Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
31,948
result(s) for
"Poisson"
Sort by:
Seafood simple
\"In its three decades at the top of New York City's restaurant scene, Le Bernardin has been celebrated as one of the finest seafood restaurants in the world and its iconic chef Eric Ripert as the expert in fish cookery. Now, in Seafood Simple, Ripert demystifies his signature cuisine, making it accessible to home cooks of all skill levels-yet still with elegance and panache. Breaking down cooking techniques in a step-by-step process along with accompanying images, this book teaches readers how to master core skills, from poaching and deep frying to filleting a fish and shucking an oyster. These techniques are then applied to 85 straightforward, delicious recipes. Dishes like Tuna Carpaccio, Crispy Fish Tacos, Shrimp Tempura, Miso Cod, and Spaghetti Vongole show us how to bring out the vibrant flavor and incredible versatility of seafood. Each recipe is complete with a gorgeous image by renowned photographer Nigel Parry, as well as step-by-step photos for each of the twenty techniques taught in the book\"-- Provided by publisher.
Book
New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications
The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate this problem. Additionally, the PML estimator is sensitive to multicollinearity. Therefore, several biased Poisson estimators have been provided to cope with this problem, such as the Poisson ridge estimator, Poisson Liu estimator, Poisson Kibria–Lukman estimator, and Poisson modified Kibria–Lukman estimator. Despite different Poisson biased regression estimators being proposed, there has been no analysis of the robust version of these estimators to deal with the two above-mentioned problems simultaneously, except for the robust Poisson ridge regression estimator, which we have extended by proposing three new robust Poisson one-parameter regression estimators, namely, the robust Poisson Liu (RPL), the robust Poisson Kibria–Lukman (RPKL), and the robust Poisson modified Kibria–Lukman (RPMKL). Theoretical comparisons and Monte Carlo simulations were conducted to show the proposed performance compared with the other estimators. The simulation results indicated that the proposed RPL, RPKL, and RPMKL estimators outperformed the other estimators in different scenarios, in cases where both problems existed. Finally, we analyzed two real datasets to confirm the results.
Journal Article
The rivals of Versailles : a novel
\"And you thought sisters were a thing to fear. In this scandalous follow-up to Sally Christie's clever and absorbing debut, we meet none other than the Marquise de Pompadour, one of the greatest beauties of her generation and the first bourgeois mistress ever to grace the hallowed halls of Versailles. I write this before her blood is even cold. She is dead, suddenly, from a high fever. The King is inconsolable, but the way is now clear. The way is now clear. The year is 1745. Marie-Anne, the youngest of the infamous Nesle sisters and King Louis XV's most beloved mistress, is gone, making room for the next Royal Favorite. Enter Jeanne-Antoinette Poisson, a stunningly beautiful girl from the middle classes. Fifteen years prior, a fortune teller had mapped out young Jeanne's destiny: she would become the lover of a king and the most powerful woman in the land. Eventually connections, luck, and a little scheming pave her way to Versailles and into the King's arms. All too soon, conniving politicians and hopeful beauties seek to replace the bourgeois interloper with a more suitable mistress. As Jeanne, now the Marquise de Pompadour, takes on her many rivals--including a lustful lady-in-waiting; a precocious fourteen-year-old prostitute, and even a cousin of the notorious Nesle sisters--she helps the king give himself over to a life of luxury and depravity. Around them, war rages, discontent grows, and France inches ever closer to the Revolution. Enigmatic beauty, social climber, actress, trendsetter, patron of the arts, spendthrift, whoremonger, friend, lover, foe. History books may say many things about the famous Marquise de Pompadour, but one thing is clear: for almost twenty years, she ruled France and the King's heart. Told in Christie's witty and modern style, this second book in the Mistresses of Versailles trilogy will delight and entrance fans as it once again brings to life the world of eighteenth century Versailles in all its pride, pestilence and glory\"-- Provided by publisher.
Book
Statistical modeling of patterns in annual reproductive rates
Reproduction by individuals is typically recorded as count data (e.g., number of fledglings from a nest or inflorescences on a plant) and commonly modeled using Poisson or negative binomial distributions, which assume that variance is greater than or equal to the mean. However, distributions of reproductive effort are often underdispersed (i.e., variance < mean). When used in hypothesis tests, models that ignore underdispersion will be overly conservative and may fail to detect significant patterns. Here we show that generalized Poisson (GP) and Conway-Maxwell-Poisson (CMP) distributions are better choices for modeling reproductive effort because they can handle both overdispersion and underdispersion; we provide examples of how ecologists can use GP and CMP distributions in generalized linear models (GLMs) and generalized linear mixed models (GLMMs) to quantify patterns in reproduction. Using a new R package, glmmTMB, we construct GLMMs to investigate how rainfall and population density influence the number of fledglings in the warbler Oreothlypis celata and how flowering rate of Heliconia acuminata differs between fragmented and continuous forest. We also demonstrate how to deal with zero-inflation, which occurs when there are more zeros than expected in the distribution, e.g., due to complete reproductive failure by some individuals.
Journal Article
Penta-graphene: A new carbon allotrope
A 2D metastable carbon allotrope, penta-graphene, composed entirely of carbon pentagons and resembling the Cairo pentagonal tiling, is proposed. State-of-the-art theoretical calculations confirm that the new carbon polymorph is not only dynamically and mechanically stable, but also can withstand temperatures as high as 1000 K. Due to its unique atomic configuration, penta-graphene has an unusual negative Poisson’s ratio and ultrahigh ideal strength that can even outperform graphene. Furthermore, unlike graphene that needs to be functionalized for opening a band gap, penta-graphene possesses an intrinsic quasi-direct band gap as large as 3.25 eV, close to that of ZnO and GaN. Equally important, penta-graphene can be exfoliated from T12-carbon. When rolled up, it can form pentagon-based nanotubes which are semiconducting, regardless of their chirality. When stacked in different patterns, stable 3D twin structures of T12-carbon are generated with band gaps even larger than that of T12-carbon. The versatility of penta-graphene and its derivatives are expected to have broad applications in nanoelectronics and nanomechanics.
Significance Carbon has many faces––from diamond and graphite to graphene, nanotube, and fullerenes. Whereas hexagons are the primary building blocks of many of these materials, except for C ₂₀ fullerene, carbon structures made exclusively of pentagons are not known. Because many of the exotic properties of carbon are associated with their unique structures, some fundamental questions arise: Is it possible to have materials made exclusively of carbon pentagons and if so will they be stable and have unusual properties? Based on extensive analyses and simulations we show that penta-graphene, composed of only carbon pentagons and resembling Cairo pentagonal tiling, is dynamically, thermally, and mechanically stable. It exhibits negative Poisson's ratio, a large band gap, and an ultrahigh mechanical strength.
Journal Article
Geometry of Miura-folded metamaterials
This paper describes two folded metamaterials based on the Miura-ori fold pattern. The structural mechanics of these metamaterials are dominated by the kinematics of the folding, which only depends on the geometry and therefore is scale-independent. First, a folded shell structure is introduced, where the fold pattern provides a negative Poisson’s ratio for in-plane deformations and a positive Poisson’s ratio for out-of-plane bending. Second, a cellular metamaterial is described based on a stacking of individual folded layers, where the folding kinematics are compatible between layers. Additional freedom in the design of the metamaterial can be achieved by varying the fold pattern within each layer.
Journal Article
Reexamining Key Applications of the Poisson Distribution
The Poisson distribution is a discrete probability model, widely used in science and engineering to describe various natural and man-made phenomena. It possesses an important feature, namely being inherently asymmetric, but as its parameter becomes large, the distribution becomes approximately symmetric. To broaden its use, multiple extensions and variations have been developed. Determining whether a data set follows a Poisson distribution involves hypothesis testing at a chosen significance level. When sampling from a Poisson distribution, confidence intervals provide an estimated range instead of a single value. Due to the discrete nature of the Poisson distribution, confidence intervals cannot be derived from a simple formula, and are therefore computed using specialized algorithms. In this paper, three alternatives are given and discussed.
Journal Article
Fractional Poisson Processes of Order k and Beyond
In this article, we introduce fractional Poisson fields of order
k
in
n
-dimensional Euclidean space of positive real valued vectors. We also work on time-fractional Poisson process of order
k
, space-fractional Poisson processes of order
k
and a tempered version of time-space fractional Poisson processes of order
k
. We discuss generalized fractional Poisson processes of order
k
in terms of Bernstein functions. These processes are defined in terms of fractional compound Poisson processes. The time-fractional Poisson process of order
k
naturally generalizes the Poisson process and the Poisson process of order
k
to a heavy-tailed waiting-times counting process. The space-fractional Poisson process of order
k
allows on average an infinite number of arrivals in any interval. We derive the marginal probabilities governing difference–differential equations of the introduced processes. We also provide the Watanabe martingale characterization for some time-changed Poisson processes.
Journal Article
The Inverse Gaussian Process as a Degradation Model
This article systematically investigates the inverse Gaussian (IG) process as an effective degradation model. The IG process is shown to be a limiting compound Poisson process, which gives it a meaningful physical interpretation for modeling degradation of products deteriorating in random environments. Treated as the first passage process of a Wiener process, the IG process is flexible in incorporating random effects and explanatory variables that account for heterogeneities commonly observed in degradation problems. This flexibility makes the class of IG process models much more attractive compared with the Gamma process, which has been thoroughly investigated in the literature of degradation modeling. The article also discusses statistical inference for three random effects models and model selection. It concludes with a real world example to demonstrate the applicability of the IG process in degradation analysis. Supplementary materials for this article are available online.
Journal Article
A FLEXIBLE REGRESSION MODEL FOR COUNT DATA
Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often over- or under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway—Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson regression generalizes the well-known Poisson and logistic regression models, and is suitable for fitting count data with a wide range of dispersion levels. With a GLM approach that takes advantage of exponential family properties, we discuss model estimation, inference, diagnostics, and interpretation, and present a test for determining the need for a COM-Poisson regression over a standard Poisson regression. We compare the COM-Poisson to several alternatives and illustrate its advantages and usefulness using three data sets with varying dispersion.
Journal Article