# Bibliography¶

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[2] | , . Point process diagnostics based on weighted second-order statistics and their asymptotic properties. 61:929–948, |

[3] | . Information Theory and an Extension of the Maximum Likelihood Principle. Proceeding of the Second International Symposium on Information Theory, 199–213, |

[4] | . The Long Tail. 12: |

[5] | , , . Self-exciting corporate defaults: contagion vs. frailty. |

[6] | , , . Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data. 85:1–12, |

[7] | , , , . Scaling limits for Hawkes processes and application to financial statistics. |

[8] | , . Second order statistics characterization of Hawkes processes and non-parametric estimation. |

[9] | , . Hawkes model for price and trades high-frequency dynamics. 14:1147–1166, |

[10] | , . Spatstat: an R package for analyzing spatial point patterns. 12:1–42, |

[11] | . First-and second-order methods for learning: between steepest descent and Newton's method. 4:141–166, |

[12] | . Simultaneous and selective inference: Current successes and future challenges. 52:708–721, |

[13] | , . Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. 57:289–300, |

[14] | , . False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters. 100:71–81, |

[15] | , , , . Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, 2546–2554, |

[16] | , . Estimating Weighted Integrals of the Second-Order Intensity of a Spatial Point Process. 51:81–92, |

[17] | , , , , , , . A 61-million-person experiment in social influence and political mobilization. 489:295–298, |

[18] | , , , , . The time-rescaling theorem and its application to neural spike train data analysis. 14:325–346, |

[19] | , . Power spectra of general shot noises and Hawkes point processes with a random excitation. 34:205–222, |

[20] | , . Multimodel Inference Understanding AIC and BIC in Model Selection. 33:261–304, |

[21] | . Bootstraps for Time Series. 17:52–72, |

[22] | , . High-dimensional inference in misspecified linear models. |

[23] | , , . Stable signal recovery from incomplete and inaccurate measurements. 59:1207–1223, |

[24] | . Unifying the derivations for the Akaike and corrected Akaike information criteria. 33:201–208, |

[25] | , , . On Stochastic Versions of the EM Algorithm. |

[26] | . Model selection and model averaging. |

[27] | , . A lognormal model for the cosmological mass distribution. 248:1–13, |

[28] | . Some Statistical Methods Connected with Series of Events. 17:129–164, |

[29] | . On the Estimation of the Intensity Function of a Stationary Point Process. 27:332–337, |

[30] | , . The fitting of complex parametric models. 99:741–747, |

[31] | , , . Power law signature of media exposure in human response waiting time distributions. 81:056101, |

[32] | , . Robust dynamic classes revealed by measuring the response function of a social system. 105:15649–15653, |

[33] | . Intensity Estimation for Spatial Point Processes Observed with Noise. 35:322–334, |

[34] | , . An introduction to the theory of point processes. 1. Elementary theory and methods: |

[35] | , . An introduction to the theory of point processes. 2. General theory and structure: |

[36] | , . A dynamic contagion process. 43:814–846, |

[37] | , , . Convergence of a stochastic approximation version of the EM algorithm. 27:94–128, |

[38] | , , . Maximum Likelihood from Incomplete Data via the EM Algorithm. 39:1–38, |

[39] | , . Dynamics of book sales: Endogenous versus exogenous shocks in complex networks. 72:016112, |

[40] | . A Kernel Method for Smoothing Point Process Data. 34:138–147, |

[41] | . Compressed sensing. 52:1289–1306, |

[42] | . How biased is the apparent error rate of a prediction rule?. 81:461–470, |

[43] | . The Estimation of Prediction Error. 99:619–632, |

[44] | , , , . Least angle regression. 32:407–499, |

[45] | , , , . Quantification of the high level of endogeneity and of structural regime shifts in commodity markets. 42:174–192, |

[46] | , . Apparent criticality and calibration issues in the Hawkes self-excited point process model: application to high-frequency financial data. |

[47] | , , . Effective measure of endogeneity for the Autoregressive Conditional Duration point processes via mapping to the self-excited Hawkes process. 22:23–37, |

[48] | , , . Regularization Paths for Generalized Linear Models via Coordinate Descent. 33:1–22, |

[49] | , . Adaptive confidence bands. 36:875–905, |

[50] | , , , . Real-time forecasts of tomorrow's earthquakes in California. 435:328–331, |

[51] | , , . Exact Simulation of Point Processes with Stochastic Intensities. 59:1233–1245, |

[52] | . Penalized Likelihood for General Semi-Parametric Regression Models. 55:245–259, |

[53] | , . Power-law and exponential tails in a stochastic priority-based model queue. 77:012101, |

[54] | , . Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. 21:3001–3008, |

[55] | . An EM algorithm for Hawkes process. 2: |

[56] | , . Last but not least: Additional positional effects on citation and readership in arXiv. 61:2381–2388, |

[57] | , , . Critical reflexivity in financial markets: a Hawkes process analysis. 86:1–9, |

[58] | , . Branching-ratio approximation for the self-exciting Hawkes process. 90:062807, |

[59] | . Point spectra of some mutually exciting point processes. 33:438–443, |

[60] | , . A cluster process representation of a self-exciting process. 11:493, |

[61] | , , . Mainshocks are aftershocks of conditional foreshocks: How do foreshock statistical properties emerge from aftershock laws. 108:2046, |

[62] | , . Adaptive Smoothing of Seismicity in Time, Space, and Magnitude for Time-Dependent Earthquake Forecasts for California. 104:809–822, |

[63] | , . Ridge Regression: Biased Estimation for Nonorthogonal Problems. 12:55–67, |

[64] | , . Regression and time series model selection in small samples. 76:297–307, |

[65] | , , . Opinion leadership and social contagion in new product diffusion. 30:195–212, |

[66] | , . The Indirect Method: Inference Based on Intermediate Statistics—A Synthesis and Examples. 19:239–263, |

[67] | , . When does more regularization imply fewer degrees of freedom? Sufficient conditions and counterexamples. 101:771–784, |

[68] | , , , , , , . Population cycles in the pine looper moth: Dynamical tests of mechanistic hypotheses. 75:259–276, |

[69] | , . Generalised information criteria in model selection. 83:875–890, |

[70] | , . Coupling a stochastic approximation version of EM with an MCMC procedure. 8:115–131, |

[71] | . The Jackknife and the Bootstrap for General Stationary Observations. 17:1217–1241, |

[72] | . On the moving block bootstrap under long range dependence. 18:405–413, |

[73] | . Effects of block lengths on the validity of block resampling methods. 121:73–97, |

[74] | . On Estimation of the Intensity Function of a Point Process. 14:567–578, |

[75] | , , , . A significance test for the lasso. 42:413–468, |

[76] | , , . Inference for a class of partially observed point process models. 65:413–437, |

[77] | , , . Estimating the parameters of a nonhomogeneous Poisson process with linear rate. 5:361–388, |

[78] | . Relaxed Lasso. 52:374–393, |

[79] | , . Stability selection. 72:417–473, |

[80] | , , . p-Values for High-Dimensional Regression. 104:1671–1681, |

[81] | , . Lasso-type recovery of sparse representations for high-dimensional data. 37:246–270, |

[82] | , , , , . Self-exciting point process modeling of crime. 106:100–108, |

[83] | . Shot noise Cox processes. 35:614–640, |

[84] | , , . Log Gaussian Cox Processes. 25:451–482, |

[85] | , . Confidence sets in sparse regression. 41:2852–2876, |

[86] | . The Markovian self-exciting process. 12:69, |

[87] | . The asymptotic behaviour of maximum likelihood estimators for stationary point processes. 30:243–261, |

[88] | . Statistical models for earthquake occurrences and residual analysis for point processes. 83:9–27, |

[89] | , . On linear intensity models for mixed doubly stochastic Poisson and self-exciting point processes. 44:269–274, |

[90] | , , . Fast likelihood computation of epidemic type aftershock-sequence model. 20:2143–2146, |

[91] | . Exclusive: YouTube hits 4 billion daily video views. |

[92] | . Maximum likelihood estimation of Hawkes' self-exciting point processes. 31:145–155, |

[93] | , , . Modeling FX market activity around macroeconomic news: a Hawkes process approach. 91:012819, |

[94] | . Bayesian inference for Hawkes processes. 15:623–642, |

[95] | . YouTube serves up 100 million videos a day online. |

[96] | . Mexican Drug Cartels Leave a Bloody Trail on YouTube. |

[97] | . Regular point processes and their detection. 18:547–557, |

[98] | , . Hierarchy of temporal responses of multivariate self-excited epidemic processes. |

[99] | . Consistent parametric estimation of the intensity of a spatial–temporal point process. 128:79–93, |

[100] | , , . On the relationship between lower magnitude thresholds and bias in epidemic-type aftershock sequence parameter estimates. 115:B04309, |

[101] | . The dress that broke the Internet 16 million views in 6 hours.. |

[102] | . On the Estimation of a Probability Density Function by the Maximum Penalized Likelihood Method. 10:795–810, |

[103] | , . Estimating a state-space model from point process observations. 15:965–991, |

[104] | , , . Volatility fingerprints of large shocks: Endogeneous versus exogeneous. |

[105] | , . Limits of declustering methods for disentangling exogenous from endogenous events in time series with foreshocks, main shocks, and aftershocks. 79:061110, |

[106] | , . Endogenous versus exogenous shocks in systems with memory. 318:577–591, |

[107] | . Endogenous versus exogenous origins of crises. Extreme events in nature and society, 95–119, |

[108] | , , , . Endogenous versus exogenous shocks in complex networks: An empirical test using book sale rankings. 93:228701, |

[109] | . Further analysts of the data by Akaike' s Information Criterion and the finite corrections. 7:13–26, |

[110] | . Regression Shrinkage and Selection via the Lasso. 58:267–288, |

[111] | , . Use of the regularization method in non-linear problems. 5:93–107, |

[112] | . Aftershocks and earthquake statistics (1): Some parameters which characterize an aftershock sequence and their interrelations. 3:129–195, |

[113] | . Filtering and parameter estimation for partially observed generalized Hawkes processes. |

[114] | , , , . On asymptotically optimal confidence regions and tests for high-dimensional models. 42:1166–1202, |

[115] | , . The Lasso, correlated design, and improved oracle inequalities. |

[116] | , . Estimation of Space–Time Branching Process Models in Seismology Using an EM–Type Algorithm. 103:614–624, |

[117] | , . High-dimensional variable selection. 37:2178–2201, |

[118] | , . A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms. 85:699–704, |

[119] | , , , , . Adaptively smoothed seismicity earthquake forecasts for Italy. |

[120] | . Almost all YouTube views come from just 30\%of films. |

[121] | . On the Convergence Properties of the EM Algorithm. 11:95–103, |

[122] | . We never thought a video would be watched in numbers greater than a 32-bit integer. |

[123] | , . Confidence intervals for low dimensional parameters in high dimensional linear models. 76:217–242, |

[124] | . Moderate deviations for Hawkes processes. 83:885–890, |

[125] | , , . On the “degrees of freedom” of the lasso. 35:2173–2192, |