Most of real data are characterized by positive, asymmetric and skewed distributions of various shapes. Modelling and forecasting of such data are addressed by proposing nonnegative conditional ...heteroscedastic time series models with Gamma distributions. Three types of time-varying parameters of Gamma distributions are adopted to construct the nonnegative GARCH models. A condition for the existence of a stationary Gamma-GARCH model is given. Parameter estimates are discussed via maximum likelihood estimation (MLE) method. A Monte-Carlo study is conducted to illustrate sample paths of the proposed models and to see finite-sample validity of the MLEs, as well as to evaluate model diagnostics using standardized Pearson residuals. Furthermore, out-of-sample forecasting analysis is performed to compute forecasting accuracy measures. Applications to oil price and Bitcoin data are given, respectively.
The large-sample properties of likelihood-based statistical inference under mixture models have received much attention from statisticians. Although the consistency of the nonparametric MLE is ...regarded as a standard conclusion, many researchers ignore the precise conditions required on the mixture model. An incorrect claim of consistency can lead to false conclusions even if the mixture model under investigation seems well behaved. Under a finite normal mixture model, for instance, the consistency of the plain MLE is often erroneously assumed in spite of recent research breakthroughs. This paper streamlines the consistency results for the nonparametric MLE in general, and in particular for the penalized MLE under finite normal mixture models.
Persistent Fault Attack in Practice Fan Zhang; Yiran Zhang; Huilong Jiang ...
IACR transactions on cryptographic hardware and embedded systems,
03/2020, Letnik:
2020, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Persistence fault analysis (PFA) is a novel fault analysis technique proposed in CHES 2018 and demonstrated with rowhammer-based fault injections. However, whether such analysis can be applied to ...traditional fault attack scenario, together with its difficulty in practice, has not been carefully investigated. For the first time, a persistent fault attack is conducted on an unprotected AES implemented on ATmega163L microcontroller in this paper. Several critical challenges are solved with our new improvements, including (1) how to decide whether the fault is injected in SBox; (2) how to use the maximum likelihood estimation to pursue the minimum number of ciphertexts; (3) how to utilize the unknown fault in SBox to extract the key. Our experiments show that: to break AES with physical laser injections despite all these challenges, the minimum and average number of required ciphertexts are 926 and 1641, respectively. It is about 38% and 28% reductions of the ciphertexts required in comparison to 1493 and 2273 in previous work where both fault value and location have to be known. Furthermore, our analysis is extended to the PRESENT cipher. By applying the persistent fault analysis to the penultimate round, the full PRESENT key of 80 bits can be recovered. Eventually, an experimental validation is performed to confirm the accuracy of our attack with more insights. This paper solves the challenges in most aspects of practice and also demonstrates the feasibility and universality of PFA on SPN block ciphers.
Moringa is a multipurpose plant due to enormous benefits but chilling temperature in winter results in various physiological and metabolic disturbances in cells of this plant which leads to ...vegetative growth inhibition in moringa. To overcome this problem, foliar application of various biostimulants might be viable option. Moringa leaf extract is also getting popularity as bio-stimulant. Therefore, present study was conducted to test the growth of moringa seedling under low temperature regime (12.9–20.1 °C) in response to foliar application of moringa leaf extract, hydrogen peroxide, ascorbic acid and salicylic acid. Seeds of moringa accessions were collected from concerning locations and seedlings were grown in polythene bags filled with equal ratio of compost, sand, silt and clay. All foliar treatments were applied twice at the age of one and three months of seedlings. Growth and biochemical parameters were recorded by using standard procedures. Foliar application of moringa leaf extract at the rate of 3% significantly improved the number of branches (66%), leaves (52%), leaflets (42%), leaf chlorophyll a (41%) and b (71%) and total chlorophyll (49%) contents, membrane stability index (45%) and leaf phenolic contents (78%) of moringa seedlings as compared to control after first round of treatment application. After second round of foliar application of moringa leaf extract at the rate of 3% significantly increased the number of branches (92%), leaves (141%), leaflets (61%), leaf chlorophyll a (51%) and b (61%), total chlorophyll contents (54%), membrane stability index (60%) and leaf phenolic contents (63%) of moringa seedlings. Vigorous growth of moringa seedlings under low temperature stress ensured the defensive potential of moringa leaf extract against low temperature stress.
•Moringa oleifera leaf extract, rich in minerals and growth hormones, is a very effective under low temperature stress.•Foliar application of MLE improved growth and biochemical parameters of moringa grown under low temperature conditions.•More growth was observed in local moringa as compared to Indian cultivar grown under low temperature conditions.
The peak wavelength of fiber Bragg grating (FBG) using the higher-order derivative of Wavelets multiresolution analysis and maximum likelihood estimation is proposed. The FBG reflected spectrum is ...decomposed at the different level using wavelet multiresolution analysis and the higher-order derivative is applied. The first-order derivative is applied to the calculated desired decomposed level to find the zero-crossing point. The 2nd order derivative is applied and the extrema at the corresponding zero-crossing points are calculated. Further, the proceeding derivative, the 4th order derivative is applied and sharper maxima corresponding zero-crossing point is calculated. The maximum likelihood estimation-based statically method is applied to predict the exact peak wavelengths with good efficiency and low error. The proposed peak detection algorithm is also used in the partially overlapped with weak and non-uniform peaks of cascaded FBGs.
This paper rigorously establishes that the existence of the maximum likelihood estimate (MLE) in high-dimensional logistic regression models with Gaussian covariates undergoes a sharp “phase ...transition.” We introduce an explicit boundary curve
h
MLE, parameterized by two scalars measuring the overall magnitude of the unknown sequence of regression coefficients, with the following property: in the limit of large sample sizes n and number of features p proportioned in such a way that p / n → κ, we show that if the problem is sufficiently high dimensional in the sense that κ >
h
MLE, then the MLE does not exist with probability one. Conversely, if κ <
h
MLE, theMLE asymptotically exists with probability one.
The motivation for this study is to develop the new family of continuous distributions called Odd Lomax-G (OLG). Also present a new flexible three-parameter distribution according to the developed ...family called the Odd Lomax-G Exponential (OLE) distribution. Using binomial series expansion, logarithmic, and expo-nential expansions, the new OLG family and OLE distribution are expanded. We find the derivative of the moments, the mgf , quantity function, ordered statistics and R´enyi entropy. Then use the MLE method to estimate the OLE model parameters. Finally, an importance of the new family is made clear experimentally through two real data applications. Then explore the performance of OLE distribution inferred from family OLG based on certain goodness of fit criteria.
This paper is concerned with the problem of top-K ranking from pairwise comparisons. Given a collection of n items and a few pairwise comparisons across them, one wishes to identify the set of K ...items that receive the highest ranks. To tackle this problem, we adopt the logistic parametric model—the Bradley–Terry–Luce model, where each item is assigned a latent preference score, and where the outcome of each pairwise comparison depends solely on the relative scores of the two items involved. Recent works have made significant progress toward characterizing the performance (e.g., the mean square error for estimating the scores) of several classical methods, including the spectral method and the maximum likelihood estimator (MLE). However, where they stand regarding top-K ranking remains unsettled.
We demonstrate that under a natural random sampling model, the spectral method alone, or the regularized MLE alone, is minimax optimal in terms of the sample complexity—the number of paired comparisons needed to ensure exact top-K identification, for the fixed dynamic range regime. This is accomplished via optimal control of the entrywise error of the score estimates. We complement our theoretical studies by numerical experiments, confirming that both methods yield low entrywise errors for estimating the underlying scores. Our theory is established via a novel leave-one-out trick, which proves effective for analyzing both iterative and noniterative procedures. Along the way, we derive an elementary eigenvector perturbation bound for probability transition matrices, which parallels the Davis–Kahan sin Θ theorem for symmetric matrices. This also allows us to close the gap between the ℓ2 error upper bound for the spectral method and the minimax lower limit.
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