Yaacov Ritov

CURRICULUM VITAE

Ya’acov Ritov October 21, 2019

University of Michigan:

— Department of Statistics

The Hebrew University of Jerusalem:

— Department of Statistics

— The center for the Study of Rationality

— Mr. and Mrs. Francis Hock Chair (emer.) in Statistics.

Academic Education:

1973 B.Sc. in Electrical Engineering (with Excellence), The Technion, Israel Institute of Technology.

1980 M.Sc. in Electrical Engineering, The Technion, Israel Institute of Technology.

1983 Ph.D. in Statistics (summa cum laude), The Hebrew University of Jerusalem Israel.

Awards:

1984 Alon fund Fellow.

2008 L. Meitner - A.v. Humboldt Research Award

2013 Medallion lecture, IMS-JSM.

Academic Appointments:

1980–1983 Teaching Assistant, The Hebrew University of Jerusalem.

1983–1984 Visiting Lecturer, Department of Statistics, University of California at Berkeley.

1983–1984 Lady Davis Postdoctoral fellow.

1984–1988 Lecturer, Department of Statistics, The Hebrew University of Jerusalem.

1984–1987 Alon fund fellow.

1989–1990 Senior Lecturer, Department of Statistics, The Hebrew University of Jerusalem.

1990–1990 Associate Professor, Department of Statistics, The Hebrew University of Jerusalem.

1990–2016 Professor, Department of Statistics, The Hebrew University of Jerusalem.

2016– Professor emiritus, Department of Statistics, The Hebrew University of Jerusalem.

1988–1990 Visiting Assoc. Prof., University of Pennsylvania.

1992–1994 Associate Editor, The Annals of Statistics.

1994–1995 Visiting Prof. University of California, Berkeley.

1995–1999 Chair, Department of Statistics, The Hebrew University.

2001–2003 President, Israel Statistical Association.

2004–2006 Associate Editor, Bernoulli.

2009-2012 Associate Editor, Annals of Statistics

2011–2015 Board of director. The Hebrew University Secondary School

2015–2016 (Visiting) Professor, Department of Statistics, U. of Michigan.

2016– Professor, Department of Statistics, U. of Michigan.

Membership in Professional Organizations: American Statistical Association.

Institute of Mathematical Statistics (fellow).

International Statistical Institute

Grants

The ISF (Israel Science Foundation) grants are in the range $30–40k/year.

1. Semiparametric methods for point processes (ISF, 2000–2004).

2. Nonstandard applications of classifiers (ISF, 2003–2006)

3. Hidden dimensions. Statistical inference for data on or near manifolds (ISF, 2006–2010).

4. Exploring the optimal forecasting frontiers (IARPA, CPI, 2012–2013).

5. Complex statistical models and time (ISF, 2010–2014).

6. Planes of Change: New Methods for Complex Non-Standard Problems.(NSF, 2017–2019, $350,000

with Moulinath Banerjee).

Courses taught (last 7 years)

1. Advanced Statistical Models A. (Multivariate Analysis)

2. Advanced Statistical Models B. (Decision Theory)

3. Introduction To Probability and Stat for CS B

4. Asymptotic Efficiency

5. Asymptotic Statistics

6. Advanced Statistical Theory (PhD seminar)

7. Probability and Random Processes

8. Regression, Statistical Applications and Computation.

9. Statistical Theorey

10. Linear Models

Publications:

Theses:

1. M.Sc. A linear pursuit game with an unknown trap (1980, adviser: M. Heymann).

2. Robust Bayes Procedures (1983, advisers: P. J. Bickel and Y. Yahav).

Books authored:

1. P.J. Bickel, C.A.G. Klaassen, Y. Ritov, and J.A. Wellner: Efficient and Adaptive Estimation in

Semiparametric Models , Johns Hopkins University Press, (1994). 2nd edition, Springer Verlag,

1998.

2. Felix Abramovich; Ya’acov Ritov (2013). Statistical Theory: A Concise Introduction. Chapman

& Hall/CRC Texts in Statistical Science

Books edited:

1. Yoel Haitovsky, Hans, Rudolf Lerche, Ya’acov Ritov (edt.) (2003): Foundations of Statistical

Inference. Physica-Verlag, Heidelberg.

2. Jianqing Fan, Ya’acov Ritov, C.F. Jeff Wu (Editors, 2013), Selected Works of Peter J. Bickel. Springer,

New York.

Articles:

1. M. Heymann and J. Ritov: On a linear pursuit game with an unknown trap. J. of Optimization

Theory and Applications 42 (1982), 421–445.

2. Y. Ritov: Robust Bayes decision procedures: gross error on the data distribution. The Annals

of Statistics, 13 (1985), 626–637.

3. M. Haviv and Y. Ritov: An approximation to the stationary distribution of a nearly completely

decomposable Markov chain and its error bounds SIAM J. of Algebraic and Discrete Methods 7

(1986), 583–586.

4. M. Haviv, U. G. Rothblum, and Y. Ritov: Iterative methods for approximating the sub-dominant

modulus of an eign value of a non-negative matrix. Linear Algebra and its Applications 87

(1987), 61–76.

5. A. Melkman and Y. Ritov: Minimax estimation of the mean of a general distribution when

the parameter of interest is restricted. The Annals of Statistics, 15 (1987), 432–442.

6. P. J. Bickel and Y. Ritov: Efficient estimation in the error in variables models. The Annals of

Statistics 15 (1987), 513–540.

7. Y. Ritov: Asymptotic results in robust quasi - Bayesian estimation. J. of Multivariate Analysis

23 (1987), 290- 302.

8. Y. Ritov: Tightness of monotone random fields. J. Roy. Statist. Soc.-B (1987) 49 , 331–333.

9. M. Haviv and Y. Ritov: The variance of the waiting time in a queuing system with jockeying.

Stochastic Models 4 (1988), 162–181.

10. Y. Ritov and J. A. Wellner: Censoring, martingale, and the Cox model, Contemporary Mathematics (AMS) (1988) Volume 80 on Statistical Inference for Stochastic processes, ed. N.H.

Prabhu, pages 191–219.

11. D. Assaf and Y. Ritov: A double sequential procedure for detecting a change in distribution.

Biometrika 75 (1988), 715–722.

12. P.J. Bickel and Y. Ritov: Estimating integrated squared density derivatives. Sankhya A-50

(1988), 381–393.

13. Z. Gilula, A. M. Krieger, and Y. Ritov: Ordinal association in contingency tables: some interpretive aspects. J. Amr. Statist. Assoc. 83 (1989), 540–545.

14. Y. Ritov: Estimating a signal with noisy parameters. Biometrika 76 (1989), 31–38.

15. Y. Ritov: Monte Carlo computation of the mean of a function with convex support. Computational Statistics and Data Analysis 7 (1989), 269–277.

16. D. Assaf and Y. Ritov: A dynamic sampling procedure for detecting a change in the drift of

Brownian motion: a non - Bayesian model. The Annals of statistics 17 (1989), 793–800.

17. Y. Ritov: Estimation of a linear regression model with censored data. The Annals of Statistics

18 (1990), 303–328.

18. Y. Ritov and P.J. Bickel: Achieving information bounds in semi and non parametric models.

The Annals of statistics 18 , (1990), 925–938.

19. Y. Ritov: Decision theoretic optimality of the CUSUM procedure. The Annals of statistics 18

(1990), 1464–1469.

20. Y. Ritov: The convergence of an algorithm for finding the distance between a ball in a subspace and a sum of subspaces. SIAM J. of Numerical Analysis, 27 (1990), 1355–1367.

21. Y. Ritov: Asymptotic efficient estimation of the change point with unknown distributions.

The Annals of Statistics 18 (1990), 1829–1839.

22. Z. Gilula and Y. Ritov: Inferential ordinal correspondence analysis: motivation derivation

and limitation. International Statistical Review, 58 (1990), 99–108.

23. P.J. Bickel and Y. Ritov (1990): Comment on Silverman et al.: A smoothed EM approach to

indirect estimation problems, with particular reference to sterology and tomography. J. of the

Royal Statist. Soc. B 52, 311–312 .

24. Y. Ritov: Estimating functions in semiparametric models, in Estimating Equations (V.P. Godambe ed.) (1991), pp. 319–336. Oxford University Press.

25. P.J. Bickel, Y. Ritov, and J.A. Wellner: Efficient estimation of a probability measure P with

known marginal distributions. The Annals of Statistics 19 (1991) 1316–1346.

26. P. J. Bickel and Y. Ritov: Large sample theory of estimation in biased sampling regression,

model I. The Annals of Statistics, 19 (1991), 797–816.

27. J. Baron, P.C. Badgio, and Y. Ritov: Departures from optimal stopping in an anagram task.

Journal of Mathematical Psychology, 35 , (1991), 41–63.

28. Y. Ritov and Z. Gilula: The order restricted MLE in RC model for order restricted contingency

tables: estimation and testing for fit. The Annals of Statistics, 19 ( 1991), 2090–2101.

29. P.J. Bickel and Y. Ritov: Testing for Goodness of Fit: A New Approach, in Nonparametric

Statistics and Related Topics (pp. 51–57), Ed.: A. K. Md. E. Saleh, Elsevier, Amsterdam. (1991)

30. D. Assaf, M. pollak and Y. Ritov: A new look at warning and action lines of surveillance

schemes. J. Amr. Statist. Assoc. 87 (1992), 889–895..

31. M. Haviv, Y. Ritov, and U. G. Rothblum: Taylor expansions of eigenvalues of perturbed matrices with applications to spectral radii of non-negative matrices. Linear Algebra and its Application 168 (1992), 159–188.

32. D. Assaf and Y. Ritov: Adaptive sampling for detecting a change point in past. Stochastic

Analysis 11 (1992), 237–255.

33. M. Haviv and Y. Ritov: On series expansions of stochastic matrices. SIAM J. of Matrix analysis,

14 (1993), 670–676.

34. S. Barasch and Y. Ritov: Pruning FFT frequencies. IEEE transactions on Signal Processing 41

(1993), 1398–1400.

35. D. Assaf, M. Pollak, Y. Ritov, and B. Yakir: Detecting a change of a normal mean by dynamic

sampling with a probability bound on a false alarm. The Annals of Statistics 21, (1993), 1155–

1165.

36. Y. Ritov and Z. Gilula: Analysis of contingency tables by correspondence models subject to

order-constraints. J. Amr. Statist. Assoc. 88 (1993), 1380–1387.

37. P. J. Bickel and Y. Ritov: Efficient estimation using both direct and indirect observations. Th. of

Prob. and Appl. 38 (1994) , 194–213. In Russian, Teorija Verojatnostei i ee Primenenija, 38, (1993),

233–258.

38. M. Haviv and Y. Ritov: Error bounds for non self-adjoint matrices. Numerische. Mathematik,

67 (1994), 491–450.

39. P.J. Bickel and Y. Ritov: Estimating linear functionals of a PET image. IEEE Tr. of Medical

Imaging, 14 , (1995), 81–87.

40. M. Fygenson and Y. Ritov: Monotone estimating equations for the censored regression model

The Annals of Statistics, 22 (1994) 732–746.

41. P. J. Bickel and Y. Ritov: “Ibragimov Hasminskii models” Fifth Purdue International Symposium

on Decision Theory and Related Topics, (1993), pp 51–60.

42. P. J. Bickel, and Y. Ritov.: Discussion of papers by Feigelson and Nousek in Statistical Challenges in Modern Astronomy (E. Feigelson and G.J. Babu eds), (1993) Springer, New York.

43. P. J. Bickel and Y. Ritov: An exponential inequality for U-statistics with applications to testing,

Probability in the Engineering and Informational Sciences, 9 (1995), .39–52.

44. Y. Ritov: PM algorithms for calculating minimum χ

2

estimators with partial observed tables.

Computational Statistics and Data Analysis, 20 (1995), 19–33.

45. P. J. Bickel and Y. Ritov: LAN for ranks in transformation models. Festschrift for Lucien Le Cam,

D. Pollard, E. Torgersen, and G. Yang edts (1997). Springer, New York.

46. P. J. Bickel and Y. Ritov: Inference in Hidden Markov Models I, Bernoulli, 2 (1996), 199–228.

47. D. Assaf and Y. Ritov: Dynamic sampling applied to problems in optimal control. J. of Optimization Th. and Appl., 95 (1997), 565–580.

48. J. M. Robins and Y. Ritov: Toward A Curse of Dimensionality Appropriate (CODA) Asymptotic Theory for Semiparametric Models. Statistics in Medicine, 17 (1997), pp. 285–319.

49. M. Haviv and Y. Ritov: Externalities, Tangible Externalities and Queue Disciplines. Management Science, 44, (1998), 850-858. .

50. Karl F. Petty, Peter Bickel, Jiming Jiang, Michael Ostland, John Rice, Ya’acov Ritov, and Frederic Schoenberg: Accurate estimation of travel times from single-loop detectors. Transportation Research: Part A—Policy and practice, 32 (1998), 1–17.

51. Peter J. Bickel, Ya’acov Ritov and Tobias Ryden: Asymptotic normality of the maximum- ´

likelihood estimator for general hidden Markov models, The Annals of Statistics, 26 (1998),

1614–1635.

52. Y. Ritov: Estimating mass and shape of domains in PET imaging. Journal of Nonparametric

Statistics, 10 (1999), 47-66.

53. H. Pasula, S. Russell, M. Ostland, and Y. Ritov, “Tracking many objects with many sensors.”

In Proc. IJCAI-99, Stockholm, 1999

54. P. J. Bickel and Y. Ritov: Non- and semiparametric statistics: compared and contrasted, J. Stat.

Plan. Infer., 91 (2000), 209–228.

55. E. Greenshtein and Y. Ritov: Sampling from a stationary process and detecting a change in

the mean of a stationary distribution. Bernoulli, 6 (2000), 679–697.

56. P. J. Bickel and Y. Ritov: On profile likelihood. Discussion of S. Murphy and A. van der Vaart

“On profile likelihood,” J. Amr. Statist. Assoc. 95 (2000), 466–468.

57. M. Osland, P. J. Bickel, K. Petty, J. Rice, Y, Ritov, and X. Zhang: “An EM/MCMC approach to

travel time estimation and origin-destination counts.” A PATH report

58. I. Bar-Gad, Y. and H. Bergman (2000): “The neuronal refractory period causes a short-term

peak in the autocorrelation function.” Journal of Neuroscience Methods, 104, 155-163.

59. M. Haviv, M. and Y. Ritov, Homogeneous Customers Renege from invisible queues at Random Times under deteriorating waiting conditions, Queueing Systems, 38 (2001), 495–508.

60. I. Bar-Gad, Y. Ritov, E. Vaadia, and H. Bergman (2001), “Failure in identification of overlapping spikes from multiple neuron recording causes artificial correlations,” Journal of Neuroscience Methods 107, 1–13.

61. Y. Ben Shaul, H. Bergman, Y. Ritov, and M. Ables: “Trial to Trial Variability in Stimulus or

Action Causes Apparent Correlation and Synchrony in Neuronal Activity,” Journal of Neuroscience Methods, J NEUROSCI METH 111 (2): 99-110 OCT 30 2001.

62. P. J. Bickel, Y. Ritov, and T. Ryden (2002): Hidden Markov model likelihoods and their deriva- ´

tives behave like i.i.d. ones. Annales de l’Institut Henri Poincare-Pr, 38 (6): 825–846 2002

63. Y. Ritov, A. Raz and H. Bergman (2002): Detection of onset of neuronal activity by allowing

for heterogeneity in the change points. Journal of Neuroscience Methods 122 25–42.

64. Bickel, P. and Ritov, Y. and Ryden, T. (2002). Hidden Markov and state space models asymptotic analysis of exact and approximate methods for prediction, filtering, smoothing and statistical inference. Proceedings of the International Congress of Mathematicians, Vol. I (Beijing, 2002)

555–556.

65. I. Bar-Gad, Y. Ritov and H. Bergman (2002): The High Frequency Discharge Of Pallidal Neurons Disrupts The Interpretation Of Pallidal Correlation Functions, The Basal Ganglia VII,

Advances in behavioral biology, Vol 52 editors: Louise F.B. Nicholson and Richard L.M. Faull,

Kluwer Academic/Plenum Publishers, Chapter 5, pp 35-42, 2002.

66. P. J. Bickel and Y. Ritov (2003), Non-Parametric Estimators Which Can Be ‘Plugged-In’, The

Annals of Statistics , 31, 1033-1053.

67. Izhar Bar-Gad, Gali Heimer, Ya’acov Ritov and Hagai Bergman: Functional correlations between neighboring neurons in the primate Globus Pallidus are weak or nonexistent. J NEUROSCI 23 (10): 4012-4016 MAY 15 2003

68. Y. Ritov (2003). Comments on: A theory of statistical models for Monte Carlo integration, by

A. Kong, P. McCullagh, D. Nicolae, Z.Tan and X.-L.MengKong. JRSS-B 65, 613.

69. P. J. Bickel and Y. Ritov (2003): The Golden Chain, a comment. Ann. Stat. 32, 91–96.

70. Bryan G. Reuben , Ya’acov Ritov, Orit Geller, Melinda A. McFarland, Alan G. Marshall, Chava

Lifshitz (2003): Applying a new algorithm for obtaining site specific rate constants for H/D

exchange of the gas phase proton-bound arginine dimer; Chemical Physics Letters, 380, 88-94.

71. G. Mosheiov, D. Oron, Y. Ritov (2004), Flow-shop batch scheduling with identical processingtime jobs. Naval Research, 51, 783–799.

72. Greenshtein, E. and Ritov, Y. (2004) “Persistence in high dimensional linear predictor-selection

and the virtue of over-parametrization,” Bernoulli, 10, 971–988.

73. Sklan, E.H, Lowenthal, A., Korner, M., Ritov, Y., Rankinen, T., Bouchard, C., Leon, A.S., Rao,

D.C., Wilmore, J.H., Skinner, J.S. and Soreq, H. (2004). Acetylcholinesterase/paraoxonase

genotype and expression predict anxiety scores in Health, Risk Factors, Exercise Training,

and Genetics study. PNAS, 101, 5512-5517.

74. P. J. Bickel, Y. Ritov, and T. Stoker (2005): Nonparametric testing of an index model. Identification and Inference for Econometric Models:A Festschrift in Honor of Thomas J.Rothenberg, ed. by D.

W. K. Andrews and J. H. Stock. Cambridge University Press, Cambridge (2005).

75. G. Mosheiov, D. Oron, Y. Ritov (2005), Minimizing flow-time on a single machine with integer

batch sizes. Operation Research Letters, 33, 497–501.

76. P. J. Bickel, Y. Ritov, and T. Stoker (2006): Tailor-made Tests for Goodness-of-Fit to Semiparametric Hypotheses. Ann. Stat., 34, 721–741.

77. Michal Rivlin-Etzion, Ya’acov Ritov, Gali Heimer, Hagai Bergman, Izhar Bar-Gad (2006) “Local shuffling of spike trains boosts the accuracy of spike train spectral analysis,” Journal of

Neurophysiology, 95, 3245–3256.

78. P. J. Bickel, Y. Ritov, and A. Zakai (2006): “Some theory for generalized boosting algorithms”

Journal of Machine Learning Research,7, 705–732.

79. Saul Lach, Ya’acov Ritov, and Avi Simhon (2006): LONGEVITY ACROSS GENERATIONS,

Maurice Falk Institute for Economic Research in Israel, Hebrew University, Discussion Paper

No. 06.01, Jerusalem2006, 21 pages

80. Jon A.Wellner,Chris A.J.Klaassen,Ya’acov Ritov (2006): Semiparametric Models: a Review of

Progress since BKRW (1993). In Frontier of Statistics, J. Fan and H. L Koul (edts.) pp. 25-44.

81. Kjell Doksum and Ya’acov Ritov (2006): Our steps on the Bickel way. In Frontier of Statistics,

J. Fan and H. L Koul (edts.) pp. 1-24.

82. Guy Leshem and Ya’acov Ritov (2007): Traffic flow prediction using Adaboost algorithm with

random forests as a weak learner. Transactions On Engineering,Computing And Technology, 193-

198.

83. Daniel Gill, Ya’acov Ritov, and Gideon Dror (2007): Is Pinocchio’s Nose Long or His Head

Small? Learning Shape Distances for Classification. Lecture Notes In Computer Science,

Proceedings of the 3rd international conference on Advances in visual computing. Part I,

652–661.

84. Ya’acov Ritov (2007): Comments following Candes and Tao: The Dantzig selector:statistical

estimation when p is much larger than n. Annals of Statistics, 35, 2370–2372.

85. Michel Broniatowski, Alexandre Depire And Ya’acov Ritov (2008). Bivariate Cox Models.

In Mathematical Methods in Survival Analysis, Reliability and Quality of Life, Catherine Huber,

Nikolaos Limnios, Mounir Mesbah, and Mikhail Nikulin Editors, ISTE and Wiley & Sons.

86. Peter J. Bickel, Ya’acov Ritov (2008) Response to Mease and Wyner, Evidence Contrary to the

Statistical View of Boosting, JMLR 9:131–156, 2008: And Yet It Overfits. Journal of Machine

Learning Research 9 (2008) 181-186.

87. Eitan Greenshtein, Junyong Park, Ya’acov Ritov (2008): Estimating the mean of high valued

observations in high dimensions, Journal of Statistical Theory and Practice, 2, 407–418.

88. Benjamin Kedem and Ya’acov Ritov (2008) Interview with Ya’acov Ritov. Journal of Statistical

Theory and Practice, 2, 493–496.

89. Y. Rabinowicz, I Roman and Y. Ritov (2008): “Advanced methodology for assessing distribution charactaristics of paris equation coefficients to improve fatigue life prediction” . Fatigue

& Fracture of Engineering Materials & Structures, 31, 262–269.

90. Peter J. Bickel and Ya’acov Ritov (2008) Discussion of: treelets — an adaptive multi-scale basis

for sparse unordered data , Annals of Applied Statistics, 2 474-477..

91. Alon Zakai and Ya’acov Ritov (2008): How Local Should a Learning Method Be? COLT 2008,

205–216.

92. Thomas Trigano, Uri Israeles, and Ya’acov Ritov (2008): Semiparametric shift estimation for

alignment of ECG data. EUSIPCO 2008.

93. Peter J. Bickel, Ya’acov Ritov, and Alexandre Tsybakov (2009). Simultaneous analysis of Lasso

and Dantzig selector. Annals of Statistics, , 37, 1705–1732.

94. E. Greenshtein and Y. Ritov (2008). Asymptotic efficiency of simple decisions for the compound decision problem, The 3rd Lehmann Symposium, IMS Lecture-Notes Monograph series.

vol. 57. J. Rojo, editor. 266–275.

95. Yair Goldberg and Ya’acov Ritov (2009). Local Procrustes for Manifold Embedding: A measure of embedding quality and embedding algorithms. Machine Learning. 77, 1–25.

96. R. Douc, E. Moulines, Y. Ritov (2009) Forgetting of the initial condition for the filter in general

state-space hidden Markov chain: a coupling approach, ELECTRONIC JOURNAL OF PROBABILITY 14 Pages: 27-49.

97. Yair Goldberg, Alon Zakai, Dan Kushnir, Ya’acov Ritov (2008). Manifold Learning: The Price

of Normalization. JMLR 9(Aug):1909–1939.

98. Yair Goldberg, Ya’acov Ritov (2008) LDR-LLE: LLE with Low-Dimensional Neighborhood

Representation. 4th International Symposium on Visual Computing (ISVC08). ADVANCES

IN VISUAL COMPUTING, PT II, PROCEEDINGS Volume: 5359 Pages: 43-54.

99. Elias, S, Ritov, Y. and Bergman, H. (2008) Balance of increases and decreases in firing rate of

the spontaneous activity of basal ganglia high-frequency discharge neurons Journal of Neurophysiology 100. 3086–3104..

100. Zakai, A., and Ritov, Y. (2009) “Consistency and Localizability.” JMLR, 10, 827–856.

101. Ya’acov Ritov and Wolfgang K. Hardle (2007): Investors preference: Estimating and demixing ¨

of the weight function in semiparametric models for biased samples. SFB 649 Discussion

Paper 2007-024, Humboldt University. Statisica Sinica, 20, 771–785.

102. Ya’acov Ritov (2009). A random walk with drift: Interview with Peter J. Bickel. Statistical

Science, to appear.

103. Adam Zaidel, Hagai Bergman, Ya’acov Ritov, and Zvi Israel (2010). Levodopa and subthalamic deep brain stimulation responses are not congruent. Movement Disorder 25 ,2379–2386.

104. Peter J. Bickel, Ya’acov Ritov, and Alexandre Tsybakov (2010). Hierarchical selection of variables in sparse high-dimensional regression. IMS Collections, Borrowing Strength: Theory Powering Applications—A Festschrift for Lawrence D. Brown 6, 56–69.

105. Natalia Bochkina and Ya’acov Ritov (2011) Bayesian Perspectives on

Sparse Empirical Bayes Analysis (SEBA). In “Inverse Problems and High-Dimensional Estimation Inverse Problems and High-Dimensional Estimation,” Alquier, Pierre; Gautier, Eric;

Stoltz, Gilles (Eds.),Lecture Notes in Statistics, Vol. 203.

106. P. Chigansky and Y. Ritov (2009). A On the Viterbi process with continuous state space.

Bernoulli, 17, 609–627.

107. M. Levy and Y. Ritov (2011): Mean-variance efficient portfolios with many assets: 50% short.

Quantitative Finance 11, 1461–1471.

108. U. Isserles, Y. Ritov and T. Trigano (2011): Semiparametric curve alignment and shift density

estimation for biological data. IEEE Transactions on Signal Processing 59, 1970–1984.

109. Yair Goldberg and Ya’acov Ritov (2012). Theoretical analysis of LLE based on its weighting

step. Journal of Computational and Graphical Statistics, 21, 380—393 .

110. Rea Mitelman, Boris Rosin, Hila Zadka, Maya Slovik, Gali Heimer, Ya’acov Ritov, Hagai

Bergman, Shlomo Elias (2011). Neighboring pallidal neurons do not exhibit more synchronous

oscillations than remote ones in the MPTP primate model of Parkinson’s disease, Frontiers in

Systems Neuroscience.

111. Song Song, Ya’acov Ritov, and Wolfgang Karl Ha”rdle (2012). Bootstrap confidence bands and

partial linear quantile regression. SFB 649 Discussion Paper 2010-002, Journal of Multivariate

Analysis, 107, 244–262.

112. Mandel, Micha; Ritov, Ya’akov (2010). The Accelerated Failure Time Model Under Biased

Sampling BIOMETRICS,

113. Ritov, Y. (2013). Introduction to four papers by Peter Bickel. In Selected Works of Peter J. Bickel,

Editors: Jianqing Fan, Ya’acov Ritov, C. F. Jeff Wu.

114. 121. Noam Cohen, Eitan Greenshtein and Ya’acov Ritov (2013).. Empirical Bayes in the presence of explanatory variables. Statistica Sinica 23, 333–357.

115. Brown, L., Greenshtein, E. and Ritov Y. (2013). The Poisson Compound Decision Problem

Revisited. Journal Of The American Statistical Association 108, 741–749.

116. Ritov, Ya’acov (2011). A Random Walk with Drift: Interview with Peter J. Bickel. STATISTICAL SCIENCE 26 150–159.

117. Shenhar-Tsarfaty S, Waiskopf N, Ofek K, Shopin L, Usher S, Berliner S, Shapira I, Bornstein

NM, Ritov Y, Soreq H, Ben Assayag E. (2013). Atherosclerosis and arteriosclerosis parameters

in stroke patients associate with paraoxonase polymorphism and esterase activities. Eur J

Neurol., 20 891–898.

118. Y. Sepulcre, T. Trigano, Y. Ritov (2013) Sparse regression algorithm for activity estimation in

Gamma spectrometry”. IEEE Transactions on Signal Processing, 61, 4347–4359 .

119. Ya’acov Ritov and Gershon Saar (2013). Remarks on Buhlmann, R ¨ utimann van de Geer and ¨

Zhang: Correlated variables in regression: clustering and sparse estimation. Journal Of Statistical Planning And Inference, 143, 1863-1865.

120. Daniel Nevo and Ya’acov Ritov (2013). Around the goal: Examining the effect of the first goal

on the second goal in soccer using survival analysis methods”, Journal of Quantitative Analysis

of Sports (JQAS), 9. 165–177.

121. Song Song, Wolfgang K. Hardle, Ya’acov Ritov (2014). Generalized Dynamic Semiparametric ¨

Factor Models for High Dimensional Nonstationary Time Series. Econometrics Journal 17, pp.

1–32.

122. Hardle, W. K., Ritov, Y. A., & Wang, W. (2015). Tie the straps: Uniform bootstrap confidence ¨

bands for semiparametric additive models. Journal of Multivariate Analysis, 134, 129-145.

123. Yair Goldberg, Ya’acov Ritov and Avishai Mandelbaum (2014). “Predicting the Continuation of a Function with Applications to Call Center Data”. Journal of Statistical Planning and

Inference, 147, 53–65.

124. Van de Geer, S., Buhlmann, P., Ritov, Y. A., & Dezeure, R. (2014). On asymptotically optimal ¨

confidence regions and tests for high-dimensional models. The Annals of Statistics, 42(3), 1166-

1202.

125. Y. Ritov, P. J. Bickel, A. Gamst, B. J. K. Kleijn (2014), The Bayesian Analysis of Complex, HighDimensional Models: Can it be CODA? Statistical Science.

126. Shenhar-Tsarfaty, S., Yayon, N., Waiskopf, N., Shapira, I., Toker, S., Zaltser, D., ... & Soreq,

H. (2015). Fear and C-reactive protein cosynergize annual pulse increases in healthy adults.

Proceedings of the National Academy of Sciences, 112(5), E467-E471.

127. Shenhar-Tsarfaty, Shani; Shapira, Itzhak; Toker, Sharon; Rogowski, Ori; Berliner, Shlomo;

Ritov, Yaacov; Soreq, Hermona (2016). Weakened Cholinergic Blockade of Inflammation Associates with Diabetes-Related Depression. Molecular Medicine. To appear.

128. Malka Gorfine, Yair Goldberg, and Ya’acov Ritov (2017). A Quantile Regression Model for

Failure Time Data with Time Dependent Covariates. Biostatistics, 18(1), 132-146..

129. Edward Ionides, Alexander Giessing, Yaacov Ritov, and Scott E. Page (2016). Response to the

ASA’s statement on p-values: context, process, and purpose. The American Statistician. 71,

88–89.

130. Daniel Nevo and Ya’acov Ritov (2016). ”On Bayesian robust regression with diverging number of predictors”. Electronic Journal of Statistics 10, No. 2, 3045–3062

131. Trigano, T., Sepulcre, Y., & Ritov, Y. A. (2017). Sparse reconstruction algorithm for nonhomogeneous counting rate estimation. IEEE Transactions on Signal Processing, 65(2), 372-385.

132. Keren Weinshall Margel, Udi Sommer, and Ya’acov Ritov (2017). A Micro-Macro Gauge of

ideological Effects on Judicial Decision Making in High Courts: The Dynamic Comparative

Attitudinal Measure and the Ideological Ideal Point Estimate. Regulation & Governance, to

appear.

133. Nevo, D., & Ritov, Y. A. (2017). Identifying a Minimal Class of Models for High–dimensional

Data. Journal of Machine Learning Research, 18(24), 1-29.

134. J. D. Rosenblatt, Y. Ritov, and J.J. Goeman (2019). Discussion of Sesia et al. and the Knockoff

Framework. Biometrika 106, pp. 29-–33.

135. Zvi Gilula, Robert McCulloch, Ya’acov Ritov, and Oleg Urminsky(2018). A Study into Mechanisms of Attitudinal Scale Conversion: A Randomized Stochastic Ordering Approach. Quantitative Marketing and Economics

136. Nhat Ho, Xuanlong Nguyen, Ya’acov Ritov (2018) Robust Estimation of Mixing Measures in

Finite Mixture Models. Bernoulli.

137. Eitan Greenstein, Ariel Mansura, and Ya’acov Ritov (2019). Empirical Bayes improvement of

Kalman filter estimators. Bernoulli.

138. Eitan Greenshtein and Ya’acov Ritov (2019). Empirical Bayes, Compound Decisions and Exchangeability. Statistical Science

Manuscripts

139. Jonathan Sidi, Ya’acov Ritov, and Lyle Ungar (2015). Regularization and Classification of

Linear Mixed Models via the Elastic Net Penalty with Application to the Good Judgment

Project.

140. Ya’acov Ritov, Yuekai Sun, Ruofei Zhao (2019): On conditional parity as a notion of nondiscrimination in machine learning

141. Eitan Greenshtein, Ya’acov Ritov (2019): Applications of Generalized Maximum Likelihood

Estimators to stratified sampling and post-stratification with many unobserved strata

142. Hamid Eftekhari, Moulinath Banerjee, Ya’acov Ritov (2019): Inference In General SingleIndex Models Under High-dimensional Symmetric Designs

143. Michael Law, Ya’acov Ritov (2019): Estimating the Random Effect in Big Data Mixed Models

144. Debarghya Mukherjee, Moulinath Banerjee, Ya’acov Ritov (2019): Non-Standard Asymptotics

in High Dimensions: Manski’s Maximum Score Estimator Revisited

145. Michael Law, Ya’acov Ritov (2019): Inference Without Compatibility

146. Jonathan Yefenof, Yair Goldberg, Jennifer Wiler, Avishai Mandelbaum, Ya’acov Ritov (2019):

Self-reporting and screening: Data with current-status and censored observations

147. Ya’acov Ritov (2015). Bayesian and robustness analyses of 14C data with application to Tel

Rehov.