Referensi

Agresti, A., Franklin, C., & Kingenberg, B. (2018). Statistics: The art and science of learning from data. Pearson-Harlow.

Agustina, M., Wasono, R., & Darsyah, M. (2015). Pemodelan geographically weighted regression (GWR) pada tingkat kemiskinan di provinsi jawa tengah. Jurnal Statistika, 3.

Akinwande MO, S. A., Dikko HG. (2015). Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open Journal of Statistics, 5, 754–767.

Amelia, M., Djuraidah, A., & Anisa, R. (2023). Estimation of factors affecting stunting cases in west java in 2021 using negative binomial spatial regression. Jurnal Matematika, Statistika Dan Komputasi, 20(10), 41–51.

Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publisher.

Arbia, G. (2006). Spatial econometrics statistical foundations and applications to regional convergence. Springer-Verlag.

Asianingrum, A. H., Djuraidah, A., & Indahwati. (2020). Robust mixed geographically and temporally weighted regression to modeling the percentage of poverty population in java in 2012-2018. International Journal of Sciences: Basic and Applied Research, 53(2), 186–197.

Bates, & M., D. (2010). lme4: Mixed-effects modeling with r. http://lme4.r-forge.r-project.org/book

Besag, J., York, J., & Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 1–20.

Chun, Y., & Griffith, D. A. (2014). A quality assessment of eigenvector spatial filtering based parameter estimates for the normal probability model. Spatial Statistics, 10, 1–11.

Djuraidah, A. (2020). Monograph penerapan dan pengembangan regresi spasial dengan studi kasus pada kesehatan and sosial dan ekonomi (Ed ke-1). IPB Press.

Djuraidah, A., Anisa, R., Tarida, A. R., Aliu, M. A., Septemberini, C., Astrini, Y. P., & Meilania, G. T. (2023). Pemodelan mixed geographically weighted regression-spatial autoregressive (MGWR-SAR) pada kasus HIV di indonesia. Jurnal Aplikasi Statistika & Komputasi Statistik, 15(2), 65–76. https://doi.org/10.34123/jurnalasks.v15i2.608

Djuraidah, A., Rizki, A., & Alfan, T. (2024). Identifying factors affecting waste generation in west java in 2021 using spatial regression. JTAM (Jurnal Teori Dan Aplikasi Matematika), 8(2), 495–505.

Dray, S., Legendre, P., & Peres-Neto, P. R. (2006). Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, 196(3-4), 483–493.

Fadila, A., Djuraidah, A., & Alamudi, A. (2021). Pemodelan jumlah penderita tuberkulosis di jawa timur dengan regresi terboboti geografis binomial negatif. In [skripsi]. Institut Pertanian Bogor.

Febriyanti, E. R., Djuraidah, A., & Alamudi, A. (2016). Penerapan model regresi spasial pada kasus infeksi virus dengue di DKI jakarta. In [skripsi]. Institut Pertanian Bogor.

Fitri, D. J., Djuraidah, A., & Wijayanto, H. (2024). Bayesian conditional negative binomial autoregressive model: A case study of stunting on java island in 2021. Commun. Math. Biol. Neurosci., 2024, Article–ID.

Fotheringham, S., Charlton, M., & Brunsdon, C. (2002). Geographically weighted regression: The analysis of spatially varying relationships. John Wiley & Sons. Inc.

Fotheringham, S., Charlton, M., Brunsdon, C., & Nakaya, T. (2005). Model selection issues in geographically weighted regression. Proceedings of the 8th International Conference on GeoComputation.

Gelfand, A., Diggle, P., Fuentes, M., & Guttorp, P. (2010). Handbook of spatial statistics. CRC Press. https://doi.org/10.1201/9781420072884

Geniaux, G., & Martinetti, D. (2018). A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Regional Science and Urban Economics. https://doi.org/10.1016/j.regsciurbeco.2017.04.001

Glaser, S. (2017). A review of spatial econometric models for count data. Hohenheim Discussion Papers in Business, Economics and Social Sicences.

Griffith, D. A. (1988). Advanced spatial statistics. Springer Dordrecht.

Griffith, D. A. (2003). Spatial autocorrelation and spatial filtering: Gaining understanding through theory and scientific visualization. Springer.

Hadi, A. F., Sa’diyah, H., & Sumertajaya, I. (2007). Penanganan ketaknormalan data pada model AMMI dengan transformasi box-cox (data non-normality on AMMI models: Box-cox transformations). J ILMU DASAR, 8(2), 165–174.

Henderson, C. R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 423–447.

Hodges, J. S. (2013). Richly parameterized linear models: Additive, time series, and spatial models using random effects. CRC Press.

Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401. https://doi.org/10.1080/13658810802672469

Hughes, J., & Haran, M. (2013). Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 75(1), 139–159.

James, G. M., Witten, D., Hastle, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in r (5th ed.). Springer.

Kosfeld, R. (2006). Spatial econometric. University of Kassel.

Kutner MH, N. J., Nachtsheim CJ. (2005). Applied linear statistical models (5th ed.). McGraw-Hill Irwin.

Leroux, B. G., Lei, X., & Breslow, N. (2000). Estimation of disease rates in small areas: A new mixed model for spatial dependence. Statistical Models in Epidemiology, the Environment, and Clinical Trials, 179–191.

LeSage, J., & Pace, R. (2009). Introduction to spatial econometrics. Chapman; Hall/CRC.

Li, F., & Sang, H. (2018). Spatial homogeneity pursuit of regression coefficients for large datasets. Journal of the American Statistical Association, 114, 1–37. https://doi.org/10.1080/01621459.2018.1529595

Lu, B., Harris, P., Charlton, M., & Brunsdon, C. (2014). The GWmodel r package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo-Spatial Information Science, 17(2), 85–101. https://doi.org/10.1080/10095020.2014.917453

Mar’ah, Z., Djuraidah, A., & Wigena, A. H. (2017). Semi-parametric geographically weighted regression modelling using linear model of coregionalization. International Journal of Sciences: Basic and Applied Research, 34(2), 178–186.

McCulloch, C. E. (2003). Generalized linear mixed models. IMS.

Murakami, D. (2017). Spmoran: An r package for moran’s eigenvector-based spatial regression analysis. arXiv Preprint arXiv:1703.04467.

Murakami, D., & Griffith, D. A. (2015). Random effects specifications in eigenvector spatial filtering: A simulation study. Journal of Geographical Systems, 17, 311–331.

Nakaya T, C. M., Fotheringham AS. (2005). Geographically weighted poisson regression for disease association mapping. Statist. Med., 24, 2695–2717.

NL, J. (1949). Systems of frequency curves generated by methods of translation. Biometrika, 36(1/2), 149–176.

Oliveira, O., Ribeiro, A., Krainski, E., Rito, T., Duarte, R., & Correia-Nevesand, M. (2020). Using bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in portugal between 2000 and 2016. Scientific Reports, 10(1), 16646–16646. https://doi.org/10.1038/s41598-020-73759-w

Pongoh, F. D. (2015). Regresi terboboti geografis dan regresi terboboti geografis campuran: Studi kasus status kesejahteraan rendah di sulawesi utara. In [tesis]. Institut Pertanian Bogor.

Purhadi. (2012). Mixed geographically weighted regression model (case study: The percentage of poor households in mojokerto 2008). European Journal of Scientific Research, 69(2), 188–196.

Putri, S. A. H., & Hajarisman, N. (2021). Pemodelan data kemiskinan di provinsi jawa barat menggunakan metode geographically weighted regression (GWR) dengan fungsi pembobot kernel bi-square. Prosiding Statistika, 7(1), 208–215.

Rahmawati, R., & Djuraidah, A. (2010). Regresi terboboti geografis dengan pembobot kernel kuadrat ganda untuk data kemiskinan di kabupaten jember. Forum Statistika Dan Komputasi, 15(2), 32–37.

Rahmawati, R., & Djuraidah, A. (2011). Analisis geographically weighted regression (GWR) dengan pembobot kernel gaussian untuk data kemiskinan. Prosiding Seminar Nasional Statistik, 326–322.

Ritonga, N. S., Angraini, Y., & Susetyo, B. (2022). Penerapan metode geographically weighted regression (GWR) pada kasus kriminalitas di provinsi sumatera utara tahun 2020. In [skripsi]. Institut Pertanian Bogor.

Sayidar, A. M., Djuraidah, A., & Sadik, K. (2024). Penerapan regresi gerombol spasial pada data kemiskinan indonesia tahun 2022. In [skripsi]. Institut Pertanian Bogor.

Shekhar, S., Li, Y., Ali, R. Y., Eftelioglu, E., Tang, X., & Jiang, Z. (2018). Spatial and spatiotemporal data mining. 264–286. https://doi.org/https://doi.org/10.1016/B978-0-12-409548-9.09594-4

Silva, A. R. da, & Rodrigues, T. C. V. (2013). Geographically weighted negative binomial regression-incorporating overdispersion. Springer Science.

Silva, A. R. da, & Rodrigues, T. C. V. (2014). Geographically weighted negative binomial regression incorporating overdispersion. Stat. Comput. https://doi.org/10.1007/s11222-013-9401-9

Stern, H., & Cressie, N. A. (1999). Inference for extremes in disease mapping. Disease Mapping and Risk Assessment for Public Health, 63–84.

Stock, K., & Guesgen, H. (2016). Geospatial reasoning with open data. Automating Open Source Intelligence, 171–204. https://doi.org/10.1016/b978-0-12-802916-9.00010-5

Sugasawa, S., & Murakami, D. (2021). Spatially clustered regression. 44, 100525.

Tiefelsdorf, M. (2000). Modelling spatial processes: The identification and analysis of spatial relationships in regression residuals by means of moran’s i. Springer.

Tobler, W. R. (1970). A computer model simulation of urban growth in the detroit region. Economic Geography, 56, 234–240. https://doi.org/10.2307/143141

Tufvesson, O., Lindström, J., & Lindström, E. (2019). Spatial statistical modelling of insurance risk: A spatial epidemiological approach to car insurance. Scandinavian Actuarial Journal, 6, 508–522.

Vega SH, E. J. (2015). The SLX model. J Of Regional Science, 0(0), 1–25. https://doi.org/10.1111/jors.12188

Walker DA, S. T. (2016). Nine pseudo R2 indices for binary logistic regression models. J. Mod. Appl. Stat. Methods, 15(1), 848–854.