Tootfinder

Pair coalescence times of ancestral lineages of two-dimensional logistic branching random walks
Matthias Birkner, Andrej Depperschmidt, Timo Schl\"uter
https://arxiv.org/abs/2405.02090

Pair coalescence times of ancestral lineages of two-dimensional logistic branching random walks
Consider two ancestral lineages sampled from a system of two-dimensional branching random walks with logistic regulation in the stationary regime. We study the asymptotics of their coalescence time for large initial separation and find that it agrees with well known results for a suitably scaled two-dimensional stepping stone model and also with Malécot's continuous-space approximation for the probability of identity by descent as a function of sampling distance. This can be viewed as a justif…

Robust estimators for the log-logistic model based on ranked set sampling
\'Angel Felipe, Mar\'ia Jaenada, Pedro Miranda, Leandro Pardo
https://arxiv.org/abs/2404.03346

Robust estimators for the log-logistic model based on ranked set sampling
In this paper we introduce a new family of estimators for the parameters of shape and scale of the log-logistic distribution being robust when rank set sample method is used to select the data. Rank set sampling arises as a way to reduce the impact of extremal data. Log-logistic distribution is an important distribution suitable for modeling many different situations ranging from Economy to Engineering and Hydrology. This new family of estimators is based on density power divergences. The choic…

A comparison of the discrimination performance of lasso and maximum likelihood estimation in logistic regression model
Gilberto P. Alc\^antara Junior, Gustavo H. A. Pereira
https://arxiv.org/abs/2404.17482

A comparison of the discrimination performance of lasso and maximum likelihood estimation in logistic regression model
Logistic regression is widely used in many areas of knowledge. Several works compare the performance of lasso and maximum likelihood estimation in logistic regression. However, part of these works do not perform simulation studies and the remaining ones do not consider scenarios in which the ratio of the number of covariates to sample size is high. In this work, we compare the discrimination performance of lasso and maximum likelihood estimation in logistic regression using simulation studies a…

This https://arxiv.org/abs/2402.16326 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression
In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural con…

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression
Agniva Chowdhury, Pradeep Ramuhalli
https://arxiv.org/abs/2402.16326 https://

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression
In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural con…

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Hamza Saleem, Amir Ziashahabi, Muhammad Naveed, Salman Avestimehr
https://arxiv.org/abs/2403.17296

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Training machine learning models on data from multiple entities without direct data sharing can unlock applications otherwise hindered by business, legal, or ethical constraints. In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models. We adopt a two-server model where data owners secret-share their data between two servers that train and evaluate the model on the joint data. A significant source of inefficiency a…

A model-free subdata selection method for classification
Rakhi Singh
https://arxiv.org/abs/2404.19127 https://arxiv.org/pdf/2404.1912…

A model-free subdata selection method for classification
Subdata selection is a study of methods that select a small representative sample of the big data, the analysis of which is fast and statistically efficient. The existing subdata selection methods assume that the big data can be reasonably modeled using an underlying model, such as a (multinomial) logistic regression for classification problems. These methods work extremely well when the underlying modeling assumption is correct but often yield poor results otherwise. In this paper, we propose …

Noisy Fixed Points: Stability of the Invariant Distribution of the Random Logistic Map
Kimberly Ayers, Ami Radunskaya
https://arxiv.org/abs/2403.13116 http…

Noisy Fixed Points: Stability of the Invariant Distribution of the Random Logistic Map
The full family of discrete logistic maps has been widely studied both as a canonical example of the period-doubling route to chaos, and as a model of natural processes. In this paper we present a study of the stochastic process described by iterations of the logistic map with a random parameter. In addition to bringing together previously known results, we present a proof that the unique invariant measure of the process in the chaotic regime is asymptotically stable.

Travelling waves in a minimal go-or-grow model of cell invasion
Carles Falc\'o, Rebecca M. Crossley, Ruth E. Baker
https://arxiv.org/abs/2404.11251 htt…

Travelling waves in a minimal go-or-grow model of cell invasion
We consider a minimal go-or-grow model of cell invasion, whereby cells can either proliferate, following logistic growth, or move, via linear diffusion, and phenotypic switching between these two states is density-dependent. Formal analysis in the fast switching regime shows that the total cell density in the two-population go-or-grow model can be described in terms of a single reaction-diffusion equation with density-dependent diffusion and proliferation. Using the connection to single-populat…

This https://arxiv.org/abs/2107.06463 has been replaced.
link: https://scholar.google.com/scholar?q=a

Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures. Various models have been proposed, such as autoregressive, softmax, logistic mixture, Gaussian mixture, and Lapla…

Repro Samples Method for High-dimensional Logistic Model
Xiaotian Hou, Linjun Zhang, Peng Wang, Min-ge Xie
https://arxiv.org/abs/2403.09984 https://…

Repro Samples Method for High-dimensional Logistic Model
This paper presents a novel method to make statistical inferences for both the model support and regression coefficients in a high-dimensional logistic regression model. Our method is based on the repro samples framework, in which we conduct statistical inference by generating artificial samples mimicking the actual data-generating process. The proposed method has two major advantages. Firstly, for model support, we introduce the first method for constructing model confidence set in a high-dime…

An information-theoretic lower bound in time-uniform estimation
John C. Duchi, Saminul Haque
https://arxiv.org/abs/2402.08794 https://

An information-theoretic lower bound in time-uniform estimation
We present an information-theoretic lower bound for the problem of parameter estimation with time-uniform coverage guarantees. Via a new a reduction to sequential testing, we obtain stronger lower bounds that capture the hardness of the time-uniform setting. In the case of location model estimation, logistic regression, and exponential family models, our $Ω(\sqrt{n^{-1}\log \log n})$ lower bound is sharp to within constant factors in typical settings.

This https://arxiv.org/abs/2205.03905 has been replaced.
link: https://scholar.google.com/scholar?q=a

Single-species population models with age structure and psychological effect in a polluted environment
This paper considers a single-population model with age structure and psychological effects in a polluted environment. We divide the single population into two stages of larval and adult structure. The model uses Logistic input, and the larvae are converted into adult bodies by constant ratio. We only consider adulthood. The role of psychological effects makes the contact between adult and environmental toxins a functional form, while the contact between larvae and environmental toxins is linea…

Bayesian Federated Inference for Survival Models
Hassan Pazira, Emanuele Massa, Jetty AM Weijers, Anthony CC Coolen, Marianne A Jonker
https://arxiv.org/abs/2404.17464

Bayesian Federated Inference for Survival Models
In cancer research, overall survival and progression free survival are often analyzed with the Cox model. To estimate accurately the parameters in the model, sufficient data and, more importantly, sufficient events need to be observed. In practice, this is often a problem. Merging data sets from different medical centers may help, but this is not always possible due to strict privacy legislation and logistic difficulties. Recently, the Bayesian Federated Inference (BFI) strategy for generalized…

This https://arxiv.org/abs/2403.16121 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Log-rank test with coarsened exact matching
It is of special importance in the clinical trial to compare survival times between the treatment group and the control group. Propensity score methods with a logistic regression model are often used to reduce the effects of confounders. However, the modeling of complex structures between the covariates, the treatment assignment and the survival time is difficult. In this paper, we consider coarsened exact matching (CEM), which does not need any parametric models, and we propose the weighted lo…

This https://arxiv.org/abs/2310.08536 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Real-time Prediction of the Great Recession and the Covid-19 Recession
A series of standard and penalized logistic regression models is employed to model and forecast the Great Recession and the Covid-19 recession in the US. The empirical analysis explores the predictive content of numerous macroeconomic and financial indicators with respect to NBER recession indicator. The predictive ability of the underlying models is evaluated using a set of statistical evaluation metrics. The recessions are scrutinized by closely examining the movement of five most influential…

Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information
Shujie Ma, Po-Yao Niu, Yichong Zhang, Yinchu Zhu
https://arxiv.org/abs/2403.14899

Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information
This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available. The model consists of two parts. One part is a low-rank matrix induced by unobserved latent factors; the other part models the effects of the observed covariates through a coefficient matrix which is composed of high-dimensional column vectors. We model the observational pattern of the responses through a logistic regression of the covariates, and allow i…

Log-rank test with coarsened exact matching
Tomoya Baba, Nakahiro Yoshida
https://arxiv.org/abs/2403.16121 https://arxiv.org/pdf/2403…

Log-rank test with coarsened exact matching
It is of special importance in the clinical trial to compare survival times between the treatment group and the control group. Propensity score methods with a logistic regression model are often used to reduce the effects of confounders. However, the modeling of complex structures between the covariates, the treatment assignment and the survival time is difficult. In this paper, we consider coarsened exact matching (CEM), which does not need any parametric models, and we propose the weighted lo…

This https://arxiv.org/abs/2301.02131 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Random perturbations for the chemotaxis-fluid model with fractional dissipation: Global pathwise weak solutions
This paper considers a stochastically perturbed Keller-Segel-Navier-Stokes (KS-SNS) system arising from the biomathematics in two dimensions, where the diffusion of fluid is expressed by a fractional Laplacian with an exponent in $[1/2,1]$. Our main result demonstrates that, under appropriate assumptions, the Cauchy problem of the KS-SNS system has a unique global probabilistically strong and analytically weak solution, which also confirms that the quadratic logistic source authentically contri…

This https://arxiv.org/abs/2302.11746 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Binary Regression and Classification with Covariates in Metric Spaces
Inspired by logistic regression, we introduce a regression model for data tuples consisting of a binary response and a set of covariates residing in a metric space without vector structures. Based on the proposed model we also develop a binary classifier for metric-space valued data. We propose a maximum likelihood estimator for the metric-space valued regression coefficient in the model, and provide upper bounds on the estimation error under various metric entropy conditions that quantify comp…

This https://arxiv.org/abs/2112.12675 has been replaced.
link: https://scholar.google.com/scholar?q=a

A general multi-scale description of metastable adaptive motion across fitness valleys
We consider a stochastic individual-based model of adaptive dynamics on a finite trait graph $G=(V,E)$. The evolution is driven by a linear birth rate, a density dependent logistic death rate an the possibility of mutations along the (possibly directed) edges in $E$. We study the limit of small mutation rates for a simultaneously diverging population size. Closing the gap between the works of Bovier, Coquille and Smadi (2019) and Coquille, Kraut and Smadi (2021), we give a precise description o…

Classification Using Global and Local Mahalanobis Distances
Annesha Ghosh, Anil K. Ghosh, Rita SahaRay, Soham Sarkar
https://arxiv.org/abs/2402.08283 https…

Classification Using Global and Local Mahalanobis Distances
We propose a novel semi-parametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic link function that uses these distances as features to estimate the posterior probabilities of the different classes. While popular parametric classifiers like linear and quadratic discriminant analyses are mainly motivated by the normality of the underlying distributions, the proposed classifier is more flexible an…

This https://arxiv.org/abs/2011.08661 has been replaced.
link: https://scholar.google.com/scholar?q=a

Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders
We consider estimation of average treatment effects given observational data with high-dimensional pretreatment variables. Existing methods for this problem typically assume some form of sparsity for the regression functions. In this work, we introduce a debiased inverse propensity score weighting (DIPW) scheme for average treatment effect estimation that delivers $\sqrt{n}$-consistent estimates when the propensity score follows a sparse logistic regression model; the outcome regression functio…

This https://arxiv.org/abs/2011.08661 has been replaced.
link: https://scholar.google.com/scholar?q=a

Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders
We consider estimation of average treatment effects given observational data with high-dimensional pretreatment variables. Existing methods for this problem typically assume some form of sparsity for the regression functions. In this work, we introduce a debiased inverse propensity score weighting (DIPW) scheme for average treatment effect estimation that delivers $\sqrt{n}$-consistent estimates when the propensity score follows a sparse logistic regression model; the outcome regression functio…