Explicit Statistical Model Research Articles

Optimal order placement in limit order markets

To execute a trade, participants in electronic equity markets may choose to submit limit orders or market orders across various exchanges where a stock is traded. This decision is influenced by characteristics of the order flows and queue sizes in each limit order book, as well as the structure of transaction fees and rebates across exchanges. We propose a quantitative framework for studying this order placement problem by formulating it as a convex optimization problem. This formulation allows the study of how the optimal order placement decision depends on the interplay between the state of order books, the fee structure, order flow properties and the aversion to execution risk. In the case of a single exchange, we derive an explicit solution for the optimal split between limit and market orders. For the general case of order placement across multiple exchanges, we propose a stochastic algorithm that computes the optimal routing policy and study the sensitivity of the solution to various parameters. Our algorithm does not require an explicit statistical model of order flow but exploits data on recent order fills across exchanges in the numerical implementation of the algorithm to acquire this information through a supervised learning procedure.

Evaluating the influence of connectivity and distance on biogeographical patterns in the south‐western deserts of North America

AbstractAimTo examine the role of geological history, connectivity and distance in shaping the biogeographical structure of North American desert clades that are restricted to habitat islands (sand dunes and relictual aquatic habitats), using statistical model choice on old and new probabilistic biogeographical models.LocationNorth America, Mojave, Sonoran and Chihuahuan Deserts.Materials and methodsDated phylogenies were estimated for three field‐sampled insect clades (Trigonoscuta,Rhaphiomidasand sand treader crickets), and five other literature‐sampled clades (the snailsAssiminea,PyrgulopsisandTryonia; the desert fringe‐toed lizardUma; and the desert pupfishCyprinodon).BioGeoBEARSwas used to statistically compare biogeographical models assuming unconstrained or connectivity‐constrained dispersal, with or without founder‐event speciation (jump dispersal) permitted. Finally, we introduce and test a novel distance‐based dispersal model (+x) where dispersal probability is multiplied by distance to the powerx.ResultsWe observed little concordance between biogeographical patterns and timing of geological events. Model comparisons were decidedly in favour of inclusion of founder‐event speciation in the models for most taxa, with only a small taxon,Uma, showing support for the model favouring vicariance. The inclusion of a constrained‐dispersal matrix was favoured by three of the eight taxa examined (Cyprinodon, sand treader crickets, andTrigonoscuta). Surprisingly, tests for distance influencing dispersal probability were mostly negative.Main conclusionsOur results do not show support for any one geological event shaping the biogeographical patterns of these desert taxa. Instead, the histories of desert dune and aquatic taxa are largely products of rare jump dispersal events, and can be considered island‐like systems. Although results are negative for the distance‐based dispersal model, this in itself demonstrates the superiority of explicit statistical model testing overa prioriassumption of fixed models in historical biogeography.

Spatial memory-based behaviors for locating sources of odor plumes.

BackgroundMany animals must locate odorant point sources during key behaviors such as reproduction, foraging and habitat selection. Cues from such sources are typically distributed as air- or water-borne chemical plumes, characterized by high intermittency due to environmental turbulence and episodically rapid changes in position and orientation during wind or current shifts. Well-known examples of such behaviors include male moths, which have physiological and behavioral specializations for locating the sources of pheromone plumes emitted by females. Male moths and many other plume-following organisms exhibit “counter-turning” behavior, in which they execute a pre-planned sequence of cross-stream movements spanning all or part of an odorant plume, combined with upstream movements towards the source. Despite its ubiquity and ecological importance, theoretical investigation of counter-turning has so far been limited to a small subset of plausible behavioral algorithms based largely on classical biased random walk gradient-climbing or oscillator models.ResultsWe derive a model of plume-tracking behavior that assumes a simple spatially-explicit memory of previous encounters with odorant, an explicit statistical model of uncertainty about the plume’s position and extent, and the ability to improve estimates of plume characteristics over sequential encounters using Bayesian updating. The model implements spatial memory and effective cognitive strategies with minimal neural processing. We show that laboratory flight tracks of Manduca sexta moths are consistent with predictions of our spatial memory-based model. We assess plume-following performance of the spatial memory-based algorithm in terms of success and efficiency metrics, and in the context of “contests” in which the winner is the first among multiple simulated moths to locate the source.ConclusionsEven rudimentary spatial memory can greatly enhance plume-following. In particular, spatial memory can maintain source-seeking success even when plumes are so intermittent that no pheromone is detected in most cross-wind transits. Performance metrics reflect trade-offs between “risk-averse” strategies (wide cross-wind movements, slow upwind advances) that reliably but slowly locate odor sources, and “risk-tolerant” strategies (narrow cross-wind movements, fast upwind advances) that often fail to locate a source but are fast when successful. Success in contests of risk-averse vs. risk-tolerant behaviors varies strongly with the number of competitors, suggesting empirically testable predictions for diverse plume-following taxa. More generally, spatial memory-based models provide tractable, explicit theoretical linkages between sensory biomechanics, neurophysiology and behavior, and ecological and evolutionary dynamics operating at much larger spatio-temporal scales.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0037-6) contains supplementary material, which is available to authorized users.

Spatially explicit models of land abandonment in the Amazon

Given growing concerns about biodiversity loss and carbon emissions stemming from tropical deforestation, it is important to identify the factors associated with land abandonment as they might indicate areas that were deforested but are unsuitable for long-term cultivation. This article utilizes a high-resolution, Landsat-based data-set called TerraClass to relate land abandonment to precipitation, slope, farmgate price of beef, market access, soil quality, and land tenure characteristics in the Brazilian Amazon. The spatially explicit statistical models reveal that rates of land abandonment increase with higher rainfall and slope values, even when controlling for distance to roads, farmgate prices, type of settlement, and soil quality. Moreover, land adjacent to a major road is more likely to be consistently cultivated, but land near large rivers and far from highways is more likely to be abandoned. Land ownership type also influences the likelihood of land abandonment as settlements based on communal property rights have higher rates of land abandonment than settlements with private lots. These results suggest that policies aimed at occupying tropical forests such as the Amazon must consider these biophysical and socioeconomic factors in order to foster human development aligned with sustainability principles.

Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data.

Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) is a valuable tool for epigenetic studies. Analysis of the data arising from ChIP-seq experiments often requires implicit or explicit statistical modeling of the read counts. The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data. Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model. Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution. We apply the Constrained Newton Method (CNM), which suggests the Negative Binomial - Negative Binomial (NB-NB) mixture model as a candidate for modeling ChIP-seq data. We illustrate fitting the NB-NB model with an accelerated EM algorithm on four data sets from three species. Zero-inflated models have been suggested as an approach to improve model fit for ChIP-seq data. We show that the NB-NB mixture model requires no zero-inflation and suggest that in some cases the need for zero inflation is driven by the model's inability to cope with both artifactual large read counts and the frequently observed very low read counts. We see that the CNM-based approach is a useful diagnostic for the assessment of model fit and inference in ChIP-seq data and beyond. Use of the suggested NB-NB mixture model will be of value not only when calling peaks or otherwise modeling ChIP-seq data, but also when simulating data or constructing blacklists de novo.

Toward a statistically explicit understanding of de novo sequence assembly

Draft de novo genome assemblies are now available for many organisms. These assemblies are point estimates of the true genome sequences. Each is a specific hypothesis, drawn from among many alternative hypotheses, of the sequence of a genome. Assembly uncertainty, the inability to distinguish between multiple alternative assembly hypotheses, can be due to real variation between copies of the genome in the sample, errors and ambiguities in the sequenced data and assumptions and heuristics of the assemblers. Most assemblers select a single assembly according to ad hoc criteria, and do not yet report and quantify the uncertainty of their outputs. Those assemblers that do report uncertainty take different approaches to describing multiple assembly hypotheses and the support for each. Here we review and examine the problem of representing and measuring uncertainty in assemblies. A promising recent development is the implementation of assemblers that are built according to explicit statistical models. Some new assembly methods, for example, estimate and maximize assembly likelihood. These advances, combined with technical advances in the representation of alternative assembly hypotheses, will lead to a more complete and biologically relevant understanding of assembly uncertainty. This will in turn facilitate the interpretation of downstream analyses and tests of specific biological hypotheses.

Supervised Classification of Multisensor and Multiresolution Remote Sensing Images With a Hierarchical Copula-Based Approach

In this paper, we develop a novel classification approach for multiresolution, multisensor [optical and synthetic aperture radar (SAR)], and/or multiband images. This challenging image processing problem is of great importance for various remote sensing monitoring applications and has been scarcely addressed so far. To deal with this classification problem, we propose a two-step explicit statistical model. We first design a model for the multivariate joint class-conditional statistics of the coregistered input images at each resolution by resorting to multivariate copulas. Such copulas combine the class-conditional marginal probability density functions (pdfs) of each input channel that are estimated by finite mixtures of well-chosen parametric families. We consider different distribution families for the most common types of remote sensing imagery acquired by optical and SAR sensors. We then plug the estimated joint pdfs into a hierarchical Markovian model based on a quad-tree structure, where each tree-scale corresponds to the different input image resolutions and to corresponding multiscale decimated wavelet transforms, thus preventing a strong resampling of the initial images. To obtain the classification map, we resort to an exact estimator of the marginal posterior mode. We integrate a prior update in this model in order to improve the robustness of the developed classifier against noise and speckle. The resulting classification performance is illustrated on several remote sensing multiresolution data sets, including very high resolution and multisensor images acquired by COSMO-SkyMed and GeoEye-1.

Study of the Concrete Block Temperature Intelligent Prediction Model during the Construction

The explicit statistical model of concrete temperature variation is difficult to reasonably reflect the nonlinear relationship between the historical information and future information. This article is based on neural network intelligence tools and uses the neural network model to describe the concrete temperature variation during the construction. The relationships between the concrete temperature and initial temperature (pouring temperature), environmental temperature, the cement hydration heat temperature increase, water cooling effect and other factors are nonlinear. Establishing the neural network model of concrete temperature variation, exploring the historical temperature information could predict the future temperature information. Applying the intelligent prediction model to a construction project shows that when compared with the traditional explicit temperature statistical model, the temperature neural network prediction model established in this paper has obvious simplicity and superiority.

Understanding recent land use and land cover dynamics in the source region of the Upper Blue Nile, Ethiopia: Spatially explicit statistical modeling of systematic transitions

The objective of this paper was to quantify long-term land use and land cover changes (LULCC) and to identify the spatial determinants of locations of most systematic transitions for the period 1957–2009 in the Jedeb watershed, Upper Blue Nile Basin. Black and white aerial photographs of 1957 and Landsat imageries of 1972 (MSS), 1986 (TM), 1994 (TM) and 2009 (TM) were used to derive ten land use and land cover classes by integrated use of Remote Sensing (RS) and Geographic Information System (GIS). Post-classification change detection analysis based on enhanced transition matrix was applied to detect the changes and identify systematic transitions. The results showed that 46% of the study area experienced a transition over the past 52 years, out of which 20% was due to a net change while 26% was attributable to swap change (i.e. simultaneous gain and loss of a given category during a certain period). The most systematic transitions are conversion of grassland to cultivated land (14.8%) followed by the degradation of natural woody vegetation and marshland to grassland (3.9%). Spatially explicit logistic regression modeling revealed that the location of these systematic transitions can be explained by a combination of accessibility, biophysical and demographic factors. The modeling approach allowed improved understanding of the processes of LULCC and for identifying explanatory factors for further in-depth analysis as well as for practical interventions for watershed planning and management.

Color call improvement in next generation sequencing using multi-class support vector machines

Background There is considerable ongoing effort towards making DNA sequencing machines faster and more affordable today. Improving the accuracy of next-generation sequencers directly lowers sequencing costs by reducing the need for resequencing, making genome-based diagnostics and research more affordable [1]. In this paper, we show how the accuracy of next-generation sequencing machines is significantly improved using supervised learning, specifically, multi-class support vector machines. We demonstrate our methods on the SOLiD 5500/5500 XL platform. Base-calling is the process of determining the order of nucleotides in the read sequence. In SOLiD, base-calling involves the process of color calling, since the SOLiD platform uses an encoding system where each adjacent pair of nucleotides is represented by one of four colored dyes [2]. Base-callers have been developed for other nextgeneration sequencing platforms, in particular Illumina and Roche 454 [1]. Most of them are based on explicit statistical models and some are based on support vector based supervised learning [3,4]. But ours is the first supervised learning method applied on a large scale directly to color space. Also, this is the first supervised learning method to be applied on a large-scale to SOLiD. Moreover, we show that our methods require less training data and hence our training times are much faster than previous methods.

Determinants of agricultural land abandonment in post-Soviet European Russia

The breakdown of socialism caused massive socio-economic and institutional changes that led to substantial agricultural land abandonment. The goal of our study was to identify the determinants of agricultural land abandonment in post-Soviet Russia during the first decade of transition from a state-controlled economy to a market-driven economy (1990–2000). We analyzed the determinants of agricultural land abandonment for approximately 150,550km2 of land area in the provinces (oblasts) of Kaluga, Rjazan, Smolensk, Tula and Vladimir in European Russia. Based on the economic assumptions of profit maximization, we integrated maps of abandoned agricultural land from five ∼185km×185km Landsat TM/ETM+ footprints with socio-economic, environmental and geographic variables, and we estimated logistic regressions at the pixel level to identify the determinants of agricultural land abandonment. Our results showed that a higher likelihood of agricultural land abandonment was significantly associated with lower average grain yields in the late 1980s and with higher distances from the nearest settlements, municipality centers, and settlements with more than 500 citizens. Hierarchical partitioning showed that the average grain yields in the late 1980s had the greatest power to explain agricultural land abandonment in our models, followed by the locational attributes of the agricultural land. We hypothesize that the termination of 90% of state subsidies for agriculture from 1990 to 2000 was an important underlying cause for the decrease of cultivation in economically and environmentally marginal agriculture areas. Thus, whereas the spatial patterns corresponded to the land rent theory of von Thünen, it was primarily the macro-scale driving forces that fostered agricultural abandonment. Our study highlighted the value of spatially explicit statistical models for studying the determinants of land-use and land-cover change in large areas.

Projected Climate‐Induced Habitat Loss for Salmonids in the John Day River Network, Oregon, U.S.A.

Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species-juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)-in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69-95%, 51-87%, and 86-100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.

Direct interaction with no correlation: An experimental pitfall in neural systems

Neural systems continuously optimize how organisms process their environment and are highly dynamic. Building predictive models of these systems is challenging due to the large number of their elements. Therefore, in experimental and descriptive neurobiology the researcher typically does not seek to catalogue all variables that affect one another, but rather tries to isolate variables that interact directly. Because of methodological limitations, observed variables are often measured near equilibrium. The presented analysis demonstrates that statistical tests performed on such equilibrium values are fundamentally incapable of detecting direct interactions in a large subset of simple dynamical systems. Some of these problems can be avoided by using explicit statistical models that include time as a variable.

The Role of Physical Barriers in the Location of Avian Suture Zones in the Guiana Shield, Northern Amazonia

Suture zones represent natural forums in which to examine the role of geography and ecology in the speciation process. Here, we conduct a comparative analysis designed to investigate the location of avian phylogeographic breaks and contact zones in the Guiana Shield, northern Amazonia. We use distributional and genetic data from 78 pairs of avian taxa to address whether phylogeographic breaks and contact zones are associated with contemporary landscape features. Using spatially explicit statistical models, we found that phylogeographic breaks and contact zones are not randomly distributed throughout the landscape. In general, geographic breaks cluster along physical barriers (rivers, nonforested habitats, and small mountain ranges), whereas contact zones aggregate where these barriers either break down or are easier to overcome, such as around rivers' headwaters. Our results indicate that although major Amazonian rivers are often key determinants of taxon boundaries, the "riverine barrier effect" is a synergistic consequence of the wide lower reaches of some rivers, coupled with nonriverine landscape features at the headwaters. Our data suggest that ancestral refugia are not necessary to explain current distribution patterns and that pairs of codistributed taxa do not seem to be the result of simultaneous diversification processes.

All Your Base: a fast and accurate probabilistic approach to base calling.

The accuracy of base calls produced by Illumina sequencers is adversely affected by several processes, with laser cross-talk and cluster phasing being prominent. We introduce an explicit statistical model of the sequencing process that generalizes current models of phasing and cross-talk and forms the basis of a base calling method which improves on the best existing base callers, especially when comparing the number of error-free reads. The novel algorithms implemented in All Your Base (AYB) are comparable in speed to other competitive base-calling methods, do not require training data and are designed to be robust to gross errors, producing sensible results where other techniques struggle. AYB is available at http://www.ebi.ac.uk/goldman-srv/AYB/.

All your base: a fast and accurate probabilistic approach to base calling

The accuracy of base calls produced by Illumina sequencers is adversely affected by several processes, with laser cross-talk and cluster phasing being prominent. We introduce an explicit statistical model of the sequencing process that generalizes current models of phasing and cross-talk and forms the basis of a base calling method which improves on the best existing base callers, especially when comparing the number of error-free reads. The novel algorithms implemented in All Your Base (AYB) are comparable in speed to other competitive base-calling methods, do not require training data and are designed to be robust to gross errors, producing sensible results where other techniques struggle. AYB is available at http://www.ebi.ac.uk/goldman-srv/AYB/

ESTIMATING A RATIO OF MEANS FROM BIVARIATE COUNTS WITH APPLICATIONS IN STEREOLOGY

Summary In survey sampling and in stereology, it is often desirable to estimate the ratio of means θ= E(Y)/E(X) from bivariate count data (X, Y) with unknown joint distribution. We review methods that are available for this problem, with particular reference to stereological applications. We also develop new methods based on explicit statistical models for the data, and associated model diagnostics. The methods are tested on a stereological dataset. For point-count data, binomial regression and bivariate binomial models are generally adequate. Intercept-count data are often overdispersed relative to Poisson regression models, but adequately fitted by negative binomial regression.

Local frequency as a key to interpreting species occurrence data when recording effort is not known

Summary1. Data on the occurrence of species in grid cells are collected by biological recording schemes, typically with the intention of publishing an atlas. Interpretation of such data is often hampered by the lack of information on the effort that went into collecting them. This is the ‘recorder effort problem’.2. One measure of recorder effort is the proportion of a suite of common species (‘benchmark species’) found at a given location and time. Benchmark species have in the past been taken as a uniform set across a territory. However, if records are available from a neighbourhood surrounding a given location, then a local set benchmark species can be defined by pooling records from the neighbourhood and selecting the commonest species in the pooled set.3. Neighbourhoods differ in species richness, so that the list of species that ‘ought’ to be found in one location may be longer than that for another. If the richness of a neighbourhood can be estimated, then a suite of benchmark species can be standardized to be the commonest of a fixed proportion of the total expected for the neighbourhood. Recording effort is then defined as the proportion of benchmark species that were found.4. A method of estimating species richness is proposed here, based on the local frequencies fj of species in neighbouring grid cells. Species discovery is modelled as a Poisson process. It is argued that when a neighbourhood is well sampled, the frequency‐weighted mean frequency /∑fj of species in the neighbourhood will assume a standard value.5. The method was applied to a data set of 2 000 000 records detailing the occurrence of bryophytes in 3695 out of the total 3854 hectads (10‐km squares) in Great Britain, Ireland, the Isle of Man and the Channel Islands.6. Three main applications are outlined: estimation of recording effort, scanning data for unexpected presences or absences and measurement of species trends over time. An explicit statistical model was used to estimate trends, modelling the probability of species j being found at location i and time t as the outcome of Poisson process with intensity Qijtxjt, where xjt is a time factor for species j, and Qijt depends on recording effort at location i and time t and on the time‐independent probability of species j being found in hectad i.

RNAcode: Robust discrimination of coding and noncoding regions in comparative sequence data

With the availability of genome-wide transcription data and massive comparative sequencing, the discrimination of coding from noncoding RNAs and the assessment of coding potential in evolutionarily conserved regions arose as a core analysis task. Here we present RNAcode, a program to detect coding regions in multiple sequence alignments that is optimized for emerging applications not covered by current protein gene-finding software. Our algorithm combines information from nucleotide substitution and gap patterns in a unified framework and also deals with real-life issues such as alignment and sequencing errors. It uses an explicit statistical model with no machine learning component and can therefore be applied "out of the box," without any training, to data from all domains of life. We describe the RNAcode method and apply it in combination with mass spectrometry experiments to predict and confirm seven novel short peptides in Escherichia coli and to analyze the coding potential of RNAs previously annotated as "noncoding." RNAcode is open source software and available for all major platforms at http://wash.github.com/rnacode.

Ontogenetic and sex-specific differences in density-dependent habitat selection of a marine fish population

The spatial dynamics of species are the result of complex interactions between density-independent and density-dependent sources of variability. Disentangling these two sources of variability has challenged ecologists working in both terrestrial and aquatic ecosystems. Using a novel spatially explicit statistical model, we tested for the presence of density-independent and density-dependent habitat selection in yellowfin sole (Limanda aspera) in the eastern Bering Sea. We found specificities in the density-dependent processes operating across ontogeny and particularly with gender. Density-dependent habitat expansion occurred primarily in females, and to a lesser degree in males. These patterns were especially evident in adult stages, while juvenile stages of both sexes exhibited a mix of different dynamics. Association of yellowfin sole with substrate type also varied by sex and to a lesser degree with size, with large females distributed over a wider range of substrates than males. Moreover, yellowfin sole expanded northward as cold subsurface waters retracted in summer, suggesting high sensitivity to arctic warming. Our findings illustrate how marginal habitats can play an important role in buffering density-dependent habitat expansion, with direct implications for resource management. Our spatially explicit modeling approach is effective in evaluating density-dependent spatial dynamics, and can easily be used to test similar hypotheses from a variety of aquatic and terrestrial ecosystems.