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At least 1,297 records · Page 72Linked to original sources
PubMed2002

Contrasting models for lactation curve analysis.

Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance for the genetic analysis of lactation curves, and to assess equivalence between sire and animal models for repeated measures analyses. It was found that, with an animal model, a character process model with 11 parameters performed better, regarding the likelihood criterion, than a quartic random regression model (with 31 parameters). However, although the likelihood was higher, the genetic variance was very different with the character process model from the unstructured model, which raises important issues concerning model selection criteria. There are advantages in combining methodologies. A quadratic random regression model for the environmental part, combined with a character process model for the residual, performed better than the quartic random regression model and had fewer parameters. A character process structure allowing for a correlation pattern modeled the residual better than a simple quadratic variance, and had only one extra parameter.

Animals↗
PubMed1982

Demographic disaggregation in the construction of regional econometric models: a statistical evaluation.

"A cohort-component method is used to construct historical annual age/sex disaggregations of total population. In econometric models of six substate areas [in the United States], regressions are estimated with and without the disaggregated data for five model variables expected to be dependent on the size of particular population subgroups. Statistical tests are performed to determine significant differences in residual variance of estimation with and without the disaggregated data. Although age/sex population projections may be a desired model output per se, the results indicate that the population subgroup data generally do not improve estimation of other model variables." (summary in FRE, ITA, JPN, )

Age Distribution↗
PubMed2006

Regularization operators for natural images based on nonlinear perception models.

Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator takes these additional features in natural images into account, it will be more robust and the choice of the regularization parameter is less critical.

Algorithms↗
PubMed1992

The demographics of labour turnover: a comparison of ordinal probit and censored count data models.

The authors use date collected in 1977 on unemployed residents of West Germany aged 15-54 years old to compare two statistical models used to analyze labor market mobility. "Two classes of statistical models seem appropriate for this type of analysis, the ordinal probit model assuming a latent continuous variable behind the observables, and the count data approach with prominent members as the Poisson and the negative binomial model.... The first point of interest is to explore whether both approaches lead to similar findings.... We outline some approaches...that enable a...researcher to judge the quality of the estimated model....[Then] we compare the performance of the ordinal probit and count data models. Section 2 presents data and hypotheses. Section 3 discusses the econometric methodology. Section 4 contains the empirical results and the model comparisons." (SUMMARY IN FRE)

Developed Countries↗
PubMed2004

A trimer model for water.

A statistical model for water is studied, where the molecules are represented by trimers in a triangular lattice. Each atom of a water molecule occupies a single site on the lattice, and the HOH bond angle is assumed to be 120 degrees. The molecules can interact via three different potentials: the excluded volume interaction, which prevents two molecules from occupying the same atom site, an attractive potential between any two nearest-neighbor atoms belonging to different molecules (the van der Waals interaction), and the hydrogen bond interaction, which occurs only for a particular orientation and displacement of a pair of molecules. The model is investigated by means of Monte Carlo simulations in the canonical and grand canonical ensembles. The Metropolis and the entropic sampling algorithms are used to obtain the thermodynamics of the system. We find that the entropic sampling prescription is the most efficient algorithm of them, providing information about the entropy and free energy of the system in a straightforward way. The curves for the polarization, number of hydrogen bonds, specific heat, and cumulant of energy were obtained as a function of the temperature and total concentration. In addition, the entropy of the noninteracting version of the model is compared to that of the angular trimers in a square lattice and triangles in a triangular lattice.

Journal Article↗
PubMed2006

Customised birthweight: coefficients for an Australian population and validation of the model.

BACKGROUND: Published birthweight references in Australia do not fully take into account constitutional factors that influence birthweight and therefore may not provide an accurate reference to identify the infant with abnormal growth. Furthermore, studies in other regions that have derived adjusted (customised) birthweight references have applied untested assumptions in the statistical modelling. AIMS: To validate the customised birthweight model and to produce a reference set of coefficients for estimating a customised birthweight that may be useful for maternity care in Australia and for future research. METHODS: De-identified data were extracted from the clinical database for all births at the Mater Mother's Hospital, Brisbane, Australia, between January 1997 and June 2005. Births with missing data for the variables under study were excluded. In addition the following were excluded: multiple pregnancies, births less than 37 completed week's gestation, stillbirths, and major congenital abnormalities. Multivariate analysis was undertaken. A double cross-validation procedure was used to validate the model. RESULTS: The study of 42,206 births demonstrated that, for statistical purposes, birthweight is normally distributed. Coefficients for the derivation of customised birthweight in an Australian population were developed and the statistical model is demonstrably robust. CONCLUSIONS: This study provides empirical data as to the robustness of the model to determine customised birthweight. Further research is required to define where normal physiology ends and pathology begins, and which segments of the population should be included in the construction of a customised birthweight standard.

Australia↗
PubMed1976

The linear model: a statistical tool applied to psychophysical research in dental prosthetics.

The construction of a linear model is described, and its function in analysing variations in the perception of comfortable mandibular occlusal positions is explained. In principle, the model combines analyses of variance and regression in a number of simple computer operations. Data from a clinical study were used to demonstrate the analytical capacity of a specific model, designed to estimate the effect of factors, which were supposed to influence the perception of comfortable mandibular positions.

Adult↗
PubMed1999

Stochastic nature of precisely timed spike patterns in visual system neuronal responses.

It is not clear how information related to cognitive or psychological processes is carried by or represented in the responses of single neurons. One provocative proposal is that precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns have the potential for carrying information that would not be available from other measures such as spike count or latency. We examined exactly timed (1-ms precision) triplets and quadruplets of spikes in the stimulus-elicited responses of lateral geniculate nucleus (LGN) and primary visual cortex (V1) neurons of the awake fixating rhesus monkey. Large numbers of these precisely timed spike patterns were found. Information theoretical analysis showed that the precisely timed spike patterns carried only information already available from spike count, suggesting that the number of precisely timed spike patterns was related to firing rate. We therefore examined statistical models relating precisely timed spike patterns to response strength. Previous statistical models use observed properties of neuronal responses such as the peristimulus time histogram, interspike interval, and/or spike count distributions to constrain the parameters of the model. We examined a new stochastic model, which unlike previous models included all three of these constraints and unlike previous models predicted the numbers and types of observed precisely timed spike patterns. This shows that the precise temporal structures of stimulus-elicited responses in LGN and V1 can occur by chance. We show that any deviation of the spike count distribution, no matter how small, from a Poisson distribution necessarily changes the number of precisely timed spike patterns expected in neural responses. Overall the results indicate that the fine temporal structure of responses can only be interpreted once all the coarse temporal statistics of neural responses have been taken into account.

Action Potentials↗
PubMed2002

Quantifying predictability in a model with statistical features of the atmosphere.

The Galerkin truncated inviscid Burgers equation has recently been shown by the authors to be a simple model with many degrees of freedom, with many statistical properties similar to those occurring in dynamical systems relevant to the atmosphere. These properties include long time-correlated, large-scale modes of low frequency variability and short time-correlated "weather modes" at smaller scales. The correlation scaling in the model extends over several decades and may be explained by a simple theory. Here a thorough analysis of the nature of predictability in the idealized system is developed by using a theoretical framework developed by R.K. This analysis is based on a relative entropy functional that has been shown elsewhere by one of the authors to measure the utility of statistical predictions precisely. The analysis is facilitated by the fact that most relevant probability distributions are approximately Gaussian if the initial conditions are assumed to be so. Rather surprisingly this holds for both the equilibrium (climatological) and nonequilibrium (prediction) distributions. We find that in most cases the absolute difference in the first moments of these two distributions (the "signal" component) is the main determinant of predictive utility variations. Contrary to conventional belief in the ensemble prediction area, the dispersion of prediction ensembles is generally of secondary importance in accounting for variations in utility associated with different initial conditions. This conclusion has potentially important implications for practical weather prediction, where traditionally most attention has focused on dispersion and its variability.

Journal Article↗
PubMed1995

The etiology and prediction of breast cancer. Fourier transform-infrared spectroscopy reveals progressive alterations in breast DNA leading to a cancer-like phenotype in a high proportion of normal women.

BACKGROUND: The authors previously have shown by gas chromatography-mass spectrometry that the hydroxyl radical (.OH) induces alterations in the DNA base structure of the female breast, which are premalignant markers of breast cancer. Fourier transform-infrared (FT-IR)-spectroscopy also has a high potential for revealing a broad array of structural changes in DNA that may provide important new insight into breast cancer etiology and prediction. METHODS: DNA from normal reduction mammoplasty tissue, invasive ductal carcinoma, and nearby microscopically normal tissue was analyzed by FT-IR spectroscopy. Statistical models based on DNA spectral properties were developed and compared with a statistical model previously used with base modifications. RESULTS: Substantial differences were found in the spectral properties of DNA from women with normal and cancerous breast tissue, indicating an ability to discriminate cancerous tissue from noncancerous tissue with a sensitivity and specificity of 83%. Most importantly, the normal population was divided into subgroups in which a nonrandom progression was identified and a cancer-like DNA phenotype that was highly correlated (r > or = 0.90) with that of the patients with cancer was exhibited in 59% of the women. The spectral data, which also were highly correlated with the base-model data, were used to establish a model for predicting the probability of breast cancer. Consistent with the high cancer reoccurrence rate in the ipsilateral breast, 8 of 10 of the microscopically normal tissue specimens remaining after tumor excision were classified as cancerous using this model. CONCLUSIONS: Progressive structural changes in the DNA of the normal female breast, leading to a premalignant cancer-like phenotype in a high proportion of women, are the basis for a new paradigm for understanding the etiology of breast cancer and predicting its occurrence at early stages of oncogenesis. The results also suggest therapeutic strategies for potentially reversing the extent of DNA damage, which may be useful in disease prevention and treatment.

Breast↗
PubMed2005

Replication, variation and normalisation in microarray experiments.

INTRODUCTION: Microarray experiments often have complex designs that include sample pooling, biological and technical replication, sample pairing and dye-swapping. This article demonstrates how statistical modelling can illuminate issues in the design and analysis of microarray experiments, and this information can then be used to plan effective studies. METHODS: A very detailed statistical model for microarray data is introduced, to show the possible sources of variation that are present in even the simplest microarray experiments. Based on this model, the efficacy of common experimental designs, normalisation methodologies and analyses is determined. RESULTS: When the cost of the arrays is high compared with the cost of samples, sample pooling and spot replication are shown to be efficient variance reduction methods, whereas technical replication of whole arrays is demonstrated to be very inefficient. Dye-swap designs can use biological replicates rather than technical replicates to improve efficiency and simplify analysis. When the cost of samples is high and technical variation is a major portion of the error, technical replication can be cost effective. Normalisation by centreing on a small number of spots may reduce array effects, but can introduce considerable variation in the results. Centreing using the bulk of spots on the array is less variable. Similarly, normalisation methods based on regression methods can introduce variability. Except for normalisation methods based on spiking controls, all normalisation requires that most genes do not differentially express. Methods based on spatial location and/or intensity also require that the nondifferentially expressing genes are at random with respect to location and intensity. Spotting designs should be carefully done so that spot replicates are widely spaced on the array, and genes with similar expression patterns are not clustered. DISCUSSION: The tools for statistical design of experiments can be applied to microarray experiments to improve both efficiency and validity of the studies. Given the high cost of microarray experiments, the benefits of statistical input prior to running the experiment cannot be over-emphasised.

Algorithms↗
PubMed2004

Correct estimation of preferential chromosome pairing in autotetraploids.

In recent work, a statistical model was proposed for the purpose of estimating parameters associated with quantitative trait locus (QTL) mapping and preferential pairing within a polyploidy framework. The statistical model contained several parameters that, when estimated from experimental data, supplied information about QTL, including a preferential pairing factor. Among the results reported were estimates of preferential pairing, many of which indicated high levels of preferential pairing (p = 0.60) that were inconsistent with biological expectations. By using the biological inconsistencies as our motivation, we present a reformulated statistical method for estimating preferential pairing, and use this method to reanalyze the same autotetraploid alfalfa data and to conduct a simulation study. Our results directly contradict the current findings of significant preferential pairing and affirm the traditional view of random chromosome segregation in alfalfa.

Chromosome Pairing↗
PubMed2004

Forecasting clinical disease in pigs: comparing a naive and a Bayesian approach.

In veterinary practice the clinician often evaluates and predicts herd health status over time according to clinical criteria. In this paper, we modeled three different clinical signs among pigs based on longitudinal clinical observations in 15 pig herds. We compared and discussed the outputs from two different approaches for making clinical forecasts in a herd: a naive approach using a simple time series model with previous disease observations as predictors and a Bayesian state space models approach, in which the time lag variable entered into the random component of the model. We used the Markov chain Monte Carlo technique to calculate posterior distributions of the forecasts. For the herd specific forecasts the results showed that there were only minor differences between the forecasts from the simple time series model and the median forecasts from the Bayesian model. However, the credibility intervals from the Bayesian model were wider than the forecasts from the simple model and, therefore the Bayesian model encompassed the variability in the forecasts better. Compared to the statistical model, the simple time series would be easier to implement in a practical setting. However, the latter lacks the inherent "generality" from the statistical model that allows the user to make statements about the distribution of the herds and to predict disease status based on the "average" correlation among the herds. The applicability of the Bayesian approach within a clinical decision-making framework was discussed, with special emphasis on the use of prior information and clinical forecasting.

Animals↗
PubMedDate not supplied

BoneMorphing versus freehand localization of anatomical landmarks: consequences for the reproducibility of implant positioning in total knee arthroplasty.

OBJECTIVE: This study analyzed the influence of the acquisition method in image-free computer-assisted total knee arthroplasty (CAS-TKA), and the reproducibility of implant planning using BoneMorphing, a 3D morphometric model obtained by a 3D-to-3D elastic registration of statistical models to sparse point clouds acquired directly on the bone surface with a pointer. MATERIALS AND METHODS: Five surgeons (one expert, four trainees) each performed a CAS-TKA hybrid protocol based on morphometric models and landmarks on a cadaveric knee 10 times. In addition, several additional landmarks were digitized during each acquisition. The reproducibility of the implant positioning and sizing, as determined by an implant planning algorithm with morphometric models, was compared to direct digitization accuracy. RESULTS: Femoral and tibial implant positioning parameters with the hybrid protocol resulted in intra-surgeon standard deviations (SDs) of less than+/-1.4 degrees for rotation and 1.9 mm for translation for all surgeons in all directions except for tibial axial rotation (the only parameter determined by a digitized landmark and not recomputed in the 3D model). The variability in individual landmark digitization varied from 2 to 5 mm SD for certain landmarks, with ranges of 15-25 mm across all surgeons. The comparison study showed an improvement in femoral rotation reproducibility with the morphometric model when using the posterior condylar axis. Tibial implant reproducibility for each method was comparable, with the morphometric model giving better results in well-digitized areas such as the tibial plateau. CONCLUSION: A CAS-TKA protocol based on a deformed statistical model offers reproducible implant positioning. Some landmarks, such as distal condyles, show sufficient reproducibility in the direction of interest, while others, such as the anterior tibial tubercle, can lead to hazardous implant positioning. This should be taken into consideration when designing a CAS-TKA system with bony landmarks. In areas where a sufficient number of points have been digitized with good coverage, such as on the distal and posterior femoral condyles or the tibial plateau areas, the information derived from the 3D model is more accurate and reproducible than that derived from digitization. Good training and a guiding user interface are essential to guarantee coverage quality.

Arthroplasty, Replacement, Knee↗
PubMed2005

Adaptive covariate adjustment in clinical trials.

In analysis of covariance (ANCOVA), as a result of covariate adjustment, the estimated mean difference between the two comparative treatment groups may have a better precision than the unadjusted estimate. The extent of improvement of precision depends on the correlation between the outcome variable and the covariate selected for adjustment. Therefore, for this purpose, it is desirable to apply a proper transformation to this covariate so that the transformed covariate has a stronger correlation with the outcome variable. The best predictor from the covariate for the outcome variable is the conditional expectation of the outcome variable given the covariate. Thus, a viable strategy is using regression modeling approach to search for a statistical model to well approximate the conditional expectation based on external and/or current trial data. We propose an adaptive strategy to achieve this goal if the current data are needed to help the search.

Algorithms↗
PubMed1995

Duration of preclinical cervical cancer and reduction in incidence of invasive cancer following negative pap smears.

BACKGROUND: An investigation has been made into the differences between estimates for the duration of preclinical cervical cancer resulting from two types of studies. A median duration of 5-10 years was suggested by the observed build-up of incidence of invasive cervical cancer after one or more negative smears. Much longer median durations of more than 15 years have been reported from fitting statistical models to screening data. METHODS: We developed one of these statistical models and fitted it to clinical incidence and screening data from British Columbia, which resulted in estimated mean durations of 12 years for pre-invasive stages, and 4-5 years for screen-detectable stages. The model is used to predict the build-up of the incidence of invasive cancer after one and after two negative smears. RESULTS: The model predictions appear to correspond closely to the observed incidence trends following negative smears. The apparent contradiction between model estimates and observed data is explained by recognizing that many of the women who have had negative smears will have further Pap smears, resulting in earlier diagnosis of invasive cervical cancers and thus an apparent faster build-up of the incidence. CONCLUSIONS: When the impact of further Pap smears is neglected, the data suggest that the risk of invasive cancer following one or more negative smears returns to close to prescreening levels within 6-10 years. This is an overestimation of the risk of clinical invasive cancer. In the case of cessation of screening it will take longer before the incidence of clinical cancer will increase. Where there is continuous screening the screen-detected cancers have a relatively favourable prognosis, thus contributing less to the serious morbidity and mortality risks associated with invasive cancer. This should be taken into account in making comparisons with the prescreening situation.

Adult↗
PubMed2002

A logistic mixture model for characterizing genetic determinants causing differentiation in growth trajectories.

The logistic or S-shaped curve of growth is one of the few universal laws in biology. It is certain that there exist specific genes affecting growth curves, but, due to a lack of statistical models, it is unclear how these genes cause phenotypic differentiation in growth and developmental trajectories. In this paper we present a statistical model for detecting major genes responsible for growth trajectories. This model is incorporated with pervasive logistic growth curves under the maximum likelihood framework and, thus, is expected to improve over previous models in both parameter estimation and inference. The power of this model is demonstrated by an example using forest tree data, in which evidence of major genes affecting stem growth processes is successfully detected. The implications for this model and its extensions are discussed.

Crosses, Genetic↗