Landslide **density** analysis based on a temporal landslide distribution over **three** different years was conducted in Kundasang, Sabah, Malaysia. The analysis involved landslides that occurred in 1984, 2009 and 2012. The objective of this study is to examine the relationship between the physical parameter and landslide **density** analysis based on temporal landslide distribution. This is the preliminary study for landslide hazard assessment. Landslides in these **three** assessment years were identified based on aerial photographs interpretation. The landslides detail has to be digitised as points and the point **density** was calculated using 1 km x 1 km grid on the landslide inventory map. From the analysis, there were 494 landslides distributed across the assessment years and by using the natural break classification, the landslide **density** map was classified into **three** classes of **density**, resulting low (1 landslide), moderate (2- 3 landslides) and high (>4 landslides). Based on the landslide **density** analysis, there are 48 km 2 that were identified as highly susceptible to landslide. Out of the high landslide **density** area, 46km 2 were indicated as the most susceptible location for landslides due to the type of lithology that may lead to land sliding.

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discussion on inflation forecasting.
As in Hendry and Hubrich (2011), the data set used in the present analysis includes the all items U.S. consumer price index (CPI) as well as four subcom- ponents, i.e. prices of: 1) food, 2) commodities less food and energy commodi- ties, 3) energy and 4) services less energy services (see Figure 1). The data set can be retrieved from the U.S. Bureau of Labor Statistics (BLS) 2 . The **time** **series** employed are monthly and seasonally adjusted (X-12 ARIMA), except for CPI services less energy services, which did not have a seasonal behaviour. We present results for models estimated using monthly changes in **year**-on- **year** inflation. In fact, we found that modelling month-on-month (rather than **year**-on-**year**) inflation and/or inflation levels (rather than inflation changes), could lead to a slight reduction in the MSFE, but generally yielded worse forecasting performances when the whole predictive distribution is evaluated.

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velocities we compute **time** **series** of leakage along the satel- lite tracks, and plot the annual averages (Fig. 7a). The results are similar along the **three** tracks, with a mean leakage of 19.8, 21.8 and 22.1 Sv along tracks 20, 198 and 122 respec- tively. The standard deviation computed from monthly av- erages is high: 4.2, 4.7 and 6.7 Sv respectively, which leads to a low cross-correlation of the detrended **time** **series**: 0.41 between tracks 20 and 198 and 0.38 between tracks 198 and 122. Note that the **time** **series** all have a small positive trend. The cross-correlation is small mostly because of the deep ocean. If the top 1500 m is considered then the cross- correlation between the transport along these tracks is 0.72 between tracks 20 and 198, and 0.81 between tracks 198 and 122. An explanation for the importance of the deep ocean in decorrelating the **time** **series** is that the signal could propa- gate slower at depth. It would then be necessary to take a lag between the different tracks to improve these correlations.

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In the **three** …rst numerical examples listed below, we investigate the performance of the PPCDE for simulated multidimensional data. For each of these examples, 100 replications were generated with sample sizes n = 100; 200 and 400. In order to reduce the computa- tional burden, we only produced results for the non-normalised PPCDE. As demonstrated in the previous sections, normalising the …nal estimates is expected to improve the perfor- mance of the PPCDE. The fourth example demonstrates an application of the method to interval predictors for the daily exchange-rate returns between the US Dollar (USD) and the British Pound (GBP). In all four examples, we tested the IC stopping rule with the number of bootstrap samples B = 50. In order to evaluate the quality of the performance of the PPCDE, the standard multivariate conditional **density** kernel estimator, referred to here simply as the kernel estimator, is set as a benchmark.

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To have a better understanding of the datasets in the UCR **Time** **Series** Data Mining Archive, we select **three** classical datasets and illustrate each of them in detail.
(a) Gun Point Dataset
This dataset is transformed from the video surveillance. The dataset contains two classes: Gun-Draw and Point, each class has 100 samples. In the Gun-Draw class, the following hands motions have been recorded: drawing a replicate gun from a hip-mounted holster, pointing it at a target for a second, and returning the gun to the holster. In the Point class, the hands motions have been recorded when actors pretended drawing a gun. The **time** **series** are converted by tracking the centroid of the actors right hands in both the horizontal axis (X-axis) and the vertical axis (Y-axis)[16]. In this dataset, only the motion in the X-axis is used. The **time** **series** data from two classes are visualized in Figure 4.4.

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Background of the Present study
The urban development authorities in India have constructed many Sewage Treatment Plants to treat the generated sewage. Accordingly Mysore Urban Development Authority (MUDA) has constructed **three** Sewage Treatment Plants at Rayankere, Vidyaranyapuram and Kesare, based on the Topography of the city. However, the efficiency of sewage treatment is affected by the frequency and increase quantity of the waste water inflow. Therefore predicting the inflow changes is necessary to have the anticipatory control over the wastewater treatment systems to manage the waste generated by the population growth. Many researchers have applied different formulas, physical laws and other empirical models to forecast the sewage inflow. To forecast the sewage inflow of Vidyaranyapuram STP of Mysore; the successful ARIMA model is developed.

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We think that the material from which the pIOL is made is an important factor that causes a continuous EC loss over a long **time**. Silicone is well known for its complications when implanted in the human eye. Its effect on the cornea, angle, tension, and retina are well known. 23 Veri ﬂ ex pIOL being made mostly of silicone is expected to cause AC reaction and subclinical chronic uveitis with a subsequent drop of ECC. Although both AC iris- ﬁ xated lenses (Verisyse and Veri ﬂ ex) are implanted almost in the same location in the AC, the response of the eye to each lens is different. An ICL made of silicone and implanted in the PC as usual resulted in total bilateral corneal decom- pensation which required corneal transplantation one **year** after surgery. 20

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2. Methodology
2.1. Introduction
Stern (in press) provides the rationale for the general type of **time** **series** model developed here.
ARMA or transfer function type models are appropriate to allow computation of the dynamic impulse response function to changing radiative forcing as well as allow the modeling of long- run equilibria between non-stationary variables using the notion of cointegration. The multicointegrating model was found to be a parsimonious representation of an ocean atmosphere system that could be easily interpreted in physical terms. The basic approach is to embed a simple physical model of the energy balance of the atmosphere and ocean in a vector autoregressive model, which is constrained by multicointegrating restrictions. I decided to model atmospheric temperature, radiative forcing and ocean heat content because there are reasonably long **time** **series** observations on these variables. Finally, the model has to be able to deal with **time** **series** of non-uniform length, as only around 50-55 years of observations are available on ocean heat content. We could estimate the model for the 50-**year** period when data on all variables is available, but this would discard two thirds of the observations on atmospheric temperature and radiative forcing. Alternatively, we could switch between two models in the two periods when ocean heat content observations are available and when they are not. One model would assume that atmospheric temperature were not affected by the ocean and the other would assume it was, with some parameters common across models. My approach is to have a uniform model and to reconstruct the missing **series** automatically as a latent variable using a model for that **series** whose parameters are constrained by the 50-55 years of observations. The Kalman filter is commonly regarded as the best algorithm for relating the available observations to latent state variables, and this is the estimation procedure that I use. The latent variable is estimated whether there is an observation or not on the variable it is supposed to represent. Therefore, the full **time** **series** on atmospheric temperature and radiative forcing can be exploited, which is very statistically advantageous.

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Each daily mortality **series** was examined in relation to daily temperature using Poisson generalised linear mod- els allowing for over-dispersion, following methods used in previous analyses for England and Wales [7]. Cubic smoothing splines of **time** with equally spaced knots were used to control for secular trends in the mortality **series** and any additional confounding by seasonally-varying factors other than temperature. The same level of seasonal control was used on each **series** with 7 degrees of freedom (df) per **year**, roughly equivalent to a two-month moving average, specified for the splines. We chose the number of df as a compromise between providing adequate control for unmeasured confounders and leaving sufficient infor- mation from which to estimate temperature effects. Sensi- tivity analyses were conducted to confirm that estimates were largely unchanged if other levels of seasonal control were considered.

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Improved worldwide support processes, including additional response to severity 2 calls, twenty-four hours a day, seven days a week.
If required, IBM provides repair or exchange service depending on the types of warranty service specified for the machine. IBM will attempt to resolve your problem over the telephone, or electronically via an IBM website. Certain machines contain remote support capabilities for direct problem reporting, remote problem determination, and resolution with IBM. You must follow the problem determination and resolution procedures that IBM specifies. Following problem determination, if IBM determines on-site service is required, scheduling of service will depend upon the **time** of your call, machine technology and redundancy, and availability of parts.

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The sealed surface fraction estimates obtained in this study were validated for a set of Landsat pixels, located in parts of the study area covered by the IKONOS images used for calibrating the sub-pixel mapping model (see section 2.2). For a set of validation pixels, not used in the calibration, and for which it was verified that sealed surface cover did not change between 1986 and 2013, the error in sealed surface fraction estimates was calculated, using the average sealed surface fraction in the underlying IKONOS pixels as ground truth. Validation pixels were selected with stratified random sampling: each of ten quantiles of all possible sealed surface fraction values are equally represented in the validation set. Next, random errors were drawn for all Landsat pixels from a multivariate normal distribution defined by the mean error vector and the variance-covariance error matrix, calculated from the errors observed at each **time** step. This way, error perturbed versions of the sealed surface maps for each of the **three** **time** steps could be produced, accounting for temporal correlation in the errors. The error simulation process was repeated 100 times, producing 100 sealed surface maps for all **time** steps. These maps were used to compute 100 population maps of 2001 and 1986 and 100 densification index maps for the periods 1986-2001 and 2001-2013. As such, we can analyse the uncertainty in the resulting population and densification maps.

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Conclusion
Our data do not allow conclusions to be drawn about the effect of these implants on adolescents or in women in the menopausal transition. The reduction observed at the dis- tal radius, although within the limit of 1 SD, must be ana- lysed with caution because it is not possible to conclude whether this loss has any clinical significance over long- term use or if it has any effecton postmenopausal fracture risk [33]. Additionally, it is important to take into account that many women use hormonal contraceptive methods for a short period of **time** and consequently any deleteri- ous effect could be counterbalance by a recovery after dis- continuation. In conclusion, BMD was significantly lower at 18 and at 36 months of use compared to pre insertion values in users of both contraceptive implants at the distal radius; however, no differences were found at the ultra- distal radius. These results could be indicating apparently a non severe impact on BMD. This cohort of women is currently being followed-up and BMD will be measured again at 60 months of use if the number of users remains adequate at that **time**.

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Sara Helland 1, 2, 3 , Stuart Gardner 2 and Nick Lauter 1, 2, 4
Abstract
The physiological parameters that dictate flowering **time** and **plant** stature in maize are difficult to discern due to the large number of mechanisms involved, as well as their dependencies on both environmental and genetic contexts. From a practical perspective, there is much progress to be made in determining the relative importance of QTL, environment, and their interactions in controlling these agronomically important phenotypes. The balance of these effects may critically govern whether or not the QTL are useful for breeding. In particular, it is valuable to assess the contribution of QTL that can be detected in genetic contrasts among cultivars that are specifically adapted to the same region, for example, between B73 and Mo17 in the central region of the North American continent. To this end, we grew the Intermated B73 x Mo17 recombinant inbred line population at **three** locations in the cornbelt over two years. Using composite interval mapping, we placed QTL for flowering **time** and shoot architecture traits on a dense genetic map. The effects of temperature,

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June 20, 2021 – Fourth Sunday after Pentecost 2 Corinthians 5:15.. “And he died for all, that those who live should no longer live for themselves but for him who died for them and was [r]

This paper studies spectral **density** estimation of a strictly stationary r-vector valued continuous **time** **series** including missing observations. The finite Fourier transform is constructed in L-joint segments of observations. The modified periodogram is de- fined and smoothed to estimate the spectral **density** matrix. We explore the proper- ties of the proposed estimator. Asymptotic distribution is discussed.

Figure 4 displays the durations **series** for the four stocks. The sample sizes differ for each stock and range between 1609 and 2717 observations many of which are small because the stocks are actively traded. We observe the typical characteristics of high frequency financial duration data for all the **series**. Fitting ACD models to the durations typically implies a high persistence, α + β is close to one as explained in Section 4, because of the high dependence between the durations. See Bauwens, Giot, Grammig, and Veredas (2004) for a model comparison using **density** forecasts between several ACD models (different conditional mean and innovation **density** specifications) using the same data as in this paper. Figure 5 displays the gamma kernel **density** estimates for the four stocks. The bandwidth parameter is obtained using the gamma rule, as explained in Section 3. There is an important concentration of observations near the origin. This makes the Gaussian kernel estimator inadequate as already explained in Section 4. The shape of the four densities is quite similar, although we remark that the Boeing and Coke price durations are more concentrated at the origin than Disney and Exxon. The confidence intervals are close to the **density** estimates because of the large sample sizes and are inverse trumpet shaped according to the results in Theorem 2.

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+ " t ;
where M t is the money demand, P t is the aggregate price level, y t is real GDP and i t is the nominal interest rate.
We apply both paired and joint cotrending rank selection procedures to the vec- tors (ln(M t =P t ); ln(y t ); i t ), (ln(M t =P t ); ln(y t ); ln(i t )), and (ln(M t =P t ); ln(y t ); ln(i t =(1 + i t )). Table 9 reports the empirical results for all **three** di¤erent speci…cations of the functional form for interest elasticity of money demand. The results are somewhat mixed depending on the choice of the penalty of the criteria and the choice of the variables. However, it is important to note that none of the procedures select (r 1 ; r 2 ) = (0; 0). This implies that there are, at least, either cotrending or weak cotrending relationships in Japanese money demand in the long-run. When M 2 is used as the monetary aggregate and when demeaned version of the von Neumann ratio is used, (r 1 ; r 2 ) = (0; 2) is selected for all cases, imply- ing that the kinked trend is likely to be a single common deterministic trend among **three** variables.

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CHAPTER 3
DIFFERENTIAL PRIVACY APPLICATIONS TO BAYESIAN AND LINEAR MIXED MODEL ESTIMATION
We consider a particular maximum likelihood estimator (MLE) and a com- putationally intensive Bayesian method for differentially private estimation of the linear mixed-effects model (LMM) with normal random errors. The LMM is important because it is used in small-area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying data. The differentially private MLE performs well compared to the regular MLE, and deteriorates as the protection increases for a problem in which the small-area variation is at the county level. More dimensions of ran- dom effects are needed to adequately represent the **time** dimension of the data, and for these cases the differentially private MLE cannot be computed. The direct Bayesian approach for the same model uses an informative, reasonably diffuse prior to compute the posterior predictive distribution for the random ef- fects. The empirical differential privacy of this approach is estimated by direct computation of the relevant odds ratios after deleting influential observations according to various criteria.

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During the short history of Bitcoin, it experienced various extreme events. Bitcoin was released in January 2009, and until April 2010 there was no exchange or market for it. In May 2010, Laszlo Hanyecz made the first real–world transaction buying two pizzas in Jacksonville, Florida for 10 , 000 Bitcoins. In June 2011, there was a massive security breach in MtGox, which was the most significant trading platform for Bitcoin at that **time**, and the Bitcoin price dropped from $32 to $2. Until August 2012, the price gradually increased to $15. With the beginning of 2013, a price rally started and continued until $266 in April 2013. In October 2013, the price dropped to $110 since FBI seized $28 . 5 million worth of Bitcoins from the accounts of a website called Silk Road, an online black market and the first darknet market best known as a platform for selling illegal drugs. After the Silk Road rumors ended, the price surged during the end of 2013 and reached to $1242. During 2014 and until the end of 2015, the Bitcoin price plunged to as low as $200 due to the bankruptcy of MtGox and a false report regarding Bitcoin ban in China. During May 2016, a hype started about the incoming halving, which was expected to occur in July 2016. Since then, the price has spiked to $2739 as of July 30, 2017.

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While the first chapter is based on a single-author paper (see Otto 2019), the latter two chapters are joint works with J¨ org Breitung (see Otto and Breitung 2019) and Nazarii Salish (see Otto and Salish 2019) respectively.
Chapter 1 The literature on unit root testing is large and comprehensive, beginning with the seminal works of Dickey and Fuller (1979), Said and Dickey (1984), Phillips (1987), and Phillips and Perron (1988). Elliott et al. (1996) presented a unit root test that exhibits optimality properties. These conventional unit root tests include the assump- tion that the deterministic component is either constant or linear. Since a misspecified trend model leads to power losses, many studies focused on unit root testing under a more flexible parametric structure for the trend component, such as structural break and smooth transition models with unknown breakpoint and magnitude, and approximations by Chebyshev polynomials and Fourier **series**. However, little attention has been devoted to the nonparametric treatment of deterministic trends.

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