二元乘积型向量有理插值

利用向量的Ｓａｍｅｌｓｏｎ逆变换，建立了二元乘积型向量连分式的有理插值公式及展开式，所得结果是二元乘积型数量连分式有理插值的推广和改进。     

WAVELET-BASED STEGANOGRAPHY

Wavelets, which are used to separate data into different components, have many applications. Steganography, one such application, is a form of secret communication where the existence of a message is concealed. Methods for applying univariate and bivariate wavelets to steganography include embedding messages into the insignificant wavelet coefficients of decomposed signals and images. Recomposing then creates stego-objects that appear unaltered from the original cover-objects. First demonstrated is a technique for hiding messages in strings of data with linear trends using the univariate Daubechies D ₄ wavelets. Then, after developing the bivariate Haar wavelets and filters, a unique process of decomposing and recomposing an image in greyscale in order to implement steganography will be illustrated.     

Dynamic infant–parent affect coupling during the face-to-face/still-face.

We examined dynamic infant–parent affect coupling using the Face-to-Face/Still-Face (FFSF). The sample included 20 infants whose older siblings had been diagnosed with Autism Spectrum Disorders (ASD-sibs) and 18 infants with comparison siblings (COMP-sibs). A series of mixed effects bivariate autoregressive models was used to represent the self-regulation and interactive dynamics of infants and parents during the FFSF. Significant bidirectional affective coupling was found between infants and parents, with infant-to-parent coupling being more prominent than parent-to-infant coupling. Further analysis of within-dyad dynamics revealed ongoing changes in concurrent infant–parent linkages both within and between different FFSF episodes. The importance of considering both inter- and intradyad differences is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparing meta-analytic moderator estimation techniques under realistic conditions.

One of the most problematic issues in contemporary meta-analysis is the estimation and interpretation of moderating effects. Monte Carlo analyses are developed in this article that compare bivariate correlations, ordinary least squares and weighted least squares (WLS) multiple regression, and hierarchical subgroup (HS) analysis for assessing the influence of continuous moderators under conditions of multicollinearity and skewed distribution of study sample sizes (heteroscedasticity). The results show that only WLS is largely unaffected by multicollinearity and heteroscedasticity, whereas the other techniques are substantially weakened. Of note, HS, one of the most popular methods, typically provides the most inaccurate results, whereas WLS, one of the least popular methods, typically provides the most accurate results. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Minimum variance hedging with bivariate regime-switching model for WTI crude oil

This paper proposes a four-regime bivariate Markov regime-switching model to estimate the daily time-varying minimum variance hedge ratios for West Texas Intermediate (WTI) crude oil, and evaluates its in- and out-of-sample hedging performances with two-regime model, CC-GARCH, TVC-GARCH, and OLS models. Empirical results reveal that the four-regime Markov switching model outperforms the other models for both in- and out-of-sample hedging performance. Based on Hansen’s SPA test (2005), the four-regime model significantly outperforms the other models for only in-sample hedging.     

Fitting Drought Duration and Severity with Two-Dimensional Copulas

This study aims to model the joint drought duration and severity distribution using two-dimensional copulas. The method of inference function for margins (IFM method) is employed to construct copulas. Two separate maximum likelihood estimations of univariate marginal distributions are performed first, then followed by a maximization of the bivariate likelihood as a function of the dependence parameters. The drought duration and severity are assumed to be exponential and gamma distributions, respectively. Several copulas are tested to determine the best data fitted copula. Droughts, defined using the Standardized Precipitation Index (SPI), of Wushantou (Taiwan) are employed as an example to illustrate the proposed methodology. The copula fitting results for drought duration and severity are quite satisfactory. The bivariate drought analyses, including the joint probabilities and bivariate return periods, based on the derived copula-based joint distribution are also investigated to demonstrate the advantages of bivariate modeling of droughts.     

关于S_5~2(Δ_(mn)~(2))插值与逼近

主要讨论了矩形区域在II—型三角剖分下,一类具有C2连续的二元五次多项式插值逼近问题,并证明了它的存在性、唯一性,给出了它的逼近度。     

Pseudocontingencies.

In 3 experiments, the authors explored a contingency illusion termed pseudocontingency (PC) that produces logically unwarranted but potentially useful inferences. PCs arise when bivariate contingencies are inferred from univariate distributions via heuristic alignment processes. For example, in the absence of information about the co-occurrence of TV habits and aggressive behavior within a school class, when the prevalence of both attributes is high, a teacher may infer a positive PC as if students who often watch TV were highly aggressive. Throughout 3 experiments, predictions of the level of 1 variable from the level of another served as a measure of PCs. The illusion could be evoked reliably whether information about target attributes was presented successively or simultaneously, whether common-cause or common-effect models were activated, and whether attributes involved 2 or more levels. The discussion centers on the cognitive processes underlying PCs and their origin and adaptive value. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

SPECTRAL ANALYSIS OF A STATIONARY BIVARIATE POINT PROCESS WITH APPLICATIONS TO NEUROPHYSIOLOGICAL PROBLEMS

Abstract. In this paper we discuss the spectral analysis of a stationary bivariate point process applied to the study of a complex physiological system. An estimate of the cross-spectral density can be obtained by smoothing the modified cross-periodogram statistic. The smoothed estimate is calculated by dividing the whole length of the data into a number of disjoint subrecords. Estimates of the coherence function and the cross-intensity function follow directly from the estimate of the cross-spectral density. It is shown that the asymptotic properties of the estimate of the cross-intensity function can be improved by inserting a convergence factor in it. Examples of the estimates are illustrated by using two data sets from neurophysiological experiments and their importance is emphasized by examining the behaviour of the complex physiological system under study.     

New sampling strategies to reduce the effect of autocorrelation on the synthetic T2 chart to monitor bivariate process

In this paper, synthetic T² chart is developed to monitor bivariate process with correlated variables and autocorrelated observations. The proposed chart is a combination of the Hotelling's T² chart and the conforming run length chart. The operation and design of the chart are described when observations are autocorrelated and cross correlated. The first‐order vector autoregressive process VAR (1) is used to model the bivariate data from an autocorrelated process of interest. Using an average run length as performance measure criterion in the VAR (1) model, it is observed that autocorrelation seriously impact the performance of the synthetic T² chart. To reduce the effect of autocorrelation on the performance of the synthetic T² chart, the skip and mixed sampling strategies are implemented to form rational subgroups in the construction of synthetic T² chart. The average run length performance of the synthetic T² chart implementing these strategies is compared with that of the standard strategy of formation of rational subgroups. It is observed that implementing skip and mixed sampling strategies within rational subgroup improves the performance of the synthetic T² chart.