@article{pantula_2011, title={Statistics: A Key to Innovation in a Data-Centric World!}, volume={106}, ISSN={["0162-1459"]}, DOI={10.1198/jasa.2011.ap10508}, abstractNote={It is a great time to be a statistician; we are needed by every scientist in the world! We have a lot to be proud of. It feels like just yesterday, I was introduced to statistics, when I entered the Indian Statistical Institute (ISI) in Kolkata. ISI’s founder, P. C. Mahalanobis, had passed away a few years before I joined ISI, but his refrain that statistics is a key technology to solve realworld problems, was still very much in the air and profoundly impressed me. To this day, I retain my unwavering faith that statistics, or statistical thinking, is the real key to innovation in a data-centric world. That is the topic of my speech tonight—Statistics—a key to innovation in a data-centric world. First, I would emphasize how our members in academia, industry, and government are making innovative contributions to advancing science. Second, I would talk about the opportunities where statisticians can play an important role in formulating new policies, have an impact, and make us more visible. Finally, I would talk about some of the challenges that our profession and our association are facing, how you can GIVE to ASA, and how we can realize a brighter future that is ahead of us. Before I jump into my main three points, and while I still have your attention, I would like to take this opportunity to thank all our ASA members for your support. Also, I would like to thank a few folks in particular who made a significant impact in my life—my classmates from ISI and Iowa State; all my excellent colleagues, alumni, staff, and students at NC State, where my heart and home are; ASA presidents, staff and board members; and Executive Directors Bill Smith and Ron Wasserstein. Ron has been very helpful to me for the past few years, and I am very glad to see his hard work for ASA, first hand. I am also grateful to my well wishers like, Tom Gerig, Dan Solomon, and Jackie Hughes-Oliver from NC State, Wayne Fuller from Iowa State, and two past ASA Presidents, Stu Hunter, and Fritz Scheuren. I would like to be upfront, and mention that, I have received a considerable amount of help with this talk, from my long term ISI buddy, Srinivas Bhogle, who works for a Washington based company called TEOCO. Finally, I want to thank my wife Sobha, and our daughter Asha, for their patience, love, and for putting up with all my jobs and travels. I am sure I forgot to mention many, but I want to go ahead [and] alert you now . . . in my talk, I will mention a few names,}, number={493}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Pantula, Sastry G.}, year={2011}, month={Mar}, pages={1–5} }
 @article{dickey_pantula_2002, title={Determining the order of differencing in autoregressive processes (Reprinted)}, volume={20}, ISSN={["1537-2707"]}, DOI={10.1198/073500102753410363}, abstractNote={One way of handling nonstationarity in time series is to compute first differences and fit a model to the differenced series unless the differenced series also looks nonstationary. In that case, second- or higher-order differencing is done. To decide if the current degree of differencing is sufficient, one can look at the autocorrelation function for slow decay. A formal statistical test for the need to difference further is available if one is willing to assume that at most one more difference will render the series stationary. In this article, we present a proper sequence of statistical tests that allows the practitioner to handle cases in which a high order of differencing may be needed. The proper sequence is not the traditional sequence, which begins with a test for a single unit root.}, number={1}, journal={JOURNAL OF BUSINESS & ECONOMIC STATISTICS}, author={Dickey, DA and Pantula, SG}, year={2002}, month={Jan}, pages={18–24} }
 @article{sun_pantula_1999, title={Testing for trends in correlated data}, volume={41}, ISSN={["0167-7152"]}, DOI={10.1016/S0167-7152(98)00131-X}, abstractNote={The problem of testing for the significance of a linear trend in the presence of positively correlated errors is considered. Test criteria based on ordinary least squares, conditional maximum likelihood, estimated generalized least squares and maximum likelihood estimates tend to have higher significance levels than nominal levels for positively correlated series of moderate length. In this paper, we study three alternative methods: (a) pre-test, (b) bias-adjusted, and (c) bootstrap-based procedures. A simulation study is used to compare the empirical level and power of different procedures. An example is used to illustrate the procedures.}, number={1}, journal={STATISTICS & PROBABILITY LETTERS}, author={Sun, HG and Pantula, SG}, year={1999}, month={Jan}, pages={87–95} }
 @book{rawling_pantula_dickey_1998, title={Applied regression analysis: A research tool}, ISBN={0387984542}, publisher={New York: Springer}, author={Rawling, J. O. and Pantula, S. G. and Dickey, D. A.}, year={1998} }
 @inbook{gumpertz_pantula_1998, title={Random coefficient regression}, booktitle={Samuel Kotz (Editor-in-chief), Encyclopedia of statistical sciences}, publisher={New York: Wiley}, author={Gumpertz, M. L. and Pantula, S. G.}, year={1998}, pages={581–588} }
 @article{park_pantula_1998, title={Variance estimators in the Chu-White test for structural change}, volume={27}, ISSN={["0361-0918"]}, DOI={10.1080/03610919808813523}, abstractNote={In this paper, we consider the Chu‐White test for a change in the trend parameter in a simple linear regression model with correlated errors. Chu‐White test requires a consistent estimator of the error variance. The main goal of this paper is to study the choice of the variance estimates. We consider several variance estimators and study the power of the Chu‐White test under different choices of the variance estimators.}, number={4}, journal={COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION}, author={Park, YJ and Pantula, SG}, year={1998}, pages={1019–1029} }