Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data

2021 ◽  
Author(s):  
Francesco Bartolucci ◽  
Francesco Valentini ◽  
Claudia Pigini
Keyword(s):  
Panel Data ◽  
Conditional Probability ◽  
Probability Function ◽  
Exponential Model ◽  
Recursive Computation ◽  
Conditional Probability Function

scholarly journals Discussion of ``V-Matrix Method of Solving Statistical Inference Problems" by Vlamidir Vapnik and Rauf Izmailov

2018 ◽  
Author(s):  
Niharika Gauraha
Keyword(s):  
Conditional Probability ◽  
Optimization Problem ◽  
Probability Function ◽  
Future Research ◽  
Short Discussion ◽  
Quadratic Optimization Problem ◽  
Inference Problems ◽  
Consistent Solution ◽  
Constrained Quadratic Optimization ◽  
Conditional Probability Function

We would like to begin by stating that we have not fully understood the formulation of V-matrix conceptually. However, We are fascinated by the idea of estimation of conditional probability function without assuming any probabilistic model. In this short discussion, we would like to present that the proposed constrained quadratic optimization problem for conditional probability estimation using v-matrix based method may not have a consistent solution always. We are sure that the paper will stimulate a deeper exploration of V-matrix based methods for inference in high-dimensional problems in future research.

Discussion of the conditional-probability function for electric fields in a plasma

1984 ◽  
Vol 30 (1) ◽  
pp. 454-467 ◽  
Author(s):  
Earl W. Smith ◽  
Roland Stamm ◽  
J. Cooper
Keyword(s):  
Conditional Probability ◽  
Electric Fields ◽  
Probability Function ◽  
Conditional Probability Function

Focus on an infrequently used quantity in the context of competing risks: The conditional probability function

2018 ◽  
Vol 101 ◽  
pp. 70-81
Author(s):  
Bastien Cabarrou ◽  
Florence Dalenc ◽  
Eve Leconte ◽  
Jean-Marie Boher ◽  
Thomas Filleron
Keyword(s):  
Conditional Probability ◽  
Competing Risks ◽  
Probability Function ◽  
Conditional Probability Function

scholarly journals Karakterisasi Paparan Long Term Particulate Matter di Puspiptek Serpong-Kota Tangerang Selatan

2016 ◽  
Vol 11 (1) ◽  
pp. 51 ◽  
Author(s):  
Leons Rixson ◽  
Etty Riani ◽  
Muhayatun Santoso
Keyword(s):  
Particulate Matter ◽  
Conditional Probability ◽  
Matrix Factorization ◽  
Probability Function ◽  
Positive Matrix Factorization ◽  
Positive Matrix ◽  
Conditional Probability Function

Pencemaran partikulat udara (PM10 dan PM2.5) tidak mengenal batas administrasi wilayah hal ini disebabkan bahwa pencemaran udara tidak hanya berasal dari wilayah lokal namun dapat bertransportasi dari provinsi maupun negara lain. Unsur sampel partikulat yang dikumpulkan di Serpong, Tangerang Selatan selama 24 jam periode tahun 2011 sampai dengan 2013 diidentifikasikan dengan Spekstroskopi Florensensi Sinar-X (XRF). Karekterisasi faktor dilakukan dengan menggunakan reseptor model positive matrix factorization (PMF) dan perkiraan lokasi sumber pencemar menggunakan metode Conditional Probability Function (CPF). Hasil menunjukkan rentang rata-rata konsentrasi massa PM2.5 adalah 12.63 ± 1.60 to 15.89 ± 1.70 μg/m3 sedangkan untuk PM10 berkisar 29.00 ± 3.96 to 31.04 ± 3:28 μg/m3. Multi unsur yang teridentifikasi dengan XRF adalah Al, Ca, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, S, Si, Ti, V dan Zn. Karakterisasi partikulat halus (PM2.5) teridentifikasi 5 faktor yaitu industri peleburan logam Pb (9.61%), debu tanah (17%), campuran industri peleburan logam dan garam laut (13.02%), trasnportasi (44.36%) serta pembakaran biomassa (22.58%).Kata kunci : polusi udara, spektroskopi XRF, sumber pembagian, fungsi probabilitasbersyarat

scholarly journals Atmospheric speciated mercury concentrations on an island between China and Korea: sources and transport pathways

2016 ◽  
Vol 16 (6) ◽  
pp. 4119-4133 ◽  
Author(s):  
Gang-San Lee ◽  
Pyung-Rae Kim ◽  
Young-Ji Han ◽  
Thomas M. Holsen ◽  
Yong-Seok Seo ◽  
...  
Keyword(s):  
Probability Function ◽  
Anthropogenic Emissions ◽  
Back Trajectory ◽  
Total Gaseous Mercury ◽  
Regional Transport ◽  
Transport Pathways ◽  
Relative Significance ◽  
Secondary Formation ◽  
Global Pollutant ◽  
Conditional Probability Function

Abstract. As a global pollutant, mercury (Hg) is of particular concern in East Asia, where anthropogenic emissions are the largest. In this study, speciated Hg concentrations were measured on Yongheung Island, the westernmost island in Korea, located between China and the Korean mainland to identify the importance of local and regional Hg sources. Various tools including correlations with other pollutants, conditional probability function, and back-trajectory-based analysis consistently indicated that Korean sources were important for gaseous oxidized mercury (GOM) whereas, for total gaseous mercury (TGM) and particulate bound mercury (PBM), regional transport was also important. A trajectory cluster based approach, considering both Hg concentration and the fraction of time each cluster was impacting the site, was developed to quantify the effect of Korean sources and out-of-Korean sources. This analysis suggests that contributions from out-of-Korean sources were similar to Korean sources for TGM whereas Korean sources contributed slightly more to the concentration variations of GOM and PBM compared to out-of-Korean sources. The ratio of GOM/PBM decreased when the site was impacted by regional transport, suggesting that this ratio may be a useful tool for identifying the relative significance of local sources vs. regional transport. The secondary formation of PBM through gas-particle partitioning with GOM was found to be important at low temperatures and high relative humidity.

Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model

2015 ◽  
Vol 37 (1) ◽  
pp. 61-88 ◽  
Author(s):  
Francesco Bartolucci ◽  
Valentina Nigro ◽  
Claudia Pigini
Keyword(s):  
Panel Data ◽  
State Dependence ◽  
Exponential Model ◽  
Individual Covariates
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