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Segmentation in Drosophila melanogaster starts with a key maternal input known as bicoid gene. The initial\ud positional information provided by this gene induces the sequential activation of segmentation network.\ud Therefore, an accurate mathematical model describing the gene expression profile of bicoid gene expects to\ud provide essential insights into the gene cross-regulations presented in that network. The significantly stochastic,\ud highly volatile and non-normal nature of the bicoid gene expression profile encouraged us to look for the best\ud distribution function describing this profile. We exploit the use of fifty-four different powerful and widely-used\ud distributions and conclude that FatigueLife(3P) fits the data more accurately than the other distributions. The\ud reliability and validity of the results are evaluated via both simulation studies and empirical evidence thereby\ud adding more confidence and value to the findings of this research

In all fields of quantitative research, analysing data with missing values is an excruciating challenge. It should be no surprise that given the fragmentary nature of fossil records, the presence of missing values in geographical databases is unavoidable. As in such studies ignoring missing values may result in biased estimations or invalid conclusions, adopting a reliable imputation method should be regarded as an essential consideration. In this study, the performance of singular spectrum analysis (SSA) based on L 1 norm was evaluated on the compiled &delta, 13 C data from East Africa soil carbonates, which is a world targeted historical geology data set. Results were compared with ten traditionally well-known imputation methods showing L 1 -SSA performs well in keeping the variability of the time series and providing estimations which are less affected by extreme values, suggesting the method introduced here deserves further consideration in practice.

Genome-wide association studies the evaluation of association between candidate gene and disease status is widely carried out using Cochran-Armitage trend test. However, only a small number of research papers have evaluated the distribution of p-values for the Cochran-Armitage trend test. In this paper, an enhanced version of Cochran-Armitage trend test based on bootstrap approach is introduced. The achieved results confirm that the distribution of p-values of the proposed approach fits better to the uniform distribution, and it is thus concluded that the proposed method, which needs less assumptions in comparison with the conventional method, can be successfully used to test the genetic association.

Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research.

Copyright © 2016 John Wiley & Sons, Ltd. We present a new approach for removing the nonspecific noise from Drosophila segmentation genes. The algorithm used for filtering here is an enhanced version of singular spectrum analysis method, which decomposes a gene profile into the sum of a signal and noise. Because the main issue in extracting signal using singular spectrum analysis procedure lies in identifying the number of eigenvalues needed for signal reconstruction, this paper seeks to explore the applicability of the new proposed method for eigenvalues identification in four different gene expression profiles. Our findings indicate that when extracting signal from different genes, for optimised signal and noise separation, different number of eigenvalues need to be chosen for each gene. Copyright © 2016 John Wiley & Sons, Ltd.

bicoid is a maternally transcribed gene which plays a pivotal role during the early developmental stage of Drosophila melanogaster by acting as an essential input to the segmentation network. Therefore, fundamental insights into gene cross-regulations of segmentation network expect to be unveiled by presenting an accurate mathematical model for bicoid gene expression profile. In this paper, an extended version of Birnbaum-Saunders with four parameters is introduced and evaluated to describe the spatial gradient of this gene. Theoretical aspects of four-parameter Birnbaum-Saunders and the estimated parameters are presented and thoroughly assessed for different embryos. The reliability and validity of the results are evaluated via both simulation studies and real data sets and thereby adding more confidence and value to the findings of this research.

Accurate forecasts of home sales can provide valuable information for not only, policy makers, but also financial institutions and real estate professionals. Given this, our analysis compares the ability of two different versions of singular spectrum analysis (SSA) methods, namely recurrent SSA (RSSA) and vector SSA (VSSA), in univariate (UV) and multivariate (MV) frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy and its four census regions (Northeast, Midwest, South and West). We compare the performance of the SSA-based models with classical and Bayesian variants of the autoregressive (AR) and vector AR models. Using an out-of-sample period of 1979:8---2014:6, given an in-sample period of 1973:1---1979:7, we find that the UVVSSA is the best performing model for the aggregate US home sales, while the MV versions of the RSSA is the outright favorite in forecasting home sales for all the four census regions. Our results highlight the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.

It is widely believed that in Drosophila melanogaster the pattern of Bicoid protein gradient plays a crucial role in the segmentation stage of embryo development. As a result of its fundamental role, modelling the Bicoid gradient has become increasingly popular for researchers from many different areas of study. The aim of this paper is to bring together the most prominent studies on this maternal gene and discuss how existing techniques for modelling this gradient have evolved over the years.

Protein diffusion offers an essential and elegant mechanism for morphogen gradient formation. Morphogens are signalling molecules that emanate from a particular region of the cell and create a gradient which has an impact on most biological processes, cell signalling and embryonic development. Using a method that is based on Singular Spectrum Analysis, we estimate parameters introduced in the Synthesis Diffusion Degradation model which is a commonly applied model for a transcription factor known as Bicoid. Our findings, consistent with simulation results, indicate that the proposed method can be practically applied as an enhanced parameter estimation technique with reduced sensitivity to various levels of noise.

Signal extraction and analysis is of great importance, not only in fields such as economics and meteorology, but also in genetics and even biomedicine. There exists a range of parametric and nonparametric techniques which can perform signal extractions. However, the aim of this paper is to define a new approach for optimising signal extraction from bicoid gene expression profile. Having studied both parametric and nonparametric signal extraction techniques, we identified the lack of specific criteria enabling users to select the optimal signal extraction parameters. Exploiting the expression profile of bicoid gene, which is a maternal segmentation coordinate gene found in Drosophila melanogaster, we introduce a new approach for optimising the signal extraction using a nonparametric technique. The underlying criteria are based on the distribution of the residual, more specifically its skewness.

This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSSA) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis.

Drosophila segmentation as a model organism is one of the most highly studied. Among many maternal segmentation coordinate genes, bicoid protein pattern plays a significant role during Drosophila embryogenesis, since this gradient determines most aspects of head and thorax development. Despite the fact that several models have been proposed to describe the bicoid gradient, due to its association with considerable error, each can only partially explain bicoid characteristics. In this paper, a modified version of singular spectrum analysis is examined for filtering and extracting the bicoid gene expression signal. The results with strong evidence indicate that the proposed technique is able to remove noise more effectively and can be considered as a promising method for filtering gene expression measurements for other applications.

The aim of this paper is to present a comparative study on the performance of the two different forecasting approaches of SSA in the presence of outliers. We examine this issue from different points of view. As our real data set, we have considered the well known WTI Spot Price series. The effect on forecasting process when confronted with outlier(s) in different parts of a time series is evaluated. Based on this study, we find evidence which suggests that the existence of outliers affect SSA reconstruction and forecasting results, and that VSSA forecasting performs better than RSSA in terms of the accuracy and robustness of forecasts.

In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.

The aim of this paper is to evaluate the impact of disaggregating the data on the performance of two different versions of SSA methods, namely RSSA and VSSA. Using monthly data for natural gas prices in the United States residential sector with an out-of-sample period of 10:2010–5:2015, given an in-sample period of 1:2002–9:2010 we find evidence which suggests that data disaggregation improves SSA performance in terms of the accuracy and robustness of forecasts.

In recent years Singular Spectrum Analysis (SSA) has been used to solve many biomedical issues and is currently accepted as a potential technique in quantitative genetics studies. Presented in this article is a review of recent published genetics studies which have taken advantage of SSA. Since Singular Value Decomposition (SVD) is an important stage of this technique which can also be used as an independent analytical method in gene expression data, we also briefly touch upon some areas of the application of SVD. The review finds that at present, the most prominent area of applying SSA in genetics is filtering and signal extraction, which proves that SSA can be considered as a valuable aid and promising method for genetics analysis.