B. Succinct Definition of Thematic Area. Statistics is so ubiquitous in science as to need no definition . Statistics cuts across any and all strategic academic focusing themes that gather and analyze data. To limit a statistical proposal to just one theme (e.g., data-enabled science and engineering) would decrease its ability to establish generalizable frameworks and thus respond to needs in other themes (e.g., human health). Therefore, this proposal is intended to complement rather than compete with any other proposals. CeQR is more than just an organizational unit. It has an intellectual focus that is consistent with advanced statistical work already ongoing at UC Merced. Here are some examples: Quantitative and Systems Biology uses statistical genetics; Cognitive and Information Science models neural networks; Human Health employs large sample epidemiological analyses; Quantitative Psychology uses Bayesian statistics and generalized additive models; data mining is one key to “big data”. What do these all have in common? They all take advantage of the increased power of computers in the last several decades to develop and apply statistics that were mostly only dreamed before. This theme is sometimes called computer-intensive statistics. Other examples are Markov chain Monte Carlo methodology, the bootstrap, nonparametric and semiparametric prediction, machine learning, data visualization and scatterplot painting, and automated fitting algorithms. C. Intellectual Components of the Strategic Initiative Why is CeQR Important? Statistics departments are often the last units built on a campus, largely because of low undergraduate major enrollment. For example, UC Irvine did not start its statistics department until 40 years after campus opening. Although we would love to see the strategic academic focusing process recommend the immediate addition of a statistics department, that seems unlikely. Yet advanced statistical capabilities remain necessary to the future growth of both research and graduate education at UC Merced. Here is just one of many possible examples. Research in such widely diverse areas as human health, cognitive science, and even the kinds of drone research proposed by CIDER all use multilevel modeling statistics—statistics that take appropriate account of nesting of observations within aggregates (e.g., students in classrooms, air samples within stations, time points within longitudinal cohorts). Graduate students need training in those models, grant proposals benefit from consultants on the analyses, and researchers need colleagues whom they can trust to provide state-of-the-art advice. CeQR can help develop such capacities. What are the Key Areas for CeQR? The key areas for CeQR will emerge from statistical hires already made and to be made in the future. Examples of those areas include statistical genetics, neural networks, Bayesian statistics, generalized additive models, data mining, Markov chain Monte Carlo methodology, the bootstrap, causal analysis, nonparametric and semiparametric prediction, machine learning, data visualization and scatterplot painting, data-enabled science and engineering, mediation analysis, and survival analysis—all examples of computer-intensive statistics. D. UCM’s role in this Theme Current and Potential Strengths. CeQR will be an interdisciplinary statistics center that encourages advanced statistical research and training. The campus already has pertinent strengths in such areas as Quantitative Psychology, Quantitative and Systems Biology, Cognitive and Information Science, and the many programs contributing to Human Health (e.g., Health Psychology, Public Health, HSRI). UC Merced will no doubt add strengths both in existing statistical topics (e.g., Bayesian statistics, neural networks, statistical genetics) and new ones (e.g., data-enabled science and engineering; biostatistics). Interaction among CeQR faculty will encourage sharing of needs and ideas that will help ensure that research and training options are cutting-edge, comprehensive and nonredundant. Distinctiveness and Competitiveness. All UC campuses have statistics departments. However, they mostly have very traditional emphases on mathematical statistics compared to CeQR’s interdisciplinary and applied focus. None represent the full array of computer-intensive statistics already present at UC Merced. What makes UC Merced distinct, then, is that the development of statistics here will be grounded in applied interdisciplinary problems across the university. Exactly how that might affect the eventual addition of a statistics doctoral program or department at UC Merced is not possible to know at this time. Hopefully, it will make that eventuality more interdisciplinary as well. Developing CeQR will increase competitiveness in two ways. First, UC Merced faculty have tried since 2004 to hire a biostatistician, which resulted numerous failed searches until recently. A key reason is that such faculty do not wish to risk being isolated within a substantive specialty without other statistical colleagues. CeQR can provide a statistical environment that will be more attractive to such candidates. Second, graduate students who enter a doctoral training program are attracted by the additional opportunity to increase their statistical skills through options like obtaining a master’s degree in statistics. We know of several such students who specifically cited the lack of such an opportunity as a reason for choosing another university to attend for graduate studies. In addition, existing programs that focus on statistics will benefit from the growth that CeQR envisions. Here is one example. The quantitative psychology program has five FTE faculty lines already, and is the only program at UC Merced to offer graduate degrees in any field of statistics. The UC Merced program is already at the size and productivity level of the other two cognate UC programs at UCLA and UC Davis. UCLA’s program is capped at five faculty, and UC Davis’s at four faculty with two of those faculty leaving. With a relatively small investment of FTEs, the quantitative psychology program can become not only the top such program in the UC system, but also one of the top ones in the nation, at the level of other top programs such as Ohio State University, University of North Carolina at Chapel Hill, Notre Dame University, and Vanderbilt University. It will also be the only such program in the nation specializing in Bayesian statistics . Bayesian statistics are of increasing interest in science as modern computing allows estimation of complex models using variations on Markov chain Monte Carlo methods. It is also of interest to funding agencies. A search of “Bayes” at nsf.gov, for example, yields 506 hits across nearly every scientific area. In addition, specialties represented in quantitative psychology (and already being taught at the graduate level) include structural equation modeling, longitudinal analysis, multilevel models, item response theory, meta-analysis, and experimental and quasi-experimental design. These are topics that are of direct interest to many of the strategic themes, with quantitative psychology faculty already providing statistical expertise to empirical studies and grants across the entire university. We anticipate other such statistical cores will develop at UC Merced. Biostatistics is a likely example. Quantitative and systems biology recently hired a statistician emphasizing Bayesian analyses of genetic data. The Public Health program has hired an epidemiologist to start this coming academic year. The Health Sciences Research Institute is preparing an advertisement for an authorized hire in biostatistics next year; and they approached CeQR to see whether an affiliation with CeQR was possible so as to provide the hire with statistical colleagues. A second example would occur if UC Merced proceeds to develop a data-enabled science and engineering theme, another unique theme that could make UC Merced distinctive. E. What bylaw units/grad groups might participate, and how would they participate? Faculty who have affiliated with CeQR are part of the following programs: School of Engineering—Electrical Engineering and Computer Science. School of Natural Sciences—Applied Mathematics, Quantitative and Systems Biology, Earth Science. School of Social Sciences, Humanities and Arts—Cognitive and Information Science, Economics, Management, Political Science, Psychological Sciences, Public Health, Sociology. Befitting its transdisciplinary status, CeQR is mentioned as a component of several strategic focusing proposals. These include the Center for Human Adaptive Systems and Environments (CHASE); the Entrepreneurship Research Institute (ERI); Computational and Data Science (CDS); Management of Innovation, Sustainability, and Technology (MIST); the School of Management and Economics; Political Science; and the Psychological Sciences Healthy Development proposal. Still other proposals mention the need for expanded graduate level statistical and quantitative education. Indeed, it is difficult to think of any scientific theme that would not benefit from a synergistic relationship with a statistical focus. For example, the Spatial Analysis and Research Center (SpARC), and the Economics Strategic Initiative do not specifically mention CeQR; but they do mention “building strength in quantitative methods and synergies” (Economics), “data creation, …data analysis and data serving services” (SpARC). Even proposals that make no specific mention of statistics, such as the California Institute of Drone Engineering Research (CIDER), in fact require advanced statistical analyses like multilevel modeling (because sampled observations are often nested within geographic areas). The aim of the present proposal is not to limit CeQR interests to any single one of these foci, but rather to help all of them. By facilitating the coordination and dissemination of statistical methods across numerous research areas on campus, CeQR can help prevent unnecessary redundancies in graduate training and foster interdisciplinary research collaborations. Of course, all these faculty are actively participating in other strategic focusing themes, as well. Their interest in CeQR lies in the transdisciplinary appeal of statistics across all the scientific disciplines. Their participation in CeQR is likely to include (a) graduate students taking statistical and quantitative coursework, (b) teaching those graduate courses, (c) seeking out the collaboration of statistical colleagues to improve the creation and analyses of data, both for publications and for extramural grants, (d) attending CeQR colloquia and workshops on statistical topics, and (e) creating a master’s program in applied statistics that all graduate students can take to improve their research and their market competitiveness. F. General description of special programmatic needs (specialized space requirements, special library collections, etc.). Computation Facilities: Statistical and quantitative work in general, especially of the computer-intensive kind that UC Merced faculty already do, requires high capacity computational facilities. The present proposal shares this need with many other programs and strategic focusing proposals. In the case of CeQR, the most desirable version of this would include both high-speed computers for extensive computer simulations, and multi-user servers with statistical software packages. A central statistical organization could also improve licensing efficiency for proprietary software. Dedicated specialty computer labs would also be useful. An example would be a computer lab for running controlled experiments with human subjects in Management or in Political Science. Faculty FTEs: Because CeQR is not a bylaw unit, it will not have faculty FTE’s placed within it. However, faculty FTEs devoted to statistical hires are a high priority. This could occur in several ways. 1. One is to allocate FTEs to existing bylaw units specifically aimed at statistical hires. For example: a. Quantitative psychology FTEs could be allocated to the Psychological Sciences bylaw unit; b. Statistical genetics biostatistical FTEs could be allocated to Quantitative and Systems Biology. c. Public Health is interested in expanding biostatistical hires; d. The management program has interest in hiring faculty who do experimental analysis like Fischbacher's z-Tree. e. Political Science is interested in hires focusing on causal analysis and methodology, and on advanced statistical methods such as Semiparametric regression. 2. A complementary mechanism would be to do a cluster hire in a topic, such as Bayesian statistics or data mining. 3. A third way could be to allocate FTEs to CeQR for competitive proposals from around UC Merced to hire statistics faculty. Budget: CeQR would benefit from funding for a colloquium series, for graduate student travel to attend conferences and workshops, to initiate a summer workshop series, and for faculty and graduate students to submit grant proposals for seed money for research. A speaker series could bring in scholars with expertise currently lacking on campus, and thus could enhance the quantitative training available to both faculty and graduate students. The participation of faculty from across disciplines in seminars and workshops would foster the exchange of knowledge across disciplines. UC Merced does not currently have a comparable venue for these sorts of exchanges. A summer workshop series could generate income and provide additional training for graduate students and faculty. Longer Term Infrastructure: Looking to 2020 and beyond, UC Merced should consider two additional developments. First might be the development of a research institute at UC Merced, similar to the Health Sciences Research Institute, that would coordinate the many efforts currently underway at UC Merced to advance diverse kinds of statistical and computational work. Such an institute could serve as an umbrella for coordinating and administering existing and proposed centers such as CeQR, including the Center for Human Adaptive Systems and Environments (CHASE), the Center for Computational Biology, and the Center for Theory and Computation. Second, statistical and quantitative research at UC Merced would benefit from the development of a statistical consulting center and a survey research center, and eventually, a statistics department. These may have to be longer range plans given existing resource constraints. The Central Valley does not have a comparable quantitative research center, yet would definitely benefit from its presence. UC Merced faculty have received numerous contacts from Valley community members seeking assistance with research, especially survey research. A quantitative research center would thus not only benefit UC Merced faculty, but would also potentially serve a need in the community. Graduate and Undergraduate Training. CeQR also aims to coordinate statistical offerings across campus. A website will list CEQR activities, a list of pertinent faculty with their quantitative interests, and a list of pertinent quantitative courses at both the graduate and undergraduate level. CeQR faculty hope to develop (1) an undergraduate interdisciplinary minor in statistics, (2) a possible summer workshop series that might serve as a source of income for CEQR and a source of training for UCM and other graduate students, and (3) a graduate master’s degree in applied statistics that could be taken by graduate students already in other programs. A number of programs at UC Merced have expressed interest in the master’s program because it both aids research and provides a credential that improves the marketability of graduate students once they enter the job market. The ability to accomplish these goals, especially the master’s degree, depends partly on continued hiring of statistical faculty.