As flood inundation risk maps have become a central piece of information for both urban and risk management planning, also a need to assess the accuracies and uncertainties of these maps has emerged. Most maps show the inundation boundaries as crisp lines on visually appealing maps, whereby many planners and decision makers, among others, automatically believe the boundaries are both accurate and reliable. However, as this study shows, probably all such maps, even those that are based on high-resolution digital elevation models (DEMs), have immanent uncertainties which can be directly related to both DEM resolution and the steepness of terrain slopes perpendicular to the river flow direction. Based on a number of degenerated DEMs, covering areas along the Eskilstuna River, Sweden, these uncertainties have been quantified into an empirically-derived disparity distance equation, yielding values of distance between true and modeled inundation boundary location. Using the inundation polygon, the DEM, a value representing the DEM resolution, and the desired level of confidence as inputs in a new-developed algorithm that utilizes the disparity distance equation, the slope and DEM dependent uncertainties can be directly visualized on a map. The implications of this strategy should benefit planning and help reduce high costs of floods where infrastructure, etc., have been placed in flood-prone areas without enough consideration of map uncertainties.

Aims: The aim of this study was to illustrate how the choice of gait measure could affect the statistical analysis of data and the resulting clinical conclusions. Methodology: A descriptive design in which the results from different tests from 10 patients with normal pressure hydrocephalus illustrates the potential to generate different clinical conclusions. Major findings and principal conclusion: The results illustrate how the choice of measure can affect the statistical results and the clinical interpretation of a study. It is possible to have the paradoxical situation in which one group has a better walking ability if the variable speed is used but the other group has a better walking ability if the variable time is used. An important message is that the choice of measurement and the transformation of data are not primarily statistical issues. If the statistical results are to be useful for clinical decisions, the variables used must be directly related to the utility for the subjects. An understanding of the clinical relevance of different outcomes is required. The distinction between when numbers are purely descriptive and when numbers represent a valuation is subtle and difficult to comprehend.

Most sports have to change their rules and scoring systems once in a while to adapt to external changes or improve the quality of their sport. The driving force behind many changes has been an attempt to make the games more exciting and suitable for television broadcasting.

For example, in April 2009, the World Squash Federation changed the rules in squash so points are awarded in every rally (ball played), compared to the traditional rules in which players only could score a point in their own serve.

We weaken the assumption of summable variations in a paper by Verbitskiy [On factors of g-measures. Indag. Math. (N.S.) 22 (2011), 315-329] to a weaker condition, Berbee's condition, in order for a one-block factor (a single-site renormalization) of the full shift space on finitely many symbols to have a g-measure with a continuous g-function. But we also prove by means of a counterexample that this condition is (within constants) optimal. The counterexample is based on the second of our main results, where we prove that there is a critical inverse temperature in a one-sided long-range Ising model which is at most eight times the critical inverse temperature for the (two-sided) Ising model with long-range interactions.

We improve and subsume the conditions of Johansson and Öberg and Berbee for uniqueness of a g-measure, i.e., a stationary distribution for chains with complete connections. In addition, we prove that these unique g-measures have Bernoulli natural extensions. We also conclude that we have convergence in the Wasserstein metric of the iterates of the adjoint transfer operator to the g-measure.

Lean production originates from the Toyota production system (TPS) and is commonly known as today's Lean. Lean Production consists of a multitude of concepts, values, principles and methods with the main purpose to continuously improve operations and productions. These concepts are known to improve long-term profitability.

The purpose was to investigate Swedish industries and their relation to internal Lean strategies and how well implemented these are in different types of industries. These are categorised as Other, Process- Manufacturing and engineering industry. In this study, the main focus is on analyzing dimensions of internal Lean production in industrial types. These dimensions are Pull System (PULL), Continuous Flow (FLOW), Time Reduction (SETUP), Statistical Process Control (SPC), Employee Involvement (EMP.INVOLV) and Total Productive Maintenance (TPM). These internal Lean dimensions derive from an empirical study by Shah & Ward (2007), with the aim to produce a deeper knowledge of the definition of Internal Lean production.

The study is based on empirical evidence and the data analysis is established from a previous data collection of the European manufacturing Survey (EMS) for Sweden in 2019, previously developed by Von Haartman, Niss & Bengtsson (2020). The used data is descriptive statistics and is limited to Swedish industries. In this study, the analysis will focus on 24 Lean questions and 6 internal Lean dimensions have been investigated and analyzed. 

It is concluded, based on results and analysis, that two Lean perspectives are the basis for businesses to achieve Lean in production. In order to succeed in implementing Lean production, it is important that these perspectives interact with each other. By implementing practical methods, tools and at the same time allowing employees to be involved in the work, quality and efficiency are increased. This contributes to businesses creating improvements in production-related functions in production.

To a large extent the Swedish industries have a Low or Medium use of internal Lean dimensions. TPM is found to be the most commonly implemented dimension for most individual industry types while the dimensions SPC and EMP.INVOLV has the lowest implementation. For individual industry types, there is no major variance in how they answered the implementation of dimensions. All dimensions have a correlation without significant differences for all types of industries. It is found, implementation of dimension PULL differ from each type of industry and Engineering industry have implemented internal Lean dimensions to a higher extent.

Hydraulic modelling is now, at increasing rates, used all over the world to provide flood risk maps for spatial planning, flood insurance, etc. This puts heavy pressure on the modellers and analysts to not only produce the maps but also information on the accuracy and uncertainty of these maps. A common means to deliver this is through performance measures or feature statistics. These look at the global agreement between the modelled flood area and the reference flood that is used. Previous studies have shown that the feature agreement statistics do not differ much between models that have been based on digital elevation models (DEMs) of different resolutions, which is somewhat surprising since most researchers agree that high-resolution DEMs are to be preferred over poor resolution DEMs. Hence, the aim of this study was to look into how and under which conditions the different feature agreement statistics differ, in order to see when the full potential of high-resolution DEMs can be utilised. The results show that although poor resolution DEMs might produce high feature agreement scores (around F > 0.80), they may fail to provide good flood extent estimations locally, particularly when the terrain is flat. Therefore, when high-resolution DEMs (1 to 5 m) are used, it is important to carefully calibrate the models by the use of the roughness parameter. Furthermore, to get better estimates on the accuracy of the models, other performance measures such as distance disparities should be considered.

In realistic decision problems there is more often than not uncertainty in the background information. As for representation of uncertain or imprecise probability values, second-order probability, i.e. probability distributions over probabilities, offers an option. With a subjective view of probability second-order probability would seem to be impractical since it is hard for a person to construct a second-order distributions that reflects his or her beliefs. From the perspective of probability as relative frequency the task of constructing or updating a second-order probability distribution from data is somewhat easier. Here a very simple model for updating lower bounds of probabilities is employed. But the difficulties in choosing second-order distributions may be further alleviated if structural properties are considered. Either some of the probability values are dependent in some way, e.g. that they are known to be almost equal, or they are not dependent in any other way than what follows from that the values sum to one. In this work we present the unique family of discrete second-order probability distributions that correspond to the case where dependence is limited. These distributions are shown to have the property that the joint distributions are equal to normalised products of marginal distributions. The distribution family introduced here is a generalisation of a special case of the multivariate Pólya distribution and is shown to be conjugate prior to a compound hypergeometric distribution.

Arbetet har skrivits på uppdrag av Exploateringskontoret i Stockholm och innefattar sexbyggnadsentreprenörer som alla bygger i området Hammarby Sjöstad där studiengenomförts. Tillsammans bygger de totalt 1126 stycken lägenheter, samt fem styckenbutiker, sex lokaler och ett dagis. Utöver detta tillkommer även två garage vars avfall ärinräknat i statistiken.Arbetet har gått ut på att undersöka hur mycket byggavfall som bildas per nyproduceradlägenhet, varför det skapas samt när i byggskedet detta sker. Kostnaderna för detta skaäven beräknas och hänsyn ska tas till inköpskostnader av det material som blir till avfallsamt avfallsentreprenörernas efterhanteringsersättning. Grunden till frågeställningen ovanbottnar i indikationer som getts från diverse byggnadsentreprenörer som menat att detbildas cirka två ton avfall per nyproducerad lägenhet.Syftet är att detta ska ge underlag för att på sikt skapa bättre rutiner för projektering ochproduktion av byggnader som möjliggör en reducering av byggavfallet, samt även för attbidra till en större medvetenhet kring avfallssorteringen hos byggarbetare ochbyggnadsentreprenörer. Rapporten bygger på personliga intervjuer, telefonintervjuer ochavfallsstatistik från de medverkande byggnadsentreprenörerna. Grova antagandenbaserade på åsikter från personer med insyn i branschen har i flera fall fått göras.Resultatet baseras på ett genomsnitt av samtliga byggnadsentreprenörers avfallsstatistikoch visar på att det bildas cirka tre och ett halvt ton avfall per nyproducerad lägenhet. Detmesta av avfallet börjar uppstå strax efter halvvägs in i byggskedet och består mestadelsav avfallsfraktionerna Osorterat och Brännbart.Uppgiften att ta reda på orsaken till avfallets uppkomst visade sig vara oss övermäktigeftersom de tidsresurser vi haft tillgodo inte har varit tillräckliga. Istället skapades ettavfallsdiagram med de ingående avfallsfraktionerna uppdelade. Detta diagram löperparallellt med en framtagen generell tidsplan för byggskedet. Det går därför att utläsa vari byggskedet arbetet befinner sig när en viss typ av avfall bildas. För att få mer detaljeradesvar är denna rapport en bra grund till vidare studier i bland annat denna fråga.

In different studies for treatment effect on dichotomous outcome of a certain population, one uses different regression models, leading to different measures of the treatment effect. In observational studies, the common measures of the treatment effect are the conditional risk ratio based on a log-linear model and the conditional odds ratio based on a logistic model; in randomized trials, the common measures are the marginal risk difference based on a linear model, the marginal risk ratio based on a log-linear model, and the marginal odds ratio based on a logistic model. In this paper we express these measures in terms of the risk of a dichotomous outcome conditional on covariates and treatment, where the risk is described by a regression model. Therefore these measures do not explicitly depend on the regression model. As a result, we are able to use one regression model in one study to estimate all these measures by their maximum likelihood estimates. We show that these measures have causal interpretations and reflect different aspects of the same underlying treatment effect under the assumption of no unmeasured confounding covariate given observed covariates. We construct approximate distributions of the maximum likelihood estimates of these measures and then by using the approximate distributions we get confidence intervals for these measures. As an illustration, we estimate these measures for the effect of a triple therapy on eradication of Helicobacter pylori among Vietnamese children and are able to compare the treatment effect in this study with those in other studies.

In observational studies for the interaction between exposures on a dichotomous outcome of a certain population, usually one parameter of a regression model is used to describe the interaction, leading to one measure of the interaction. In this article we use the conditional risk of an outcome given exposures and covariates to describe the interaction and obtain five different measures of the interaction, that is, difference between the marginal risk differences, ratio of the marginal risk ratios, ratio of the marginal odds ratios, ratio of the conditional risk ratios, and ratio of the conditional odds ratios. These measures reflect different aspects of the interaction. By using only one regression model for the conditional risk, we obtain the maximum-likelihood (ML)-based point and interval estimates of these measures, which are most efficient due to the nature of ML. We use the ML estimates of the model parameters to obtain the ML estimates of these measures. We use the approximate normal distribution of the ML estimates of the model parameters to obtain approximate non-normal distributions of the ML estimates of these measures and then confidence intervals of these measures. The method can be easily implemented and is presented via a medical example.

We use logistic model to get point and interval estimates of the marginal risk difference in observational studies and randomized trials with dichotomous outcome. We prove that the maximum likelihood estimate of the marginal risk difference is unbiased for finite sample and highly robust to the effects of dispersing covariates. We use approximate normal distribution of the maximum likelihood estimates of the logistic model parameters to get approximate distribution of the maximum likelihood estimate of the marginal risk difference and then the interval estimate of the marginal risk difference. We illustrate application of the method by a real medical example. 

Suppose that a sequence of treatments are assigned to influence an outcome of interest that occurs after the last treatment. Between treatments, there are time-dependent covariates that may be post-treatment variables of the earlier treatments and confounders of the subsequent treatments. In this article, we study identification and estimation of the net effect of each treatment in the treatment sequence. We construct a point parametrization for the joint distribution of treatments, time-dependent covariates and the outcome, in which the point parameters of interest are the point effects of treatments considered as single-point treatments. We identify net effects of treatments by their expressions in terms of point effects of treatments and express patterns of net effects of treatments by constraints on point effects of treatments. We estimate net effects of treatments through their point effects under the constraint by maximum likelihood and reduce the number of point parameters in the estimation by the treatment assignment condition. As a result, we obtain an unbiased consistent maximum-likelihood estimate for the net effect of treatment even in a long treatment sequence. We also show by simulation that the interval estimation of the net effect of treatment achieves the nominal coverage probability.

In sequential causal inference, two types of causal effects are of practical interest, namely, the causal effect of the treatment regime (called the sequential causal effect) and the blip effect of treatmenton on the potential outcome after the last treatment. The well-known G-formula expresses these causal effects in terms of the standard paramaters. In this article, we obtain a new G-formula that expresses these causal effects in terms of the point observable effects of treatments similar to treatment in the framework of single-point causal inference. Based on the new G-formula, we estimate these causal effects by maximum likelihood via point observable effects with methods extended from single-point causal inference. We are able to increase precision of the estimation without introducing biases by an unsaturated model imposing constraints on the point observable effects. We are also able to reduce the number of point observable effects in the estimation by treatment assignment conditions.

In observational studies with dichotomous outcome of a population, researchers usually report treatment effect alone, although both baseline risk and treatment effect are needed to evaluate the significance of the treatment effect to the population. In this article, we study point and interval estimates including confidence region of baseline risk and treatment effect based on logistic model, where baseline risk is the risk of outcome of the population under control treatment while treatment effect is measured by the risk difference between outcomes of the population under active versus control treatments. Using approximate normal distribution of the maximum-likelihood (ML) estimate of the model parameters, we obtain an approximate joint distribution of the ML estimate of the baseline risk and the treatment effect. Using the approximate joint distribution, we obtain point estimate and confidence region of the baseline risk and the treatment effect as well as point estimate and confidence interval of the treatment effect when the ML estimate of the baseline risk falls into specified range. These interval estimates reflect nonnormality of the joint distribution of the ML estimate of the baseline risk and the treatment effect. The method can be easily implemented by using any software that generates normal distribution. The method can also be used to obtain point and interval estimates of baseline risk and any other measure of treatment effect such as risk ratio and the number needed to treat. The method can also be extended from logistic model to other models such as log-linear model.

Cancer diagnosis is part of a complex stochastic process, in which patients' personal and social characteristics influence the choice of diagnosing methods, diagnosing methods in turn influence the initial assessment of cancer stage, cancer stage in turn influences the choice of treating methods, and treating methods in turn influence cancer outcomes such as cancer survival. To evaluate the performance of diagnoses, one needs to estimate and test the sequential causal effect (SCE) under a specified regime of diagnoses and treatments in such a complex observational study, where the data-generating mechanism is unknown and modeling is needed for statistical inference. In this article, we introduce a method of statistical modeling to estimate and test SCEs under regimes of treatments (diagnoses and treatments in cancer diagnosis) in complex observational studies. By applying the alternative G-formula, we express the SCE in terms of the point effects of treatments in the sequence, so that the modeling can be conducted via the point effects in the framework of single-point causal inference. We illustrate our method by a medical example of cancer diagnosis with data from a Swedish prognosis study of cardia cancer.

In this article we estimate confidence regions of the common measures of (baseline, treatment effect) in observational studies, where the measure of a baseline is baseline risk or baseline odds while the measure of a treatment effect is odds ratio, risk difference, risk ratio or attributable fraction, and where confounding is controlled in estimation of both the baseline and treatment effect. We use only one logistic model to generate approximate distributions of the maximum-likelihood estimates of these measures and thus obtain the maximum-likelihood-based confidence regions for these measures. The method is presented via a real medical example.

When estimating treatment effect on count outcome of given population, one uses different models in different studies, resulting in non-comparable measures of treatment effect. Here we show that the marginal rate differences in these studies are comparable measures of treatment effect. We estimate the marginal rate differences by log-linear models and show that their finite-sample maximum-likelihood estimates are unbiased and highly robust with respect to effects of dispersing covariates on outcome. We get approximate finite-sample distributions of these estimates by using the asymptotic normal distribution of estimates of the log-linear model parameters. This method can be easily applied to practice.

In observational studies for the interaction between treatments, one needs to estimate and present both the treatment effects and the interaction to learn the significance of the interaction to the treatment effects. In this article, we estimate the treatment effects and the interaction jointly by using only one logistic model and based on maximum-likelihood. We present the interaction by (1) point estimate and confidence interval of the interaction, (2) point estimate and confidence region of (treatment effect, interaction) and (3) point estimate and confidence interval of the interaction when the maximum-likelihood estimate of one treatment effect falls into specified range.

Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. Unlike the operational reliability, storage reliability for certain types of products may not be always 100% at the beginning of storage since there are existing possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new combinatorial approach, the non-parametric measure for the estimates of the number of failed products and the current reliability at each testing time in storage, and the parametric measure for the estimates of the initial reliability and the failure rate based on the exponential reliability function, is proposed for estimating and predicting the storage reliability with possible initial failures. The proposed method has taken into consideration that, the initial failure and the reliability testing data before and during the storage process, are available for providing more accurate estimates of both initial failure probability and the probability of storage failures. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is provided in this paper. Finally, numerical examples are given to illustrate the method. Furthermore, a detailed comparison between the proposed method and the traditional method, for examining the rationality of assessment and prediction on the storage reliability, is presented. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality.