This two-way scheme does not form a set of disjoint classes. For instance, everyone would, I think, say that a typical article has both conceptual and methodological aspects; and there are, of course, overlaps in the topics, too. The reason I'm spelling this out is that I believe I am a little unusual (in the world of statistical research) because I have been heavily invested in category (1), especially review articles, for reasons articluated here . Also, the explanation for my somewhat quirky attraction to category (2), even when the details may seem minor, is captured by this quote from the great David Blackwell, "I never wanted to do research, I just wanted to understand. And sometimes, in order to understand, you have to do research." Good examples of what I'm calling "details," which also illuminate concepts, are a 1990 paper on data-translated likelihood, a 2006 paper with Valerie Ventura on spike count correlation and trial-to-trial variability , a 2008 paper with Shinsuke Koyama on the relationship between GLM point process models and LIF models, and a 2019 paper on the form in which history effects enter GLM point process models and its effect on model "stability."

The big questions I've had, and still have, are these: What is the relationship between mathematically-defined methods and concepts (i.e, the verbalization of methods, their motivations, their accomplishments)? What is the relationship between theory and methods? When and how is it useful for statistical models to be statements about the empirical world (e.g., in neuroscience)? What are effective ways to communicate statistical ideas?

Klein, N., Orellana, J., Brincat, S., Miller, E.K., and Kass, R.E. (2019) Torus graphs for multivariate phase coupling analysis, Annals of Applied Statistics, to appear. Supplementary material here, tutorial and code here.

Yang, Y., Tarr, M.J., Kass, R.E., and Aminoff, E.M. (2019) Exploring spatio-temporal neural dynamics of the human visual cortex , Human Brain Mapping, 40: 4213-4238.

Chen, Y., Xin, Q., Ventura, V., and Kass, R.E. (2019) Stability of point process spiking neuron models , Journal of Computational Neuroscience, 46: 19-32.

Kass, R.E., Amari, S.-I., Arai, K., Brown, E.N., Diekman, C.O., Diesmann, M., Doiron, B., Eden, U.T., Fairhall, A.L., Fiddyment, G.M., Fukai, T., Grün, S., Harrison, M.T., Helias, M., Nakahara, H., Teramae, J.-N., Thomas, P.J., Reimers, M., Rodu, J., Rotstein, H.G., Shea-Brown, E., Shimazaki, H., Shinomoto, S., Yu, B.M., and Kramer, M.A. (2018) Computational neuroscience: Mathematical and statistical perspectives , Annual Review of Statistics and its Application, 5: 183-214. Pre-publication draft available here.

Rodu, J., Klein, N., Brincat, S.L., Miller, E.K., and Kass, R.E. (2018) Detecting multivariate cross-correlation between brain regions , Journal of Neurophysiology, 120: 1962-1972.

Vinci, G., Ventura, V., Smith, M.A., and Kass, R.E. (2018) Adjusted regularization of cortical covariance , Journal of Computational Neuroscience, 45: 83-101.

Vinci, G., Ventura, V., Smith, M.A., and Kass, R.E. (2018) Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data , Annals of Applied Statistics, 12: 1068-1095. Pre-publication draft and supplementary material.

Zhou, P., Resendez, S.L., Rodriguez-Romaguera, J., Stuber, G.D., Jimenez, J.C., Hen, R., Keirbek, M.A., Neufeld, S.Q., Sabatini, B.L., Kass, R.E., and Paninski, L. (2018) Efficient and accurate extraction of * in vivo * calcium signals from microendoscopic video data, eLife, 7: e28728 DOI: 10.7554/elife.28728.

Arai, K. and Kass, R.E. (2017) Inferring oscillatory modulation in neural spike trains , PLoS Computational Biology, 13: e1005596.

Koerner, F. S., Anderson, J. R., Fincham, J. M., and Kass, R. E. (2017) Change-point detection of cognitive states across multiple trials in functional neuroimaging, Statistics in Medicine, 36: 618-642.

Orellana, J., Rodu, J., and Kass, R.E. (2017) Population vectors can provide near optimal integration of information, Neural Computation, 29: 2021-2029.

Suway, S.B., Orellana, J. McMorland, A.J.C., Fraser, G.W., Liu, Z., Velliste, M., Chase, S.M., Kass, R.E., and Schwartz, A.B. (2017) Temporally segmented directionality in the motor cortex, Cerebral Cortex, 7: 1-14.

Wood, J., Simon, N.W., Koerner, F.S., Kass, R.E., and Moghaddam, B. (2017) Networks of VTA neurons encode real-time information about uncertain numbers of actions executed to earn a reward, Frontiers in Behavioral Neuroscience, 11: 140.

Yang, Y., Xu, Y., Jew, C.A., Pyles, J.A., Kass, R.E., and Tarr, M.J. (2017) Exploring the spatio-temporal neural basis of face learning, Journal of Vision, 17: 1.doi:10.1167/17.6.1.

Zhang, Q., Borst, J.P., Kass, R.E., and Anderson, J.A. (2017) Inter-subject alignment of MEG datasets in a common representational space, Human Brain Mapping, 38: 4287-4301.

Yang, Y., Aminoff, E., Tarr, M. and Kass, R.E. (2016) A state-space model of cross-region dynamic connectivity in MEG/EEG, Advances in Neural Information Processing Systems (NIPS), 29: 1226--1234.

Hefny, A., Kass, R.E., Khanna, S., Smith, M., and Gordon, G.J. (2016) Fast and improved SLEX analysis of high-dimensional time series , in Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner, Lecture Notes on Artificial Intelligence, edited by G. Cecchi, K.K. Chang, G. Langs, B. Murphy, I. Rish, and L. Wehbe, Springer, Volume 9444, pp 94--103.

Kass, R.E., Caffo, B., Davidian, M., Meng, X.-L., Yu, B., and Reid, N. (2016) Ten simple rules for effective statistical practice, PLoS Computational Biology, 12:e1004961.

Vinci, G., Ventura, V., Smith, M.A., and Kass, R.E. (2016) Separating spike count correlation from firing rate correlation , Neural Computation, 28: 849--881.

Yang, Y., Tarr, M.J., and Kass, R.E. (2016) Estimating learning effects: a short-time Fourier transform regression model for MEG source localization , in Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner, Lecture Notes on Artificial Intelligence, edited by G. Cecchi, K.K. Chang, G. Langs, B. Murphy, I. Rish, and L. Wehbe, Springer, Volume 9444, pp 69--82.

Castellanos, L., Vu, V.Q., Perel, S., Schwartz, A., and Kass, R.E. (2015) A multivariate Gaussian process factor analysis model for hand shape during reach-to-grasp movements , Statistica Sinica, 25: 5--24. supplementary material

Kass, R.E. (2015) The gap between statistics education and statistical practice. (Comment on "Mere renovaton is too little too late: we need to re-think our undergraduate curriculum from the ground up" by George Cobb), *The American Statistician, 69. Online Discussion: Special Issue on Statistics and the Undergraduate Curriculum.*.

Scott, J.G., Kelly, R.C., Smith, M.A., Zhou, P. and Kass, R.E. (2015) False discovery rate regression: an application to neural synchrony detection in primary visual cortex , Journal of the American Statistical Association, 110: 459--471.

Wang, W., Tripathy, S.J., Padmanabhan, K., Urban, N.N., and Kass, R.E. (2015) An empirical model for reliable spiking activity , Neural Computation, August 2015, 27: 8, 1609--1623.

Zhou, P., Burton, S.D., Snyder, A.C., Smith, M.A., Urban, N.N. and Kass, R.E. (2015) Establishing a statistical link between network oscillations and neural synchrony, PLoS Computational Biology, 11: e1004549. doi: 10.1371/journal.pcbi.1004549

Kass, R.E. (2014) Spike train , in * Encyclopedia of Computational Neuroscience, * edited by D. Jaeger and R. Jung. Springer.

Kass, R.E. and many others (2014) Statistical Research and Training Under the BRAIN Initiative , report of a working group of the American Statistical Association.

Harrison, M.T., Amarasingham, A., and Kass, R.E. (2013) Statistical identification of synchronous spiking.
In * Spike Timing: Mechanisms and Function, * edited by P. Di Lorenzo and J. Victor. Taylor and Francis, pp. 77--120.

Koyama, S., Omi, T., Kass, R.E. and Shinomoto, S. (2013) Information transmission using non-Poisson regular firing, Neural Computation, 25: 854--876.

Perez, O., Kass, R.E., and Merchant, H. (2013) Trial time warping to discriminate stimulus-related from movement-related neural activity, Journal of Neuroscience Methods, 212: 203-210.

Kelly, R.C. and Kass, R.E. (2012) A framework for evaluating pairwise and multiway synchrony among stimulus-driven neurons, Neural Computation, 24: 2007--2032. (Note: title of first draft was "Detecting multi-way synchrony in the presence of two-way synchrony among stimulus-driven neurons.")

Zhang, Y., Schwartz, A.B., Chase, S.M., and Kass, R.E. (2012) Bayesian learning in assisted brain-computer interface tasks, Engineering in Medicine and Biology, 2012 Annual International Conference of the IEEE (EMBC), 2740--2743.

Xu, Y., Sudre, G.P., Wang, W., Weber, D.J., and Kass, R.E. (2011) Characterizing global statistical significance of spatio-temporal hot spots in MEG/EEG source space via excursion algorithms, Statistics in Medicine, 30: 2854--2866.

Kass, R.E. (2011) Statistical inference: the big picture, with discussion by Andrew Gelman, discussion by Steven Goodman, discussion by Rob McCulloch, discussion by Hal Stern, and rejoinder , Statistical Science, 26: 1--20.

Kass, R.E., Kelly, R.C., and Loh, W.-L. (2011) Assessment of synchrony in multiple neural spike trains using loglinear point process models, Annals of Applied Statistics, 5: 1262--1292.

Chase, S.M., Schwartz, A.B., and Kass, R.E. (2010) Latent inputs improve estimates of neural encoding in motor cortex, Journal of Neuroscience, 30: 13873--13882.

Kass, R.E. (2010) Comment: How should indirect evidence be used? Statistical Science, 25: 166-169.

Kelly, R.C, Smith, M.A., Kass, R.E., and Lee, T.-S. (2010) Accounting for network effects in neuronal responses using L1 penalized point process models, Advances in Neural Information Processing Systems (NIPS), 23.

Kelly, R.C., Smith, M.A., Kass, R.E., and Lee, T.-S. (2010) Local field potentials indicate network state and account for neuronal response variability, Journal of Computational Neuroscience, 29: 567--579.

Koyama, S., Castellanos Perez-Bolde, L., Shalizi, C.R., and Kass, R.E. (2010) Approximate methods for state-space models, Journal of the American Statistical Association, 105: 170-180. DOI: 10.1198/jasa.2009.tm08326.

Koyama, S., Chase, S.M., Whitford, A.S., Velliste, M., Schwartz, A.B., and Kass, R.E.(2010) Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control, Journal Computational Neuroscience, 29: 73--87.

Tokdar, S., Xi, P., Kelly, R.C., and Kass, R.E. (2010) Detection of bursts in extracelluar spike trains using hidden semi-Markov point process models, Journal of Computational Neuroscience, 29: 203--212.

Wang, W., Sudre, G.P., Xu, Y., Kass, R.E., Collinger, J.L., Degenhart, A.D., Bagic, A.I., and Weber, D.J. (2010) Decoding and cortical source localization for intended movement direction with MEG, Journal of Neurophysiology, 104: 2451--2461.

Behseta, S., Berdyyeva, T., Olson, C.R., and Kass, R.E. (2009) Bayesian correction for attenuation of correlation in multi-trial spike count data, Journal of Neurophysiology, 101:2186-2193.

Brown, E.N. and Kass, R.E. (2009) What is Statistics? (with discussion), American Statistician, 63:105-123.

Chase, S.M., Schwartz, A.B., and Kass, R.E. (2009) Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain computer interface algorithms, Neural Networks, 22:1203-1213.

Kass, R.E. (2009) The importance of Jeffreys's Legacy (Comment on Robert, Chopin, and Rousseau) Statistical Science, 24: 2, 179-182.

Vu, V.Q., Yu, B. and Kass, R.E. (2009) Information in the non-stationary case, Neural Computation, 21:688-703.

Jarosiewicz, B., Chase, S.M., Fraser, G.W., Velliste, M. Kass, R.E., and Schwartz, A.B.(2008) Functional network reorganization during learning in a brain-machine interface paradigm, Proceedings of the National Academy of Sciences,105:19486-19491.

Koyama, S. and Kass, R.E. (2008) Spike train probability models for stimulus-driven leaky integrate-and-fire neurons, Neural Computation, 20:1776-1795.

Wallstrom, G., Liebner, J., and Kass, R.E. (2008) An implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R wrappers, Journal of Statistical Software, 26: 1-25. (online at http://www.jstatsoft.org).

Behseta, S., Kass, R.E., Moorman, D. and Olson, C. (2007) Testing Equality of Several Functions: Analysis of Single-Unit Firing Rate Curves Across Multiple Experimental Conditions, Statistics in Medicine, 26: 3958-3975.

Brockwell, A.E., Kass, R.E., and Schwartz, A.B. (2007) Statistical signal processing and the motor cortex, Proceedings of the IEEE, 95: 881-898.

Vu, V.Q., Yu, B., and Kass, R.E. (2007) Coverage Adjusted Entropy Estimation Statistics in Medicine, 26: 4039-4060.

Kass, R.E. (2006) Kinds of Bayesians (Comment on articles by Berger and by Goldstein), Bayesian Analysis, 1: 437-440.

Kass, R.E. and Ventura, V. (2006) Spike count correlation increases with length of time interval in the presence of trial-to-trial variation, Neural Computation, 18:2583-2591.

Behseta, S., Kass, R.E., and Wallstrom, G.L. (2005) Hierarchical models for assessing variability among functions, Biometrika, 92: 419-434.

Behseta, S. and Kass, R.E. (2005) Testing Equality of Two Functions using BARS, Statistics in Medicine, 24:3523-34.

Kass, R.E., Ventura, V., and Brown, E.N. (2005) Statistical issues in the analysis of neuronal data, Journal of Neurophysiology, 94: 8-25.

Kaufman, C.G., Ventura, V., and Kass, R.E. (2005) Spline-based nonparametric regression for periodic functions and its application to directional tuning of neurons, Statistics in Medicine, 24: 2255-2265.

Ventura, V., Cai, C., and Kass, R.E. (2005) Statistical assessment of time-varying dependence between two neurons, Journal of Neurophysiology, 94: 2940-2947.

Ventura, V. Cai, C., and Kass, R.E. (2005) Trial-to-trial variability and its effect on time-varying dependence between two neurons, Journal of Neurophysiology, 94: 2928-2939.

Brockwell, A.E., Rojas, A.L., and Kass, R.E. (2004) Recursive Bayesian decoding of motor cortical signals by particle filtering, Journal of Neurophysiology, 91: 1899--1907.

Brown, E.N., Kass, R.E., and Mitra, P.N. (2004) Multiple neural spike trains analysis: state-of-the-art and future challenges. Nature Neuroscience, 7: 456--461.

Wallstrom, G.A., Kass, R.E., Miller, A., Cohn, J.F., and Fox, N.A.(2004) Automatic correction of ocular artifacts in the EEG: A comparison of regression-based and component-based methods, International Journal of Psychophysiology, 53: 105--119.

Kass, R.E., Ventura, V. and Cai, C. (2003) Statistical smoothing of neuronal data, Network: Computation in Neural Systems, 14: 5--15.

Brown, B.N., Barbieri, R, Ventura V., Kass R.E., and Frank L.M. (2002) The time-rescaling theorem and its applications to neural spike train data analysis, Neural Computation, 14: 325--346.

Ventura V., Carta R., Kass R.E., Gettner, S.N., and Olson, C.R., (2002) Statistical analysis of temporal evolution in single-neuron firing rates, Biostatistics, 1: 1--20.

Wallstrom, G.L, Kass, R.E., Miller, A., Cohn, J.F., and Fox, N.A. (2002) Correction of ocular artifacts in the EEG using Bayesian adaptive regression splines, in Case Studies in Bayesian Statistics, Vol VI, eds. Gatsonis, C., Carriquiry, A., Higdon, D., Kass, R.E., Pauler, D., and Verdinelli, I., pp. 351-366, Springer-Verlag.

Daniels, M.J. and Kass, R.E. (2001) Shrinkage estimators for covariance matrices, Biometrics, 57, 1173-1184.

DiMatteo, I., Genovese, C.R., and Kass, R.E. (2001) Bayesian curve-fitting with free-knot splines, Biometrika, 88: 1055-1071.

Kass, R.E., and Ventura, V. (2001) A spike-train probability model, Neural Computation, 13: 1713-1720.

Daniels, M.J. and Kass, R.E. (1999) Nonconjugate Bayesian estimation of covariance matrices and its use in hierarchical models, Journal of the American Statistical Association, 94, 1254-1263.

Daniels, M.J. and Kass, R.E. (1998) A note on first-stage approximation in two-stage hierarchical models, Sankhya: The Indian Journal of Statistics, 60, B: 19-30.

Kass, R.E. (1998) Comment on R.A. Fisher in the 21st Century, by Bradley Efron, Statistical Science, 13: 95-122

Kass, R.E., Carlin, B.P., Gelman, A., and Neal, R. (1998) MCMC in practice: a roundtable discussion, American Statistician, 52: 93-100.

Kass, R.E. and Wasserman, L.A. (1996) The selection of prior distributions by formal rules, Journal of the American Statistical Association, 91: 1343-1370.

Kass, R.E. and Wasserman, L.A. (1995) A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion, Journal of the American Statistical Association, 90: 928-934.

Kass, R.E. and Raftery, A. (1995) Bayes Factors, Journal of the American Statistical Association, 90: 773-795.

Kass, R.E. and Slate, E.H. (1994) Some diagnostics of maximum likelihood and posterior nonnormality, The Annals of Statistics, 22: 668-695.

Kass, R.E. (1991) More about "Theory of Probability" by H. Jeffreys, Chance, 4: no. 2, p. 13.

Kass, R.E. (1990) Data-translated likelihood and Jeffreys's rules, Biometrika, 77: 107-114.

Kass, R.E., Tierney, L. and Kadane, J.B. (1990) The validity of posterior expansions based on Laplace's method, Essays in Honor of George Bernard, eds. S. Geisser, J.S. Hodges, S.J. Press, and A. Zellner, Amsterdam: North Holland, 473-488.

Kass, R.E. and Steffey, D. (1989) Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes modes), Journal of the American Statistical Association, 84: 717-726.

Tierney, L., Kass, R.E. and Kadane, J.B. (1989) Fully exponential Laplace approximations to posterior expectations and variances, Journal of the American Statistical Association, 84: 710-716.

Kass, R.E. (1989) The geometry of asymptotic inference (with discussion) Statistical Science, 4: 188-234.

Buja, A. and Kass, R.E. (1985) Comment: Some observations on ACE methodology, Journal of the American Statistical Association, 80: 602-607.

Kass, R.E. (1983) Bayes Methods for Combining the Results of Cancer Studies in Humans and Other Species: Comment, Journal of the American Statistical Association, 78: 312-313.