linear models versus additive models are addressed.

National Basketball Association basketball is a dynamic sport played by various 5-person units (lineups) from two teams for 48 minutes. Researchers have attempted to model the dynamics of the game using play-by-play data, which contains a game log of events on the court and times at which those events happened. Recent work involving NBA play-by-play data used discrete-time Markov chains to model team possession-related events to predict final game scores. Moreover, the availability of optical player tracking data has allowed researchers to model game progression on an individual player level. While the work captured details of games, game progression driven by lineups used by each team and the amount of time played by each lineup on the court were not considered.

Using NBA play-by-play data, we sought models for time series detailing transitions of lineups (states) and the times at which a new lineup was used. Because substitutions occur at random times, we built a continuous-time Markov chain (CTMC) model for each NBA team in which each state corresponds to a unique lineup. Using the 2014-15 NBA season, we correctly predicted 12 out of 15 playoff outcomes. Nevertheless, all CTMC models suffered from absorbing states.

Each time series might have a final state which has never been reached previously. In this case, the final state will end up being an absorbing state for the maximum-likelihood-estimate Markov model. In the long run, an absorbing Markov chain has equilibrium distribution supported entirely on the set of absorbing states. To remedy this problem in a basketball simulation, we considered three strategies: i) reroute to a more probable state if an absorbing state is reached, ii) remove lineups that played less than 2 minutes and lineups corresponding to absorbing states in a season, and iii) allow simulation to stay in the absorbing state if it is the final state reached before the game is over. Combining these strategies with scoring-rate models built using singular value decomposition and weighted observations, we obtained best training and test accuracy of 75% and 70%, training on the first 75 games and testing on the remaining 7 games for the 2015-16 NBA season. A test accuracy of 70% compares favorably with state-of-the-art models.

These ad hoc absorbing-state strategies, developed for NBA data, however, might not be valid in other applications; hence, we developed stable optimization algorithms that systematically solve the issue of absorbing states so that the Markov chains reach desired equilibria and achieve zero long-term training error. We defined long-term training error as the one-norm difference between the equilibrium distribution of an MLE derived Markov chain and the actual fraction of time spent in each state on the training data. We then applied the optimization methods to the NBA data and to two finite-state, discrete-time biomedical data sets. The results showed smaller long-term training and test errors compared to naive MLE models. Furthermore, for the biomedical data, the methods yield similar long-term errors as hidden Markov models while requiring significantly less training time.

Antimycin A (antimycin), one of the first known and most potent inhibitors of the mitochondrial respiratory chain, binds to the quinone reduction site of the cytochrome bc1 complex. Structure-activity-relationship studies have shown that the N-formylamino-salicyl-amide group is responsible for most of the binding specificity, and suggested that a low pKa for the phenolic OH group and an intramolecular H-bond between that OH and the carbonyl O of the salicylamide linkage are important. Two previous X-ray structures of antimycin bound to vertebrate bc1 complex gave conflicting results. A new structure reported here of the bovine mitochondrial bc1 complex at 2.28 Angstrom resolution with antimycin bound, allows us for the first time to reliably describe the binding of antimycin and shows that the intramolecular hydrogen bond described in solution and in the small-molecule structure is replaced by one involving the NH rather than carbonyl O of the amide linkage, with rotation of the amide group relative to the aromatic ring. The phenolic OH and formylamino N form H-bonds with conserved Asp228 of cyt b, and the formylamino O H-bonds via a water molecule to Lys227. A strong density the right size and shape for a diatomic molecule is found between the other side of the dilactone ring and the alpha-A helix.