Statistical Inference

We present a simple way of assessing dynamic or time-dependent changes in displacement during single-subject radioligand positron emission tomography (PET) activation studies. The approach is designed to facilitate dynamic activa tion studies using selective radioligands. These studies are, in principle, capable of characterising functional neurochemistry by analogy with the study of functional neuroanatomy using rCBF activation studies. The proposed approach combines time-dependent compartmental models of tracer kinetics and the general linear model used in statistical parametric mapping.

In this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on statistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. Misspecification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented.

I demonstrate the application of hierarchical regression modeling, a state-of-the-art technique for statistical inference, to language research. First, a stable sociolinguistic variable in Philadelphia (Labov, 2001) is reconsidered, with attention paid to the treatment of collinearities among socioeconomic predictors. I then demonstrate the use of hierarchical models to account for the random sampling of subjects and items in an experimental setting, using data from a study of word-learning in the face of tonal variation (Quam and Swingley ...

The development of school students' understanding of comparing two data sets is explored through responses of students in individual interview settings. Eighty-eight students in grades 3 to 9 were presented with data sets in graphical form for comparison. Student responses were analysed according to a developmental cycle which was repeated in two contexts: one where the numbers of values in the data sets were the same and the other where they were different. Strategies observed within the developmental cycles were visual, numerical, or a combination of the two. The correctness of outcomes associated with using and combining these strategies varied depending upon the task and the developmental level of the response. Implications for teachers, educational planners and researchers are discussed in relation to the beginning of statistical inference during the school years.

Recent developments in ecological statistics have reached behavioral ecology, and an increasing number of studies now apply analytical tools that incorporate alternatives to the conventional null hypothesis testing based on significance levels. However, these approaches continue to receive mixed support in our field. Because our statistical choices can influence research design and the interpretation of data, there is a compelling

This note is an answer to some open problems connected with recent developments for appropriate methodologies for making inferences on the tail of a distribution function (d.f.). Namely, in Fraga Alves and Gomes (1996), the Gumbel statistic, based on the top part of a sample, is used in a semi-parametric approach, in order to fit an appropriate tail to the underlying model to a data set. The problem of statistical inference about extremal observations is handled there according to a test for choosing the most appropriate domain of attraction for the tail distribution, which gives preference to the Gumbel domain for the null hypothesis. The asymptotic behaviour of the referred statistic is derived therein under that null hypothesis and here we present similar extended results under the alternative conditions, i.e., for d.f. that belongs to the other Generalized Extreme Value domains, as an accomplishment to the promise made in last chapters of Fraga Alves and Gomes (1995; 1996).

Statistical inference on the Lagrange gamma distribution is considered in this paper. The method of moments, the maximum-likelihood estimation technique, and the estimation of the scale parameter by order statistics are discussed. A linear rank test statistic is proposed for the distribution. A numerical example on the application of the Lagrange gamma distribution is given. In the numerical example, the Lagrange gamma distribution provided a better fit than the gamma distribution.

Phylogenetic mixture models are statistical models of character evolution allowing for heterogeneity. Each of the classes in some unknown partition of the characters may evolve by different processes, or even along different trees. The fundamental question of whether parameters of such a model are identifiable is difficult to address, due to the complexity of the parameterization. We analyze mixture models on large trees, with many mixture components, showing that both numerical and tree parameters are indeed identifiable in these models when all trees are the same. We also explore the extent to which our algebraic techniques can be employed to extend the result to mixtures on different trees.

Informal inferential reasoning has shown some promise in developing students’ deeper understanding of statistical processes. This paper presents a framework to think about three key principles of informal inference – eneralizations ‘beyond the data,’ probabilistic language, and data as evidence. The authors use primary school classroom episodes and excerpts of interviews with the teachers to illustrate the framework and reiterate the importance of embedding statistical learning within the context of statistical inquiry. Implications for the teaching of more powerful satistical concepts at the primary school level are discussed.

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Confidence intervals are in many ways a more satisfactory basis for statistical inference than hypothesis tests. This article explains a simple method for using bootstrap resampling to derive confidence intervals. This method can be used for a wide variety of statistics-including the mean and median, the difference of two means or proportions, and correlation and regression coefficients. It can be implemented by an Excel spreadsheet, which is available to readers on the Web. The rationale behind the method is transparent, and it relies on almost no sophisticated statistical concepts.

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In this paper, we propose a new study of a stochastic lognormal diffusion process (SLDP), with three parameters, which can be considered as an extension of the bi-parametric lognormal process with the addition of a threshold parameter. From the Kolmogorov equation, we obtain the probability density function and the moments of this process. The statistical inference of the parameter is studied by considering discrete sampling of the sample paths of the model and then using the maximum likelihood (ML) method. The estimation of the threshold parameter requires the solution of a nonlinear equation. To do so, we propose two methods: the classical Newton-Raphson (NR) method and one based on simulated annealing (SA). This methodology is applied to an example with simulated data corresponding to the process with known parameters. From this, we obtain the estimators of the parameters by both methods (NR and SA). Finally, the methodology studied is applied to a real case concerning the mean age of males in Spain at the date of their first wedding.

Since publication use of the WHOQOL-Bre`f has rapidly risen. However, as yet no population norms have been published as a reference point against which researchers can interpret their findings. This study provides preliminary population norms for this purpose. Randomly sampled community residents from two studies were pooled and used to examine the properties of the WHOQOL-Bre`f by age group, gender and health status. The results showed that general norms for the WHOQOL-Bre`f domains were 73.5 (SD=18.1) for the Physical health domain, 70.6 (14.0) for Psychological wellbeing, 71.5 (18.2) for Social relationships and 75.1 (13.0) for the Environment domain. In general scores declined slightly by age group. For females scores were stable across the lifespan with an accelerated decline after the age of 60 years. Males exhibited a more consistent and even decline across the lifespan. There were significant differences in WHOQOL-Bre`f scores when reported by health status, with those in poor health obtaining scores that were up to 50% lower than those in excellent health. Effect sizes between different health status levels are reported. These preliminary norms and effect sizes may be used as reference points for interpreting WHOQOL-Bre`f scores. They provide additional information to the numerous national studies already reporting on the validity of the WHOQOL-Bre`f. KEY WORDS: effect sizes, population norms, profile health status instruments, quality of life measurement, WHOQOL-Bre`f

In this paper, we develop and apply a framework for estimating the potential global-scale impact of reservoir construction on riverine sediment transport to the ocean. Using this framework, we discern a large, global-scale, and growing impact from anthropogenic impoundment. Our study links information on 633 of the world's largest reservoirs (LRs) ( z 0.5 km 3 maximum storage capacity) to the geography of continental discharge and uses statistical inferences to assess the potential impact of the remaining >44,000 smaller reservoirs (SRs). Information on the LRs was linked to a digitized river network at 30V (latitude  longitude) spatial resolution. A residence time change (Ds R ) for otherwise free-flowing river water is determined locally for each reservoir and used with a sediment retention function to predict the proportion of incident sediment flux trapped within each impoundment. The discharge-weighted mean Ds R for individual impoundments distributed across the globe is 0.21 years for LRs and 0.011 years for SRs. More than 40% of global river discharge is intercepted locally by the LRs analyzed here, and a significant proportion ( c 70%) of this discharge maintains a theoretical sediment trapping efficiency in excess of 50%. Half of all discharge entering LRs shows a local sediment trapping efficiency of 80% or more. Analysis of the recent history of river impoundment reveals that between 1950 and 1968, there was tripling from 5% to 15% in global LR sediment trapping, another doubling to 30% by 1985, and stabilization thereafter. Several large basins such as the Colorado and Nile show nearly complete trapping due to large reservoir construction and flow diversion. From the standpoint of sediment retention rates, the most heavily regulated drainage basins reside in Europe. North America, Africa, and Australia/Oceania are also strongly affected by LRs. Globally, greater than 50% of basin-scale sediment flux in regulated basins is potentially trapped in artificial impoundments, with a discharge-weighted sediment trapping due to LRs of 30%, and an additional contribution of 23% from SRs. If we consider both regulated and unregulated basins, the interception of global sediment flux by all registered reservoirs 0921-8181/03/$ -see front matter D Global and Planetary Change 39 (2003) 169 -190 (n c 45,000) is conservatively placed at 4 -5 Gt year À 1 or 25 -30% of the total. There is an additional but unknown impact due to still smaller unregistered impoundments (n c 800,000). Our results demonstrate that river impoundment should now be considered explicitly in global elemental flux studies, such as for water, sediment, carbon, and nutrients. From a global change perspective, the long-term impact of such hydraulic engineering works on the world's coastal zone appears to be significant but has yet to be fully elucidated. D

The purpose of this paper is to pnxide a brief and selective survey of statistical inference in nonparametric, deterministic, linear programming-based frontier models. The survey starts with nonparametric regularity tests, sensitivity analysis, two-stage analysis with regression, and nonparametric statistical tests. It then turns to the more recent literature which shows that DEA-type estimators are maximum likelihood, and, mom importantly the results concerning the asymptotic propetties of these estimators. Also included is a discussion of recent attempts to employ resampling methods to derive empirical distributions for hypothesis testing.

The Quinlain’s ID3 algorithm makes extensive use of decision trees. A decision tree is a tree in which each non-leaf node is labeled with an attribute or a question of some sort, and in which the branches at that node correspond to the possible values of the attribute, or answers to the question. One starts at the root of the tree, and, taking appropriate branches according to the attribute or question asked about at each branch node, one eventually comes to a leaf node. The label on that leaf node is the class for that instance.

Testing for IIA with the Hausman-McFadden Test * The Independence of Irrelevant Alternatives assumption inherent in multinomial logit models is most frequently tested with a Hausman-McFadden test. As is confirmed by many findings in the literature, this test sometimes produces negative outcomes, in contradiction of its asymptotic χ 2 distribution. This problem is caused by the use of an improper variance matrix and may lead to an invalid statistical inference even when the test value is positive. With a correct specification of the variance, the sampling distribution for small samples is indeed close to a χ 2 distribution.

The Weibull and Log-Normal distributions are frequently used in reliability to analyze lifetime (or failure time) data. The ratio of maximized likelihood (RML) has been extensively used in choosing between the two distributions. The Kullback-Leibler information is a measure of uncertainty between two densities. We examine the use of Kullback-Leibler Divergence (KLD) in discriminating either the Weibull or Log-Normal distribution. An advantage of the KLD is that it incorporates entropy of each model. We explain the applicability of the KLD by a real data set and the consistency of the KLD with the RML is established.

She is currently teaching in the English and Technical Communication department at Missouri S & T. Ms Bashyal has published several conference papers in the area of sustainability and is ready to submit her Master ' s thesis related to sustainability communication in June 2012. Her research area includes: sustainability communication, green marketing, consumer behavior, web usability and classical rhetoric.

Jan. 8, 2016: Perhaps wherever I noted "the estimated standard error of the random factors of the estimated residuals," I should have said "the estimated standard deviation of the random factors of the estimated residuals." I saw some things by a couple of people that made me realize my language was sloppy here. But I also read where this may be fairly common. At any rate, this is a fixed value to be estimated. It is a standard deviation.
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Previous notes:
The classical ratio estimator (CRE) is very simple, has a long history, and has a stunningly broad range of application, especially with regard to econometrics, and to survey statistics, particularly establishment survey statistics. The CRE has a number of desirable properties, one of which is that the sum of its estimated residuals is always zero. It is easily extended to multiple regression, and a property shown in Sarndal, Swensson and Wretman (1992) may be used to indicate the desirability of this zero sum of estimated residuals feature when constructing regression weights for multiple regression. In the single regressor form, the zero sum of estimated residuals property is related to an interesting phenomenon expressed in Fox (1997). Finally, relationships of the CRE to some other statistics are also considered.
-- Note added November 2014: As noted in other works I have done, and elsewhere, for this model, only the individual values of x corresponding to individual y values need to be known, as long as the sum of the remaining x (for out-of-sample cases) is known, and then one can still estimate variance. If we do not know the sum of those remaining N-n x-values (where n is sample size selected minus cases to be imputed), but we know a range for that subtotal of x's, then we know a range of estimated variances for the estimated y-totals to go with that range. --

The bootstrap, extensively studied during the last decade, has become a powerful tool in different areas of Statistical Inference. In this work, we present the main ideas of bootstrap methodology in several contexts, citing the most relevant contributions and illustrating with examples and simulation studies some interesting aspects.

This paper is concerned with modern approaches to mechanistic modeling of the process of cancer detection. Measurements of tumor size at diagnosis represent a valuable source of information to enrich statistical inference on the processes underlying tumor latency. One possible way of utilizing this information is to model cancer detection as a quantal response variable. In doing so, one relates the chance of detecting a tumor to its current size. We present various theoretical results emerging from this approach and illustrate their usefulness with numerical examples and analyses of epidemiological data. An alternative approach based on a threshold type mechanism of tumor detection is brie¯y described. Ó (A.Yu. Yakovlev). 0025-5564/01/$ -see front matter Ó 2001 Elsevier Science Inc. All rights reserved. PII: S 0 0 2 5 -5 5 6 4 ( 0 1 ) 0 0 0 5 8 -X 114 R. Bartoszy nski et al. / Mathematical Biosciences 171 (2001) 113±142

A novel semiparametric regression model for censored data is proposed as an alternative to the widely used proportional hazards survival model. The proposed regression model for censored data turns out to be flexible and practically meaningful. Features include physical interpretation of the regression coefficients through the mean response time instead of the hazard functions, and a rigorous proof of consistency of the posterior distribution. It is shown that the regression model obtained by a mixture of parametric families, has a proportional mean structure (as in an accelerated failure time models). The statistical inference is based on a nonparametric Bayesian approach that uses a Dirichlet process prior for the mixing distribution. Consistency of the posterior distribution of the regression parameters in the Euclidean metric is established. Finite sample parameter estimates along with associated measure of uncertainties can be computed by a MCMC method. Simulation studies are p...

A prominent problem in actuarial science is to define, or describe, premium calculation principles (pcp's) that satisfy certain properties. A frequently used resolution of the problem is achieved via distorting (e.g., lifting) the decumulative distribution function, and then calculating the expectation with respect to it. This leads to coherent pcp's. Not every pcp can be arrived at in this way. Hence, in this paper we suggest and investigate a broad class of pcp's, which we call weighted premiums, that are based on weighted loss distributions. Different weight functions lead to different pcp's: any constant weight function leads to the net premium, an exponential weight function leads to the Esscher premium, and an indicator function leads to the conditional tail expectation. We investigate properties of weighted premiums such as ordering (and in particular loading), invariance. In addition, we derive explicit formulas for weighted premiums for several important classes of loss distributions, thus facilitating parametric statistical inference. We also provide hints and references on non-parametric statistical inferential tools in the area.

Partial least squares regression Partial least squares path modeling PLS Symmetric PLS Asymmetric PLS Task PLS Behavior PLS Seed PLS Multi-block PLS Multi-table PLS Canonical variate analysis Co-inertia analysis Multiple factor analysis STATIS Barycentric discriminant analysis Multiple factor analysis Common factor analysis

Extreme events are defined as extreme high (or low) values of whatever statistics of the output of the system we are interested in. These values play an important role because they may correspond to abnormal or dangerous operating conditions. Classical statistical inference techniques provide a good description of central behaviour, but not of extreme events. This was our motivation for resorting to extreme-value theory, which provides a framework and tools to model these extreme events. We show in this paper how some of these tools can be used in the context of system reliability, and the resulting methodology is illustrated on an example of circuit design, representative of a wide new field of applications for extreme-value theory.

Adolescence period is characterised by various incidences. This allows for confirmed inferences of different magnitude. This study looked into contemporary clothing habits and sexual behaviour of adolescents in the South Western Nigeria with a view to establish whether there is a relationship between adolescents ’ clothing habits and their sexual behaviour and also, what influences their clothing habits. 520 adolescent students were randomly selected using quota-sampling method from available higher institutions in the South Western Nigeria. Clothing Habits and Sexual Behaviour Questionnaire (CHSBQ) was used to collect data, which was analysed by t-test statistics inference at a 0.05 level of significance. The result shows that there is no significant difference in the factors influencing adolescents ’ dressing habits and also the fact that there is relationship between clothing habits and their sexual behaviour.

This guide book is especially designed for beginners in SPSS ans have some background knowledge of Statistical techniques. For ease of students and users different renowned books which are easily available and taught in universities are used for the solution examples by using SPSS. This guide covers most of topics of Statistics till Bachelor 's level and for further guidance and understanding, our You Tube channel may be referred.

Diagnosis and prognosis are processes of assessment of a system's health -past, present and future -based on observed data and available knowledge about the system. Due to the nature of the observed data and the available knowledge, the diagnostic and prognostic methods are often a combination of statistical inference and machine learning methods. The development (or selection) of appropriate methods requires appropriate formulation of the learning and inference problems that support the goals of diagnosis and prognosis. An important aspect of the formulation is modeling -relating the real system to its mathematical abstraction. This paper explores the impact of diagnostic and prognostic goals on modeling and reasoning system requirements, with the purpose of developing a common software framework that can be applied to a large class of systems. In particular, the role of failure-dependency modeling in the overall decision problem is discussed. The applicability of Qualtech Systems' modeling and diagnostic software tools to the presented framework for both the development and implementation of diagnostics and prognostics is assessed. Finally, a potential application concept for advancing the reliability of Navy shipboard Condition Based Maintenance (CBM) systems and processes is discussed.

is Professor of Integrated Marketing at the Centre for Integrated Marketing, University of Luton Business School, and the founder and CEO of Stepping Stones Consultancy Ltd. He has made a signifi cant contribution to practical thought leadership in integrated marketing, CRM and one-to-one marketing, and in integrated, media-neutral communications, for over 20 years, with over 100 publications and briefi ngs. Firsts include a wide range of marketing tools now in everyday global practice, including the pen portrait / customer community tool ' CustomerPrints ' and ' CRM-based touchpoint management ' . He was formerly CEO of The Computing Group (when it was European CRM market leader) and before that General Manager / Marketing Director of JBA International plc, the industry-tailored ERP market leader. His clients include Barclays, IBM, ICG, Masterfoods, MediaEdge:CIA, OgilvyOne, Olympus, Novartis, the National Trust and Vodafone.

This paper presents the usage of different types of inferential statistics, the relevance of statistics to various disciplines, its usage in different types of research, and its relevance to the writer.

ABSTRACT
Inferential statistics plays an important role in various fields of human endeavor as well as in personal development. It is a way of measuring uncertainties as well as a formal way of expressing probabilities, predictions, extrapolations, and other extensions of reasoning and thought that go beyond the available data.
According to the parameters of scientific practice, inferential statistics may be used erroneously and thus produce incorrect results. With correct usage, inferential is accepted as a key element in scientific research procedures, particularly in doctoral dissertations because, by definitions, a doctoral dissertation is a formal research paper that creates new knowledge including new hypotheses or models.
The first chapter introduces key terminology, history, importance and advantages, dangers and limitations of statistics in general and inferential statistics in particular. Chapter 2 explains how formal numerical inference is used in various fields, and in different types of research papers. Chapter 3 defines science and presents key concepts that prove that inferential statistics is, indeed, a scientific methodology. The concluding chapter explains how writing this research paper is relevant to the writer as a student and as a professional.

Generalized Information (GI) is a measurement of the degree to which a program can be said to generalize a dataset. It is calculated by creating a program to model the data set, measuring the Active Information in the model, and subtracting out the size of the model. Active Information allows GI to be usable with both exact and inexact models.

Sampling of a population is frequently required to understand trends and patterns in natural resource management because financial and time constraints preclude a complete census. A rigorous probability-based survey design specifies where to sample so that inferences from the sample apply to the entire population. Probability survey designs should be used in natural resource and environmental management situations because they provide the mathematical foundation for statistical inference. Development of long-term monitoring designs demand survey designs that achieve statistical rigor and are efficient but remain flexible to inevitable logistical or practical constraints during field data collection. Here we describe an approach to probability-based survey design, called the Reversed Randomized Quadrant-Recursive Raster, based on the concept of spatially balanced sampling and implemented in a geographic information system. This provides environmental managers a practical tool to generate flexible and efficient survey designs for natural resource applications. Factors commonly used to modify sampling intensity, such as categories, gradients, or accessibility, can be readily incorporated into the spatially balanced sample design.