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This paper revisits the controversial question of whether long-run abnormal returns are associated with major corporate events. Our analyses investigate initial public offerings (IPOs), seasoned equity offerings (SEOs), mergers and acquisitions (M&As), and dividend initiations. In an attempt to resolve ambiguous empirical evidence with respect to these events, we conduct a variety of tests for abnormal long-run performance, including buy-and-hold returns (BHARs), different calendar time approaches, and a recent standardized test. Empirical tests for these different methods consistently detect significant long-run abnormal returns for all four corporate events. We conclude that long-run abnormal returns exist with respect to these major corporate actions.

Finite mixture regression (FMR) is widely used for modeling data that originate from heterogeneous populations. In these settings, FMR can offer increased predictive power compared to more traditional one-class models. However, existing FMR methods rely heavily on mixtures of linear models, where the linear predictor must be given as an input. A flexible FMR model is presented using a combination of the random forest learner and a penalized linear FMR. The performance of the new method is assessed by predictive log-likelihood in extensive simulation studies. The method is shown to achieve equal performance with the existing FMR methods when the true regression functions are in fact linear and superior performance in cases where at least one of the regression functions is nonlinear. The method can handle a large number of covariates, and its predictive ability is not greatly affected by surplus variables.

This paper considers the problem of estimating a high-dimensional covariance matrix in a low sample support situation where the sample size is smaller, or not much larger, than the dimensionality of the data, which could potentially be very large. We develop a regularized sample covariance matrix (RSCM) estimator which can be applied in commonly occurring high-dimensional data problems. The proposed RSCM estimator is based on estimators of the unknown optimal (oracle) shrinkage parameters that yield the minimum mean squared error between the RSCM and the true covariance matrix when the data is sampled from an unspecified elliptically symmetric distribution. We propose two variants of the RSCM estimator which differ in the approach in which they estimate the underlying sphericity parameter involved in the theoretical optimal shrinkage parameter. The performance of the proposed RSCM estimators are evaluated with numerical simulation studies. In particular, when the sample sizes are low, the proposed RSCM estimators often show a significant improvement over the conventional RSCM estimator by Ledoit and Wolf (2004). We further evaluate the performance of the proposed estimators in a portfolio optimization problem with real data wherein the proposed methods are able to outperform the benchmark methods.

Cost-benefit analyses on climate change have drawn considerable critique, primarily due to contestable choices of discounting and high uncertainties in climate sensitivity and climatic damages. Consequentially, it is argued that cost-benefit analysis can suggest mitigation rates that are nearly arbitrary. This article investigates how firm conclusions can be made from cost-benefit analysis if the main uncertainties are considered endogenously in the analysis and an extensive sensitivity analysis is carried out regarding the contestable assumptions. The SCORE model is used to calculate optimal emission pathways and carbon prices that hedge against climate sensitivity and damage risks using a wide range of plausible parametrizations. In a vast majority of cases the near-term emissions fall between 1.5 °C and 2 °C emission pathways, giving thus support for the Paris Agreement targets. Sequential decision-making allows hedging against uncertainties and readjusting mitigation efforts over time to reflect new information, leading to diverse stabilization temperatures in the long-term. Assumptions on mitigation costs, climate sensitivity and damages affect optimal near-term mitigation and long-term stabilization temperature more strongly than the discount rate choice. The consideration of parametric uncertainty on climate sensitivity and damages adds a substantial risk component to carbon pricing, while learning can induce significant price volatility in a decadal timescale.

Forest canopies create dynamic light environments in their understorey, where spectral composition changes among patterns of shade and sunflecks, and through the seasons with canopy phenology and sun angle. Plants use spectral composition as a cue to adjust their growth strategy for optimal resource use. Quantifying the ever‐changing nature of the understorey light environment is technically challenging with respect to data collection. Thus, to capture the simultaneous variation occurring in multiple regions of the solar spectrum, we recorded spectral irradiance from forest understoreys over the wavelength range 300–800 nm using an array spectroradiometer. It is also methodologically challenging to analyze solar spectra because of their multi‐scale nature and multivariate lay‐out. To compare spectra, we therefore used a novel method termed thick pen transform (TPT), which is simple and visually interpretable. This enabled us to show that sunlight position in the forest understorey (i.e., shade, semi‐shade, or sunfleck) was the most important factor in determining shape similarity of spectral irradiance. Likewise, the contributions of stand identity and time of year could be distinguished. Spectra from sunflecks were consistently the most similar, irrespective of differences in global irradiance. On average, the degree of cross‐dependence increased with increasing scale, sometimes shifting from negative (dissimilar) to positive (similar) values. We conclude that the interplay of sunlight position, stand identity, and date cannot be ignored when quantifying and comparing spectral composition in forest understoreys. Technological advances mean that array spectroradiometers, which can record spectra contiguously over very short time intervals, are being widely adopted, not only to measure irradiance under pollution, clouds, atmospheric changes, and in biological systems, but also spectral changes at small scales in the photonics industry. We consider that TPT is an applicable method for spectral analysis in any field and can be a useful tool to analyze large datasets in general.

Motivation The collective and co-ordinated synaptic activity of large neuronal populations is relevant to neuronal development as well as a range of neurological diseases. Quantification of synaptically-mediated neuronal signalling permits further downstream analysis as well as potential application in target validation and in vitro screening assays. Our aim is to develop a phenotypic quantification for neuronal activity imaging data of large populations of neurons, in particular relating to the spatial component of the activity. Results We extend the use of Markov random field (MRF) models to achieve this aim. In particular, we consider Bayesian posterior densities of model parameters in Gaussian MRFs to directly model changes in calcium fluorescence intensity rather than using spike trains. The basis of our model is defining neuron ‘neighbours’ by the relative spatial positions of the neuronal somata as obtained from the image data whereas previously this has been limited to defining an artificial square grid across the field of view and spike binning. We demonstrate that our spatial phenotypic quantification is applicable for both in vitro and in vivo data consisting of thousands of neurons over hundreds of time points. We show how our approach provides insight beyond that attained by conventional spike counting and discuss how it could be used to facilitate screening assays for modifiers of disease-associated defects of communication between cells. Availability and implementation We supply the MATLAB code and data to obtain all of the results in the paper. Supplementary information Supplementary data are available at Bioinformatics online.

Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc), has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are of interest to the field. Such evidence is direly needed before a complete approach to the estimation of SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc.

We use elections data in which a large number of ties in vote counts between candidates are resolved via a lottery to study the personal incumbency advantage. We benchmark non‐experimental regression discontinuity design (RDD) estimates against the estimate produced by this experiment that takes place exactly at the cutoff. The experimental estimate suggests that there is no personal incumbency advantage. In contrast, conventional local polynomial RDD estimates suggest a moderate and statistically significant effect. Bias‐corrected RDD estimates that apply robust inference are, however, in line with the experimental estimate. Therefore, state‐of‐the‐art implementation of RDD can meet the replication standard in the context of close elections. Close elections experiment incumbency advantage regression discontinuity design C21 C52 D72

MotivationIn the analysis of metabolism using omics data, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and Stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, produce results that are readily interpretable in terms of metabolic flux modes, however, they are not best suited for exploratory analysis on a large set of samples.ResultsWe propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and Stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a Stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology.AvailabilityMatlab software for PMFA and SPMFA is available in https://github.com/ aalto-ics-kepaco/PMFA.Contactsahely@iitpkd.ac.in, juho.rousu@aalto.fi, Peter.Blomberg@vtt.fi, Sandra.Castillo@vtt.fiSupplementary informationDetailed results are in Supplementary files. Supplementary data are available at https://github.com/aalto-ics-kepaco/PMFA/blob/master/Results.zip.

The inadequate control of sample sizes in surveys using stratified sampling and area estimation may occur when the overall sample size is small or auxiliary information is insufficiently used. Very small sample sizes are possible for some areas. The proposed allocation based on multi-objective optimization uses a small-area model and estimation method and semi-collected empirical data annually collected empirical data. The assessment of its performance at the area and at the population levels is based on design-based sample simulations. Five previously developed allocations serve as references. The model-based estimator is more accurate than the design-based Horvitz–Thompson estimator and the model-assisted regression estimator. Two trade-off issues are between accuracy and bias and between the area- and the population-level qualities of estimates. If the survey uses model-based estimation, the sampling design should incorporate the underlying model and the estimation method. peerReviewed

Income distributions are commonly unimodal and skew with a heavy right tail. Different skew models, such as the lognormal and the Pareto, have been proposed as suitable descriptions of income distribution and applied in specific empirical situations. More wide-ranging tools have been introduced as measures for general comparisons. In this study, we review the income analysis methods and apply them to specific Lorenz models.

Preparation of Moodle quizzes which are data-based and contemporary tends to be tedious and time-consuming. By using innovative tools, this process can be simplified and automated, providing a substantial benefit to the teacher wishing to employ such quizzes, and ultimately improving student learning experience. The purpose of this article is to show how to create data-driven, up-to-date quizzes for Moodle in an easy fashion. The methodology is based on several popular, open-source, free tools, and its implementation details are demonstrated with an example. This makes the methodology readily-available to the practitioners.

Independent component analysis is a standard tool in modern data analysis and numerous different techniques for applying it exist. The standard methods however quickly lose their effectiveness when the data are made up of structures of higher order than vectors, namely matrices or tensors (for example, images or videos), being unable to handle the high amounts of noise. Recently, an extension of the classic fourth order blind identification (FOBI) specifically suited for tensor-valued observations was proposed and showed to outperform its vector version for tensor data. In this paper we extend another popular independent component analysis method, the joint approximate diagonalization of eigen-matrices (JADE), for tensor observations. In addition to the theoretical background we also provide the asymptotic properties of the proposed estimator and use both simulations and real data to show its usefulness and superiority over its competitors., 10 pages, 3 figures

Efforts to understand anthropogenic stress in marine environments have introduced methods of cumulative human pressure modeling in the broad-scale mapping of large sea areas. This paper examines th...

Studies of the early warning systems (EWSs) for banking crises usually rely on linear classifiers, estimated with international datasets. I construct an EWS based on an artificial neural network (ANN) model, and I also account for regional heterogeneity in order to improve the generalization ability of EWS models. All of the banking crises in my test set are then predictable at a 24-month horizon, using information from earlier crises. For some countries, estimation with a regional dataset significantly improves the predictions. The ANN outperforms the usual logit regression, assessed by the area under the receiver operating characteristics curve.

We first study the drift parameter estimation of the fractional Ornstein–Uhlenbeck process (fOU) with periodic mean for every 1 2 H 1 . More precisely, we extend the consistency proved in Dehling et al. (2016) for 1 2 H 3 4 to the strong consistency for any 1 2 H 1 on the one hand, and on the other, we also discuss the asymptotic normality given in Dehling et al. (2016). In the second main part of the paper, we study the strong consistency and the asymptotic normality of the fOU of the second kind with periodic mean for any 1 2 H 1 .

The obligate intracellular pathogen Chlamydia pneumoniae remains a difficult target for antimicrobial therapy. Owing to the permeability barrier placed by bacterial and host vacuolar membranes, as well as the propensity of the bacterium for persistent infections, treatment failures are common. Despite the urgent need for new antichlamydial compounds, their discovery is challenged by the technically demanding assay procedures and lack of validated targets. An alternative strategy of using naturally occurring compounds and their derivatives against C. pneumoniae is presented. The strategy consists of the application of ligand-based virtual screening to a natural product library of 502 compounds with the ChemGPS-NP chemography tool followed by in vitro antichlamydial assays. The reference set used for the 2D similarity search was constructed of 19 known antichlamydial compounds of plant origin. Based on the similarity screen, 53 virtual hits were selected for in vitro testing. Six compounds (leads) were identified that cause ≥50% C. pneumoniae growth inhibition and showed no impact on host cell viability. The leads fall into completely new antichlamydial chemotypes, one of them being mycophenolic acid (IC

Background Declining participation rates in health examination surveys may impair the representativeness of surveys and introduce bias into the comparison of results between population groups if participation rates differ between them. Changes in the characteristics of non-participants over time may also limit comparability with earlier surveys. Methods We studied the association of socio-economic position with participation, and its changes over the past 25 years. Occupational class and educational level are used as indicators of socio-economic position. Data from six cross-sectional FINRISK surveys conducted between 1987 and 2012 in Finland were linked to national administrative registers, which allowed investigation of the differences between survey participants and non-participants. Results Our results show that individuals with low occupational class or low level of education were less likely to participate than individuals with high occupational class or high level of education. Participation rates decreased in all subgroups of the population but the decline was fastest among those with low level of education. Conclusions The differences in participation rates must be taken into account to avoid biased estimates because socio-economic position has also been shown to be strongly related to health, health behaviour and biological risk factors. Particular attention should be paid to the recruitment of the less-educated population groups. peerReviewed

We calculate the regular conditional future law of the fractional Brownian motion with index H ∈ ( 0 , 1 ) conditioned on its past. We show that the conditional law is continuous with respect to the conditioning path. We investigate the path properties of the conditional process and the asymptotic behavior of the conditional covariance.

Motivation Incorporating gene interaction data into the identification of ‘hit’ genes in genomic experiments is a well-established approach leveraging the ‘guilt by association’ assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach. Results We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen. Availability and implementation We provide all of the data and code related to the results in the paper. Supplementary information Supplementary data are available at Bioinformatics online.

Despite many treatment options, cancer remains a growing problem and has become the second leading cause of death globally. Here, we present fluorescence molecular tomography (FMT) data regarding the reversion of third generation co-cultured U87+DBTRG and patient-derived GBM tumor model after treatment with novel IL17A inhibitor named FLVM and FLVZ (organic derivatives of caffeic acid). FMT was used to determine tumor angiogenesis volume (assessment of number of blood vessel; the expression of angiogenic factors CD34 and other angiogenic cancer bio-markers) in U87+DBTRG and patient-derived gliomas. Immunohistochemistry was used to determine microvessel density [CD34], and cell proliferation [Ki67]. Western blot was used to assess the interleukin 17A [IL17A], vascular endothelial growth factor [VEGF] and hypoxia-inducible factor-1α [HIF-1α]. Antibody array was used to assess the cancer bio-markers in co-cultured U87+DBTRG gliomas. Animal survival was found to be significantly increased (P

The widely used fixed-point FastICA algorithm has been derived and motivated as being an approximate Newton-Raphson (NR) algorithm. In the original derivation, the Lagrangian multiplier is treated as a constant and an ad hoc approximation is used for Jacobian matrix in the NR update. In this letter, we provide an alternative derivation of the FastICA algorithm without approximation. We show that any solution to the FastICA algorithm is a solution to the exact NR algorithm as well. In addition, we propose a novel power iteration algorithm for FastICA which is remarkably more stable than the fixed-point algorithm, when the sample size is not orders of magnitudes larger than the dimension. Our proposed algorithm can be run on parallel computing nodes.

Blind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The mixing system and the distributions of the latent variables are unknown. The aim is to find an estimate of an unmixing matrix which then transforms the observed data back to latent sources. In this paper we present the R packages JADE and BSSasymp. The package JADE offers several BSS methods which are based on joint diagonalization. Package BSSasymp contains functions for computing the asymptotic covariance matrices as well as their data-based estimates for most of the BSS estimators included in package JADE. Several simulated and real datasets are used to illustrate the functions in these two packages. peerReviewed

In benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data has recently been developed. Taxa identification errors can, however, have strong effects on the indices and thus on the determination of the ecological status. In order to shift the biomonitoring process towards automated expert systems, we need a clear understanding on the bias caused by automation. In this paper, we examine eleven classification methods in the case of macroinvertebrate image data and show how their classification errors propagate into different biological indices. We evaluate 14 richness, diversity, dominance and similarity indices commonly used in biomonitoring. Besides the error rate of the classification method, we discuss the potential effect of different types of identification errors. Finally, we provide recommendations on indices that are least affected by the automatic identification errors and could be used in automated biomonitoring. We thank the Academy of Finland (projects 288584 (Kiranyaz), 295854 (Iosidis), 289364 (Gabbouj), 289076 (rje, Krkkinen) and 289104 (Meissner)) and the Ellen and Artturi Nyyssnen foundation for the grant of rje. The authors would like to thank Marko Vikstedt for the preparation of the monitoring data and Tuomas Turpeinen for the image data. We kindly thank Antti Penttinen for fruitful discussions and support. Scopus

Knowledge of current fishing mortality rates is an important prerequisite for formulating management plans for the recovery of threatened stocks. We present a method for estimating migration and fishing mortality rates for anadromous fishes that combines tag return data from commercial and recreational fisheries with expert opinion in a Bayesian framework. By integrating diverse sources of information and allowing for missing data, this approach may be particularly applicable in data-limited situations.Wild populations of anadromous sea trout (Salmo trutta) in the northern Baltic Sea have undergone severe declines, with the loss of many populations. The contribution of fisheries to this decline has not been quantified, but is thought to be significant. We apply the Bayesian mark-recapture model to two reared sea trout stocks from the Finnish Isojoki and Lestijoki Rivers. Over the study period (1987–2012), the total harvest rate was estimated to average 0.82 y–1 for the Isojoki River stock and 0.74 y−1 for the Lestijoki River stock. Recreational gillnet fishing at sea was estimated to be the most important source of fishing mortality for both stocks, particularly during the 1980s and 1990s. Our results indicate a high probability of unsustainable levels of fishing mortality for both stocks, and illustrate the importance of considering the effect of recreational fisheries on fish population dynamics.

In this paper, we study linear backward stochastic differential equations driven by a class of centered Gaussian non-martingales, including fractional Brownian motion with Hurst parameter $H\in (0,1)\setminus \{\frac12\}$. We show that, for every choice of deterministic coefficient functions, there is a square integrable terminal condition such that the equation has no solution.

Highlights • We predict a substantial decline in the carriage prevalence of vaccine serotypes. • About a 56% reduction in invasive pneumococcal disease is also predicted. • The decline is predicted to be sustainable ten years post-vaccination. • The current vaccination schedule is unlikely to achieve elimination of vaccine serotypes., Background In 2011, Kenya introduced the 10-valent pneumococcal conjugate vaccine together with a catch-up campaign for children aged

For the realistic case where there is no secondary snapshot that does not contain the desired signal and exhibits the statistical characteristics similar to the snapshot under test, direct data domain (D3) beamforming approaches have been proposed to estimate a desired signal in the presence of interference. However, the basic idea of the D3 methods is realized by making significant sacrifices with respect to the number of degrees of freedom (DoFs). In this paper, we present a multi-snapshot approach for D3 beamforming. Using the least-squares method with multiple snapshots, we can eliminate the interference without causing a severe reduction in the number of DoFs. In addition, to consider a mismatch between nominal and actual target steering vectors, we propose a D3 approach combined with a probability constraint to prevent the self-nulling effect, and the relationship between the probability constraint and norm constraint is discovered. The simulations verify that the proposed method provides better performance and robustness than the conventional D3 approaches.

Motivation A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations. Results We present a novel approach that leverages on systematic integration of data sources to identify response predictive features of multiple drugs. To solve the modeling task we implement a Bayesian linear regression method. To further improve the usefulness of the proposed model, we exploit the known human cancer kinome for identifying biologically relevant feature combinations. In case studies with a synthetic dataset and two publicly available cancer cell line datasets, we demonstrate the improved accuracy of our method compared to the widely used approaches in drug response analysis. As key examples, our model identifies meaningful combinations of features for the well known EGFR, ALK, PLK and PDGFR inhibitors. Availability and Implementation The source code of the method is available at https://github.com/suleimank/mvlr. Supplementary information Supplementary data are available at Bioinformatics online.

A combined Lagrange multiplier (LM) test for autoregressive conditional heteroskedastic (ARCH) errors in vector autoregressive (VAR) models is proposed by replacing an exact Monte Carlo (MC) test by a bootstrap MC test when the model includes lags. The test circumvents the problem of high dimensionality in multivariate tests for ARCH in VAR models. It only requires computing univariate statistics. A computational advantage is therefore that the number of parameters to be estimated is independent of the dimension of the VAR process. The bootstrap MC test is shown to be asymptotically valid. Monte Carlo simulations show that the test has good finite-sample properties. The test is robust against a non-normal error distribution. Two financial applications of multivariate LM tests for ARCH to credit default swap (CDS) prices and Euribor interest rates are presented. The results indicate that the errors are skewed and heavy-tailed, and that there are significant ARCH effects.