On Spatio-Temporal Model with Diverging Number of Thresholds and its Applications in Housing Market
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Bai, J.: Estimation of a change point in multiple regression models. Rev. Econ. Stat. 79(4), 551–563 (1997)
Bai, J., Perron, P.: Computation and analysis of multiple structural change models. J. Appl. Economet. 18(1), 1–22 (2003)
Bel, L., Bar-Hen, A., Petit, R., Cheddadi, R.: Spatio-temporal functional regression on paleoecological data. J. Appl. Stat. 38(4), 695–704 (2011)
Bernardi, M.S., Sangalli, L.M., Mazza, G., Ramsay, J.O.: A penalized regression model for spatial functional data with application to the analysis of the production of waste in venice province. Stoch. Env. Res. Risk Assess. 31(1), 23–38 (2017)
Bleakley, K., Vert, JP.: The group fused lasso for multiple change-point detection. arXiv preprint arXiv:1106.4199 (2011)
Blundell, R., Bond, S.: Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87(1), 115–143 (1998)
Breheny, P., Huang, J.: Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Stat. Comput. 25(2), 173–187 (2015)
Brooks, C., Tsolacos, S.: Real estate modelling and forecasting. Cambridge University Press, Cambridge (2010)
Cliff, A., Ord, J.: Spatial autocorrelation. Pion, London (1973)
De Wachter, S., Tzavalis, E.: Detection of structural breaks in linear dynamic panel data models. Comput. Stat. Data Anal. 56(11), 3020–3034 (2012)
Elhorst, J.P.: Specification and estimation of spatial panel data models. Int. Reg. Sci. Rev. 26(3), 244–268 (2003)
Elhorst, JP.: Spatial econometrics: from cross-sectional data to spatial panels. Springer Science & Business Media (2013)
Fan, J., Li, R.: Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96(456), 1348–1360 (2001)
Fan, J., Hall, P., Martin, M., Patil, P.: Adaptation to high spatial inhomogeneity using wavelet methods. Stat. Sin. 9, 85–102 (1999)
Fan, J., Feng, Y., Wu, Y., et al.: Network exploration via the adaptive lasso and scad penalties. The Ann. Appl. Stat. 3(2), 521–541 (2009)
Fearnhead, P., Vasileiou, D.: Bayesian analysis of isochores. J. Am. Stat. Assoc. 104(485), 132–141 (2009)
Finley, A., Sang, H., Banerjee, S., Gelfand, A.: Improving the performance of predictive process modeling for large datasets. Comput. Stat. Data Anal. 53, 2873–2884 (2009)
Frick, K., Munk, A., Sieling, H.: Multiscale change point inference. J. Royal Stat. Soc.: Series B (Statistical Methodology) 76(3), 495–580 (2014)
Fryzlewicz, P.: Wild binary segmentation for multiple change-point detection. Ann. Stat. 42(6), 2243–2281 (2014)
Giraldo, R., Dabo-Niang, S., Martinez, S.: Statistical modeling of spatial big data: An approach from a functional data analysis perspective. Stat. Probab. Lett. 136, 126–129 (2018)
Harchaoui, Z., Lévy-Leduc, C.: Multiple change-point estimation with a total variation penalty. J. Am. Stat. Assoc. 105(492), 1480–1493 (2010)
Hepşen, A., Vatansever, M.: Forecasting future trends in dubai housing market by using box-jenkins autoregressive integrated moving average. Int. J. Housing Markets Anal. (2011)
Huang, B., Wu, B., Barry, M.: Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int. J. Geogr. Inf. Sci. 24(3), 383–401 (2010)
Huang, H., Cressie, N.: Spatio-temporal prediction of snow water equivalent using the kalman filter. Comput. Stat. Data Anal. 22, 159–175 (1996)
Jandhyala, V., Fotopoulos, S., MacNeill, I., Liu, P.: Inference for single and multiple change-points in time series. J. Time Ser. Anal. 34(4), 423–446 (2013)
Jin, B., Shi, X., Wu, Y.: A novel and fast methodology for simultaneous multiple structural break estimation and variable selection for nonstationary time series models. Statistics and Computing 1–11 (2013)
Jin, B., Wu, Y., Shi, X.: Consistent two-stage multiple change-point detection in linear models. Can. J. Stat. 44(2), 161–179 (2016)
Jin, X., Carlin, B.: Multivariate parametric spatio-temporal models for county level breast cancer survival data. Lifetime Data Anal. 11, 5–27 (2003)
Joseph, L., Wolfson, D.B.: Estimation in multi-path change-point problems. Commun. Stat.-Theory Methods 21(4), 897–913 (1992)
Joseph, L., Wolfson, D.B.: Maximum likelihood estimation in the multi-path change-point problem. Ann. Inst. Stat. Math. 45(3), 511–530 (1993)
Ke, Y., Li, J., Zhang, W.: Structure identification in panel data analysis. Ann. Stat. 44, 1193–1233 (2016)
Kim, D.: Common breaks in time trends for large panel data with a factor structure. Economet. J. 17(3), 301–337 (2014)
Lee, D.J., Zhu, Z., Toscas, P.: Spatio-temporal functional data analysis for wireless sensor networks data. Environmetrics 26(5), 354–362 (2015)
Lee, L.F., Yu, J.: Some recent developments in spatial panel data models. Reg. Sci. Urban Econ. 40(5), 255–271 (2010)
LeSage, J.P., Pace, R.K.: Models for spatially dependent missing data. The J. Real Estate Fin. Econ. 29(2), 233–254 (2004)
Li, CW.: Spatial autocorrelation and liquidity in hong kong’s real estate market. HKU Theses Online (HKUTO) (2010)
Niu, YS., Hao, N., Zhang, H.: Multiple change-point detection: a selective overview. Statistical Science 611–623 (2016)
Pace, R.K., Barry, R., Gilley, O.W., Sirmans, C.: A method for spatial-temporal forecasting with an application to real estate prices. Int. J. Forecast. 16(2), 229–246 (2000)
Perron, P., et al.: Dealing with structural breaks. Palgrave Handbook of Econ. 1(2), 278–352 (2006)
Qu, Z., Perron, P.: Estimating and testing structural changes in multivariate regressions. Econometrica 75(2), 459–502 (2007)
Ramsay, JO., Ramsay, T., Sangalli, LM.: Spatial functional data analysis. In: Recent advances in functional data analysis and related topics, Springer, 269–275 (2011)
Ren, Q., Banerjee, S., Finley, A., Hodges, J.: Variational bayesian methods for spatial data analysis. Comput. Stat. Data Anal. 55, 3197–3217 (2011)
Rodrigues, G., Prangle, D., Sisson, S.: Recalibration: a post-processing method for approximate bayesian computation. Comput. Stat. Data Anal. 126, 53–66 (2011)
Smith, RL., Kolenikov, S., Cox, LH.: Spatiotemporal modeling of pm2. 5 data with missing values. J. Geophys. Res.: Atmos. 108(D24) (2003)
Wang, H., Leng, C.: A note on adaptive group lasso. Comput. Stat. Data Anal. 52(12), 5277–5286 (2008)
West, T., Worthington, A.C.: Macroeconomic risk factors in australian commercial real estate, listed property trust and property sector stock returns. J. Financ. Manag. Prop. Constr. 11(2), 105 (2006)
Yang, Y., Zou, H.: A fast unified algorithm for solving group-lasso penalize learning problems. Stat. Comput. 25(6), 1129–1141 (2015)
Yanosky, J.D., Paciorek, C.J., Laden, F., Hart, J.E., Puett, R.C., Liao, D., Suh, H.H.: Spatio-temporal modeling of particulate air pollution in the conterminous united states using geographic and meteorological predictors. Environ. Health 13(1), 63 (2014)
Yao, YC., Au, ST.: Least-squares estimation of a step function. Sankhyā: The Indian J. Stati., Series A 370–381 (1989)
Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Royal Stat. Soc.: Series B (Statistical Methodology) 68(1), 49–67 (2006)
