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1. Stochastic estimation of Green's functions with application to diffusion and advection-diffusion-reaction problems

Author

Keanini, Russell G., Dahlberg, Jerry, Brown, Philip, Morovati, Mehdi, Moradi, Hamidreza, Jacobs, Donald, and Tkacik, Peter T.

Subjects
Mathematics - Numerical Analysis, 35Qxx (Primary), 60Gxx (Secondary), G.1, G.2, J.2
Abstract

A stochastic method is described for estimating Green's functions (GF's), appropriate to linear advection-diffusion-reaction transport problems, evolving in arbitrary geometries. By allowing straightforward construction of approximate, though high-accuracy GF's, within any geometry, the technique solves the central challenge in obtaining Green's function solutions. In contrast to Monte Carlo solutions of individual transport problems, subject to specific sets of conditions and forcing, the proposed technique produces approximate GF's that can be used: a) to obtain (infinite) sets of solutions, subject to any combination of (random and deterministic) boundary, initial, and internal forcing, b) as high fidelity direct models in inverse problems, and c) as high quality process models in thermal and mass transport design, optimization, and process control problems., Comment: 47 pages, 8 figures

Published
2022

2. Biased Hypothesis Formation From Projection Pursuit

Author

Patterson, John, Avery, Chris, Grear, Tyler, and Jacobs, Donald J.

Subjects
Computer Science - Machine Learning, Physics - Biological Physics, Quantitative Biology - Biomolecules, Statistics - Machine Learning
Abstract

The effect of bias on hypothesis formation is characterized for an automated data-driven projection pursuit neural network to extract and select features for binary classification of data streams. This intelligent exploratory process partitions a complete vector state space into disjoint subspaces to create working hypotheses quantified by similarities and differences observed between two groups of labeled data streams. Data streams are typically time sequenced, and may exhibit complex spatio-temporal patterns. For example, given atomic trajectories from molecular dynamics simulation, the machine's task is to quantify dynamical mechanisms that promote function by comparing protein mutants, some known to function while others are nonfunctional. Utilizing synthetic two-dimensional molecules that mimic the dynamics of functional and nonfunctional proteins, biases are identified and controlled in both the machine learning model and selected training data under different contexts. The refinement of a working hypothesis converges to a statistically robust multivariate perception of the data based on a context-dependent perspective. Including diverse perspectives during data exploration enhances interpretability of the multivariate characterization of similarities and differences., Comment: 12 pages, 5 figures

Published
2022

6. Distribution of volume, microvoid percolation, and packing density in globular proteins

Author

Farmer, Jenny, Green, Sheridan B., and Jacobs, Donald J.

Subjects
Physics - Biological Physics, Condensed Matter - Soft Condensed Matter, Physics - Computational Physics
Abstract

A fast and accurate grid-based method with low memory requirement is presented to calculate volume characteristics in molecular systems. The distribution of volume and packing density is characterized in globular proteins, where void space is decomposed into microvoid volume and cavities based on a spherical test probe with variable radius. A scan over test probe radius is mapped onto a site percolation problem for microvoid volume. Finite-size scaling is applied to determine critical exponents, which are found to be consistent with connectivity percolation exponents in three dimensions. Disparate results in the literature regarding packing density in the core of a protein compared to on its surface, and with respect to protein size, is elucidated in terms of microvoid volume within a unified implicit-solvent model. By parameterizing the model to match the results of explicit-solvent models that agree with experimental data, we verify that packing density within globular proteins is spatially uniform and independent of protein size., Comment: 19 pages, 11 figures

Published
2018

9. Outcomes of thoracic endovascular aortic repair in patients with concomitant blunt thoracic aortic injury and traumatic brain injury from the Aortic Trauma Foundation global registry

Author

Saqib, Naveed, Charlton-Ouw, Kristofer, Afifi, Rana, McNutt, Michelle, Al-Rustum, Zain, Shrestha, Binod, Quiroga, Elina, Starnes, Ben, Gilani, Rami, Turay, David, Luo-Owen, Xian, Bee, Tiffany, Moyer, Suzanne, DuBose, Joe, Shutze, William, Dockery, William, Petrey, Laura, Phelps, Timothy N., Fox, Chuck, Moore, Ernest, Cralley, Alexis, Teixeira, Pedro, Leede, Emily, Buchanan, Frank, Ramos, Emilio, Ngoue, Marielle, Fox, Nicole, Shea, Lisa, Zielinski, Martin, Fischmann, Marianna Martini, Inaba, Kenji, Khor, Desmond, Magee, Gregory, Sheahan, Malachi, Unruh, Marie, Parry, Neil, Dubois, Luc, Berne, John, Puente, Ivan, Gomez, Mario F., Mederos, Dalier R., Bini, John, Herzing, Karen, Hardman, Claire, Schanzer, Andres, Aiello, Francesco, Arous, Edward, Arous, Elias, Jones, Douglas, Judelson, Dejah, Messina, Louis, Nguyen, Tammy, Simons, Jessica, Steppacher, Robert, Rezende-Neto, Joao, Haan, James, Lightwine, Kelly, Dunn, Julie, Smoot, Brittany, Horer, Tal, McGreevy, David, Riambau, Vincent, Mestres, Gaspar, Yugueros, Xavier, Passman, Marc, Beck, Adam W., Patterson, Mark, Pearce, Ben, Spangler, Emily, McFarland, Graeme, Sutzko, Danielle, Smeds, Matt, Zakhary, Emad, Williams, Michael, Wittgen, Catherine, Vogel, Todd, Eagleton, Matt, Azizzadeh, Ali, Gewertz, Bruce, Barmparas, Galinos, Arbabi, Cassra, DuBose, Joseph, Kundi, Rishi, Morrison, Jonathan, Rossi, Peter, Pacini, Davide, Botta, Luca, Amodio, Ciro, Rimoldi, Pierantonio, D'Alessio, Ilenia, Compagnoni, Nicola Monzio, Aftab, Muhammad, Al-Musawi, Mohammed, Reece, T. Brett, Pal, Jay D., Jacobs, Donald, Malgor, Rafael D., Zarkowsky, Devin, Rajani, Ravi, Benarroch-Gampel, Jaime, Ramos, Christopher R., Schermerhorn, Marc, Wyers, Mark, Hamdan, Allen, Stangenberg, Lars, Lee, Andy, Davies, Mark, Jayakumar, Lalithapriya (Priya), Sideman, Matthew J., Mitromaras, Christopher, Miserlis, Dimitrios, Brahmbhatt, Reshma, Darling, Ralph, Caliste, Xzabia, Chang, Benjamin B., Hnath, Jeffrey C., Kreienberg, Paul B., Kryszuk, Alexander, Laser, Adriana, Roddy, Sean P., Saltzberg, Stephanie, Shah, Melissa, Warner, Courtney, Yeh, Chin-Chin, Reva, Viktor, Zhigalo, Viktor, Krasikov, Alexander V., Trimarchi, Santi, Domanin, Maurizio, Babrowski, Trissa, Milner, Ross, Pocivavsek, Luka, Skelly, Christopher, Malka, Kimberly, Nolan, Brian, D’Oria, Mario, Lepidi, Sandro, Arbabi, Cassra N., Starnes, Benjamin W., and Miller, Charles

Published
2022
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10. Outcomes and practice patterns of medical management of blunt thoracic aortic injury from the Aortic Trauma Foundation global registry

Author

Saqib, Naveed, Charlton-Ouw, Kristofer, Afifi, Rana, McNutt, Michelle, Al-Rustum, Zain, Shrestha, Binod, Dipasupil, Edmundo, Quiroga, Elina, Starnes, Ben, Gilani, Rami, Turay, David, Luo-Owen, Xian, Bee, Tiffany, Moyer, Suzanne, DuBose, Joe, Shutze, William, Dockery, William, Petrey, Laura, Phelps, Timothy N., Fox, Chuck, Moore, Ernest, Cralley, Alexis, Teixeira, Pedro, Leede, Emily, Buchanan, Frank, Ramos, Emilio, Ngoue, Marielle, Fox, Nicole, Shea, Lisa, Zielinski, Martin, Fischmann, Marianna Martini, Inaba, Kenji, Khor, Desmond, Magee, Gregory, Sheahan, Malachi, Unruh, Marie, Parry, Neil, Dubois, Luc, Berne, John, Puente, Ivan, Gomez, Mario F., Mederos, Dalier R., Bini, John, Herzing, Karen, Hardman, Claire, Schanzer, Andres, Aiello, Francesco, Arous, Edward, Arous, Elias, Jones, Douglas, Judelson, Dejah, Messina, Louis, Nguyen, Tammy, Simons, Jessica, Steppacher, Robert, Rezende-Neto, Joao, Haan, James, Lightwine, Kelly, Dunn, Julie, Smoot, Brittany, Horer, Tal, McGreevy, David, Riambau, Vincent, Mestres, Gaspar, Yugueros, Xavier, Passman, Marc, Beck, Adam W., Patterson, Mark, Pearce, Ben, Spangler, Emily, McFarland, Graeme, Sutzko, Danielle, Smeds, Matt, Zakhary, Emad, Williams, Michael, Wittgen, Catherine, Vogel, Todd, Eagleton, Matt, Azizzadeh, Ali, Gewertz, Bruce, Barmparas, Galinos, Arbabi, Cassra, DuBose, Joseph, Kundi, Rishi, Morrison, Jonathan, Rossi, Peter, Pacini, Davide, Botta, Luca, Amodio, Ciro, Rimoldi, Pierantonio, D'Alessio, Ilenia, Compagnoni, Nicola Monzio, Aftab, Muhammad, Al-Musawi, Mohammed, Reece, T. Brett, Pal, Jay D., Jacobs, Donald, Malgor, Rafael D., Zarkowsky, Devin, Rajani, Ravi, Benarroch-Gampel, Jaime, Ramos, Christopher R., Schermerhorn, Marc, Wyers, Mark, Hamdan, Allen, Stangenberg, Lars, Lee, Andy, Davies, Mark, Jayakumar, Lalithapriya (Priya), Sideman, Matthew J., Mitromaras, Christopher, Miserlis, Dimitrios, Brahmbhatt, Reshma, Darling, Ralph, Caliste, Xzabia, Chang, Benjamin B., Hnath, Jeffrey C., Kreienberg, Paul B., Kryszuk, Alexander, Laser, Adriana, Roddy, Sean P., Saltzberg, Stephanie, Shah, Melissa, Warner, Courtney, Yeh, Chin-Chin, Reva, Viktor, Zhigalo, Viktor, Krasikov, Alexander V., Trimarchi, Santi, Domanin, Maurizio, Babrowski, Trissa, Milner, Ross, Pocivavsek, Luka, Skelly, Christopher, Malka, Kimberly, Nolan, Brian, D’Oria, Mario, Lepidi, Sandro, Arbabi, Cassra N., Starnes, Benjamin W., and Miller, Charles

Published
2022
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12. Documenting the Recovery of Vascular Services in European Centres Following the Initial COVID-19 Pandemic Peak: Results from a Multicentre Collaborative Study

Author

Ruffino, Maria Antonella, Chan, Sharon, Coughlin, Patrick, Awopetu, Ayoola, Stather, Philip, Lane, Tristan, Theodosiou, Dimitrios, Ahmed, Mohamed Abozeid, Vasudevan, Thodur, Ibrahim, Mohammed, Al Maadany, Faraj, Eljareh, Mohamed, Alkhafeefi, Fatimah Saad, Coscas, Raphael, Ünal, Ertekin Utku, Pulli, Raffaele, Zacà, Sergio, Angiletta, Domenico, Kotsis, Thomas, Moawad, Magdy, Tozzi, Matteo, Patelis, Nikolaos, Lazaris, Andreas M., Chuen, Jason, Croo, Alexander, Tsolaki, Elpiniki, Zenunaj, Gladiol, Kamal, Dhafer, Tolba, Mahmoud MH., Maresch, Martin, Khetarpaul, Vipul, Mills, Joseph, Gangwani, Gaurav, Elahwal, Mohamed, Khalil, Rana, Azab, Mohammed A., Mahomed, Anver, Whiston, Richard, Contractor, Ummul, Esposito, Davide, Pratesi, Carlo, Giacomelli, Elena, Troncoso, Martín Veras, Elkouri, Stephane, Johansson, Flavia Gentile, Dodos, Ilias, Benezit, Marie, Vidoedo, José, Rocha-Neves, João, Pereira-Neves, António Henrique, Dias-Neto, Marina Felicidade, Campos Jácome, Ana Filipa, Loureiro, Luis, Silva, Ivone, Garza-Herrera, Rodrigo, Canata, Victor, Bezard, Charlotte, Bowser, Kathryn, Tobar, Jorge Felipe, Vera, Carlos Gomez, Parra, Carolina Salinas, Lopez, Eugenia, Serra, Yvis Gadelha, Varela, Juan, Rubio, Vanessa, Victoria, Gerardo, Johnson, Adam, O’Banion, Leigh Ann, Makar, Ragai, Tantawy, Tamer Ghatwary, Storck, Martin, Jongkind, Vincent, falah, Orwa, McBride, Olivia, Isik, Arda, Papaioannou, Athanasios, Ocke Reis, Paulo Eduardo, Bracale, Umberto Marcello, Atkins, Ellie, Tinelli, Giovanni, Scott, Emma, Wales, Lucy, Sivaharan, Ashwin, Priona, Georgia, Nesbitt, Craig, Grainger, Tabitha, Shelmerdine, Lauren, Chong, Patrick, Bajwa, Adnan, Arwynck, Luke, Hadjievangelou, Nancy, Elbasty, Ahmed, Rubio, Oscar, Ricardo, Michael, Ulloa, Jorge H., Tarazona, Marcos, Pabon, Manuel, Pitoulias, Georgios, Corless, Kevin, Ioannidis, Orestis, Friedrich, Oliver, Van Herzeele, Isabelle, Vijaynagar, Badri, Cohnert, Tina, Bell, Rachel, Moore, Hayley, Saha, Prakash, Gifford, Edward, Laine, Matti, Barkat, Adel, Karkos, Christos, Binti Safri, Lenny Suryani, Buitron, Gabriel, Del Castillo, Javier, Carrera, Paul, Salinas, Nilson, Biagioni, Rodrigo Bruno, Benites, Sergio, Mafla, César Andrés, Pian, Putera Mas, Albino, Pereira, Serrano, Ernesto, Marin, Andres, González, Marco, Foreroga, Marsha, Russo, Alejandro, Reyes, Andrés, Guglielmone, Daniel, Grillo, Lorena, Flumignan, Ronald, Palones, Francisco Gomez, Silveira, Pierre Galvagni, Ramely, Rosnelifaizur Bin, Edeiken, Sara, Chetter, Ian, Green, Lucy, Sudarsanam, Abhilash, Lyons, Oliver, Lemmon, Gary, Neville, Richard, Castelli, Mariano, Hinojosa, Carlos A., Carvajal, Rubén Rodríguez, Rivera, Aksim, Wong, Peng, Drudi, Laura, Perkins, Jeremy, Sieunarine, Kishore, Attia, Doaa, Atef, Mahmoud, Eftychios, Lostoridis, Weaver, Fred, Ren, Leong Chuo, Alomari, Mohannad, Jamjoom, Reda, Aljarrah, Qusai, Abbas, Ayman, Alomran, Faris, Kumar, Ambrish, Altoijri, Abdulmajeed, ElSanhoury, Kareem T., Alhumaid, Ahmed, Fekry, Tamer, Sekhar, Raghuram, Theodoridis, Panagiotis, Panagiotis, Theodoridis, Roditis, Konstantinos, Tsiantoula, Paraskevi, Antoniou, Afroditi, Soler, Raphael, Hasemaki, Natasha, Baili, Efstratia, Mpaili, Eustratia, Huasen, Bella, Wallace, Tom, Duncan, Andrew, Metcalfe, Matthew, Mannoia, Kristyn, Bechara, Carlos F., Tsilimparis, Nikolaos, Aranson, Nathan, Riding, David, Palena, Mariano, McDonnell, Ciarán, Mouawad, Nicolas J., Banegas, Shonda, Rossi, Peter, Oshodi, Taohid, Diaz, Rodney, Afifi, Rana, Dindyal, Shiva, Thapar, Ankur, Kordzadeh, Ali, Pullas, Gonzalo, Lin, Stephanie, Davies, Chris, Darvall, Katy, Kodama, Akio, Gooneratne, Thushan, Gunawansa, Nalaka, Munoz, Alberto, Jie, Ng Jun, Bradley, Nicholas, Al-Jundi, Wissam, Meyer, Felicity, Lee, Cheong, Malina, Martin, Renton, Sophie, Lui, Dennis, Batchelder, Andrew, Oszkinis, Grzegorz, Freyrie, Antonio, Giordano, Jacopo, Saratzis, Nikolaos, Tigkiropoulos, Konstantinos, Kyriakos, Stavridis, Popov, Guriy, Cheema, Muhammad Usman, Lapolla, Pierfrancesco, Ling Patricia, Yih Chun, Ennab, Raed, Ullery, Brant W., Pasenidou, Ketino, Tam, Jacky, Sidel, Gabriel, Jayaprakash, Vivek Vardhan, Bennett, Lisa, Hardy, Simon, Davies, Emma, Baker, Sara, Wijesinghe, Lasantha, Tam, Adam, McCune, Ken, Chana, Manik, Lowe, Chris, Goh, Aaron, Powezka, Katarzyna, Kyrou, Ioanna, Altaf, Nishath, Harkin, Denis, Travers, Hannah, Cragg, James, sharif, Atif, Akhtar, Tasleem, Chávez, José Antonio, Ordonez, Claudia, Mazzurco, Martin, Choke, Edward, Asghar, Imran, Summerour, Virginia, Dunlop, Paul, Morley, Rachel, Hardy, Thomas, Bevis, Paul, Cuff, Robert, Stavroulakis, Konstantinos, Beropoulis, Efthymios, Argyriou, Angeliki, Loftus, Ian, Azhar, Bilal, Sheth, Sharvil, Usai, Marco Virgilio, Choudhry, Asad, Nicole, Kira, Boyle, Emily, Joyce, Doireann, Abdelaty Hassan, Mohammed Hassan, Saltiel, Alberto, Frahm-Jensen, Gert, Antoniou, George, Elhadi, Muhammed, Kimyaghalam, Ali, Malgor, Rafael, O'Banion, Leigh Ann, Telve, Diego, Isaak, Andrej, Schmidli, Jürg, McKevitt, Kevin, Siddiqui, Tam, Asciutto, Giuseppe, Floros, Nikolaos, Papadopoulos, George, Kafetzakis, Alexandros, Koutsias, Stylianos G., Nana, Petroula, Giannoukas, Athanasios, Kakkos, Stavros, Moulakakis, Konstantinos G., Shafique, Natasha, Jawien, Arkadiusz, Popplewell, Matthew, Imray, Chris, Abayasekara, Kumar, Rowlands, Timothy, Kuhan, Ganesh, Rajagopalan, Sriram, Jaipersad, Anthony, Sadia, Uzma, Kobe, Isaac, Mittapalli, Devender, Enemosah, Ibrahim, Behrendt, Christian-Alexander, Beck, Adam, Almudhafer, Muayyad, Ancetti, Stefano, Jacobs, Donald, Jayakumar, Priya, Malekpour, Fatemeh, Shalhub, Sherene, Keldiyorov, Boboyor, Simon, Meryl, Khashram, Manar, Rich, Nicole, Shepherd, Amanda, Meecham, Lewis, Doherty, Daniel, and Benson, Ruth A.

Published
2022
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14. Methods, design, and initial results of an angiographic core lab from VOYAGER-PAD

Author

Rogers, R Kevin, primary, Herold, Joerg, additional, Govsyeyev, Nicholas, additional, Iezzi, Roberto, additional, Morrison, Justin, additional, Hogan, Shea E, additional, Nehler, Mark, additional, Bricker, Rory, additional, Andring, Brice, additional, Bergmark, Brian, additional, Cavender, Matt, additional, Malgor, Emily, additional, Jacobs, Donald, additional, Young, Michael N, additional, Capell, Warren, additional, Yčas, Joseph W, additional, Anand, Sonia S, additional, Berkowitz, Scott D, additional, Debus, E Sebastian, additional, Haskell, Lloyd P, additional, Muehlhofer, Eva, additional, Patel, Manesh R, additional, Hess, Connie N, additional, Bauersachs, Rupert M, additional, Anderson, Victoria, additional, and Bonaca, Marc P, additional

Published
2024
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16. Nonparametric Maximum Entropy Probability Density Estimation

Author

Farmer, Jenny and Jacobs, Donald J.

Subjects
Mathematics - Probability, Statistics - Methodology
Abstract

Given a sample of independent and identically distributed random variables, a novel nonparametric maximum entropy method is presented to estimate the underlying continuous univariate probability density function (pdf). Estimates are found by maximizing a log-likelihood function based on single order statistics after transforming through a sequence of trial cumulative distribution functions that iteratively improve using a Monte Carlo random search method. Improvement is quantified by assessing the random variables against the statistical properties of sampled uniform random data. Quality is determined using an empirically derived scoring function that is scaled to be sample size invariant. The scoring function identifies atypical fluctuations, for which threshold values are set to define objective criteria that prevent under-fitting as trial iterations continue to improve the model pdf, and, stopping the iteration cycle before over-fitting occurs. No prior knowledge about the data is required. An ensemble of pdf models is used to reflect uncertainties due to statistical fluctuations in random samples, and the quality of the estimates is visualized using scaled residual quantile plots that show deviations from size-invariant statistics. These considerations result in a tractable method that holistically employs key principles of random variables and their statistical properties combined with employing orthogonal basis functions and data-driven adaptive algorithms. Benchmark tests show that the pdf estimates readily converge to the true pdf as sample size increases. Robust results are demonstrated on several test probability densities that include cases with discontinuities, multi-resolution scales, heavy tails and singularities in the pdf, suggesting a generally applicable approach for statistical inference.

Published
2016

19. Handbook of Empirical Literary Studies

Author

Donald Kuiken, Arthur M. Jacobs, Donald Kuiken, Arthur M. Jacobs and Donald Kuiken, Arthur M. Jacobs, Donald Kuiken, Arthur M. Jacobs

Published
2021

33. Network rigidity at finite temperature: Relationships between thermodynamic stability, the non-additivity of entropy and cooperativity in molecular systems

Author

Jacobs, Donald J., Dallakyan, S., Wood, G. G., and Heckathorne, A.

Subjects
Condensed Matter - Soft Condensed Matter, Condensed Matter - Statistical Mechanics, Quantitative Biology - Biomolecules
Abstract

A statistical mechanical distance constraint model (DCM) is presented that explicitly accounts for network rigidity among constraints present within a system. Constraints are characterized by local microscopic free energy functions. Topological re-arrangements of thermally fluctuating constraints are permitted. The partition function is obtained by combining microscopic free energies of individual constraints using network rigidity as an underlying long-range mechanical interaction -- giving a quantitative explanation for the non-additivity in component entropies exhibited in molecular systems. Two exactly solved 2-dimensional toy models representing flexible molecules that can undergo conformational change are presented to elucidate concepts, and to outline a DCM calculation scheme applicable to many types of physical systems. It is proposed that network rigidity plays a central role in balancing the energetic and entropic contributions to the free energy of bio-polymers, such as proteins. As a demonstration, the distance constraint model is solved exactly for the alpha-helix to coil transition in homogeneous peptides. Temperature and size independent model parameters are fitted to Monte Carlo simulation data, which includes peptides of length 10 for gas phase, and lengths 10, 15, 20 and 30 in water. The DCM is compared to the Lifson-Roig model. It is found that network rigidity provides a mechanism for cooperativity in molecular structures including their ability to spontaneously self-organize. In particular, the formation of a characteristic topological arrangement of constraints is associated with the most probable microstates changing under different thermodynamic conditions., Comment: 24 pages, 15 figures, 5 tables, submitted to Phys. Rev. E

Published
2003
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34. CDW Corporation : 2002

Author

Jacobs, Donald P. Author, Kellogg School of Management. Publisher, and Jacobs, Donald P. Author

Published
2004

47. Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching.

Author

Merino, Zach D., Farmer, Jenny, and Jacobs, Donald J.

Subjects
CUMULATIVE distribution function, NONPARAMETRIC estimation, MAXIMUM entropy method, PROBABILITY density function, PROBABILITY theory, TREE branches
Abstract

We present a novel nonparametric adaptive partitioning and stitching (NAPS) algorithm to estimate a probability density function (PDF) of a single variable. Sampled data is partitioned into blocks using a branching tree algorithm that minimizes deviations from a uniform density within blocks of various sample sizes arranged in a staggered format. The block sizes are constructed to balance the load in parallel computing as the PDF for each block is independently estimated using the nonparametric maximum entropy method (NMEM) previously developed for automated high throughput analysis. Once all block PDFs are calculated, they are stitched together to provide a smooth estimate throughout the sample range. Each stitch is an averaging process over weight factors based on the estimated cumulative distribution function (CDF) and a complementary CDF that characterize how data from flanking blocks overlap. Benchmarks on synthetic data show that our PDF estimates are fast and accurate for sample sizes ranging from 2 9 to 2 27 , across a diverse set of distributions that account for single and multi-modal distributions with heavy tails or singularities. We also generate estimates by replacing NMEM with kernel density estimation (KDE) within blocks. Our results indicate that NAPS(NMEM) is the best-performing method overall, while NAPS(KDE) improves estimates near boundaries compared to standard KDE. [ABSTRACT FROM AUTHOR]

Published
2023
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