log-concave density

Top PDF log-concave density:

Semi-Parametric Mixture Models Through Log-Concave Density Estimation

Semi-Parametric Mixture Models Through Log-Concave Density Estimation

progress in log-concave density estimation by [26]. We further investigate the identifiabili- ty conditions of the proposed semiparametric mixture models and propose two innovative algorithms to estimate θ without assuming a parametric form for the contaminated densi- ty f (x). Extensive simulation studies demonstrate that our methods provide comparable performance to traditional MLE whether the data are clean and much better performance when the data contain outliers.

122 Read more

LogConcDEAD: An R Package for Maximum Likelihood Estimation of a Multivariate Log-Concave Density

LogConcDEAD: An R Package for Maximum Likelihood Estimation of a Multivariate Log-Concave Density

The function σ is convex, but not differentiable, so standard gradient-based convex optimiza- tion techniques such as Newton’s method are not suitable. Nevertheless, the notion of a subgradient is still valid: a subgradient at y of σ is any direction which defines a supporting hyperplane to σ at y. Shor (1985) developed a theory of subgradient methods for handling convex, non-differentiable optimization problems. The r-algorithm, described in Shor (1985, Chapter 3) and implemented as SolvOpt in C by Kappel and Kuntsevich (2000), was found to work particularly well in practice. A main feature of the LogConcDEAD package is an implementation of an adaptation of this r-algorithm for the particular problem encountered in log-concave density estimation.
Show more

20 Read more

Recent Progress in Log-Concave Density Estimation

Recent Progress in Log-Concave Density Estimation

The main points to note in this algorithm are that in each iteration of the inner while loop, the active set decreases strictly (which ensures this loop terminates eventually), and that after each iteration of the outer while loop, the log-likelihood has strictly increased, and the current iterate ψ belongs to K ∩ V ∗ (A) for some A ⊆ { 2, . . . , n − 1 } . It follows that, up to machine precision, the algorithm terminates with the exact so- lution in finitely many steps. See Figure 1. Dümbgen, Rufibach and Schuhmacher (2014) study the more in- volved problem of estimating a log-concave (sub)- probability density in settings where observations may be subject to various different types of censoring, in- cluding right and interval censoring. In their R pack- age logconcens, they propose an EM algorithm for computation (Dümbgen, Rufibach and Schuhmacher, 2013).
Show more

17 Read more

Maximum Likelihood Estimation of Discrete Log-Concave Distributions with Applications

Maximum Likelihood Estimation of Discrete Log-Concave Distributions with Applications

Notably, We show that random variables from a continuous log-concave density can be grouped/binned (e.g. rounded to some accuracy level), the resulted discrete mass function will fall into our new defined distribution class under certain conditions (Proposition 2.3.1). We also show that under which condition the class of generalized log-concave is extendible- log-concave (Proposition 2.2.1). Moreover we prove there exist a unique extendible-log- concave PMF which minimize the distance to a given true PMF in term of KL divergence, and this minimizer is the true PMF itself if the true PMF is extendible-log-concave.
Show more

172 Read more

Semiparametric Multivariate Density Estimation Using Copulas and Shape-Constraints

Semiparametric Multivariate Density Estimation Using Copulas and Shape-Constraints

Maximum likelihood estimation of a log-concave density has certain advantages over other nonparametric approaches, such as kernel density estimation, which re- quires a bandwidth selection. Furthermore, finding the optimal bandwidth gets more difficult as a dimension increases. On the other hand, the shape-constrained approach is automatic and does not need any tuning parameters. However, for both the kernel and log-concave estimators, the rate of convergence slows down as the dimension d increases. To handle this “curse of dimensionality”, we study an intermediate semi- parametric copula approach and we estimate the marginals using the log-concave shape-constrained MLE and use a parametric approach to fit the copula parameters. We prove √ n rate of convergence for the parametric estimator and that the joint density converges at a rate of n −2/5 regardless of dimension. This is faster than the
Show more

199 Read more

Transparent and flexible capacitors based on nanolaminate Al2O3/TiO2/Al2O3

Transparent and flexible capacitors based on nanolaminate Al2O3/TiO2/Al2O3

ellipsometer (J. A. Woollam alpha-SE, J. A. Woollam Co. Inc., Lincoln, NE, USA) was used to determine the film thicknesses. Standard photolithography and wet etching process were used to define the capacitor areas. The final capacitor device was approximately 100 × 100 μm 2 in area. The capacitance density versus voltage (C-V) and leakage current density versus voltage (I-V) charac- teristics were measured by a semiconductor device analyzer (Keithley 4200, Keithley Instruments, Solon, OH, USA). The optical transmittance was measured in a wavelength range of 300 to 800 nm by using a UV– VIS-NIR spectrophotometer (Varian Cary 5000, Triad Scientific, Manasquan, NJ, USA). The surface morphology of ITO and ATA films was measured by an atomic force microscopy (SPM-9500 J3, Shimadzu, Kyoto, Japan).
Show more

5 Read more

Concave Learners for Rankboost

Concave Learners for Rankboost

In this paper we present a new weak ranking learner that enforces consistency in preference and confidence for the ranking function by being monotonic and concave. We start with a discussion of these regularization properties, theoretically justify them, and show what they mean in terms of the final ranking function. Then we present a new learner, Minimum Weighted Group Ranks (MWGR), that satisfies these properties and can be readily learned. This learner is tested and compared with the binary learner of rankboost on combining multiple face recognition systems from the FERET study (Phillips et al., 2000) and on an information retrieval combination task from TREC (Voorhees and Harman, 2001).
Show more

22 Read more

Generating Pseudo-Synthetic Seismogram with Resistivity Logs Considering the Effect of Gas: Application to Bizzy Field, Onshore, Niger-Delta, Nigeria

Generating Pseudo-Synthetic Seismogram with Resistivity Logs Considering the Effect of Gas: Application to Bizzy Field, Onshore, Niger-Delta, Nigeria

Again, the property of rock units embedded in a formation is also one of the factors that influence the output of well log signals and resistivity log is not an exception. Therefore, their physical characteristics such as mineral constituents, volume of pores and their connectivity should be put into consideration in order to get a log that reflect the formation properties as accurate as possible. However, unlike other field with complex mineralogy, this is not a problem in the Niger Delta which is the study area because it consists of majorly sands and shales. The basis of synthetic seismogram is the Zoeppritz’s equation, which is calculated as the product of velocity and density of subsurface layers. The generation of a synthetic seismogram requires velocity and density. The inverse of sonic log is used to replace velocity data which might be absent when calculating acoustic impedance because of the observed
Show more

13 Read more

Convex and Concave Functions

Convex and Concave Functions

Abstract- In mathematics there is one of the most important properties of real numbers is comparability. In real number system we can compare two distinct real numbers and we can say one of them is smaller or larger than other. The inequalities which we drive are totally dependent on these properties. In this work we study convex and concave functions and use them to drive arithmetic-geometric mean inequality, A-G-H mean inequality, Chebyshey's inequality, Holder inequality, Minkowski's inequality and Jensen's inequality.

7 Read more

Bayesian Inference for Sparse Generalized Linear Models

Bayesian Inference for Sparse Generalized Linear Models

Fig. 2. Left: Comparison between Gaussian and Laplace prior for the reduced model. Hyperparameters are chosen by crossvalidation (see text). The negative log-likelihood value on the test dataset is plotted as a function of dataset size of the training set. Errorbars are obtained by sampling from the approximative posterior distribution and correspond to 2 standard deviations. Right: Receptive fields (shown are posterior means) under model with Gaussian (upper) and with Laplace prior (lower), for different training set sizes. Curves below show marginal posteriors (absolute value of mean, one std. dev. error bars, cut off at zero), decreasing order.
Show more

12 Read more

Study of the Influence of Crop, Machine & Operating Parameters on Performance of Cereal Threshers

Study of the Influence of Crop, Machine & Operating Parameters on Performance of Cereal Threshers

 Increasing the bulk density increases the threshing efficiency (Fig. 7). This might be due to the crop stream between the cylinder and the concave becoming denser, thus providing less cushioning for the grains, since volume flow rate is expressed as feed rate divided by material density. The losses decreased as bulk density increased thus revealing a true image of the efficiency picture (fig. 7b). The thresher capacity increased as bulk density increased (fig. 8). Also the flow of unthreshed grain was found to decrease as the bulk density increased(fig 8b).
Show more

9 Read more

Correlation between maximum density and log exposure range of quality prints

Correlation between maximum density and log exposure range of quality prints

was no were prefer technical to the were cases it photographs to with compared pattern probably due had and that in many judges glossy long as subjects nine be and same Science Photo pho[r]

78 Read more

On the Berry Esséen bound of frequency polygons for ϕ mixing samples

On the Berry Esséen bound of frequency polygons for ϕ mixing samples

As pointed out by Scott [], the frequency polygon has convergence rates similar to those of kernel density estimators and greater than the rate for a histogram. As for com- putation, the computational effort of the frequency polygon is equivalent to the one of the histogram. For large bivariate data sets, the computational simplicity of the frequency polygon and the ease of determining exact equiprobable contours may outweigh the in- creased accuracy of a kernel density estimator. Bivariate contour plots based on millions of observations are increasingly required in applications including high-energy physics sim- ulation experiments, cell sorters and geographical data representation. Moreover, such data are usually collected in a binned form. Therefore, the frequency polygon can be a useful tool for examination and presentation of data. Since the frequency polygon has the advantages mentioned above, it attracts the attention of some scholars, and they have de- rived some results. For the explicit results obtained, one can refer to the references listed in Yang and Liang [] and Xing et al. [], which gave the strong consistency of frequency polygons. Among the obtained results, the study on asymptotic normality can be found in Carbon et al. []. The relevant Berry-Esséen bound for φ-mixing samples has not been seen. This motivates us to investigate the Berry-Esséen bound of frequency polygon under φ -mixing samples. Under the given assumptions, we give the corresponding Berry-Esséen bound. Furthermore, by the obtained Berry-Esséen bound, the relevant convergence rate of uniformly asymptotic normality is also derived, which is nearly O(n –/ ) under the given
Show more

11 Read more

Advertising and Collusion in Retail Markets

Advertising and Collusion in Retail Markets

Allowing that the support of possible costs may be large, we thus establish a general sense in which Bagwell and Ramey’s main …ndings extend to the private-information setting. When legal or other considerations lead to the absence of advertising, if the distribution of types is log- concave and demand is su¢ ciently inelastic, then the market is less concentrated than it would be were advertising competition to occur. Furthermore, the average transaction price is lower, and social welfare is thus higher, when entry is endogenized and …rms compete in advertising. Note, however, that some …ndings such as Proposition 2 and Proposition 3 (i) are not straightforward, given downward-sloping demand. For a given number of …rms, pooling at zero advertising acts to increase the pro…t at the top but sorting through advertising acts to increase expected information rents when demand is substantially larger for lower prices. This con‡ict suggests that market concentration could be lower in the advertising equilibrium than in the random equilibrium, when demand is su¢ ciently elastic. Thus, the established positive association between advertising and market concentration employs additional assumptions on the distribution of types and the elasticity of demand in the general private-information setting.
Show more

53 Read more

Fourier Inequalities in Lorentz and Lebesgue Spaces

Fourier Inequalities in Lorentz and Lebesgue Spaces

A major part of the work is on Fourier coefficients. Several different sufficient condi- tions and necessary conditions for the boundedness of Fourier transform on T , viewed as a map between Lorentz Λ and Γ spaces are established. For a large range of Lorentz in- dices, necessary and sufficient conditions for boundedness are given. A number of known inequalities for generalized quasi concave functions are generalized and improved as part of the preparation for the proofs of the Fourier series results.

101 Read more

Studying the relationship between logging characteristics and the code quality of platform software

Studying the relationship between logging characteristics and the code quality of platform software

Abstract Platform software plays an important role in speeding up the development of large scale applications. Such platforms provide functionalities and abstraction on which applications can be rapidly developed and easily deployed. Hadoop and JBoss are examples of popular open source platform software. Such platform software generate logs to assist operators in monitoring the applications that run on them. These logs capture the doubts, concerns, and needs of developers and operators of platform software. We believe that such logs can be used to better understand code quality. However, logging characteristics and their relation to quality has never been explored. In this paper, we sought to empirically study this relation through a case study on four releases of Hadoop and JBoss. Our findings show that files with logging statements have higher post-release defect densities than those without logging statements in 7 out of 8 studied releases. Inspired by prior studies on code quality, we defined log-related product metrics, such as the number of log lines in a file, and log-related process metrics such as the number of changed log lines. We find that the correlations between our log-related metrics and post-release defects are as strong as their correlations with traditional process metrics, such as the number of pre-release defects, which is known to be one the metrics with the strongest correlation with post- release defects. We also find that log-related metrics can complement traditional product and process metrics resulting in up to 40 % improvement in explanatory power of defect proneness. Our results show that logging characteristics provide
Show more

27 Read more

Agrawal, Aniket
  

(2018):


	Non Gaussianity of primordial gravitational waves and cosmic density and velocity fields.


Dissertation, LMU München: Fakultät für Physik

Agrawal, Aniket (2018): Non Gaussianity of primordial gravitational waves and cosmic density and velocity fields. Dissertation, LMU München: Fakultät für Physik

2. It is direct. The log-normal mock generator takes the observed galaxy two-point correlation function as an input so that we can avoid post-processing steps (halo finding, HOD, for example) connecting the non-linear density field to mock galaxies. 3. It is instructive. Upon assuming log-normal PDF of the galaxy density field, all higher-order correlation functions are given in terms of the two-point correlation function of the log-transformed field (43). This allows us to quantitatively study highly non-linear mode-coupling effects in both the signal and covariance matrix that demand knowledge about the density field on non-linear scales. One such example is mode-coupling due to the survey window function. By using a thousand log-normal mock catalogs, Ref. (39) has quantified the effect from a duplicated, sparse (instead of contiguous) angular selection function, and deduced the optimal analysis strategy. In this chapter, we extend the real-space log-normal mock generator presented in Ref. (39) by including the velocity field in a consistent manner. We then generate the log-normal mock in redshift space by applying the real-to-redshift space mapping. Again, equipped with perfect knowledge about the statistical properties of the galaxy density and velocity fields, such a mock catalog serves as an excellent test bed for modelling RSD due to this non-linear mapping (148). To test the RSD effect on the two-point statistics of the log-normal mock catalog, we begin with the real-space galaxy two-point correlation func- tion as an input. We use a log-normal PDF to generate a three-dimensional galaxy density field, as well as a matter density field. Finally, we generate a velocity field consistent with the matter density field by using the linearised continuity equation (see section 4.3 for more details). We then measure the galaxy two-point statistics (correlation function and power spectrum) both in real and redshift space, and the pairwise line-of-sight velocity PDFs from the log-normal mock catalog. We also calculate the mean pairwise velocity using log-normal statistics and show that it agrees with the measurement from the catalog.
Show more

138 Read more

CFD Study of Concave Turbine

CFD Study of Concave Turbine

field for a given set of fluids, impellers, and tank geometries [1,2]. Rushton turbine is a traditional six-blade disc turbine, which is widely used. The flat blade of the Rushton turbine leads to the formation of a pair of high-speed, low-pressure trailing vortices at the rear of each blade [3,4]. Recently, different modifications in the blade geometry have been considered such as shaping the blade from a flat plate to one with various degrees of streamlining in cross-section. These new curved blade turbines have a cavity structure. The original concave blade concept was developed in the 1970s at Delft University by a group led by Jhon M.Smith. Van´t Riet et al. (1976) studied a variety of impeller styles and introduced the concept of concave blades [5]. Wong, C.W. et al. (1988) studied the curved-blade turbine [6]. Warmoeskerken and Smith. (1989) extended that work and explained the improved performance of the concave blades compared to flat blades [7]. The newer blade designs with deeper concavity were proposed by Hjorth (1988) and Middleton (1993) [8,9]. Galindo, E. et al. (1993) studied a similar design on parabolic-blade Scaba 6SRGT [10]. Bakker et al. (1994) studied the performance of impellers with a semicircular blade shape, the Chemineer CD-6 [11]. A comparative analysis of the fluid dynamic performance of the concave turbines and hydrofoil impellers was provided by Neinow.A.W (1996) [12]. Bakker et al. (1998) designed a new impeller BT-6 that has been optimized to take into account different flow conditions above and below the disc [13]. D.Pinelli et al. (2003) studied the behavior of the asymmetric concave blade (BT-6) and compared it with the behavior of other impellers [14]. S.D.Vlaev et al. (2004) reported the
Show more

16 Read more

Assessing interpretability of visual symbols of varied colors across demographic profiles

Assessing interpretability of visual symbols of varied colors across demographic profiles

Usually, if a shape is Convex shapes appear to be the figure whereas concave shapes are seen as ground.. concave, it is the result of convex shapes surrounding it.[r]

320 Read more

A note on Schur concave functions

A note on Schur concave functions

If we compute f (x) = nx((n–)x+M) M–x , we have that f is nondecreasing on (, M]. Since f (M) = , it follows that f (x) ≤  for every  < x ≤ M. In the following, we extend the class of Schur-concave functions which verifies (.). Consider the elementary symmetric functions of n variables given by

9 Read more

Show all 10000 documents...